import fs from "fs";
import path from "path";
import { fileURLToPath } from "url";
import PizZip from "pizzip";
import {
  registerImageBuffer,
  renderDocxReport,
  patchAnomalyTemplateXml,
  chunkDetailImageRows,
} from "../src/server/reportService/docxReportBuilder.js";
import {
  buildDetectorSectionPayload,
  pickKeyTurbines,
  buildSensorHeatmapData,
  buildDataCompletenessHeatmapData,
  buildDetectorHeatmapData,
  buildSensorRadarData,
  buildDetectorRadarData,
  chunkHeatmapData,
  buildSensorAnomalyRows,
  mapAnomalyCoverFields,
  summarizeTurbineAnomalyRate,
  buildAnomalySummaryRows,
  buildConclusionRows,
  formatSensorTypeText,
} from "../src/server/reportService/anomalyReportMapper.js";
import {
  DETECTOR_TEMPLATE_CONFIG,
  buildAnomalyRadarOption,
  buildDetectorPlotOption,
  buildEmptyPlaceholderOption,
  buildFarmPlotJsons,
  buildCompletenessHeatmapOption,
  buildReportHeatmapOption,
  listDetectorPlotOptions,
  listFarmPlotOptionsFromTurbines,
  listFarmChartOptions,
  optionHasData,
  plotJsonHasData,
  turbinePlotsToFarmJson,
} from "../src/server/reportService/anomalyChartBuilder.js";
import { renderEchartsOption } from "../src/server/reportService/echartsRenderer.js";
import {
  initChartService,
  shutdownChartService,
} from "../src/server/utils/chartService/index.js";
import {
  diagnosticFarmSeriesColor,
  diagnosticFarmStyle,
  diagnosticSeriesColor,
  diagnosticSeriesSymbol,
  diagnosticSingleSeriesZ,
  DIAGNOSTIC_COLORS,
  PITCH_MIN_REPORT_COUNT_COLORS,
  PITCH_MIN_REPORT_COLOR_STOPS,
  PAGE_SHUTDOWN_COLORS,
} from "../shared/anomalyDiagnosticVisual.mjs";
import {
  farmAggregateToPanels,
  formatReportValueAxisLabel,
  resolvePanelSubtitle,
} from "../shared/anomalyDetectorPlotAdapter.mjs";
import {
  fillFarmPanelsToRule,
  getFarmPanelRule,
} from "../shared/anomalyFarmChartRules.mjs";

const __dirname = path.dirname(fileURLToPath(import.meta.url));

function samplePlot(labelPrefix = "") {
  return {
    title: `${labelPrefix}偏航`,
    xaxis: "时间",
    yaxis: "偏移 (°)",
    data: [
      {
        label: "正常偏航角",
        mode: "markers",
        timeData: [
          "2026-07-14 04:00:00",
          "2026-07-14 08:00:00",
          "2026-07-14 12:00:00",
        ],
        yData: [180, 200, 190],
      },
      {
        label: "12h均值",
        mode: "lines",
        timeData: [
          "2026-07-14 04:00:00",
          "2026-07-14 08:00:00",
          "2026-07-14 12:00:00",
        ],
        yData: [185, 198, 120],
      },
    ],
  };
}

function assert(condition, message) {
  if (!condition) throw new Error(message);
}

/** 图例项可能是字符串或 { name, itemStyle } 对象，统一取名称 */
function legendNames(option) {
  const data = option?.legend?.data || [];
  return data.map((item) =>
    item && typeof item === "object" ? item.name : item,
  );
}

const headerCover = mapAnomalyCoverFields({
  fieldCode: "SVLgVZfi",
  fieldName: "新疆鄂能风电场",
  fieldMeta: { provinceName: "大唐新疆公司" },
  modelList: [],
});
assert(
  headerCover.Province === "新疆",
  `页眉 Province 应去重为「新疆」，实际为「${headerCover.Province}」`,
);
assert(
  headerCover.Wind_farm === "鄂能风电场",
  `页眉 Wind_farm 应去掉省份前缀并保留风电场，实际为「${headerCover.Wind_farm}」`,
);
assert(
  headerCover.Overview_of_the_Wind_Farm ===
    "新疆鄂能风电场位于大唐新疆公司，机型—，共安装0台风机",
  `项目概要不应重复公司名或多余句号，实际为「${headerCover.Overview_of_the_Wind_Farm}」`,
);
const duplicateLocationCover = mapAnomalyCoverFields({
  fieldCode: "SVLgVZfi",
  fieldName: "新疆鄂能风电场",
  fieldMeta: {
    provinceName: "大唐新疆公司",
    cityName: "大唐新疆公司",
  },
  modelList: [{ machineTypeCode: "GW82-1500" }, { machineTypeCode: "GW82-1500" }],
});
assert(
  duplicateLocationCover.Overview_of_the_Wind_Farm ===
    "新疆鄂能风电场位于大唐新疆公司，机型GW82-1500，共安装2台风机",
  `省/市同名时应去重，实际为「${duplicateLocationCover.Overview_of_the_Wind_Farm}」`,
);
assert(
  !duplicateLocationCover.Overview_of_the_Wind_Farm.includes("。"),
  "项目概要正文不应自带句号（模板已有）",
);
const thirteenCover = mapAnomalyCoverFields({
  fieldCode: "FIELD13",
  fieldName: "新疆十三师风电场",
  fieldMeta: { provinceName: "大唐新疆能源有限公司" },
  overview: { fieldName: "鄂能风电场" },
  modelList: [],
});
assert(
  thirteenCover.Province === "新疆" &&
    thirteenCover.Wind_farm === "十三师风电场" &&
    thirteenCover.Company === "大唐新疆能源有限公司",
  `选中风场应优先用于页眉，实际 Province=${thirteenCover.Province} Wind_farm=${thirteenCover.Wind_farm}`,
);
assert(
  !`${headerCover.Province}${headerCover.Wind_farm}`.includes("大唐大唐"),
  "页眉不应出现「大唐大唐」重复前缀",
);
const conclusionSample = buildConclusionRows({
  yaw_error: {
    anomalyTurbines: 3,
    anomalyPoints: 12,
    turbineNames: ["25", "4", "10", "4号"],
  },
  yaw_static: {
    anomalyTurbines: 0,
    anomalyPoints: 0,
    turbineNames: [],
  },
});
assert(conclusionSample.length === 1, "无异常检测器不应进入检测结论表");
assert(
  conclusionSample[0].problem_desc.startsWith("主要表现为") &&
    conclusionSample[0].problem_desc.includes("静态偏差"),
  `问题描述应从「主要表现为」截取，实际为「${conclusionSample[0].problem_desc}」`,
);
assert(
  conclusionSample[0].suggestion.startsWith("建议"),
  `建议意见应使用排查建议，实际为「${conclusionSample[0].suggestion}」`,
);
assert(
  conclusionSample[0].problem_nature === "偏航与扭缆系统异常",
  `问题性质应为模块大标题，实际为「${conclusionSample[0].problem_nature}」`,
);
assert(
  conclusionSample[0].turbine_names === "#04、#10、#25",
  `问题机组应统一罗列，实际为「${conclusionSample[0].turbine_names}」`,
);
const conclusionWithoutDeload = buildConclusionRows({
  ctrl_deload: {
    anomalyTurbines: 20,
    anomalyPoints: 80,
    turbineNames: ["01", "02", "03"],
  },
  yaw_error: {
    anomalyTurbines: 1,
    anomalyPoints: 2,
    turbineNames: ["05"],
  },
});
assert(
  conclusionWithoutDeload.length === 1 &&
    !conclusionWithoutDeload.some((row) =>
      `${row.problem_desc}${row.suggestion}`.includes("降载"),
    ),
  "表10-1 检测结论不应统计降载判定",
);
assert(
  !plotJsonHasData({ data: [] }) && !plotJsonHasData(null),
  "空 JSON 应判定为无图表数据",
);
assert(
  plotJsonHasData({
    data: [{ label: "散点", xData: [1], yData: [2] }],
  }),
  "含坐标点的 JSON 应判定为有数据",
);

const templateSource = path.join(
  __dirname,
  "../src/public/file/异常检测数据分析报告模板（大唐版）_修订版_人工对齐版_业务化.docx",
);
if (fs.existsSync(templateSource)) {
  const sourceXml = new PizZip(fs.readFileSync(templateSource))
    .file("word/document.xml")
    .asText();
  const patchedXml = patchAnomalyTemplateXml(sourceXml);
  const tocParas = patchedXml.match(/<w:p[\s\S]*?<\/w:p>/g) || [];
  let tocHas42 = false;
  let tocHasFigure = false;
  let tocHasModuleTag = false;
  tocParas.slice(0, 70).forEach((paragraph) => {
    const plain = paragraph.replace(/<[^>]+>/g, "").trim();
    const isToc = /w:pStyle w:val="(?:26|29|30|31)"/.test(paragraph);
    if (!isToc) return;
    if (/^4\.2 按功能诊断项统计/.test(plain)) tocHas42 = true;
    if (/^图[34]-[12]/.test(plain)) tocHasFigure = true;
    if (/show_module_/.test(plain)) tocHasModuleTag = true;
  });
  assert(tocHas42, "目录应保留 4.2 按功能诊断项统计 条目");
  assert(!tocHasFigure, "目录中不应出现图3/图4 图题");
  assert(!tocHasModuleTag, "目录中不应插入模块条件标签");
  const detectorLoopOpen =
    patchedXml.indexOf(
      "{#zn-techcn-replace-tags-data_detector_anomaly-generalFiles}",
    );
  const detectorCaption = patchedXml.indexOf(
    "{figure_caption}",
    detectorLoopOpen,
  );
  const detectorImage = patchedXml.indexOf("{%image}", detectorCaption);
  const detectorLoopClose = patchedXml.indexOf(
    "{/zn-techcn-replace-tags-data_detector_anomaly-generalFiles}",
    detectorImage,
  );
  assert(
    detectorLoopOpen >= 0 &&
      detectorLoopOpen < detectorCaption &&
      detectorCaption < detectorImage &&
      detectorImage < detectorLoopClose,
    "第4章概览应按图题、图片逐项循环",
  );
  assert(
    patchAnomalyTemplateXml(patchedXml) === patchedXml,
    "异常模板补丁应可重复执行",
  );
  const detailGrid = patchedXml.indexOf(
    "{#zn-techcn-replace-tags-wind_power_curve-generalFiles}",
  );
  const detailImage = patchedXml.indexOf("{%detail_image}", detailGrid);
  const detailClose = patchedXml.indexOf(
    "{/zn-techcn-replace-tags-wind_power_curve-generalFiles}",
    detailImage,
  );
  assert(
    detailGrid >= 0 && detailGrid < detailImage && detailImage < detailClose,
    "分图应排成一行三列",
  );
  assert(
    patchedXml.includes("{#detail_slot1}") &&
      patchedXml.includes("{#detail_slot2}") &&
      patchedXml.includes("{#detail_slot3}"),
    "分图应预留三列",
  );
  assert(
    patchedXml.includes(
      "{#zn-techcn-replace-tags-wind_power_curve-generalFiles}{#detail_slot1}",
    ) &&
      patchedXml.includes(
        "{/detail_slot3}{/zn-techcn-replace-tags-wind_power_curve-generalFiles}",
      ),
    "分图循环标签应与槽位标签同段，避免第一列下移",
  );
  const farmImage = patchedXml.indexOf(
    "{#zn-techcn-replace-tags-wind_power_curve-farmSummary}",
  );
  assert(
    farmImage >= 0 &&
      patchedXml.indexOf("{%image}", farmImage) > farmImage &&
      patchedXml.indexOf("{%image}", farmImage) <
        patchedXml.indexOf(
          "{/zn-techcn-replace-tags-wind_power_curve-farmSummary}",
          farmImage,
        ),
    "总图应保持一行一张",
  );
  const chunked = chunkDetailImageRows([
    { image: "a" },
    { image: "b" },
    { image: "c" },
    { image: "d" },
  ]);
  assert(
    chunked.length === 2 &&
      chunked[0].detail_slot3[0].detail_image === "c" &&
      chunked[1].detail_slot1[0].detail_image === "d" &&
      chunked[1].detail_slot2.length === 0,
    "分图应按 3 张一组，末行空位留白",
  );
  assert(
    patchedXml.includes("{#conclusionRows}") &&
      patchedXml.includes("{problem_desc}") &&
      patchedXml.includes("{problem_nature}") &&
      patchedXml.includes("{/detectorSummaryRows}"),
    "表10-1 检测结论应改为 conclusionRows 循环，表4-1 应闭合 detectorSummaryRows",
  );
  assert(
    (patchedXml.match(/\{sensor_anomaly_type\}/g) || []).length >= 16,
    "各检测器明细表应保留数据感知异常类型列",
  );
  assert(
    patchedXml.includes("{#show_module_wind}") &&
      patchedXml.includes("{/show_module_aero}"),
    "各检测模块章节应包一层 show_module 条件标签",
  );
  const patchedPlain = patchedXml.replace(/<[^>]+>/g, "");
  const patchedLoops = [
    ...new Set([...patchedPlain.matchAll(/\{#([^{}]+)\}/g)].map((m) => m[1])),
  ];
  const danglingLoops = patchedLoops.filter(
    (name) => !patchedPlain.includes(`{/${name}}`),
  );
  assert(
    danglingLoops.length === 0,
    `模板循环应全部闭合，未闭合：${danglingLoops.join("、")}`,
  );
  assert(
    patchedXml.includes("{/wind_power_curveRows}") &&
      patchedXml.includes("{/aero_tsr_windRows}"),
    "检测明细表循环应补齐结尾标签",
  );
  const patchedParas = patchedXml.match(/<w:p[\s\S]*?<\/w:p>/g) || [];
  patchedParas.slice(0, 70).forEach((paragraph) => {
    const plain = paragraph.replace(/<[^>]+>/g, "").trim();
    const isToc = /w:pStyle w:val="(?:26|29|30|31)"/.test(paragraph);
    if (isToc && /show_module_/.test(plain)) {
      throw new Error("目录 TOC 段落中仍含有 show_module 标签");
    }
  });
}

