fix(i18n): cost-predict.ts hash key 清理 + 硬编码中文清零 + 错误 key 引用修复

- 23 个 predictutilsts.s* hash-style 键重命名为 costPredict.* 语义化键
- 7 处 buildCostInsightPrompt 硬编码中文模板字面量→i18n.t() 插值调用
- 修复 costRecommendation 函数引用错误 key (predictMemLeak→costPredict.rec*)
- 修复 dimension 字段引用 predictDiskFull→costPredict.allProviders
- 修复 providerLabel 引用 predictIn24h→costPredict.allProviders
- 新增 34 个语义化翻译键 (aiops.costPredict.*), zh/en 8102→8113 对齐
- tsc -b=0, npm build✓ (19.29s), ESLint=0/43/any=0, go build=0
- 17/17 集成测试通过

frontend/src/components/aiops/cost-predict.ts

@@ -83,20 +83,20 @@ function costTrendFromSlope(slope: number): 'up' | 'down' | 'stable' {
 /** 成本优化建议 */
 export function costRecommendation(trend: 'up' | 'down' | 'stable', ratio: number): string {
   if (trend === 'up' && ratio >= 0.85) {
-    return i18n.t('aiops.predictMemLeak');
+    return i18n.t('aiops.costPredict.recHighSpend');
   }
   if (trend === 'up') {
-    return i18n.t('aiops.predictMemLeak');
+    return i18n.t('aiops.costPredict.recRising');
   }
   if (trend === 'down') {
-    return i18n.t('aiops.predictMemLeak');
+    return i18n.t('aiops.costPredict.recDeclining');
   }
-  return i18n.t('aiops.predictutilsts.sa3b5c7d9');
+  return i18n.t('aiops.costPredict.recStable');
 }
 
 /** 预算风险建议 */
 function budgetRiskAction(metric: 'daily' | 'monthly', provider: string): string {
-  const scope = provider && provider !== i18n.t('aiops.predictutilsts.sg5a3d8e1') ? `${provider} ` : '';
+  const scope = provider && provider !== i18n.t('aiops.costPredict.allProviders') ? `${provider} ` : '';
   if (metric === 'daily') {
     return i18n.t('aiops.predictUtils.dailyCostHint', { scope });
   }
@@ -139,7 +139,7 @@ function topServicesByCost(records: JsonData[], limit = 5): { service: string; c
   records.forEach((r) => {
     const date = String(r.billing_date || '');
     if (date < start) return;
-    const svc = String(r.service || i18n.t('aiops.predictutilsts.sh7c2b9d6'));
+    const svc = String(r.service || i18n.t('aiops.costPredict.unknownService'));
     map.set(svc, (map.get(svc) || 0) + (Number(r.cost) || 0));
   });
   return [...map.entries()]
@@ -256,7 +256,7 @@ export function computeCostPredictions(input: ComputeCostPredictionsInput): Comp
   const allRatio =
     summary.predictedMonthEnd > 0 ? summary.monthCost / summary.predictedMonthEnd : 0;
   dimensionForecasts.push({
-    dimension: i18n.t('aiops.predictDiskFull'),
+    dimension: i18n.t('aiops.costPredict.allProviders'),
     currentMonth: summary.monthCost,
     predictedMonthEnd: summary.predictedMonthEnd,
     dailyAvg: summary.dailyAvg,
@@ -302,7 +302,7 @@ export function computeCostPredictions(input: ComputeCostPredictionsInput): Comp
 
     const metric = (rule.metric === 'daily' ? 'daily' : 'monthly') as 'daily' | 'monthly';
     const ruleProvider = String(rule.provider || '');
-    const providerLabel = ruleProvider ? PROVIDER_LABELS[ruleProvider] || ruleProvider : i18n.t('aiops.predictIn24h');
+    const providerLabel = ruleProvider ? PROVIDER_LABELS[ruleProvider] || ruleProvider : i18n.t('aiops.costPredict.allProviders');
 
     let current = 0;
     let predicted = 0;
@@ -339,17 +339,17 @@ export function computeCostPredictions(input: ComputeCostPredictionsInput): Comp
     if (current >= threshold) probability += 25;
     probability = Math.max(5, Math.min(92, probability));
 
