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 {
  DETECTOR_TEMPLATE_CONFIG,
  buildDetectorPlotOption,
  buildFarmPlotJsons,
  listDetectorPlotOptions,
} from "../src/server/reportService/anomalyChartBuilder.js";
import { buildDetectorSectionPayload } from "../src/server/reportService/anomalyReportMapper.js";
import { renderEchartsOption } from "../src/server/reportService/echartsRenderer.js";
import {
  initChartService,
  shutdownChartService,
} from "../src/server/utils/chartService/index.js";

const __dirname = path.dirname(fileURLToPath(import.meta.url));
const EMPTY_PNG = Buffer.from(
  "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8z8BQz0AEYBxVSF+FABJADveWkH6oAAAAAElFTkSuQmCC",
  "base64",
);

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.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 === "scatter",
);
assert(farmScatterSeries.length > 0, "全场散点应输出 scatter 系列");
assert(
  farmScatterSeries.every((item) => Number(item.symbolSize) >= 10),
  "全场散点 symbolSize 过小",
);
assert(
  farmScatterSeries.every((item) => item.large === false),
  "报告散点不应开启 large 模式",
);

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(
  !pitchFarm[0].data.some((row) => /阈值/.test(row.originalLabel || "")),
  "变桨一致性不应展示阈值参考",
);

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");

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: "功率因数", xaxis: "x", yaxis: "y", data: [{ label: "点", xData: [1], yData: [1] }] },
          { panelTitle: "不平衡度", xaxis: "x", yaxis: "y", data: [{ label: "点", xData: [1], yData: [1] }] },
          { panelTitle: "频率", xaxis: "x", yaxis: "y", data: [{ label: "点", xData: [1], yData: [1] }] },
        ],
      },
    },
  ],
  "ctrl_power_quality",
  "全场电能质量",
);
assert(pqFarm.length === 3, "电能质量总图应为 3 张");
console.log("farm chart rules ok");

await initChartService();
const imageBufferMap = {};
registerImageBuffer(imageBufferMap, "anomaly_sensor_dist", EMPTY_PNG);

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 > 20000, "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-data_sensor_anomaly-generalFiles": [
    { image: "anomaly_sensor_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();