const scatterFarm = buildFarmPlotJsons(
  [
    {
      engineName: "A",
      plotJson: {
        xaxis: "风速",
        yaxis: "功率",
        data: [
          { label: "散点", mode: "markers", xData: [1, 2], yData: [3, 4] },
          { label: "上限", mode: "lines", xData: [1, 2], yData: [8, 9] },
          { label: "下限", mode: "lines", xData: [1, 2], yData: [1, 2] },
          {
            label: "参考功率曲线",
            mode: "lines",
            xData: [1, 2],
            yData: [5, 6],
          },
        ],
      },
    },
    {
      engineName: "B",
      plotJson: {
        xaxis: "风速",
        yaxis: "功率",
        data: [
          { label: "散点", mode: "markers", xData: [2, 3], yData: [4, 5] },
          {
            label: "参考功率曲线",
            mode: "lines",
            xData: [1, 2],
            yData: [5, 6],
          },
        ],
      },
    },
  ],
  "wind_power_scatter",
  "全场散点",
);
assert(
  scatterFarm[0].data.filter((row) => row.__shared).length === 1,
  "参考功率曲线应只保留一条",
);
assert(
  scatterFarm[0].data[scatterFarm[0].data.length - 1].__shared,
  "参考/合同功率曲线应放在图例最后",
);
assert(
  !scatterFarm[0].data.some((row) => /上限|下限/.test(row.label)),
  "散点总图不应含上下限",
);
assert(
  scatterFarm[0].data.some((row) => row.label === "A") &&
    scatterFarm[0].data.some((row) => row.label === "B"),
  "散点总图应按风机分系列",
);

const farmScatterOption = buildDetectorPlotOption(
  scatterFarm[0],
  scatterFarm[0].title,
  "anomalyScatterPO",
);
const farmScatterSeries = (farmScatterOption.series || []).filter(
  (item) => item.type === "scatter3D",
);
assert(farmScatterSeries.length > 0, "全场散点应输出 scatter3D 系列");
assert(
  farmScatterSeries.every((item) => {
    const size = Number(item.symbolSize);
    return size >= 4 && size <= 6;
  }),
  "全场散点报告正常点应收小，避免糊成色块",
);
const emphasisScatter = buildDetectorPlotOption(
  {
    detector: "wind_power_scatter",
    chartType: "series",
    xaxis: "风速 (m/s)",
    yaxis: "有功功率 (kW)",
    turbines: Array.from({ length: 12 }, (_, index) => ({
      turbine: String(index + 1).padStart(2, "0"),
      xType: "number",
      xData: [4, 8, 12],
      yData: [200, 800, 1400],
      pointAnomaly: [false, true, false],
    })),
  },
  "全场风功率散点分析",
  "anomalyScatterPO",
);
const emphasisNormal = (emphasisScatter.series || []).find(
  (item) => item.type === "scatter3D" && /正常/.test(item.name),
);
const emphasisAnomaly = (emphasisScatter.series || []).find(
  (item) =>
    item.type === "scatter3D" &&
    /异常/.test(item.name) &&
    !/正常/.test(item.name),
);
assert(emphasisNormal && emphasisAnomaly, "全场散点应按点拆成正常/异常");
assert(
  Number(emphasisAnomaly.symbolSize) - Number(emphasisNormal.symbolSize) === 2,
  "报告全场散点异常点只比正常点大一圈",
);
assert(
  Number(emphasisAnomaly.itemStyle?.opacity) >
    Number(emphasisNormal.itemStyle?.opacity),
  "报告全场散点异常点应比正常点更不透明",
);
assert(
  emphasisAnomaly.itemStyle?.color === "#FF1E1E",
  "报告全场散点异常点应为亮红",
);
assert(
  !emphasisAnomaly.itemStyle?.borderWidth,
  "报告全场散点异常点不描白边",
);
assert(farmScatterOption.grid3D, "风功率散点应输出 3D 图表");

const singleScatterOption = buildDetectorPlotOption(
  {
    title: "07 风功率散点分析",
    xaxis: "风速 (m/s)",
    yaxis: "有功功率 (kW)",
    data: [
      {
        label: "正常运行 (271)",
        mode: "markers",
        xData: [6, 8, 10],
        yData: [400, 900, 1500],
      },
      {
        label: "近90日功率曲线",
        mode: "markers",
        xData: [3, 6, 9, 12, 15],
        yData: [0, 400, 1200, 1800, 1850],
      },
    ],
  },
  "07 风功率散点分析",
  "anomalyScatterPO",
);
const curveSeries = (singleScatterOption.series || []).find(
  (item) => item.name === "近90日功率曲线",
);
assert(curveSeries?.type === "line", "近90日功率曲线应渲染为折线而非散点");
assert(curveSeries?.showSymbol === false, "近90日功率曲线折线不应显示散点符号");

assert(
  diagnosticSeriesColor({ label: "正常运行 (271)" }) === DIAGNOSTIC_COLORS.normal &&
    diagnosticSeriesColor({ label: "异常点" }) === DIAGNOSTIC_COLORS.anomaly &&
    diagnosticSeriesColor({ label: "降载" }) === DIAGNOSTIC_COLORS.deload &&
    diagnosticSeriesColor({ label: "停机" }) === DIAGNOSTIC_COLORS.shutdown,
  "单机风功率散点应按正常蓝/异常红/降载黄/停机黑上色",
);
assert(
  diagnosticSeriesColor({ label: "累计降载率" }) !== DIAGNOSTIC_COLORS.deload,
  "累计降载率曲线不应被当成散点降载状态色",
);
// 页面深色底可换成浅灰；报告与浅色页必须保持 shutdown 深色
assert(
  diagnosticSeriesColor({ label: "停机" }, 0, {
    pageTheme: true,
    darkTheme: true,
  }) === PAGE_SHUTDOWN_COLORS.dark &&
    diagnosticSeriesColor({ label: "停机" }, 0, {
      pageTheme: true,
      darkTheme: false,
    }) === DIAGNOSTIC_COLORS.shutdown &&
    diagnosticSeriesColor({ label: "停机" }) === DIAGNOSTIC_COLORS.shutdown,
  "停机点色仅在页面深色主题切换，报告保持 shutdown 深色",
);
assert(
  diagnosticSeriesColor({ label: "正常运行 (271)" }, 0, {
    pageTheme: true,
    darkTheme: true,
  }) === DIAGNOSTIC_COLORS.normal,
  "页面主题选项不应影响停机以外的状态色",
);

const singleCurveWithContract = buildDetectorPlotOption(
  {
    title: "07 功率曲线分析",
    xaxis: "风速 (m/s)",
    yaxis: "有功功率 (kW)",
    data: [
      {
        label: "近90天功率曲线",
        mode: "lines",
        xData: [3, 6, 9, 12],
        yData: [0, 400, 1200, 1500],
      },
      {
        label: "合同功率曲线",
        mode: "lines",
        xData: [3, 6, 9, 12],
        yData: [0, 500, 1300, 1500],
      },
    ],
  },
  "07 功率曲线分析",
  "anomalyPowercurvePO",
);
const singleContractSeries = (singleCurveWithContract.series || []).find(
  (item) => item.name === "合同功率曲线",
);
const singlePowerCurveSeries = (singleCurveWithContract.series || []).find(
  (item) => /近90/.test(item.name || ""),
);
assert(singleContractSeries, "单机功率曲线应含合同功率曲线");
assert(
  Number(singleContractSeries.z) < Number(singlePowerCurveSeries?.z || 2),
  "单机合同功率曲线层级应低于近90天功率曲线",
);
assert(
  diagnosticSingleSeriesZ({ label: "合同功率曲线" }) <
    diagnosticSingleSeriesZ({ label: "正常运行" }),
  "单机合同功率曲线 z 应低于散点",
);

const singleStatusScatter = buildDetectorPlotOption(
  {
    title: "07 风功率散点分析",
    xaxis: "风速 (m/s)",
    yaxis: "有功功率 (kW)",
    data: [
      { label: "正常", mode: "markers", xData: [6], yData: [400] },
      { label: "异常", mode: "markers", xData: [8], yData: [500] },
      { label: "降载", mode: "markers", xData: [7], yData: [300] },
      { label: "停机", mode: "markers", xData: [5], yData: [0] },
      {
        label: "合同功率曲线",
        mode: "lines",
        xData: [3, 12],
        yData: [0, 1500],
      },
    ],
  },
  "07 风功率散点分析",
  "anomalyScatterPO",
);
const colorByName = Object.fromEntries(
  (singleStatusScatter.series || []).map((item) => [
    item.name,
    item.itemStyle?.color || item.lineStyle?.color,
  ]),
);
assert(
  String(colorByName["正常"] || "").startsWith(DIAGNOSTIC_COLORS.normal),
  "正常应为蓝色",
);
assert(
  String(colorByName["异常"] || "").startsWith("#FF1E1E"),
  "报告散点异常点应为亮红",
);
assert(
  String(colorByName["降载"] || "").startsWith(DIAGNOSTIC_COLORS.deload),
  "降载应为黄色",
);
assert(
  String(colorByName["停机"] || "").startsWith(DIAGNOSTIC_COLORS.shutdown),
  "停机应为黑色",
);
assert(
  Number(
    (singleStatusScatter.series || []).find((item) => item.name === "合同功率曲线")
      ?.z,
  ) <
    Number(
      (singleStatusScatter.series || []).find((item) => item.name === "正常")?.z,
    ),
  "单机散点图合同功率曲线应在散点下方",
);

const scatterFarmWith90d = buildFarmPlotJsons(
  [
    {
      engineName: "A",
      plotJson: {
        xaxis: "风速",
        yaxis: "功率",
        data: [
          { label: "散点", mode: "markers", xData: [1, 2], yData: [3, 4] },
          {
            label: "近90日功率曲线",
            mode: "markers",
            xData: [1, 2, 3],
            yData: [5, 6, 7],
          },
        ],
      },
    },
    {
      engineName: "B",
      plotJson: {
        xaxis: "风速",
        yaxis: "功率",
        data: [
          { label: "散点", mode: "markers", xData: [2, 3], yData: [4, 5] },
          {
            label: "近90日功率曲线",
            mode: "markers",
            xData: [1, 2, 3],
            yData: [5, 6, 7],
          },
        ],
      },
    },
  ],
  "wind_power_scatter",
  "全场散点",
);
assert(
  scatterFarmWith90d[0].data.filter((row) => row.__shared).length === 1,
  "近90日功率曲线全场应只保留一条",
);
assert(
  scatterFarmWith90d[0].data.some((row) => row.label === "近90日功率曲线"),
  "近90日功率曲线应出现在全场散点图例",
);

const qualityPanels = listDetectorPlotOptions(
  {
    panels: [
      {
        panelTitle: "功率因数 | 异常点: 0",
        xaxis: "时间",
        yaxis: "功率因数",
        data: [
          {
            label: "功率因数",
            mode: "markers",
            timeData: ["2026-08-01 00:00:00", "2026-08-01 01:00:00"],
            yData: [0.98, 0.97],
          },
        ],
      },
      {
        panelTitle: "电流不平衡度",
        xaxis: "时间",
        yaxis: "电流不平衡度",
        data: [
          {
            label: "电流不平衡度",
            mode: "markers",
            timeData: ["2026-08-01 00:00:00", "2026-08-01 01:00:00"],
            yData: [0.12, 0.15],
          },
        ],
      },
    ],
  },
  "DT01 电能质量分析",
  "anomalyPowerqualityPO",
);
assert(qualityPanels.length === 2, "电能质量单机图应按 panel 全部出图");
assert(
  qualityPanels[1].yAxis.name === "电流不平衡度",
  "第二张单机图应使用对应 panel 的 Y 轴",
);
assert(
  (qualityPanels[1].series || []).some(
    (item) => Array.isArray(item.data) && item.data.length > 0,
  ),
  "单机图系列不能为空",
);