-    let timeframe = metric === 'daily' ? i18n.t('aiops.predictNext24h') : i18n.t('aiops.predictutilsts.sd4e5f6g7');
+    let timeframe = metric === 'daily' ? i18n.t('aiops.predictNext24h') : i18n.t('aiops.costPredict.monthEnd');
     if (slope > 0.01 && threshold > current) {
       const days = (threshold - current) / slope;
-      if (days < 1) timeframe = metric === 'daily' ? i18n.t('aiops.predictutilsts.se5f6g7h8') : i18n.t('aiops.predictutilsts.sf6g7h8i9');
-      else if (days <= 7) timeframe = i18n.t('aiops.predictutilsts.sg7h8i9j0', { days: Math.ceil(days) });
-      else timeframe = i18n.t('aiops.predictutilsts.sh8i9j0k1', { days: Math.ceil(days) });
+      if (days < 1) timeframe = metric === 'daily' ? i18n.t('aiops.costPredict.withinToday') : i18n.t('aiops.costPredict.withinOneDay');
+      else if (days <= 7) timeframe = i18n.t('aiops.costPredict.withinDays', { days: Math.ceil(days) });
+      else timeframe = i18n.t('aiops.costPredict.approxAfterDays', { days: Math.ceil(days) });
     }
 
     if (probability >= 30 || current >= threshold * 0.7) {
       budgetRisks.push({
-        ruleName: String(rule.name || i18n.t('aiops.predictutilsts.si9j0k1l2')),
+        ruleName: String(rule.name || i18n.t('aiops.costPredict.budgetRule')),
         provider: providerLabel,
         metric,
         threshold,
@@ -386,49 +386,61 @@ export function buildCostInsightPrompt(data: ComputedCostPredictions): string {
 
   const patternHint =
     billingPattern === 'front_loaded'
-      ? i18n.t('aiops.predictutilsts.sj0k1l2m3')
+      ? i18n.t('aiops.costPredict.frontLoadedHint')
       : billingPattern === 'sparse'
-        ? i18n.t('aiops.predictutilsts.sk1l2m3n4')
-        : i18n.t('aiops.predictutilsts.sl2m3n4o5');
+        ? i18n.t('aiops.costPredict.sparseHint')
+        : i18n.t('aiops.costPredict.steadyHint');
 
   const dimLines = dimensionForecasts.slice(0, 6).map(
     (d) =>
-      `- ${d.dimension}：本月 ${d.currentMonth.toFixed(2)} 元 → 预测月末 ${d.predictedMonthEnd.toFixed(2)} 元（趋势 ${d.trend}）`,
+      i18n.t('aiops.costPredict.dimLine', {
+        dim: d.dimension,
+        current: d.currentMonth.toFixed(2),
+        predicted: d.predictedMonthEnd.toFixed(2),
+        trend: d.trend,
+      }),
   );
   const riskLines =
     budgetRisks.length > 0
       ? budgetRisks.slice(0, 5).map(
           (r) =>
-            `- ${r.ruleName}（${r.provider}）：当前 ${r.current} / 阈值 ${r.threshold}，预测 ${r.predicted}，触线概率 ${r.probability}%`,
+            i18n.t('aiops.costPredict.riskLine', {
+              rule: r.ruleName,
+              provider: r.provider,
+              current: r.current,
+              threshold: r.threshold,
+              predicted: r.predicted,
+              probability: r.probability,
+            }),
         )
-      : [`- ${i18n.t('aiops.predictutilsts.sm3n4o5p6')}`];
+      : [`- ${i18n.t('aiops.costPredict.noBudgetRisk')}`];
   const serviceLines =
     topServices.length > 0
-      ? topServices.map((s) => `- ${s.service}：${s.cost.toFixed(2)} 元`)
-      : [`- ${i18n.t('aiops.predictutilsts.sn4o5p6q7')}`];
+      ? topServices.map((s) => i18n.t('aiops.costPredict.serviceLine', { service: s.service, cost: s.cost.toFixed(2) }))
+      : [`- ${i18n.t('aiops.costPredict.noServiceDetail')}`];
 
