performance-test/downLoadServer/scripts/smoke-anomaly-report.mjs

import fs from "fs";
import path from "path";
import { fileURLToPath } from "url";
import {
  registerImageBuffer,
  renderDocxReport,
} from "../src/server/reportService/docxReportBuilder.js";
import {
  buildDetectorSectionPayload,
  pickKeyTurbines,
  buildSensorHeatmapData,
  buildDetectorHeatmapData,
  buildSensorRadarData,
  buildDetectorRadarData,
  chunkHeatmapData,
  buildSensorAnomalyRows,
} from "../src/server/reportService/anomalyReportMapper.js";
import {
  DETECTOR_TEMPLATE_CONFIG,
  buildAnomalyRadarOption,
  buildDetectorPlotOption,
  buildEmptyPlaceholderOption,
  buildFarmPlotJsons,
  buildReportHeatmapOption,
  listDetectorPlotOptions,
  listFarmPlotOptionsFromTurbines,
  listFarmChartOptions,
  optionHasData,
  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,
  diagnosticSeriesSymbol,
  FARM_ANOMALY_WARM_COLORS,
  FARM_NORMAL_COOL_COLORS,
} from "../shared/anomalyDiagnosticVisual.mjs";
import { farmAggregateToPanels } 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);
}

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) => Number(item.symbolSize) >= 3),
  "全场散点 3D symbolSize 过小",
);
assert(farmScatterOption.grid3D, "风功率散点应输出 3D 图表");

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(
    { originalLabel: "正常", turbineIndex: 1 },
    0,
    "ctrl_deload",
  ) ===
    diagnosticFarmSeriesColor(
      { originalLabel: "异常", turbineIndex: 1 },
      1,
      "ctrl_deload",
    ),
  "降载图同一风机的正常和异常点应使用相同颜色",
);
assert(
  FARM_NORMAL_COOL_COLORS.includes(
    diagnosticFarmSeriesColor(
      { originalLabel: "正常", turbineIndex: 0 },
      0,
      "pitch_coord",
    ),
  ),
  "总图正常风机应使用冷色",
);
assert(
  FARM_ANOMALY_WARM_COLORS.includes(
    diagnosticFarmSeriesColor(
      { originalLabel: "异常", turbineIndex: 0 },
      0,
      "pitch_coord",
    ),
  ),
  "总图异常风机应使用红橙暖色",
);
assert(
  diagnosticFarmSeriesColor(
    { extra: { anomaly: true }, originalLabel: "正常", turbineIndex: 3 },
    0,
    "ctrl_deload",
  ) ===
    diagnosticFarmSeriesColor(
      { extra: { anomaly: true }, originalLabel: "异常", turbineIndex: 3 },
      1,
      "ctrl_deload",
    ) &&
    FARM_ANOMALY_WARM_COLORS.includes(
      diagnosticFarmSeriesColor(
        { extra: { anomaly: true }, turbineIndex: 3 },
        0,
        "ctrl_deload",
      ),
    ),
  "异常降载风机应整机暖色且正常/异常点同色",
);
assert(
  diagnosticSeriesSymbol({ originalLabel: "正常" }) !==
    diagnosticSeriesSymbol({ originalLabel: "异常" }),
  "降载图应使用不同点形区分正常和异常",
);

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 === "风机机位", "3D X 轴应为风机机位");
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 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) >= 12,
  "全场变桨协调散点应加大",
);

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(Array.isArray(pitchColorOption.visualMap), "功率-转速图应按桨距角着色");
assert(
  pitchColorOption.series.some((item) => item.name === "正常数据"),
  "桨距角着色图应按正常/异常分系列而非每台风机",
);
assert(
  pitchColorOption.series.find((item) => item.name === "正常数据")?.data
    .length === 4 &&
    pitchColorOption.series.find((item) => item.name === "异常点")?.data
      .length === 1,
  "桨距角着色图合并系列时不得丢失原始点",
);
const pitchSpeedOption = buildDetectorPlotOption(
  pitchCoordFarmTwo[1],
  pitchCoordFarmTwo[1].title,
  "anomalyPitchcoordPO",
);
assert(
  pitchSpeedOption.series.some((item) => item.name === "001") &&
    pitchSpeedOption.series.some((item) => item.name === "005"),
  "桨距角-转速图应按风机分色",
);