const pitchFarm = buildFarmPlotJsons(
  [
    {
      engineName: "101",
      plotJson: {
        panels: [
          {
            panelTitle: "桨距角时序",
            xaxis: "时间",
            yaxis: "桨距角",
            data: [
              {
                label: "桨叶 1",
                mode: "markers",
                timeData: ["2026-01-01 00:00"],
                yData: [1],
              },
              {
                label: "桨叶 2",
                mode: "lines",
                timeData: ["2026-01-01 00:00"],
                yData: [2],
              },
              {
                label: "桨叶 3",
                mode: "lines",
                timeData: ["2026-01-01 00:00"],
                yData: [3],
              },
              {
                label: "阈值参考",
                mode: "lines",
                timeData: ["2026-01-01 00:00"],
                yData: [4],
              },
            ],
          },
          {
            panelTitle: "桨距角差",
            xaxis: "时间",
            yaxis: "差值",
            data: [
              {
                label: "桨叶 1",
                mode: "markers",
                timeData: ["2026-01-01 00:00"],
                yData: [1],
              },
            ],
          },
        ],
      },
    },
  ],
  "pitch_regulation",
  "全场变桨一致性",
);
assert(pitchFarm.length === 2, "变桨一致性总图应为 2 张");
assert(
  /各桨叶分风速段/.test(pitchFarm[0].title || ""),
  "变桨一致性第一张应为各桨叶分风速段中位数趋势",
);
assert(
  /极差/.test(pitchFarm[1].title || ""),
  "变桨一致性第二张应为桨叶极差时序及异常识别",
);
assert(
  !pitchFarm[0].data.some((row) => /阈值/.test(row.originalLabel || "")),
  "变桨一致性不应展示阈值参考",
);
const pitchFarmReportOptions = listFarmChartOptions(
  [
    {
      engineName: "101",
      plotJson: {
        panels: [
          {
            panelTitle: "桨距角时序",
            xaxis: "时间",
            yaxis: "桨距角",
            data: [
              {
                label: "桨叶 1",
                mode: "markers",
                timeData: ["2026-01-01 00:00"],
                yData: [1],
              },
              {
                label: "桨叶 2",
                mode: "lines",
                timeData: ["2026-01-01 00:00"],
                yData: [2],
              },
              {
                label: "桨叶 3",
                mode: "lines",
                timeData: ["2026-01-01 00:00"],
                yData: [3],
              },
              {
                label: "阈值参考",
                mode: "lines",
                timeData: ["2026-01-01 00:00"],
                yData: [4],
              },
            ],
          },
          {
            panelTitle: "桨距角差",
            xaxis: "时间",
            yaxis: "差值",
            data: [
              {
                label: "桨叶 1",
                mode: "markers",
                timeData: ["2026-01-01 00:00"],
                yData: [1],
              },
            ],
          },
        ],
      },
    },
  ],
  "全场变桨一致性",
  "anomalyPitchregulationPO",
  { templateKey: "pitch_regulation", farmMode: true },
);
assert(pitchFarmReportOptions.length === 2, "报告变桨一致性应出 2 张图");

const deloadFarm = buildFarmPlotJsons(
  [
    {
      engineName: "101",
      plotJson: {
        panels: [
          {
            panelTitle: "风速",
            xaxis: "时间",
            yaxis: "风速",
            data: [{ label: "风速", timeData: ["t"], yData: [1] }],
          },
          {
            panelTitle: "有功功率时序",
            xaxis: "时间",
            yaxis: "有功功率",
            data: [
              { label: "正常", mode: "markers", timeData: ["t"], yData: [1] },
              { label: "异常", mode: "markers", timeData: ["t"], yData: [2] },
            ],
          },
        ],
      },
    },
  ],
  "ctrl_deload",
  "全场降载",
);
assert(deloadFarm.length === 1, "降载总图只出有功功率时序");
assert(
  /有功功率/.test(deloadFarm[0].title + deloadFarm[0].yaxis),
  "降载总图应为有功功率",
);
assert(
  diagnosticFarmSeriesColor(
    { extra: { anomaly: false }, originalLabel: "正常", turbineIndex: 1 },
    0,
    "ctrl_deload",
  ) === DIAGNOSTIC_COLORS.normal &&
    diagnosticFarmSeriesColor(
      { extra: { anomaly: false }, originalLabel: "异常", turbineIndex: 1 },
      1,
      "ctrl_deload",
    ) === DIAGNOSTIC_COLORS.normal,
  "正常降载机组整机应统一蓝色",
);
assert(
  diagnosticFarmSeriesColor(
    { originalLabel: "正常", turbineIndex: 0 },
    0,
    "pitch_coord",
  ) === DIAGNOSTIC_COLORS.normal,
  "变桨协调总图正常点应统一蓝色",
);
assert(
  diagnosticFarmSeriesColor(
    { originalLabel: "异常", turbineIndex: 0 },
    0,
    "pitch_coord",
  ) === DIAGNOSTIC_COLORS.anomaly,
  "变桨协调总图异常点应统一红色",
);
assert(
  diagnosticFarmSeriesColor(
    { label: "07号 正常", originalLabel: "正常", turbineIndex: 0 },
    0,
    "wind_power_scatter",
  ) === DIAGNOSTIC_COLORS.normal &&
    diagnosticFarmSeriesColor(
      { label: "07号 异常", originalLabel: "异常", turbineIndex: 0 },
      0,
      "wind_power_scatter",
    ) === DIAGNOSTIC_COLORS.anomaly &&
    diagnosticFarmSeriesColor(
      { label: "08号 正常", originalLabel: "正常", turbineIndex: 1 },
      1,
      "wind_power_curve",
    ) === DIAGNOSTIC_COLORS.normal &&
    diagnosticFarmSeriesColor(
      { label: "08号 异常", originalLabel: "异常", turbineIndex: 1 },
      1,
      "wind_power_curve",
    ) === DIAGNOSTIC_COLORS.anomaly,
  "风功率曲线/散点 3D 总图应按正常蓝、异常红上色",
);
assert(
  diagnosticFarmSeriesColor(
    { extra: { anomaly: true }, originalLabel: "正常", turbineIndex: 3 },
    0,
    "ctrl_deload",
  ) === DIAGNOSTIC_COLORS.anomaly &&
    diagnosticFarmSeriesColor(
      { extra: { anomaly: true }, originalLabel: "异常", turbineIndex: 3 },
      1,
      "ctrl_deload",
    ) === DIAGNOSTIC_COLORS.anomaly,
  "异常降载机组整机应统一红色",
);
assert(
  diagnosticFarmSeriesColor(
    { originalLabel: "降载", engineName: "08号", turbineIndex: 0 },
    0,
    "ctrl_deload",
  ) === DIAGNOSTIC_COLORS.anomaly &&
    diagnosticFarmSeriesColor(
      { originalLabel: "停机", engineName: "08号", turbineIndex: 0 },
      0,
      "ctrl_deload",
    ) === DIAGNOSTIC_COLORS.anomaly,
  "全场降载总图残留降载/停机系列名应标红而非黄/黑",
);
assert(
  diagnosticFarmSeriesColor(
    { extra: { anomaly: true }, originalLabel: "桨叶 1", turbineIndex: 0 },
    0,
    "pitch_regulation",
  ) === DIAGNOSTIC_COLORS.anomaly &&
    diagnosticFarmSeriesColor(
      { extra: { anomaly: false }, originalLabel: "桨叶 2", turbineIndex: 1 },
      1,
      "pitch_regulation",
    ) === DIAGNOSTIC_COLORS.normal,
  "变桨一致性总图应按机组状态统一红/蓝",
);
assert(
  diagnosticFarmStyle(
    { extra: { anomaly: true }, originalLabel: "桨叶 1" },
    "pitch_regulation",
  ).z >
    diagnosticFarmStyle(
      { extra: { anomaly: false }, originalLabel: "桨叶 2" },
      "pitch_regulation",
    ).z,
  "变桨一致性异常机组应叠在正常机组之上",
);
assert(
  diagnosticFarmSeriesColor(
    { anomaly: true, originalLabel: "异常", turbineIndex: 0 },
    0,
    "aero_cp",
  ) === DIAGNOSTIC_COLORS.anomaly &&
    diagnosticFarmSeriesColor(
      { anomaly: false, originalLabel: "正常", turbineIndex: 1 },
      1,
      "aero_tsr",
    ) === DIAGNOSTIC_COLORS.normal &&
    diagnosticFarmSeriesColor(
      {
        anomaly: false,
        extra: { anomaly: true },
        originalLabel: "正常",
        turbineIndex: 0,
      },
      0,
      "aero_cp",
    ) === DIAGNOSTIC_COLORS.normal,
  "气动总图应按 pointAnomaly 拆分后的点状态上色（行级优先于整机）",
);
assert(
  diagnosticSeriesSymbol({ originalLabel: "正常" }) === "circle" &&
    diagnosticSeriesSymbol({ originalLabel: "异常" }) === "circle" &&
    diagnosticSeriesSymbol({ originalLabel: "降载" }) === "circle",
  "降载总图正常/异常/降载点形应统一为圆形，靠颜色区分",
);

const deloadRateOption = listDetectorPlotOptions(
  {
    panels: [
      {
        panelTitle: "有功功率时序",
        xaxis: "时间",
        yaxis: "有功功率",
        data: [
          {
            label: "正常运行",
            mode: "markers",
            timeData: ["2026-08-22 00:00:00", "2026-08-22 04:00:00"],
            yData: [1000, 1100],
          },
        ],
      },
      {
        panelTitle: "累计降载率随时间变化",
        xaxis: "时间",
        yaxis: "累计降载率 (%)",
        data: [
          {
            label: "累计降载率",
            mode: "markers",
            timeData: [
              "2026-08-22 00:00:00",
              "2026-08-22 04:00:00",
              "2026-08-22 08:00:00",
            ],
            yData: [14, 2, 3],
          },
          {
            label: "正常上限 5%",
            mode: "markers",
            timeData: ["2026-08-22 00:00:00", "2026-08-22 08:00:00"],
            yData: [5, 5],
          },
          {
            label: "频繁阈值 20%",
            mode: "markers",
            timeData: ["2026-08-22 00:00:00"],
            yData: [20],
          },
        ],
      },
    ],
  },
  "002 降载判定分析",
  "anomalyDeloadPO",
  { templateKey: "ctrl_deload" },
);
assert(deloadRateOption.length === 2, "单机降载应按 panel 出图");
const rateChart = deloadRateOption[1];
assert(
  rateChart.series.every((item) => item.type === "line"),
  "累计降载率应为折线图",
);
assert(
  rateChart.series.find((item) => item.name === "累计降载率")?.lineStyle
    .type === "solid",
  "累计降载率应为实线",
);
assert(
  rateChart.series.find((item) => /正常上限/.test(item.name))?.lineStyle
    .type === "dashed",
  "正常上限应为虚线",
);
assert(
  rateChart.series.find((item) => /频繁阈值/.test(item.name))?.lineStyle
    .type === "dashed",
  "频繁阈值应为虚线",
);
assert(
  (rateChart.series.find((item) => /频繁阈值/.test(item.name))?.data || [])
    .length >= 2,
  "阈值线应拉满时间轴",
);

const opFarm = buildFarmPlotJsons(
  [
    {
      engineName: "101",
      plotJson: {
        panels: [
          {
            panelTitle: "投影",
            xaxis: "x",
            yaxis: "y",
            data: [{ label: "簇", xData: [1], yData: [1] }],
          },
          {
            panelTitle: "转速-功率散点",
            xaxis: "转速",
            yaxis: "功率",
            data: [{ label: "散点", xData: [1], yData: [2] }],
          },
        ],
      },
    },
  ],
  "ctrl_op_state",
  "全场运行状态",
);
assert(opFarm.length === 1, "运行状态总图只出一张");
assert(
  /转速/.test(opFarm[0].title + opFarm[0].xaxis),
  "运行状态总图应为转速-功率",
);

const pqFarm = buildFarmPlotJsons(
  [
    {
      engineName: "101",
      plotJson: {
        panels: [
          {
            panelTitle: "三相电流不平衡度 | 异常点: 0",
            xaxis: "时间",
            yaxis: "电流不平衡度",
            data: [{ label: "点", xData: [1], yData: [1] }],
          },
          {
            panelTitle: "三相电压不平衡度 | 异常点: 0",
            xaxis: "时间",
            yaxis: "电压不平衡度",
            data: [{ label: "点", xData: [1], yData: [1] }],
          },
          {
            panelTitle: "功率因数 | 异常点: 0",
            xaxis: "时间",
            yaxis: "功率因数",
            data: [{ label: "点", xData: [1], yData: [1] }],
          },
        ],
      },
    },
  ],
  "ctrl_power_quality",
  "全场电能质量",
);
assert(pqFarm.length === 3, "电能质量总图应出三张");
assert(
  pqFarm.length === getFarmPanelRule("ctrl_power_quality").count,
  "电能质量图数必须与共享报告模板规则一致",
);
assert(
  /电流不平衡/.test(pqFarm[0].title + pqFarm[0].yaxis),
  "第一张应为三相电流不平衡度",
);
assert(
  /电压不平衡/.test(pqFarm[1].title + pqFarm[1].yaxis),
  "第二张应为三相电压不平衡度",
);
assert(
  /功率因数/.test(pqFarm[2].title + pqFarm[2].yaxis),
  "第三张应为功率因数",
);

function loadLocalFarmJson(name) {
  const filePath = path.resolve(__dirname, `../../SVLgVZfi/${name}`);
  if (!fs.existsSync(filePath)) return null;
  return JSON.parse(fs.readFileSync(filePath, "utf8"));
}

function dummyFarmPanel(title, yaxis) {
  return {
    title,
    xaxis: "x",
    yaxis,
    data: [{ label: "占位", xData: [1], yData: [1] }],
  };
}