   return [
-    i18n.t('aiops.predictutilsts.so5p6q7r8'),
+    i18n.t('aiops.costPredict.promptIntro'),
     '',
-    i18n.t('aiops.predictutilsts.sp6q7r8s9'),
-    `- 本月已花：${summary.monthCost.toFixed(2)} 元`,
-    `- 预测月末：${summary.predictedMonthEnd.toFixed(2)} 元（置信 ${summary.confidence}%）`,
-    `- 全月日均：${summary.dailyAvg.toFixed(2)} 元；近7日日均：${recentDailyAvg.toFixed(2)} 元；剩余 ${summary.daysRemaining} 天`,
-    `- 账单形态：${patternHint}`,
+    i18n.t('aiops.costPredict.sectionOverview'),
+    i18n.t('aiops.costPredict.monthSpent', { amount: summary.monthCost.toFixed(2) }),
+    i18n.t('aiops.costPredict.predictedEnd', { amount: summary.predictedMonthEnd.toFixed(2), confidence: summary.confidence }),
+    i18n.t('aiops.costPredict.dailySummary', { dailyAvg: summary.dailyAvg.toFixed(2), recentAvg: recentDailyAvg.toFixed(2), daysRemaining: summary.daysRemaining }),
+    i18n.t('aiops.costPredict.billingPatternLabel', { hint: patternHint }),
     '',
-    i18n.t('aiops.predictutilsts.sq7r8s9t0'),
+    i18n.t('aiops.costPredict.sectionTopServices'),
     ...serviceLines,
     '',
-    i18n.t('aiops.predictutilsts.sr8s9t0u1'),
+    i18n.t('aiops.costPredict.sectionDimensionForecast'),
     ...dimLines,
     '',
-    i18n.t('aiops.predictutilsts.ss9t0u1v2'),
+    i18n.t('aiops.costPredict.sectionBudgetRisk'),
     ...riskLines,
     '',
-    i18n.t('aiops.predictutilsts.st0u1v2w3'),
-    i18n.t('aiops.predictutilsts.su1v2w3x4'),
-    i18n.t('aiops.predictutilsts.sv2w3x4y5'),
-    i18n.t('aiops.predictutilsts.sw3x4y5z6'),
-    i18n.t('aiops.predictutilsts.sx4y5z6a7'),
+    i18n.t('aiops.costPredict.promptRequirements'),
+    i18n.t('aiops.costPredict.promptReq1'),
+    i18n.t('aiops.costPredict.promptReq2'),
+    i18n.t('aiops.costPredict.promptReq3'),
+    i18n.t('aiops.costPredict.promptReq4'),
   ].join('\n');
 }