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].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 === "function",
  "最小桨距角气泡大小应跟随点数",
);

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(
  String(opStateOptions[1].title?.subtext || "").includes("PC1"),
  "运行状态 PCA 面板应展示主成分定义",
);

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)),
  "风场总图应把 engineId 映射成风机名",
);

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[0].series[0].type === "bar", "风场偏航误差总图应为柱状对比");
assert(
  farmYawErrorOptions[0].series.some((item) => item.name === "风场均值"),
  "风场 value 型总图应绘制均值线",
);

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],
      },
    ],
  },
  "全场静态偏航分析检测汇总",
  "anomalyStaticyawPO",
  { templateKey: "yaw_static", farmMode: true },
);
assert(
  farmYawStaticOptions[0].series.length === 2,
  "风场静态偏航总图应按 pointAnomaly 拆成正常/异常点",
);

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[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(
  yawErrorFarmFromTurbines[0].series.some((item) => item.name === "风场均值"),
  "报告 value 型总图应绘制均值线",
);

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",
);

const emptyRowsPayload = buildDetectorSectionPayload(
  DETECTOR_TEMPLATE_CONFIG.find((cfg) => cfg.templateKey === "pitch_coord"),
  { farmImages: [], turbineImages: [], rows: [] },
);
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 sensorHeat = buildSensorHeatmapData([
  {
    engineName: "09",
    sensorAnomalyPowerRatio: 0.12,
    sensorAnomalyWindRatio: 0,
  },
  { engineName: "16", sensorAnomalyPowerRatio: 0, sensorAnomalyWindRatio: 0.2 },
]);
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.length === 8, "数据感知热力图应为8行");
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: 0.9247,
    model4CtrlparamDeloadRatio: 0,
  },
]);
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] ===
    92.47,
  "功能诊断热力图占比换算错误",
);

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");
const detectorRadar = buildDetectorRadarData(
  { model1Count: 3, model2Count: 1 },
  20,
);
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",
);
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 farmPlots = buildFarmPlotJsons(
  [
    { engineName: "111", plotJson: samplePlot("111") },
    { engineName: "112", plotJson: samplePlot("112") },
  ],
  "yaw_static",
  "全场静态偏航分析检测汇总",
);
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 静态偏航分析"),
  { 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) >= 8,
  "密点单机图 symbolSize 过小",
);
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: "静态偏航分析异常点数最多（3）",
  sensorAnomalyRows: [
    {
      turbine_name: "111",
      sensor_anomaly_type: "风速异常",
      anomaly_points: "1",
      ratio: "—",
    },
  ],
  detectorSummaryRows: [
    {
      detector_name: "静态偏航分析",
      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: "1",
      anomaly_points: "3",
      anomaly_rate: "12.00%",
      main_anomaly_type: "静态偏航分析",
    },
  ],
  priorityList: "111",
  keyTurbineLoop: [
    {
      turbine_name: "111",
      turbineDetailRows: [
        {
          detector_name: "静态偏航分析",
          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_type: "静态偏航分析",
      turbine_names: "111",
      anomaly_points: "3",
      risk_level: "P2",
      suggestion: "核对偏航角越限情况。",
    },
  ],
  "zn-techcn-replace-tags-anomaly_overview-generalFiles": [
    { image: "anomaly_sensor_radar" },
    { image: "anomaly_detector_radar" },
  ],
  "zn-techcn-replace-tags-data_sensor_anomaly-generalFiles": [
    { image: "anomaly_sensor_dist" },
    { image: "anomaly_detector_dist" },
  ],
  "zn-techcn-replace-tags-key_turbine-generalFiles": [],
};

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%",
            comment: "功能诊断异常",
          },
        ],
        anomalyPoints: 3,
        anomalyRate: 0.12,
        anomalyTurbines: 1,
      }),
    );
    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）.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);

await shutdownChartService();