const localPitchFarm = loadLocalFarmJson("farm_pitch_regulation.json");
if (localPitchFarm) {
  const overlay = farmAggregateToPanels(localPitchFarm, {
    templateKey: "pitch_regulation",
  });
  const filled = fillFarmPanelsToRule(
    overlay,
    [
      dummyFarmPanel("各桨叶分风速段中位数趋势", "桨距角 (°)"),
      dummyFarmPanel("桨叶极差时序及异常识别", "桨叶极差 (°)"),
    ],
    "pitch_regulation",
  );
  assert(filled.length === 2, "变桨一致性页面总图应为 2 面");
  assert(
    /各桨叶分风速段/.test(filled[0].title),
    "第一面应为各桨叶分风速段中位数趋势",
  );
  assert(/极差/.test(filled[1].title), "第二面应为桨叶极差时序及异常识别");
  assert(filled[1].data.length > 1, "极差面应保留 farm JSON 真实叠加");
}

const localCoordFarm = loadLocalFarmJson("farm_pitch_coord.json");
if (localCoordFarm) {
  const overlay = farmAggregateToPanels(localCoordFarm, {
    templateKey: "pitch_coord",
  });
  const filled = fillFarmPanelsToRule(
    overlay,
    [
      dummyFarmPanel("功率-转速散点（桨距角着色）", "有功功率 (kW)"),
      dummyFarmPanel("桨距角-转速散点", "桨距角 (°)"),
    ],
    "pitch_coord",
  );
  assert(filled.length === 2, "变桨协调页面总图应为 2 面");
  assert(/着色/.test(filled[0].title), "变桨协调第一面应为桨距角着色");
  assert(
    /桨距角-转速/.test(filled[1].title),
    "变桨协调第二面应为桨距角-转速散点",
  );
}

const localPqFarm = loadLocalFarmJson("farm_ctrl_power_quality.json");
if (localPqFarm) {
  const overlay = farmAggregateToPanels(localPqFarm, {
    templateKey: "ctrl_power_quality",
  });
  const filled = fillFarmPanelsToRule(
    overlay,
    [
      dummyFarmPanel("三相电流不平衡度", "电流不平衡度"),
      dummyFarmPanel("三相电压不平衡度", "电压不平衡度"),
      dummyFarmPanel("功率因数", "功率因数"),
    ],
    "ctrl_power_quality",
  );
  assert(filled.length === 3, "电能质量页面总图应为 3 面");
  assert(/电流不平衡/.test(filled[0].title), "电能质量第一面应为电流不平衡");
  assert(/电压不平衡/.test(filled[1].title), "电能质量第二面应为电压不平衡");
  assert(/功率因数/.test(filled[2].title), "电能质量第三面应为功率因数");
  assert(filled[2].data.length > 1, "功率因数面应保留 farm JSON 真实叠加");
}

const rawPointCount = 9001;
const rawX = Array.from({ length: rawPointCount }, (_, index) => index);
const rawY = rawX.map((value) => value * 2);
const rawPointFarm = buildFarmPlotJsons(
  [
    {
      engineName: "101",
      plotJson: {
        panels: [
          {
            panelTitle: "转速-功率散点",
            xaxis: "转速",
            yaxis: "功率",
            data: [
              { label: "散点", mode: "markers", xData: rawX, yData: rawY },
            ],
          },
        ],
      },
    },
  ],
  "ctrl_op_state",
  "全场运行状态",
);
assert(
  rawPointFarm[0].data[0].xData.length === rawPointCount,
  "全场图不得抽样原始数据点",
);
const rawPointOption = buildDetectorPlotOption(
  rawPointFarm[0],
  rawPointFarm[0].title,
  "anomalyOperationPO",
);
assert(
  rawPointOption.series[0].data.length === rawPointCount,
  "报告图渲染不得抽样原始数据点",
);
console.log("farm chart rules ok");

const curveFarm = buildFarmPlotJsons(
  [
    {
      engineName: "DT01",
      plotJson: {
        xaxis: "风速 (m/s)",
        yaxis: "有功功率 (kW)",
        data: [
          {
            label: "30天功率曲线",
            mode: "lines",
            timeData: ["2026-01-01 00:00:00", "2026-01-01 01:00:00"],
            xData: [4, 12],
            yData: [200, 2000],
          },
          {
            label: "合同功率曲线",
            mode: "lines",
            timeData: ["2026-01-01 00:00:00", "2026-01-01 01:00:00"],
            xData: [4, 12],
            yData: [180, 2100],
          },
        ],
      },
    },
    {
      engineName: "DT02",
      plotJson: {
        xaxis: "风速 (m/s)",
        yaxis: "有功功率 (kW)",
        data: [
          {
            label: "30天功率曲线",
            mode: "lines",
            timeData: ["2026-01-01 00:00:00", "2026-01-01 01:00:00"],
            xData: [5, 11],
            yData: [300, 1900],
          },
          {
            label: "合同功率曲线",
            mode: "lines",
            xData: [4, 12],
            yData: [180, 2100],
          },
        ],
      },
    },
  ],
  "wind_power_curve",
  "全场功率曲线",
);
assert(curveFarm.length === 1, "功率曲线总图应 1 张");
assert(
  curveFarm[0].data.filter((row) => row.__shared).length === 1,
  "合同功率曲线全场只保留一条",
);
assert(
  curveFarm[0].data[curveFarm[0].data.length - 1].label === "合同功率曲线",
  "合同功率曲线应在图例末尾",
);
const curveOption = buildDetectorPlotOption(
  curveFarm[0],
  curveFarm[0].title,
  "anomalyPowercurvePO",
);
assert(curveOption.grid3D, "功率曲线应输出 3D 图表");
assert(curveOption.xAxis3D?.name === "风机名称", "3D X 轴应为风机名称");
assert(
  curveOption.xAxis3D?._turbineLabelMeta?.labels &&
    typeof curveOption.xAxis3D._turbineLabelMeta.labels === "object",
  "功率曲线风机轴应带可序列化抽样标签，供报告渲染重建 formatter",
);
assert(/风速/.test(String(curveOption.yAxis3D?.name || "")), "3D Y 轴应为风速");
assert(/功率/.test(String(curveOption.zAxis3D?.name || "")), "3D Z 轴应为功率");
assert(
  curveOption.series.some(
    (item) =>
      (item.type === "scatter3D" || item.type === "line3D") &&
      (item.data?.[0]?.[1] === 4 || item.data?.[0]?.[1] === 5),
  ),
  "功率曲线 3D Y 轴应使用风速",
);
assert(
  curveOption.series.some(
    (item) => item.type === "line3D" && item.data?.[0]?.[0] > 0,
  ),
  "功率曲线各风机系列 X 轴应为机组序号",
);
assert(
  !curveOption.series.some(
    (item) =>
      item.type === "scatter3D" && item.name && !/参考|合同/.test(item.name),
  ),
  "功率曲线风机数据不应为 scatter3D",
);

const farmWithContractJson = {
  detector: "wind_power_scatter",
  chartType: "series",
  xaxis: "风速 (m/s)",
  yaxis: "有功功率 (kW)",
  turbines: [
    {
      turbine: "03",
      anomaly: true,
      xType: "number",
      xData: [3.25, 8.25],
      yData: [56.45, 243.89],
    },
    {
      turbine: "06",
      anomaly: false,
      xType: "number",
      xData: [3.25, 8.25],
      yData: [61.4, 238.51],
    },
  ],
  referenceCurve: {
    label: "合同功率曲线",
    xData: [0, 12, 25],
    yData: [0, 595, 850],
    mode: "lines",
  },
};
const farmWithContract = farmAggregateToPanels(farmWithContractJson);
assert(
  farmWithContract[0].data
    .filter((row) => !row.__shared)
    .every((row) => row.mode === "markers"),
  "风功率散点总图风机数据应为散点",
);
assert(
  farmWithContract[0].data.some(
    (row) => row.__shared && row.label === "合同功率曲线" && row.mode === "lines",
  ),
  "总图应绘制 referenceCurve 合同功率曲线",
);
const contractFarmOption = buildDetectorPlotOption(
  farmWithContractJson,
  "全场风功率散点分析",
  "anomalyScatterPO",
);
const contractSeries = (contractFarmOption.series || []).find(
  (item) => item.name === "合同功率曲线",
);
const turbineScatter = (contractFarmOption.series || []).find(
  (item) =>
    item.name !== "合同功率曲线" &&
    (item.type === "scatter3D" || item.type === "scatter"),
);
assert(contractSeries, "合同功率曲线应出现在总图中");
assert(
  contractSeries.type === "line3D" || contractSeries.type === "line",
  "合同功率曲线应为折线",
);
assert(turbineScatter, "风功率散点总图风机数据应为散点");
assert(
  /#7C3AED|rgb\(124,\s*58,\s*237\)/i.test(
    String(contractSeries.lineStyle?.color || ""),
  ),
  `合同功率曲线应与异常红区分，实际为 ${contractSeries.lineStyle?.color}`,
);
assert(
  Number(contractSeries.lineStyle?.width) >= 3,
  "合同功率曲线应比风机散点更醒目",
);
assert(contractSeries.lineStyle?.type === "solid", "合同功率曲线应为实线");

const misplacedX = buildDetectorPlotOption(
  {
    title: "DT01 功率曲线分析",
    xaxis: "风速 (m/s)",
    yaxis: "有功功率 (kW)",
    data: [
      {
        label: "30天功率曲线",
        mode: "lines",
        timeData: ["2026-01-01 00:00:00", "2026-01-01 01:00:00"],
        xData: [6, 10],
        yData: [400, 1800],
      },
    ],
  },
  "DT01 功率曲线分析",
  "anomalyPowercurvePO",
);
assert(misplacedX.series[0].data[0][0] === 6, "单机功率曲线应取 xData 风速");
assert(!misplacedX.grid3D, "单机功率曲线应保持 2D");
assert(misplacedX.xAxis?.type === "value", "单机功率曲线风速轴应为数值轴");

const pitchCoordFarm = buildFarmPlotJsons(
  [
    {
      engineName: "DT40",
      plotJson: {
        xaxis: "转速 (r/min)",
        yaxis: "有功功率 (kW)",
        data: [
          {
            label: "散点",
            mode: "lines",
            timeData: ["2026-01-01 00:00:00", "2026-01-01 01:00:00"],
            xData: [8, 12],
            yData: [400, 1800],
          },
        ],
      },
    },
  ],
  "pitch_coord",
  "全场变桨协调",
);
const pitchCoordOption = buildDetectorPlotOption(
  pitchCoordFarm[0],
  pitchCoordFarm[0].title,
  "anomalyPitchcoordPO",
);
assert(pitchCoordOption.series[0].type === "scatter", "变桨协调应输出散点");
assert(pitchCoordOption.series[0].data[0][0] === 8, "变桨协调 X 轴应为转速");
assert(
  Number(pitchCoordOption.series[0].symbolSize) >= 3 &&
    Number(pitchCoordOption.series[0].symbolSize) <= 5,
  "全场变桨协调报告散点应更小",
);
const rangedOpFarm = listFarmChartOptions(
  [
    {
      engineName: "01",
      plotJson: {
        panels: [
          {
            panelTitle: "转速-功率散点图",
            xaxis: "转速(r/min)",
            yaxis: "有功功率(kW)",
            xrange: [0, 20],
            yrange: [0, 500],
            data: [
              {
                label: "异常点",
                mode: "markers",
                xData: [12, 40],
                yData: [200, 900],
              },
            ],
          },
        ],
      },
    },
  ],
  "全场运行状态分析",
  "anomalyOperationPO",
  { templateKey: "ctrl_op_state" },
);
assert(rangedOpFarm.length === 1, "运行状态总图应保留转速-功率散点");
assert(
  rangedOpFarm[0].xAxis.min == null && rangedOpFarm[0].xAxis.max == null,
  "运行状态总图横轴应自适应",
);
assert(
  rangedOpFarm[0].yAxis.min == null && rangedOpFarm[0].yAxis.max == null,
  "运行状态总图纵轴应自适应",
);
assert(
  rangedOpFarm[0].xAxis.scale === true && rangedOpFarm[0].yAxis.scale === true,
  "运行状态总图应按数据自适应缩放",
);
const rangedPitch = listDetectorPlotOptions(
  {
    detector_name: "pitch_coord",
    panels: [
      {
        panelTitle: "功率-转速散点（桨距角着色）",
        xaxis: "转速 (r/min)",
        yaxis: "有功功率 (kW)",
        xrange: [5, 18],
        yrange: [0, 1500],
        data: [
          {
            label: "异常点",
            mode: "markers",
            xData: [8, 30],
            yData: [400, 2200],
          },
        ],
      },
    ],
  },
  "01 变桨-转速-功率协调分析",
  "anomalyPitchcoordPO",
  { templateKey: "pitch_coord" },
);
assert(
  rangedPitch[0].xAxis.min === 1000 && rangedPitch[0].xAxis.max == null,
  "变桨协调分图横轴应从 1000 起，上限自适应",
);
assert(
  rangedPitch[0].yAxis.min === 0 && rangedPitch[0].yAxis.max === 1500,
  "变桨协调分图纵轴仍按 yrange",
);