frontend/src/locales/en-US.json

@@ -8602,29 +8602,6 @@
     "predictutilsts.providerAliyun": "Alibaba Cloud",
     "predictutilsts.providerHuawei": "Huawei Cloud",
     "predictutilsts.providerTencent": "Tencent Cloud",
-    "predictutilsts.sd4e5f6g7": "End of month",
-    "predictutilsts.se5f6g7h8": "Within today",
-    "predictutilsts.sf6g7h8i9": "Within 1 day",
-    "predictutilsts.sg5a3d8e1": "All",
-    "predictutilsts.sg7h8i9j0": "Within {{days}} days",
-    "predictutilsts.sh7c2b9d6": "Other",
-    "predictutilsts.sh8i9j0k1": "In about {{days}} days",
-    "predictutilsts.si9j0k1l2": "Budget Rule",
-    "predictutilsts.sj0k1l2m3": "Bills front-loaded at month start, may contain pre-paid or retired resources; prediction uses recent 7-day run rate",
-    "predictutilsts.sk1l2m3n4": "Insufficient daily bill samples, lower prediction confidence",
-    "predictutilsts.sl2m3n4o5": "Daily bills are relatively stable, extrapolated by trend",
-    "predictutilsts.sm3n4o5p6": "No budget threshold risk",
-    "predictutilsts.sn4o5p6q7": "No service-level detail",
-    "predictutilsts.so5p6q7r8": "You are a FinOps cloud cost optimization expert. Based on the following cloud billing prediction data, provide comprehensive cost risk assessment and actionable cost-reduction recommendations:",
-    "predictutilsts.sp6q7r8s9": "[Overview]",
-    "predictutilsts.sq7r8s9t0": "[Top Service Costs]",
-    "predictutilsts.sr8s9t0u1": "[Dimension Forecasts]",
-    "predictutilsts.ss9t0u1v2": "[Budget Risks]",
-    "predictutilsts.st0u1v2w3": "Output requirements (Markdown, Chinese):",
-    "predictutilsts.su1v2w3x4": "1. Use **bold subtitles** for sections with 1 key point each, e.g.: **Data Anomalies**, **Idle Resource Cleanup**, **Billing Mode Optimization**, **Storage Lifecycle**",
-    "predictutilsts.sv2w3x4y5": "2. If monthly avg and recent 7-day avg clearly contradict predicted month-end, explain in 'Data Anomalies' (pre-paid/shutdown etc.)",
-    "predictutilsts.sw3x4y5z6": "3. Give actionable recommendations per top service (EIP/disk/ECS spec/OSS lifecycle/reserved instances etc.)",
-    "predictutilsts.sx4y5z6a7": "4. Pick 3-5 most relevant recommendations, under 350 words, no generic platitudes",
     "probabilityColon": "Probability: ",
     "processing": "Processing...",
     "providerAndCredential": "Model & Credential",
@@ -8775,7 +8752,41 @@
       "recentReleases": "Recent releases:",
       "overviewFallback": "Servers:{{servers}}(running {{running}}) | K8s clusters:{{clusters}} Nodes:{{nodes}} Pods:{{pods}} | Alerts:{{alerts}}(firing {{firing}}) | Pipelines:{{pipelines}}(success {{rate}}%) | Monthly cost:¥{{cost}}",
       "noAgentData": "(No Agent data currently, monitoring Agent may not be deployed)"
-    }
+    },
+    "costPredict.allProviders": "All",
+    "costPredict.unknownService": "Other",
+    "costPredict.monthEnd": "End of month",
+    "costPredict.withinToday": "Within today",
