营销归因漏斗图生成

{
  "workflow_id": "marketing_attribution_funnel_generator",
  "input": {
    "report_text": {
      "field_name": "report_text",
      "default": "",
      "description": "用户提供的原始营销报告全文或核心段落文本",
      "example": "本季度全渠道营销归因漏斗分析\n本季度公司围绕\"曝光—点击—注册—转化\"的完整用户旅程，投入全渠道营销预算共计 500 万元，构建起一条层层递进的营销归因漏斗，旨在精准追踪各触点的引流效率与转化贡献。\n\n第一层 · 社交媒体曝光触点：作为漏斗最顶端的入口，社交媒体投入占总预算的 40%（200 万元），承担着大规模品牌曝光与流量蓄水的职责，通过内容种草、信息流广告等方式触达海量潜在用户，奠定后续转化的流量基础。\n\n第二层 · 搜索引擎点击触点：占预算 30%（150 万元），承接由曝光激发出的主动搜索需求。从曝光到点击的转化率为 5%，意味着每 100 次有效曝光中约有 5 次转化为真实点击，体现了用户从\"被动看见\"到\"主动了解\"的兴趣升级。\n\n第三层 · 私域社群注册触点：占预算 20%（100 万元），聚焦于将公域流量沉淀为私域资产。点击到注册的转化率高达 15%，反映出社群运营在用户留资、建立信任关系方面的高效能，是漏斗中关键的\"用户资产化\"环节。\n\n第四层 · 电商转化触点：作为漏斗最底端，投入占 10%（50 万元），直接驱动最终成交。注册到转化的转化率为 25%，表明已注册用户具备较强的购买意愿，临门一脚的促销与复购策略成效显著。\n\n漏斗整体洞察：从投入结构看，预算呈现\"顶部重、底部轻\"的金字塔式分布，与用户体量自上而下逐级收窄的漏斗形态高度契合；从转化效率看，三段转化率（5%→15%→25%）逐级递增，说明越接近成交环节、用户质量越高、转化越高效。该归因模型可清晰呈现各触点的流量损耗与价值贡献，为后续预算的精细化再分配提供数据支撑——例如适度优化顶部曝光的引流精度，以提升整体漏斗的转化效能。",
      "required": true,
      "type": "string"
    },
    "aspect_ratio": {
      "field_name": "aspect_ratio",
      "default": "16:9",
      "description": "生成图的画面比例，可选值：1:1、3:4、4:3、16:9、9:16",
      "example": "1:1",
      "required": false,
      "type": "string"
    }
  },
  "steps": [
    {
      "point_code": "",
      "id": "step_1_intent_analyzer",
      "result": [],
      "backend_type": "prompt_api",
      "api_code": "intent_analyzer",
      "status": "PENDING",
      "name": "深度解析漏斗图生成意图与专业标准",
      "action": "run",
      "params": {
        "merge_context": [],
        "user_requirement": "我有一个全渠道多触点的营销规因分析报告，我需要生成一个多触点归因漏斗图，注意图中文字尽量使用中文，可选生成图的画面比例，也许报告中没有营销规因分析就需要分析原始报告生成"
      }
    },
    {
      "point_code": "",
      "id": "step_2_attribution_analyzer",
      "result": [],
      "api_code": "attribution_analyzer",
      "backend_type": "prompt_api",
      "status": "PENDING",
      "name": "从营销报告中抽取/推断结构化归因数据",
      "action": "run",
      "params": {
        "merge_context": {
          "upstream_content": "{{@step_1_intent_analyzer.result.data.structured_intent.deep_intent}}",
          "fusion_strategy": "semantic",
          "priority_rules": [
            "数据逻辑一致性优先",
            "推断数据需标注"
          ]
        },
        "success_criteria": "{{@step_1_intent_analyzer.result.data.structured_intent.success_criteria}}",
        "report_text": "{{@input.report_text}}"
      },
      "depends_on": [
        "step_1_intent_analyzer"
      ]
    },
    {
      "point_code": "",
      "id": "step_3_funnel_visual_prompt_render",
      "result": [],
      "api_code": "funnel_visual_prompt_render",
      "backend_type": "prompt_api",
      "status": "PENDING",
      "name": "将结构化归因数据渲染为精准漏斗图生图提示词",
      "action": "run",
      "params": {
        "merge_context": {
          "upstream_content": "{{@step_2_attribution_analyzer.result.data.result}}",
          "fusion_strategy": "semantic_structural_stylistic",
          "priority_rules": [
            "数据逻辑一致性优先",
            "中文标注优先",
            "权重宽度视觉映射"
          ]
        },
        "success_criteria": "{{@step_1_intent_analyzer.result.data.structured_intent.success_criteria}}",
        "structured_funnel_data": "{{@step_2_attribution_analyzer.result.data.structured_funnel_data}}",
        "user_aspect_ratio": "{{@input.aspect_ratio}}"
      },
      "depends_on": [
        "step_2_attribution_analyzer"
      ]
    },
    {
      "point_code": "1",
      "id": "step_4_generate_funnel_image",
      "result": [],
      "api_code": "3373",
      "backend_type": "sys_api",
      "status": "PENDING",
      "name": "生成漏斗图图像",
      "action": "run",
      "params": {
        "prompt": "{{@step_3_funnel_visual_prompt_render.result.data.optimized_prompt}}",
        "aspect_ratio": "{{@input.aspect_ratio}}"
      },
      "depends_on": [
        "step_3_funnel_visual_prompt_render"
      ]
    },
    {
      "point_code": "",
      "id": "step_5_poll_image_status",
      "result": [],
      "backend_type": "sys_api",
      "api_code": "polling",
      "status": "PENDING",
      "name": "轮询漏斗图生成状态",
      "action": "run",
      "params": {
        "result_url": "{{@step_4_generate_funnel_image.result.data.result_url}}"
      },
      "depends_on": [
        "step_4_generate_funnel_image"
      ],
      "polling": {
        "interval_seconds": 5,
        "timeout_seconds": 600,
        "success_condition": "{{@step_5_poll_image_status.result.data.task_status}} === 'SUCCEEDED'"
      }
    }
  ],
  "output": {
    "optimized_prompt": {
      "field_name": "optimized_prompt",
      "description": "精准优化后的生图提示词",
      "value": "{{@step_3_funnel_visual_prompt_render.result.data.optimized_prompt}}",
      "type": "string"
    },
    "funnel_image_url": {
      "field_name": "funnel_image_url",
      "description": "生成的多触点归因漏斗图URL",
      "value": "{{@step_5_poll_image_status.result.data.result_url}}",
      "type": "string"
    },
    "structured_funnel_data": {
      "field_name": "structured_funnel_data",
      "description": "结构化漏斗归因数据（包含各触点层级、权重、转化率等）",
      "value": "{{@step_2_attribution_analyzer.result.data.structured_funnel_data}}",
      "type": "object"
    }
  }
}