const pitchCoordFarmTwo = buildFarmPlotJsons(
  [
    {
      engineName: "001",
      plotJson: {
        panels: [
          {
            panelTitle: "功率-转速散点（桨距角着色）",
            xaxis: "转速 (r/min)",
            yaxis: "有功功率 (kW)",
            data: [
              {
                label: "正常数据",
                mode: "markers",
                xData: [8, 12],
                yData: [400, 1800],
                colorbar: [1, 8],
                colorbarTitle: "桨距角 (°)",
              },
              {
                label: "异常点",
                mode: "markers",
                xData: [10],
                yData: [900],
                colorbar: [12],
              },
            ],
          },
          {
            panelTitle: "桨距角-转速散点",
            xaxis: "转速 (r/min)",
            yaxis: "桨距角 (°)",
            data: [
              {
                label: "正常数据",
                mode: "markers",
                xData: [8, 12],
                yData: [1, 8],
              },
            ],
          },
        ],
      },
    },
    {
      engineName: "005",
      plotJson: {
        panels: [
          {
            panelTitle: "功率-转速散点（桨距角着色）",
            xaxis: "转速 (r/min)",
            yaxis: "有功功率 (kW)",
            data: [
              {
                label: "正常数据",
                mode: "markers",
                xData: [9, 11],
                yData: [500, 1600],
                colorbar: [2, 7],
                colorbarTitle: "桨距角 (°)",
              },
            ],
          },
          {
            panelTitle: "桨距角-转速散点",
            xaxis: "转速 (r/min)",
            yaxis: "桨距角 (°)",
            data: [
              {
                label: "正常数据",
                mode: "markers",
                xData: [9, 11],
                yData: [2, 7],
              },
            ],
          },
        ],
      },
    },
  ],
  "pitch_coord",
  "全场变桨协调分析检测汇总",
);
assert(pitchCoordFarmTwo.length === 2, "变桨协调总图应为 2 张");
assert(
  /功率-转速散点（桨距角着色）/.test(pitchCoordFarmTwo[0].title),
  "第一张应为功率-转速散点（桨距角着色）",
);
assert(
  /桨距角-转速散点/.test(pitchCoordFarmTwo[1].title),
  "第二张应为桨距角-转速散点",
);
const pitchColorOption = buildDetectorPlotOption(
  pitchCoordFarmTwo[0],
  pitchCoordFarmTwo[0].title,
  "anomalyPitchcoordPO",
);
assert(!pitchColorOption.visualMap, "功率-转速全场图应按状态色而非桨距角着色");
assert(
  pitchColorOption.series.some((item) => /001/.test(item.name)) &&
    pitchColorOption.series.some((item) => /005/.test(item.name)),
  "功率-转速全场图应按机组保留系列",
);
assert(
  pitchColorOption.series.reduce(
    (sum, item) => sum + (Array.isArray(item.data) ? item.data.length : 0),
    0,
  ) === 5,
  "功率-转速全场图合并后不得丢失原始点",
);
const pitchSpeedOption = buildDetectorPlotOption(
  pitchCoordFarmTwo[1],
  pitchCoordFarmTwo[1].title,
  "anomalyPitchcoordPO",
);
assert(
  pitchSpeedOption.series.some((item) => /001/.test(item.name)) &&
    pitchSpeedOption.series.some((item) => /005/.test(item.name)),
  "桨距角-转速图应按机组保留系列",
);

const pitchStyleOption = listDetectorPlotOptions(
  {
    xaxis: "风速分箱中心值 (m/s)",
    yaxis: "桨距角中位数 (°)",
    data: [
      {
        label: "桨叶 1",
        mode: "lines",
        xData: [2, 6],
        yData: [1, 2],
        timeData: ["t1", "t2"],
      },
      {
        label: "桨叶 2",
        mode: "lines",
        xData: [2, 6],
        yData: [1.1, 2.1],
      },
      {
        label: "桨叶 3",
        mode: "lines",
        xData: [2, 6],
        yData: [1.2, 2.2],
      },
    ],
  },
  "DT01 变桨一致性分析",
  "anomalyPitchregulationPO",
  { templateKey: "pitch_regulation" },
)[0];
assert(pitchStyleOption.series.length === 3, "单机变桨一致性应绘出三支桨叶");
assert(pitchStyleOption.series[0].symbol === "rect", "桨叶1应为方形");
assert(pitchStyleOption.series[0].lineStyle.type === "dashed", "桨叶1应为虚线");
assert(pitchStyleOption.series[1].symbol === "circle", "桨叶2应为圆形");
assert(pitchStyleOption.series[1].lineStyle.type === "solid", "桨叶2应为实线");
assert(pitchStyleOption.series[2].symbol === "triangle", "桨叶3应为三角");
assert(pitchStyleOption.series[2].lineStyle.type === "dotted", "桨叶3应为点线");
assert(
  pitchStyleOption.series[0].data[0][0] === 2,
  "变桨一致性 X 轴应使用分箱风速",
);

const yawErrorOptions = listDetectorPlotOptions(
  {
    title: "静态偏航误差分析",
    xaxis: "对风角度/偏航误差 (°)",
    yaxis: "平均有功功率 (kW)",
    data: [
      {
        label: "4.5-5.0 m/s",
        mode: "lines+markers",
        xData: [-2, 0, 4],
        yData: [300, 420, 380],
      },
      {
        label: "5.0-5.5 m/s",
        mode: "lines+markers",
        xData: [-1, 2, 5],
        yData: [410, 500, 430],
      },
    ],
  },
  "01 静态偏航误差分析",
  "anomalyYawErrorPO",
  { templateKey: "yaw_error" },
);
assert(yawErrorOptions.length === 1, "偏航误差单机图应按 data[] 出一张");
assert(yawErrorOptions[0].series.length === 2, "偏航误差应按风速箱分系列");
assert(yawErrorOptions[0].xAxis.min === -20, "偏航误差单机图 X 轴最小应为 -20");
assert(yawErrorOptions[0].xAxis.max === 20, "偏航误差单机图 X 轴最大应为 20");
assert(
  yawErrorOptions[0].series[0].markPoint?.data?.[0]?.coord?.[0] === 0,
  "偏航误差应标注平均功率最大的对风角度",
);

const yawCountOptions = listDetectorPlotOptions(
  {
    title: "偏航次数检测（CW/CCW 跳变）",
    xaxis: "时间",
    yaxis: "状态 (0/1)",
    data: [
      {
        label: "CW 偏航状态",
        mode: "lines",
        xData: ["2026-09-11 00:00:00", "2026-09-11 00:01:00"],
        yData: [0, 1],
        timeData: ["2026-09-11 00:00:00", "2026-09-11 00:01:00"],
      },
      {
        label: "CCW 偏航状态",
        mode: "lines",
        xData: ["2026-09-11 00:00:00", "2026-09-11 00:01:00"],
        yData: [0, 0],
        timeData: ["2026-09-11 00:00:00", "2026-09-11 00:01:00"],
      },
    ],
  },
  "01 偏航次数分析",
  "anomalyYawCountPO",
  { templateKey: "yaw_count" },
);
assert(yawCountOptions[0].yAxis.min === -0.05, "偏航次数 Y 轴应为 0/1");
assert(yawCountOptions[0].series.length === 2, "偏航次数应绘制 CW/CCW");
assert(yawCountOptions[0].xAxis.type === "time", "偏航次数横轴应为时间");

const pitchMinOptions = listDetectorPlotOptions(
  {
    title: "最小桨距角分布",
    xaxis: "时间",
    yaxis: "桨距角 (°)",
    xrange: ["2026-09-05", "2026-09-11"],
    yrange: [-1.0, 4.5],
    data: [
      {
        label: "最小桨距角分布",
        mode: "markers",
        xData: ["2026-09-05", "2026-09-06"],
        yData: [-0.5, 0.2],
        colorbar: [120, 40],
        colorbarTitle: "点数",
      },
    ],
  },
  "01 最小桨距角分析",
  "anomalyMinpitchPO",
  { templateKey: "pitch_min" },
);
assert(pitchMinOptions[0].series[0].type === "scatter", "最小桨距角应为散点");
assert(
  typeof pitchMinOptions[0].series[0].symbolSize === "number",
  "最小桨距角点径应统一，不再按点数缩放气泡",
);
{
  const vm = pitchMinOptions[0].visualMap;
  const colors = Array.isArray(vm) ? vm[0]?.inRange?.color : vm?.inRange?.color;
  const step = (colors?.length || 1) - 1;
  const stopsOk = PITCH_MIN_REPORT_COLOR_STOPS.every(([ratio, color]) => {
    const index = Math.round(ratio * step);
    return colors?.[index]?.toLowerCase() === color.toLowerCase();
  });
  assert(
    stopsOk &&
      colors?.[0]?.toLowerCase() ===
        PITCH_MIN_REPORT_COLOR_STOPS[0][1].toLowerCase() &&
      colors?.slice(-1)[0]?.toLowerCase() ===
        PITCH_MIN_REPORT_COLOR_STOPS.at(-1)[1].toLowerCase(),
    "最小桨距角报告色带应与 PITCH_MIN_REPORT_COLOR_STOPS 停靠一致",
  );
}

const opStateOptions = listDetectorPlotOptions(
  {
    detector_name: "ctrl_op_state",
    title: "运行状态综合异常检测",
    panels: [
      {
        panelTitle: "转速-功率散点图",
        xaxis: "转速(r/min)",
        yaxis: "有功功率(kW)",
        xrange: [0, 30],
        yrange: [0, 800],
        data: [
          {
            label: "正常数据",
            mode: "markers",
            xData: [12, 18],
            yData: [320, 500],
          },
        ],
      },
      {
        panelTitle: "PCA 降维投影",
        xaxis: "PC1",
        yaxis: "PC2",
        componentDefinition: [
          {
            name: "PC1",
            explainedVarianceRatio: 0.63,
            loadings: [
              {
                feature: "p_active",
                displayName: "z(功率)",
                coefficient: 0.81,
                standardized: true,
              },
            ],
          },
        ],
        data: [
          {
            label: "投影点",
            mode: "markers",
            xData: [0.1],
            yData: [0.2],
          },
        ],
      },
    ],
  },
  "01 运行状态分析",
  "anomalyOperationPO",
  { templateKey: "ctrl_op_state" },
);
assert(opStateOptions.length === 2, "运行状态单机图应按 panels[] 全部出图");
assert(
  opStateOptions[0].xAxis.min == null && opStateOptions[0].xAxis.max == null,
  "运行状态转速-功率分图横轴应自适应",
);
assert(
  opStateOptions[0].yAxis.min == null && opStateOptions[0].yAxis.max == null,
  "运行状态转速-功率分图纵轴应自适应",
);
assert(
  opStateOptions[0].xAxis.scale === true && opStateOptions[0].yAxis.scale === true,
  "运行状态分图应按数据自适应缩放",
);
assert(
  opStateOptions[1].xAxis.min == null && opStateOptions[1].yAxis.min == null,
  "运行状态没有 xrange/yrange 的分图应自适应",
);
assert(
  opStateOptions[1].xAxis.scale === true && opStateOptions[1].yAxis.scale === true,
  "运行状态缺省轴范围时应自适应缩放",
);
assert(
  !String(opStateOptions[1].title?.subtext || "").includes("PC1"),
  "componentDefinition 不应再被前端拼接为小标题",
);
assert(
  resolvePanelSubtitle({ subtitle: "算法小标题" }, {}) === "算法小标题",
  "小标题应仅透传 JSON subtitle 字段",
);

const farmCurveOptions = listDetectorPlotOptions(
  {
    detector: "wind_power_curve",
    farm: "XX风电场",
    chartType: "series",
    xaxis: "风速 (m/s)",
    yaxis: "有功功率 (kW)",
    xrange: [3.25, 25.25],
    turbines: [
      {
        turbine: "a1",
        anomaly: false,
        xType: "number",
        xData: [3.25, 4],
        yData: [120, 200],
        ratio: 1.02,
        status: "正常",
      },
      {
        turbine: "a2",
        anomaly: true,
        xType: "number",
        xData: [3.25, 4],
        yData: [80, 150],
        ratio: 0.7,
        status: "欠发",
      },
    ],
  },
  "全场功率曲线分析检测汇总",
  "anomalyPowercurvePO",
  {
    templateKey: "wind_power_curve",
    farmMode: true,
    nameMap: { a1: "01", a2: "02" },
  },
);
assert(farmCurveOptions.length === 1, "风场功率曲线总图应为一张叠加图");
assert(farmCurveOptions[0].grid3D, "风场功率曲线总图应按全场 3D 渲染");
assert(
  farmCurveOptions[0].series.some((item) => /01号/.test(item.name)),
  "风场功率曲线总图应画出正常机组",
);
assert(
  !(Array.isArray(farmCurveOptions[0].legend)
    ? farmCurveOptions[0].legend
    : [farmCurveOptions[0].legend]
  ).some((item) => (item?.data || []).some((name) => /01号/.test(name))),
  "风场功率曲线总图图例不展示正常机组",
);
assert(
  farmCurveOptions[0].series.some((item) => /02号/.test(item.name)),
  "风场功率曲线总图应展示异常机组",
);
assert(
  farmCurveOptions[0].legend?.orient === "vertical" &&
    farmCurveOptions[0].legend?.right != null &&
    farmCurveOptions[0].grid3D?.right >= 140,
  "功率曲线图例应在三维图右侧",
);
const manyAnomalyTurbines = Array.from({ length: 9 }, (_, index) => ({
  turbine: `b${index + 1}`,
  anomaly: true,
  xType: "number",
  xData: [3.25, 4],
  yData: [80, 150],
  status: "异常",
}));
const packedCurveOptions = listDetectorPlotOptions(
  {
    detector: "wind_power_curve",
    chartType: "series",
    xaxis: "风速 (m/s)",
    yaxis: "有功功率 (kW)",
    turbines: [
      {
        turbine: "ok",
        anomaly: false,
        xData: [3.25, 4],
        yData: [120, 200],
        status: "正常",
      },
      ...manyAnomalyTurbines,
    ],
  },
  "全场功率曲线分析检测汇总",
  "anomalyPowercurvePO",
  { templateKey: "wind_power_curve", farmMode: true },
);
assert(
  packedCurveOptions[0].series.length === 10,
  "功率曲线总图应画出正常机组和全部异常机组",
);
assert(
  !Array.isArray(packedCurveOptions[0].legend) &&
    packedCurveOptions[0].legend?.orient === "vertical" &&
    packedCurveOptions[0].legend?.data?.length === 9,
  "异常机组较多时图例仍在右侧且不遗漏",
);