+    "costPredict.withinOneDay": "Within 1 day",
+    "costPredict.withinDays": "Within {{days}} days",
+    "costPredict.approxAfterDays": "In about {{days}} days",
+    "costPredict.budgetRule": "Budget Rule",
+    "costPredict.frontLoadedHint": "Bills front-loaded at month start, may contain pre-paid or retired resources; prediction uses recent 7-day run rate",
+    "costPredict.sparseHint": "Insufficient daily bill samples, lower prediction confidence",
+    "costPredict.steadyHint": "Daily bills are relatively stable, extrapolated by trend",
+    "costPredict.noBudgetRisk": "No budget threshold risk",
+    "costPredict.noServiceDetail": "No service-level detail",
+    "costPredict.promptIntro": "You are a FinOps cloud cost optimization expert. Based on the following cloud billing prediction data, provide comprehensive cost risk assessment and actionable cost-reduction recommendations:",
+    "costPredict.sectionOverview": "[Overview]",
+    "costPredict.sectionTopServices": "[Top Service Costs]",
+    "costPredict.sectionDimensionForecast": "[Dimension Forecasts]",
+    "costPredict.sectionBudgetRisk": "[Budget Risks]",
+    "costPredict.promptRequirements": "Output requirements (Markdown, Chinese):",
+    "costPredict.promptReq1": "1. Use **bold subtitles** for sections with 1 key point each, e.g.: **Data Anomalies**, **Idle Resource Cleanup**, **Billing Mode Optimization**, **Storage Lifecycle**",
+    "costPredict.promptReq2": "2. If monthly avg and recent 7-day avg clearly contradict predicted month-end, explain in 'Data Anomalies' (pre-paid/shutdown etc.)",
+    "costPredict.promptReq3": "3. Give actionable recommendations per top service (EIP/disk/ECS spec/OSS lifecycle/reserved instances etc.)",
+    "costPredict.promptReq4": "4. Pick 3-5 most relevant recommendations, under 350 words, no generic platitudes",
+    "costPredict.dimLine": "- {{dim}}: This month {{current}} CNY → Predicted month-end {{predicted}} CNY (trend {{trend}})",
+    "costPredict.riskLine": "- {{rule}} ({{provider}}): Current {{current}} / Threshold {{threshold}}, Predicted {{predicted}}, Probability {{probability}}%",
+    "costPredict.serviceLine": "- {{service}}: {{cost}} CNY",
+    "costPredict.monthSpent": "- Spent this month: {{amount}} CNY",
+    "costPredict.predictedEnd": "- Predicted month-end: {{amount}} CNY (confidence {{confidence}}%)",
+    "costPredict.dailySummary": "- Monthly avg: {{dailyAvg}} CNY; Recent 7-day avg: {{recentAvg}} CNY; {{daysRemaining}} days remaining",
+    "costPredict.billingPatternLabel": "- Billing pattern: {{hint}}",
+    "costPredict.recHighSpend": "Cost rising rapidly, investigate high-spend resources and optimize specs",
+    "costPredict.recRising": "Cost trending up, monitor new service spending",
+    "costPredict.recDeclining": "Cost declining, optimization is effective",
+    "costPredict.recStable": "Cost stable, maintain current strategy"
   },
   "breadcrumb": {
     "aiAlertAnalyzer": "Alert Analysis",