const farmYawErrorOptions = listDetectorPlotOptions(
  {
    detector: "yaw_error",
    chartType: "value",
    turbines: [
      {
        turbine: "a1",
        anomaly: true,
        value: 15,
        powerloss: 2.1,
        coverage: 0.9,
      },
      {
        turbine: "a2",
        anomaly: false,
        value: 3,
        powerloss: 0.1,
        coverage: 0.95,
      },
    ],
  },
  "全场静态偏航误差分析检测汇总",
  "anomalyYawErrorPO",
  {
    templateKey: "yaw_error",
    farmMode: true,
    nameMap: { a1: "01", a2: "02" },
  },
);
assert(
  farmYawErrorOptions.length === 1,
  "风场静态偏航误差总图应为一张三档着色柱图",
);
assert(
  farmYawErrorOptions[0].series[0].type === "bar",
  "风场偏航误差总图应为柱状对比",
);
assert(
  farmYawErrorOptions[0].legend?.data
    ?.map((item) => item?.name ?? item)
    .join(",") === "[0,3],(3,5],>5",
  "风场偏航误差总图应按绝对值三档着色",
);
assert(
  !farmYawErrorOptions[0].series.some((item) => item.name === "风场均值"),
  "风场偏航误差总图不应再画均值线",
);
assert(
  farmYawErrorOptions[0].yAxis.name === "静态偏航误差（度）",
  "风场偏航误差纵轴应直接表示 JSON value",
);
const signedYaw = listDetectorPlotOptions(
  {
    detector: "yaw_error",
    chartType: "value",
    turbines: [{ turbine: "a1", anomaly: true, value: -8 }],
  },
  "全场静态偏航误差分析检测汇总",
  "anomalyYawErrorPO",
  { templateKey: "yaw_error", farmMode: true },
);
const signedBar = signedYaw[0].series
  .flatMap((item) => item.data || [])
  .find((item) => item && item.value != null);
assert(signedBar?.value === -8, "静态偏航误差应使用 JSON value，不取绝对值");
assert(
  farmYawErrorOptions[0].series.find((item) => item.name === ">5")?.data?.[0]
    ?.itemStyle?.color === "#F04438",
  "大于5°的机组应标红",
);
assert(
  farmYawErrorOptions[0].series.find((item) => item.name === "[0,3]")?.data?.[1]
    ?.itemStyle?.color === "#12B76A",
  "小于等于3°的机组应标绿",
);

const farmYawCountOptions = listDetectorPlotOptions(
  {
    detector: "yaw_count",
    chartType: "value",
    turbines: [
      { turbine: "a1", anomaly: true, value: 18, cw: 10, ccw: 8 },
      { turbine: "a2", anomaly: false, value: 6, cw: 3, ccw: 3 },
      { turbine: "a3", anomaly: true, value: 22, cw: 12, ccw: 10 },
    ],
  },
  "全场偏航次数分析检测汇总",
  "anomalyYawCountPO",
  {
    templateKey: "yaw_count",
    farmMode: true,
    nameMap: { a1: "01", a2: "02", a3: "03" },
  },
);
assert(
  legendNames(farmYawCountOptions[0]).join(",") === "正常机组,01号,03号",
  "偏航次数总图图例应列出正常色和全部异常机组",
);
assert(
  farmYawCountOptions[0].series.find((item) => item.name === "正常机组")?.data?.[1]
    ?.itemStyle?.color === DIAGNOSTIC_COLORS.normal,
  "偏航次数正常机组应统一蓝色",
);
assert(
  farmYawCountOptions[0].series.find((item) => item.name === "01号")?.data?.[0]
    ?.itemStyle?.color === DIAGNOSTIC_COLORS.anomaly,
  "偏航次数异常机组应标红",
);
assert(
  farmYawCountOptions[0].series.some((item) => item.name === "03号"),
  "偏航次数图例应包含全部异常机组",
);

const farmPitchMinOptions = listDetectorPlotOptions(
  {
    detector: "pitch_min",
    chartType: "value",
    turbines: [
      { turbine: "a1", anomaly: true, value: 2.4, p10: 1.2, baseline: -0.2 },
      { turbine: "a2", anomaly: false, value: 0.3, p10: 0.2, baseline: 0 },
    ],
  },
  "全场最小桨距角分析检测汇总",
  "anomalyMinpitchPO",
  {
    templateKey: "pitch_min",
    farmMode: true,
    nameMap: { a1: "01", a2: "02" },
  },
);
assert(
  legendNames(farmPitchMinOptions[0]).join(",") === "正常机组,01号",
  "最小桨距角总图图例应列出正常色和异常机组",
);
assert(
  farmPitchMinOptions[0].series.find((item) => item.name === "01号")?.data?.[0]
    ?.itemStyle?.color === DIAGNOSTIC_COLORS.anomaly,
  "最小桨距角异常机组应标红",
);

const farmAeroOptions = listDetectorPlotOptions(
  {
    detector: "aero_cp",
    chartType: "series",
    xaxis: "风速 (m/s)",
    yaxis: "Cp",
    turbines: [
      {
        turbine: "t1",
        anomaly: true,
        xData: [6, 8],
        yData: [0.4, 0.45],
      },
      {
        turbine: "t2",
        anomaly: false,
        xData: [6, 8],
        yData: [0.42, 0.48],
      },
    ],
  },
  "全场Cp功率系数分析检测汇总",
  "anomalyCpPO",
  { templateKey: "aero_cp", farmMode: true },
);
assert(
  legendNames(farmAeroOptions[0]).includes("正常机组"),
  "Cp总图应标明正常机组为蓝色",
);
assert(
  legendNames(farmAeroOptions[0]).includes("t1"),
  "Cp总图图例应展示异常机组",
);
assert(
  !legendNames(farmAeroOptions[0]).includes("t2"),
  "Cp总图有异常时图例不列正常机组",
);
assert(
  farmAeroOptions[0].series.find((item) => item.name === "t1")?.itemStyle
    ?.color === DIAGNOSTIC_COLORS.anomaly ||
    String(
      farmAeroOptions[0].series.find((item) => item.name === "t1")?.itemStyle
        ?.color || "",
    ).startsWith(DIAGNOSTIC_COLORS.anomaly),
  "Cp异常机组应标红",
);

const farmAeroPointOptions = listDetectorPlotOptions(
  {
    detector: "aero_cp",
    chartType: "series",
    xaxis: "功率 (kW)",
    yaxis: "功率系数 Cp",
    turbines: [
      {
        turbine: "26",
        anomaly: true,
        xData: [100, 200, 300],
        yData: [0.2, 0.3, 0.25],
        pointAnomaly: [false, true, false],
      },
      {
        turbine: "27",
        anomaly: false,
        xData: [120, 220],
        yData: [0.22, 0.28],
        pointAnomaly: [false, false],
      },
    ],
  },
  "全场Cp功率系数分析检测汇总",
  "anomalyCpPO",
  { templateKey: "aero_cp", farmMode: true, nameMap: { 26: "26", 27: "27" } },
);
const aeroDataSeries = (farmAeroPointOptions[0].series || []).filter(
  (item) => Array.isArray(item.data) && item.data.length > 0,
);
assert(
  aeroDataSeries.length === 3,
  "Cp总图应按 pointAnomaly 拆分：26/27号正常点各一条，26号异常点一条",
);
const aero26NormalSeries = aeroDataSeries.find(
  (item) =>
    String(item.name || "").includes("26") &&
    /正常/.test(String(item.name || "")),
);
const aero26AnomalySeries = aeroDataSeries.find(
  (item) =>
    String(item.name || "").includes("26") &&
    !/正常/.test(String(item.name || "")),
);
assert(
  aero26NormalSeries &&
    String(aero26NormalSeries.itemStyle?.color || "").startsWith(
      DIAGNOSTIC_COLORS.normal,
    ),
  "Cp总图 pointAnomaly=false 的点应为蓝色",
);
assert(
  aero26AnomalySeries &&
    String(aero26AnomalySeries.itemStyle?.color || "").startsWith(
      DIAGNOSTIC_COLORS.anomaly,
    ),
  "Cp总图 pointAnomaly=true 的点应为红色",
);
assert(
  aero26NormalSeries?.data?.length === 2 &&
    aero26AnomalySeries?.data?.length === 1,
  "26号正常点保留 2 个（下标 0/2），异常点仅 1 个（下标 1）",
);
assert(
  !(farmAeroPointOptions[0].series || []).some(
    (item) => String(item.name || "") === "27号" && item.data?.length,
  ),
  "27号无异常点时不单独成异常系列",
);

const farmYawStaticOptions = listDetectorPlotOptions(
  {
    detector: "yaw_static",
    chartType: "series",
    xaxis: "时间",
    yaxis: "偏航角 (°)",
    turbines: [
      {
        turbine: "t1",
        anomaly: true,
        xType: "time",
        xData: [
          "2026-09-11 00:00:00",
          "2026-09-11 00:01:00",
          "2026-09-11 00:02:00",
        ],
        yData: [10, 80, 12],
        pointAnomaly: [false, true, false],
      },
    ],
  },
  "全场偏航分析检测汇总",
  "anomalyStaticyawPO",
  { templateKey: "yaw_static", farmMode: true },
);
const yawStaticDataSeries = farmYawStaticOptions[0].series.filter(
  (item) => item.data?.length,
);
assert(
  yawStaticDataSeries.length === 2,
  "风场静态偏航总图应按 pointAnomaly 拆成正常/异常点",
);
assert(
  farmYawStaticOptions[0].series.every((item) => {
    const size = Number(item.symbolSize) || 0;
    if (!item.data?.length) return true;
    return size > 0 && size <= 4;
  }) &&
    new Set(
      farmYawStaticOptions[0].series
        .filter((item) => item.data?.length)
        .map((item) => Number(item.symbolSize)),
    ).size === 1,
  "全场静态偏航报告正常点与异常点大小应一致",
);
assert(
  Number(yawStaticDataSeries[0].itemStyle?.opacity) <= 0.7,
  "全场静态偏航报告散点透明度应更低",
);
assert(
  legendNames(farmYawStaticOptions[0]).includes("正常机组"),
  "偏航总图应标明正常点为蓝色",
);
assert(
  legendNames(farmYawStaticOptions[0]).includes("t1"),
  "偏航总图有异常时应在图例展示异常机组",
);
assert(
  farmYawStaticOptions[0].series.some(
    (item) =>
      item.name === "t1" && item.itemStyle?.color?.startsWith("#F04438"),
  ),
  "偏航总图异常点应为统一红色",
);
assert(
  farmYawStaticOptions[0].series.some(
    (item) =>
      /正常/.test(item.name) && item.itemStyle?.color?.startsWith("#2E90FA"),
  ),
  "偏航总图正常点应为统一蓝色",
);
assert(
  typeof farmYawStaticOptions[0].yAxis?.axisLabel?.formatter === "function",
  "报告 Y 轴应保留 2 位小数",
);
assert(
  formatReportValueAxisLabel(-1416.9293302406602) === "-1,416.93",
  "Y 轴刻度应格式化为 2 位小数",
);

const stitchedFarmJson = turbinePlotsToFarmJson(
  [
    {
      engineId: "a1",
      engineName: "01",
      plotJson: {
        title: "功率曲线",
        xaxis: "风速 (m/s)",
        yaxis: "有功功率 (kW)",
        data: [
          {
            label: "实测功率",
            mode: "lines",
            xData: [3.25, 4],
            yData: [120, 200],
            ratio: 1.02,
            status: "正常",
          },
        ],
      },
    },
    {
      engineId: "a2",
      engineName: "02",
      po: { detectorIsAnomaly: 1 },
      plotJson: {
        title: "功率曲线",
        xaxis: "风速 (m/s)",
        yaxis: "有功功率 (kW)",
        data: [
          {
            label: "实测功率",
            mode: "lines",
            xData: [3.25, 4],
            yData: [80, 150],
            ratio: 0.7,
            status: "欠发",
          },
        ],
      },
    },
  ],
  "wind_power_curve",
);
assert(stitchedFarmJson?.chartType === "series", "单机 JSON 拼总图应为 series");
assert(
  stitchedFarmJson.turbines.length === 2,
  "单机 JSON 拼总图应包含全部风机",
);
const stitchedFarmOptions = listFarmPlotOptionsFromTurbines(
  [
    {
      engineId: "a1",
      engineName: "01",
      plotJson: {
        title: "功率曲线",
        xaxis: "风速 (m/s)",
        yaxis: "有功功率 (kW)",
        data: [{ label: "实测功率", xData: [3.25, 4], yData: [120, 200] }],
      },
    },
  ],
  "全场功率曲线分析检测汇总",
  "anomalyPowercurvePO",
  {
    templateKey: "wind_power_curve",
    nameMap: { a1: "01" },
  },
);
assert(stitchedFarmOptions[0].grid3D, "报告总图应与页面一样走全场 3D");

const yawErrorFarmFromTurbines = listFarmPlotOptionsFromTurbines(
  [
    {
      engineId: "a1",
      engineName: "01",
      po: { detectorIsAnomaly: 1, detectorAnomalyRate: 15 },
      plotJson: {
        xaxis: "对风角度/偏航误差 (°)",
        yaxis: "平均有功功率 (kW)",
        data: [
          {
            label: "4.5-5.0 m/s",
            xData: [-2, 0, 4],
            yData: [300, 420, 380],
          },
          {
            label: "5.0-5.5 m/s",
            xData: [-1, 2, 5],
            yData: [410, 500, 430],
          },
        ],
      },
    },
    {
      engineId: "a2",
      engineName: "02",
      po: { detectorIsAnomaly: 0, detectorAnomalyRate: 3 },
      plotJson: {
        data: [
          {
            label: "4.5-5.0 m/s",
            xData: [-1, 3],
            yData: [200, 260],
          },
        ],
      },
    },
  ],
  "全场静态偏航误差分析检测汇总",
  "anomalyYawErrorPO",
  {
    templateKey: "yaw_error",
    nameMap: { a1: "01", a2: "02" },
  },
);
assert(
  yawErrorFarmFromTurbines.length === 1,
  "报告偏航误差总图应为一张三档着色柱图",
);
assert(
  yawErrorFarmFromTurbines[0].series[0].type === "bar",
  "报告偏航误差总图应与页面一样为单值柱状",
);
assert(
  legendNames(yawErrorFarmFromTurbines[0]).join(",") === "[0,3],(3,5],>5",
  "报告偏航误差总图应按绝对值三档着色",
);

const emptyOption = buildEmptyPlaceholderOption("全场检测");
assert(!optionHasData(emptyOption), "占位图不应视为有数据");
assert(
  JSON.stringify(emptyOption.title).includes("暂无"),
  "无 JSON 时应输出暂无占位",
);

const watchlist = pickKeyTurbines(
  [
    { engineId: "1", turbineName: "DT01", anomalyPoints: 99 },
    { engineId: "2", turbineName: "DT02", anomalyPoints: 1 },
  ],
  [
    { engineId: "2", model3PitchPitchcoordRatio: 0.6 },
    { engineId: "1", model3PitchPitchcoordRatio: 0.1 },
  ],
);
assert(
  watchlist.length === 1 && watchlist[0].turbineName === "DT02",
  "第6章应只取关注机组",
);
assert(
  pickKeyTurbines([{ engineId: "1" }], []).length === 0,
  "无关注机组时应为空",
);
assert(
  pickKeyTurbines(
    [
      { engineId: "1", turbineName: "DT01" },
      { engineId: "2", turbineName: "DT02" },
      { engineId: "3", turbineName: "DT03" },
      { engineId: "4", turbineName: "DT04" },
    ],
    [
      { engineId: "1", model2YawYawcountRatio: 0.9 },
      { engineId: "2", model2YawYawerrorRatio: 0.9 },
      { engineId: "3", model3PitchMinpitchRatio: 0.9 },
      { engineId: "4", model4CtrlparamDeloadRatio: 0.9 },
      { engineId: "5", model1WindpwrPowercurveRatio: 0.9 },
    ],
  ).length === 0,
  "偏航次数/静态偏航误差/最小桨距角/降载/功率曲线不应参与关注机组判定",
);

const emptyRowsPayload = buildDetectorSectionPayload(
  DETECTOR_TEMPLATE_CONFIG.find((cfg) => cfg.templateKey === "pitch_coord"),
  {
    farmImages: [{ image: "demo" }],
    turbineImages: [{ image: "demo" }],
    rows: [],
    hasChartData: true,
  },
);
assert(
  emptyRowsPayload.pitch_coordRows[0].turbine_name === "暂无",
  "异常机组清单无数据时应写暂无",
);

const emptySensorRows = buildSensorAnomalyRows([]);
assert(
  emptySensorRows[0].turbine_name === "暂无",
  "数据感知统计无数据时应写暂无",
);

const sensorTableRows = buildSensorAnomalyRows([
  {
    engineName: "10",
    sensorAnomalyWindPwr: 1,
    sensorAnomalyWindPwrRatio: 0.086,
  },
]);
assert(sensorTableRows.length === 1, "数据感知表应按异常类型分行");
assert(
  sensorTableRows[0].ratio === "8.60%",
  `数据感知占比应来自 ratio 字段，实际 ${sensorTableRows[0].ratio}`,
);
assert(
  sensorTableRows[0].sensor_anomaly_type === "风速-功率逻辑异常",
  "数据感知表异常类型应与 flag 字段对应",
);

const majorTempRows = buildSensorAnomalyRows([
  {
    engineName: "10",
    sensorAnomalyMajorTemp: 1,
    sensorAnomalyMajorTempRatio: 0.3,
  },
]);
assert(
  majorTempRows.length === 1 &&
    majorTempRows[0].sensor_anomaly_type === "大部件温度异常" &&
    majorTempRows[0].ratio === "30.00%",
  "数据感知表应支持大部件温度异常行",
);
assert(
  formatSensorTypeText({ sensorAnomalyType: "9" }) === "大部件温度异常",
  "数据感知类型 9 应翻译为大部件温度异常",
);
assert(
  formatSensorTypeText({ sensorAnomalyType: "1,5" }) === "功率异常、扭矩异常",
  "多个数据感知类型编码应按顿号拼接",
);
assert(
  formatSensorTypeText({}) === "暂无异常",
  "无数据感知异常时应写暂无异常",
);

const sensorHeat = buildSensorHeatmapData([
  {
    engineName: "09",
    sensorAnomalyPowerRatio: 0.12,
    sensorAnomalyWindRatio: 0,
  },
  {
    engineName: "16",
    sensorAnomalyPowerRatio: 0,
    sensorAnomalyWindRatio: 0.2,
    sensorAnomalyMajorTempRatio: 0.35,
  },
]);
assert(
  sensorHeat.xLabels.join(",") === "09号,16号",
  "数据感知热力图风机名应带号",
);
assert(sensorHeat.yLabels[0] === "功率异常", "数据感知热力图首行应为功率异常");
assert(
  sensorHeat.yLabels.includes("偏航角、扭缆角、偏航误差等偏航"),
  "数据感知热力图应含偏航角、扭缆角、偏航误差等偏航",
);
assert(
  sensorHeat.yLabels.includes("大部件温度异常"),
  "数据感知热力图应含大部件温度异常",
);
assert(sensorHeat.yLabels.length === 9, "数据感知热力图应为9行");
assert(
  sensorHeat.matrix[8][1] === 35 && sensorHeat.matrix[8][0] === 0,
  "大部件温度异常行应按 sensorAnomalyMajorTempRatio 取占比",
);
assert(sensorHeat.matrix[0][0] === 12, "数据感知热力图应按占比×100");
assert(chunkHeatmapData(sensorHeat, 1).length === 2, "热力图应按风机分片");

const detectorHeat = buildDetectorHeatmapData([
  {
    engineName: "01",
    model2YawYawerrorRatio: 18,
    model2YawYawcountRatio: 110,
    model3PitchMinpitchRatio: 1,
    model4CtrlparamDeloadRatio: 0.01,
  },
]);
assert(
  detectorHeat.yLabels.includes("静态偏航误差"),
  "功能诊断热力图应含静态偏航误差",
);
assert(
  detectorHeat.matrix[detectorHeat.yLabels.indexOf("静态偏航误差")][0] === 18,
  "静态偏航误差热力图应按角度原值",
);
assert(
  detectorHeat.matrix[detectorHeat.yLabels.indexOf("偏航次数")][0] === 110,
  "偏航次数热力图应按次数原值",
);
assert(
  detectorHeat.matrix[detectorHeat.yLabels.indexOf("最小桨距角异常")][0] === 1,
  "最小桨距角热力图应按角度原值",
);
assert(
  detectorHeat.matrix[detectorHeat.yLabels.indexOf("降载情况")][0] === 1,
  "功能诊断热力图占比换算错误",
);
const detectorHeatOption = buildReportHeatmapOption(
  detectorHeat,
  "各机组功能诊断异常分布",
);
const heatRamp = (label) => {
  const y = detectorHeat.yLabels.indexOf(label);
  const seriesIndex = detectorHeatOption.series.findIndex((item) =>
    (item.data || []).some((cell) => cell.value[1] === y),
  );
  const maps = [].concat(detectorHeatOption.visualMap || []);
  return (maps.find((item) => item.seriesIndex === seriesIndex)?.inRange?.color || []).join(",");
};
assert(
  Array.isArray(detectorHeatOption.visualMap) &&
    detectorHeatOption.visualMap.length > 1 &&
    heatRamp("静态偏航误差") !== heatRamp("降载情况") &&
    heatRamp("偏航次数") !== heatRamp("静态偏航误差") &&
    heatRamp("风功率曲线情况") !== heatRamp("降载情况"),
  "功能诊断热力图应按符合度、角度、次数、异常率分色",
);
assert(
  summarizeTurbineAnomalyRate(
    [
      { title: "降载判定分析", points: 477, rate: 0.3286 },
      { title: "变桨协调分析", points: 8, rate: 0.0052 },
    ],
    { model4CtrlparamDeloadRatio: 0.3286, model3PitchPitchcoordRatio: 0.0052 },
  ).toFixed(4) === "0.3233",
  "表4-2 机组异常率应按异常点数加权，并优先使用热力图单项占比",
);
const summaryWithoutDeload = buildAnomalySummaryRows([
  {
    turbineName: "07",
    anomalyDetectorCount: 1,
    anomalyPoints: 250,
    anomalyRate: 0.0893,
    mainAnomalyType: "降载判定分析",
    detectorHits: [{ title: "降载判定分析", points: 250, rate: 0.0893 }],
  },
  {
    turbineName: "08",
    anomalyDetectorCount: 2,
    anomalyPoints: 220,
    anomalyRate: 0.1,
    mainAnomalyType: "降载判定分析",
    detectorHits: [
      { title: "降载判定分析", points: 200, rate: 0.12 },
      { title: "变桨协调分析", points: 20, rate: 0.01 },
    ],
  },
]);
assert(
  summaryWithoutDeload.length === 1 &&
    summaryWithoutDeload[0].turbine_name === "08" &&
    summaryWithoutDeload[0].anomaly_detector_count === "变桨协调分析" &&
    summaryWithoutDeload[0].main_anomaly_type === "变桨协调异常率" &&
    summaryWithoutDeload[0].anomaly_rate === "1.00%",
  "表4-2 不应收录仅降载异常机组，其余检测项应分行给出名称、异常率和说明",
);
const summaryMetricRows = buildAnomalySummaryRows([
  {
    turbineName: "009",
    detectorHits: [
      { title: "静态偏航误差分析", templateKey: "yaw_error", points: 4, rate: 8 },
      { title: "偏航次数分析", templateKey: "yaw_count", points: 2, rate: 110 },
      { title: "最小桨距角分析", templateKey: "pitch_min", points: 1, rate: 1.5 },
      { title: "变桨一致性分析", templateKey: "pitch_regulation", points: 3, rate: 0.021 },
    ],
  },
]);
assert(
  summaryMetricRows.map((row) => row.anomaly_detector_count).join(",") ===
    "静态偏航误差分析,变桨一致性分析,偏航次数分析,最小桨距角分析",
  "表4-2 功能诊断项应为检测器名称",
);
assert(
  summaryMetricRows.map((row) => row.anomaly_rate).join(",") ===
    "8°,2.10%,110次,1.5°",
  "表4-2 数值应按度、次数或异常率分别格式化",
);
assert(
  summaryMetricRows.map((row) => row.main_anomaly_type).join(",") ===
    "静态偏航误差度,变桨一致性异常率,偏航次数,最小桨距角",
  "表4-2 数值说明应写明指标含义",
);

const heatOption = buildReportHeatmapOption(
  sensorHeat,
  "各机组数据感知异常分布",
);
assert(heatOption.series[0].type === "heatmap", "图3-1应为热力图");
assert(
  heatOption.series[0].data[0]?.name?.includes("%"),
  "报告热力图单元格应预置占比文本",
);
assert(optionHasData(heatOption), "数据感知热力图应视为有数据");