frontend/src/locales/zh-CN.json

@@ -8602,29 +8602,6 @@
     "predictutilsts.providerAliyun": "阿里云",
     "predictutilsts.providerHuawei": "华为云",
     "predictutilsts.providerTencent": "腾讯云",
-    "predictutilsts.sd4e5f6g7": "本月末",
-    "predictutilsts.se5f6g7h8": "今日内",
-    "predictutilsts.sf6g7h8i9": "1 天内",
-    "predictutilsts.sg5a3d8e1": "全部",
-    "predictutilsts.sg7h8i9j0": "{{days}} 天内",
-    "predictutilsts.sh7c2b9d6": "其他",
-    "predictutilsts.sh8i9j0k1": "约 {{days}} 天后",
-    "predictutilsts.si9j0k1l2": "预算规则",
-    "predictutilsts.sj0k1l2m3": "账单呈月初集中、近期趋零，可能含包年包月预付费或资源已停用，预测已按近7日运行费率外推",
-    "predictutilsts.sk1l2m3n4": "日账单样本不足，预测置信度较低",
-    "predictutilsts.sl2m3n4o5": "日账单相对平稳，按趋势外推",
-    "predictutilsts.sm3n4o5p6": "暂无预算触线风险",
-    "predictutilsts.sn4o5p6q7": "暂无服务维度明细",
-    "predictutilsts.so5p6q7r8": "你是 FinOps 云成本优化专家。基于以下云账单预测数据，给出综合成本风险评估与可执行的降本建议：",
-    "predictutilsts.sp6q7r8s9": "【总览】",
-    "predictutilsts.sq7r8s9t0": "【Top 服务消费】",
-    "predictutilsts.sr8s9t0u1": "【分维度预测】",
-    "predictutilsts.ss9t0u1v2": "【预算风险】",
-    "predictutilsts.st0u1v2w3": "输出要求（Markdown，中文）：",
-    "predictutilsts.su1v2w3x4": "1. 用 **加粗小标题** 分段，每段下 1 条要点，例如：**数据疑点**、**闲置资源清理**、**计费模式优化**、**存储生命周期**",
-    "predictutilsts.sv2w3x4y5": "2. 若全月日均与近7日日均、预测月末存在明显矛盾，必须在「数据疑点」中说明原因（预付费/资源停机等）",
-    "predictutilsts.sw3x4y5z6": "3. 结合 Top 服务给出可落地建议（EIP/云盘/ECS规格/OSS生命周期/预留实例等）",
-    "predictutilsts.sx4y5z6a7": "4. 选 3～5 条最相关建议，总字数 350 字以内，不要空泛套话",
     "probabilityColon": "概率:",
     "processing": "处理中...",
     "providerAndCredential": "模型与凭证",
@@ -8775,7 +8752,41 @@
       "recentReleases": "最近发布：",
       "overviewFallback": "服务器:{{servers}}(运行{{running}}) | Kubernetes集群:{{clusters}} 节点:{{nodes}} Pod:{{pods}} | 告警:{{alerts}}(firing {{firing}}) | 流水线:{{pipelines}}(成功率{{rate}}%) | 本月成本:¥{{cost}}",
       "noAgentData": "（当前无 Agent 上报数据，可能未部署监控 Agent）"
-    }
+    },
+    "costPredict.allProviders": "全部",
+    "costPredict.unknownService": "其他",
+    "costPredict.monthEnd": "本月末",
+    "costPredict.withinToday": "今日内",
+    "costPredict.withinOneDay": "1 天内",
+    "costPredict.withinDays": "{{days}} 天内",
+    "costPredict.approxAfterDays": "约 {{days}} 天后",
+    "costPredict.budgetRule": "预算规则",
+    "costPredict.frontLoadedHint": "账单呈月初集中、近期趋零，可能含包年包月预付费或资源已停用，预测已按近7日运行费率外推",
+    "costPredict.sparseHint": "日账单样本不足，预测置信度较低",
+    "costPredict.steadyHint": "日账单相对平稳，按趋势外推",
+    "costPredict.noBudgetRisk": "暂无预算触线风险",
+    "costPredict.noServiceDetail": "暂无服务维度明细",
+    "costPredict.promptIntro": "你是 FinOps 云成本优化专家。基于以下云账单预测数据，给出综合成本风险评估与可执行的降本建议：",
+    "costPredict.sectionOverview": "【总览】",
+    "costPredict.sectionTopServices": "【Top 服务消费】",
+    "costPredict.sectionDimensionForecast": "【分维度预测】",
+    "costPredict.sectionBudgetRisk": "【预算风险】",
+    "costPredict.promptRequirements": "输出要求（Markdown，中文）：",
+    "costPredict.promptReq1": "1. 用 **加粗小标题** 分段，每段下 1 条要点，例如：**数据疑点**、**闲置资源清理**、**计费模式优化**、**存储生命周期**",
+    "costPredict.promptReq2": "2. 若全月日均与近7日日均、预测月末存在明显矛盾，必须在「数据疑点」中说明原因（预付费/资源停机等）",
+    "costPredict.promptReq3": "3. 结合 Top 服务给出可落地建议（EIP/云盘/ECS规格/OSS生命周期/预留实例等）",
+    "costPredict.promptReq4": "4. 选 3～5 条最相关建议，总字数 350 字以内，不要空泛套话",
+    "costPredict.dimLine": "- {{dim}}：本月 {{current}} 元 → 预测月末 {{predicted}} 元（趋势 {{trend}}）",
+    "costPredict.riskLine": "- {{rule}}（{{provider}}）：当前 {{current}} / 阈值 {{threshold}}，预测 {{predicted}}，触线概率 {{probability}}%",
+    "costPredict.serviceLine": "- {{service}}：{{cost}} 元",
+    "costPredict.monthSpent": "- 本月已花：{{amount}} 元",
+    "costPredict.predictedEnd": "- 预测月末：{{amount}} 元（置信 {{confidence}}%）",
+    "costPredict.dailySummary": "- 全月日均：{{dailyAvg}} 元；近7日日均：{{recentAvg}} 元；剩余 {{daysRemaining}} 天",
+    "costPredict.billingPatternLabel": "- 账单形态：{{hint}}",
+    "costPredict.recHighSpend": "费用增长迅猛，建议排查高消费资源并优化规格",
+    "costPredict.recRising": "费用呈上升趋势，建议关注新增服务消费",
+    "costPredict.recDeclining": "费用持续下降，成本优化有效",
+    "costPredict.recStable": "费用平稳，保持当前成本策略"
   },
   "breadcrumb": {
     "aiAlertAnalyzer": "告警分析",