const sensorRadar = buildSensorRadarData(
  { sensorAnomalyPowerCount: 2, sensorAnomalyWindCount: 1 },
  20,
);
assert(sensorRadar.values[0] === 2, "数据感知雷达图应映射异常台数");
assert(sensorRadar.max === 3, "数据感知雷达图应按异常台数自适应上限");
assert(
  sensorRadar.indicators.length === 9 &&
    sensorRadar.indicators[8].name === "大部件温度异常" &&
    sensorRadar.values[8] === 0,
  "数据感知雷达图应含大部件温度异常轴",
);
const majorTempRadar = buildSensorRadarData(
  { sensorAnomalyMajorTempCount: 5 },
  20,
);
assert(
  majorTempRadar.values[8] === 5,
  "大部件温度异常台数应取自 sensorAnomalyMajorTempCount",
);
const detectorRadar = buildDetectorRadarData(
  { model1Count: 3, model2Count: 1 },
  20,
);
assert(detectorRadar.max === 4, "功能诊断雷达图应按异常台数自适应上限");
const sensorRadarOption = buildAnomalyRadarOption(
  sensorRadar,
  "数据感知异常台数",
);
assert(sensorRadarOption.series[0].type === "radar", "概览区应输出雷达图");
assert(
  Number(sensorRadarOption.series[0].lineStyle?.width) >= 2,
  "雷达图 series 应配置连线宽度",
);
assert(
  sensorRadarOption.radar.indicator[0].name.includes("2台"),
  "雷达图维度应标注台数",
);
assert(
  sensorRadarOption.radar.indicator.every((item) => item.max === 3),
  "雷达图外圈应按本图最大台数自适应",
);
assert(sensorRadarOption.radar.splitNumber === 3, "雷达图刻度应落到整数台数");
console.log("axis/watchlist/placeholder rules ok");

await initChartService();
const imageBufferMap = {};
const sensorRadarBuf = await renderEchartsOption(sensorRadarOption, {
  width: 520,
  height: 360,
});
registerImageBuffer(imageBufferMap, "anomaly_sensor_radar", sensorRadarBuf);
const detectorRadarBuf = await renderEchartsOption(
  buildAnomalyRadarOption(detectorRadar, "功能诊断异常台数"),
  { width: 520, height: 360 },
);
registerImageBuffer(imageBufferMap, "anomaly_detector_radar", detectorRadarBuf);
const sensorHeatBuf = await renderEchartsOption(heatOption, {
  width: 920,
  height: 420,
});
registerImageBuffer(imageBufferMap, "anomaly_sensor_dist", sensorHeatBuf);
const detectorHeatBuf = await renderEchartsOption(
  buildReportHeatmapOption(detectorHeat, "各机组功能诊断异常分布"),
  { width: 920, height: 560 },
);
registerImageBuffer(imageBufferMap, "anomaly_detector_dist", detectorHeatBuf);

const completenessHeatmap = buildDataCompletenessHeatmapData(
  [
    {
      engineName: "111",
      sourceDatetime: new Date("2026-08-01").getTime(),
      minuteDataCompleteness: 96.5,
    },
    {
      engineName: "112",
      sourceDatetime: new Date("2026-08-01").getTime(),
      minuteDataCompleteness: 88.2,
    },
    {
      engineName: "111",
      sourceDatetime: new Date("2026-08-02").getTime(),
      minuteDataCompleteness: 91,
    },
  ],
  "minuteDataCompleteness",
);
assert(
  completenessHeatmap.yLabels.length === 2 &&
    completenessHeatmap.xLabels.length === 2,
  "数据完整度热力图应正确展开机组×日期",
);
assert(
  completenessHeatmap.xLabels.join(",") === "08-01,08-02",
  "数据完整度 X 轴应为日期",
);
assert(
  completenessHeatmap.yLabels.length === 2 &&
    completenessHeatmap.yLabels.every((name) => /111|112/.test(name)),
  "数据完整度 Y 轴应为风机",
);
const completenessMinuteBuf = await renderEchartsOption(
  buildCompletenessHeatmapOption(
    completenessHeatmap,
    "分钟级数据逐日完整度（近90天，%）",
  ),
  { width: 920, height: 420 },
);
const completenessOption = buildCompletenessHeatmapOption(
  completenessHeatmap,
  "分钟级数据逐日完整度（近90天，%）",
);
assert(
  completenessOption.xAxis?.name === "日期" &&
    completenessOption.yAxis?.name === "风机",
  "数据完整度热力图轴名称应为日期/风机",
);
registerImageBuffer(
  imageBufferMap,
  "anomaly_data_completeness_minute",
  completenessMinuteBuf,
);
registerImageBuffer(
  imageBufferMap,
  "anomaly_data_completeness_second",
  completenessMinuteBuf,
);

const farmPlots = buildFarmPlotJsons(
  [
    { engineName: "111", plotJson: samplePlot("111") },
    { engineName: "112", plotJson: samplePlot("112") },
  ],
  "yaw_static",
  "全场偏航分析检测汇总",
);
const farmLabels = (farmPlots[0]?.data || []).map((row) => row.label);
if (farmLabels.join(",") !== "111,112") {
  throw new Error(`全场偏航分色失败: ${farmLabels.join(",")}`);
}
if (
  (farmPlots[0]?.data || []).some((row) => /均值/.test(row.originalLabel || ""))
) {
  throw new Error("全场偏航仍包含 2h/12h 均值");
}
const farmBuf = await renderEchartsOption(
  buildDetectorPlotOption(
    farmPlots[0],
    farmPlots[0].title,
    "anomalyStaticyawPO",
  ),
  { width: 820, height: 420 },
);
registerImageBuffer(imageBufferMap, "anomaly_farm_yaw_static", farmBuf);

const turbineBuf = await renderEchartsOption(
  buildDetectorPlotOption(samplePlot("111"), "111 偏航分析"),
  { width: 760, height: 400 },
);
registerImageBuffer(imageBufferMap, "anomaly_111_yaw_static", turbineBuf);

const denseCount = 4500;
const denseTimes = Array.from({ length: denseCount }, (_, i) => {
  const t = new Date("2026-08-01T00:00:00").getTime() + i * 60000;
  const pad = (n) => String(n).padStart(2, "0");
  const d = new Date(t);
  return `${d.getFullYear()}-${pad(d.getMonth() + 1)}-${pad(d.getDate())} ${pad(
    d.getHours(),
  )}:${pad(d.getMinutes())}:00`;
});
const denseOption = buildDetectorPlotOption(
  {
    title: "DT01 电能质量分析",
    xaxis: "时间",
    yaxis: "电流不平衡度",
    data: [
      {
        label: "电流不平衡度",
        mode: "markers",
        timeData: denseTimes,
        yData: Array.from(
          { length: denseCount },
          (_, i) => 0.08 + (i % 20) / 200,
        ),
      },
    ],
  },
  "DT01 电能质量分析",
  "anomalyPowerqualityPO",
);
assert(denseOption.series[0].large === false, "密点单机图不应使用 large");
assert(
  Number(denseOption.series[0].symbolSize) <= 6,
  "报告散点 symbolSize 应缩小以提升清晰度",
);
assert(denseOption.backgroundColor === "#eef4fb", "报告图表应使用浅蓝灰底色");
const denseBuf = await renderEchartsOption(denseOption, {
  width: 760,
  height: 420,
});
assert(denseBuf.length > 10000, "密点单机图截图过小，可能未画出数据");

const renderData = {
  reportNo: "AD-SMOKE-20260824",
  Province: "测试省",
  Wind_farm: "烟雾风场",
  Year_now: "2026",
  Month_now: "08",
  machineTypeCode: "WD",
  turbine_count: "2",
  anomaly_turbine_count: "1",
  total_anomaly_points: "3",
  anomaly_rate: "50.00",
  Overview_of_the_Wind_Farm: "烟雾风场用于模板渲染校验。",
  target_date: "2026-08-24",
  anomaly_module_count: "1",
  main_problem_modules: "偏航与扭缆",
  top_problem_description: "偏航分析异常点数最多（3）",
  sensorAnomalyRows: buildSensorAnomalyRows([
    {
      engineName: "111",
      sensorAnomalyWindPwr: 1,
      sensorAnomalyWindPwrRatio: 0.125,
    },
    {
      engineName: "16",
      sensorAnomalyWind: 1,
      sensorAnomalyWindRatio: 0.2,
      sensorAnomalyMajorTemp: 1,
      sensorAnomalyMajorTempRatio: 0.3,
    },
  ]),
  detectorSummaryRows: [
    {
      detector_name: "偏航分析",
      module_name: "偏航与扭缆",
      data_granularity: "秒级",
      anomaly_turbines: "1",
      anomaly_points: "3",
      avg_anomaly_rate: "12.00%",
    },
  ],
  anomalySummaryRows: [
    {
      turbine_name: "111",
      anomaly_detector_count: "偏航分析",
      anomaly_points: "3",
      anomaly_rate: "12.00%",
      main_anomaly_type: "偏航异常率",
    },
  ],
  priorityList: "111",
  keyTurbineLoop: [
    {
      turbine_name: "111",
      turbineDetailRows: [
        {
          detector_name: "偏航分析",
          anomaly_points: "3",
          anomaly_rate: "12.00%",
          conclusion: "建议结合现场复核",
        },
      ],
      "zn-techcn-replace-tags-key_turbine-generalFiles": [
        { image: "anomaly_111_yaw_static" },
      ],
    },
  ],
  conclusionRows: [
    {
      index: "1",
      problem_desc: "主要表现为偏航角越限、突变或长时间不动作。",
      suggestion:
        "建议检查偏航编码器、风向标、偏航制动器及对风控制参数，对异常机组进行现场复核。",
      problem_nature: "偏航与扭缆系统异常",
      turbine_names: "#111",
    },
  ],
  "zn-techcn-replace-tags-data_sensor_anomaly-generalFiles": [
    {
      image: "anomaly_sensor_radar",
      figure_caption: "图3-1 数据感知异常台数",
    },
    {
      image: "anomaly_sensor_dist",
      figure_caption: "图3-2 各机组数据感知异常分布",
    },
  ],
  "zn-techcn-replace-tags-data_detector_anomaly-generalFiles": [
    {
      image: "anomaly_detector_radar",
      figure_caption: "图4-1 功能诊断异常台数",
    },
    {
      image: "anomaly_detector_dist",
      figure_caption: "图4-2 各机组功能诊断异常分布",
    },
  ],
  "zn-techcn-replace-tags-data_completeness_minute-farmSummary": [
    { image: "anomaly_data_completeness_minute" },
  ],
  "zn-techcn-replace-tags-data_completeness_second-farmSummary": [
    { image: "anomaly_data_completeness_second" },
  ],
  "zn-techcn-replace-tags-key_turbine-generalFiles": [],
  show_module_wind: [],
  show_module_yaw: [{}],
  show_module_pitch: [],
  show_module_run: [],
  show_module_aero: [],
};

DETECTOR_TEMPLATE_CONFIG.forEach((cfg) => {
  if (cfg.templateKey === "yaw_static") {
    Object.assign(
      renderData,
      buildDetectorSectionPayload(cfg, {
        farmImages: [{ image: "anomaly_farm_yaw_static" }],
        turbineImages: [{ image: "anomaly_111_yaw_static" }],
        rows: [
          {
            turbine_name: "111",
            anomaly_points: "3",
            anomaly_rate: "12.00%",
            sensor_anomaly_type: "大部件温度异常",
            comment: "功能诊断异常",
          },
        ],
        anomalyPoints: 3,
        anomalyRate: 0.12,
        anomalyTurbines: 1,
        hasChartData: true,
      }),
    );
    return;
  }
  const farmTag = `zn-techcn-replace-tags-${cfg.templateKey}-farmSummary`;
  const fileTag = `zn-techcn-replace-tags-${cfg.templateKey}-generalFiles`;
  renderData[`show-${fileTag}`] = [];
  renderData[farmTag] = [];
  renderData[fileTag] = [];
  renderData[`${cfg.templateKey}Rows`] = [];
});

const buffer = await renderDocxReport({
  templateName: "异常检测数据分析报告模板（大唐版）_修订版_人工对齐版.docx",
  renderData,
  imageBufferMap,
});

const outDir = path.join(__dirname, "../templates");
fs.mkdirSync(outDir, { recursive: true });
const outPath = path.join(outDir, "_smoke_anomaly_report.docx");
fs.writeFileSync(outPath, buffer);
console.log("smoke anomaly report written:", outPath, "bytes=", buffer.length);

const smokeZip = new PizZip(buffer);
Object.keys(smokeZip.files)
  .filter((name) => /^word\/footer\d+\.xml$/.test(name))
  .sort()
  .forEach((name) => {
    const xml = smokeZip.file(name).asText();
    const plain = xml.replace(/<[^>]+>/g, "").trim();
    assert(!/PAGE/.test(xml), `${name} 不应含页码域`);
    assert(!plain, `${name} 页脚应为空，实际为「${plain}」`);
  });

const smokeDocText = smokeZip
  .file("word/document.xml")
  .asText()
  .replace(/<[^>]+>/g, "");
assert(
  smokeDocText.includes("数据感知异常类型"),
  "报告中检测器明细表应包含数据感知异常类型列",
);
assert(
  smokeDocText.includes("大部件温度异常"),
  "报告应输出大部件温度异常",
);
assert(
  smokeDocText.includes("大部件温度（齿轮箱油温、发电机轴承"),
  "表3-1 应包含大部件温度关联测点说明",
);
assert(
  !/\{[A-Za-z_]/.test(smokeDocText),
  "报告中不应残留模板占位符",
);

await shutdownChartService();