PRA-only-demo/scripts/gen-morning-flow.py

# -*- coding: utf-8 -*-
"""生成「全家桶 · 晨间自动化助手」流程 JSON。"""
import json
import os

BASE = r"C:\Users\Administrator\Desktop\RPA整理测试"
EXCEL = "C:/Users/Administrator/Desktop/淘宝手机比价.xlsx"
OUT_XLSX = "C:/Users/Administrator/Desktop/低价手机清单.xlsx"
SHOT = BASE + "\\整理完成截图.png"

nodes = []
edges = []

def node(id, componentId, label, values, output_var=None, timeout=30000, parent=None, slot=None, pos=None, options_extra=None):
    options = {"timeoutMs": timeout}
    if output_var:
        options["outputVar"] = output_var
    if options_extra:
        options.update(options_extra)
    n = {
        "id": id,
        "componentId": componentId,
        "label": label,
        "values": values,
        "options": options,
    }
    if parent:
        n["parentId"] = parent
        n["slot"] = slot
        n["position"] = {"x": 18, "y": 0}
    else:
        n["position"] = pos
    nodes.append(n)
    return n

def link(a, b):
    edges.append({"id": f"edge_{a}_{b}", "source": a, "target": b})

# ============ 主链 ============
node("node_start", "Trigger.Manual", "立即执行", {"entryName": "立即执行"}, pos={"x": 240, "y": 70})

# ---- 板块 A：文件整理 ----
node("node_scan", "File_GetFileList_V1", "扫描测试文件夹", {
    "folder_path": BASE, "key_word": "", "ignore": "",
    "recursive": "0", "is_full_path": "1", "sort_type": "1",
}, output_var="dlFiles", pos={"x": 240, "y": 210})

node("node_mkdir_zip", "File_MakeDir", "建分类文件夹：压缩包", {"path": BASE + "\\压缩包", "name": "", "overwrite": "False"}, pos={"x": 240, "y": 340})
node("node_mkdir_img", "File_MakeDir", "建分类文件夹：图片", {"path": BASE + "\\图片", "name": "", "overwrite": "False"}, pos={"x": 240, "y": 460})
node("node_mkdir_doc", "File_MakeDir", "建分类文件夹：文档", {"path": BASE + "\\文档", "name": "", "overwrite": "False"}, pos={"x": 240, "y": 580})
node("node_mkdir_other", "File_MakeDir", "建分类文件夹：其他", {"path": BASE + "\\其他", "name": "", "overwrite": "False"}, pos={"x": 240, "y": 700})

node("node_loop", "logic_control_array_for_v3", "遍历文件逐个分类", {
    "array_name": "dlFiles", "reverse": "0", "start_index": "0", "end_index": "-1",
}, pos={"x": 240, "y": 840})

# 循环体内：三层 IF 嵌套分类
node("node_if_zip", "logic_control_if_else", "是压缩包？", {
    "condition": '${contains(lower(item), ".zip") || contains(lower(item), ".rar") || contains(lower(item), ".7z")}',
}, parent="node_loop", slot="array_for_fn")
node("node_move_zip", "File_File_Move", "移入「压缩包」", {
    "file_type": "file", "file_path": "${item}", "target_path": BASE + "\\压缩包", "overwrite": "True",
}, parent="node_if_zip", slot="statement_if")
node("node_if_img", "logic_control_if_else", "是图片？", {
    "condition": '${contains(lower(item), ".png") || contains(lower(item), ".jpg") || contains(lower(item), ".jpeg") || contains(lower(item), ".gif") || contains(lower(item), ".bmp")}',
}, parent="node_if_zip", slot="statement_else")
node("node_move_img", "File_File_Move", "移入「图片」", {
    "file_type": "file", "file_path": "${item}", "target_path": BASE + "\\图片", "overwrite": "True",
}, parent="node_if_img", slot="statement_if")
node("node_if_doc", "logic_control_if_else", "是文档？", {
    "condition": '${contains(lower(item), ".doc") || contains(lower(item), ".pdf") || contains(lower(item), ".txt") || contains(lower(item), ".xlsx") || contains(lower(item), ".ppt")}',
}, parent="node_if_img", slot="statement_else")
node("node_move_doc", "File_File_Move", "移入「文档」", {
    "file_type": "file", "file_path": "${item}", "target_path": BASE + "\\文档", "overwrite": "True",
}, parent="node_if_doc", slot="statement_if")
node("node_move_other", "File_File_Move", "移入「其他」", {
    "file_type": "file", "file_path": "${item}", "target_path": BASE + "\\其他", "overwrite": "True",
}, parent="node_if_doc", slot="statement_else")

# ---- 板块 B：Excel 数据加工 ----
node("node_read_excel", "Bivartable_DoOpenExcel", "读取淘宝比价 Excel", {
    "filePath": EXCEL, "sheetName": "Sheet1", "indexx": "False",
}, output_var="priceTable", pos={"x": 240, "y": 990})

node("node_sort", "Bivartable_SortDf", "按价格升序排序", {
    "df": "${priceTable}", "name": "价格", "sort": "True", "showTable": "0",
}, output_var="sortedTable", pos={"x": 240, "y": 1110})

node("node_filter", "Bivartable_FilterDf", "筛选 3000 元以下", {
    "df": "${sortedTable}", "filter_mode": "ordinary", "col_name": "价格",
    "choice": "4", "criteria": "3000", "rule": '["价格"] <= 3000', "showTable": "0",
}, output_var="filteredTable", pos={"x": 240, "y": 1230})

node("node_min", "Bivartable_GetMinValue", "算最低价", {
    "df": "${priceTable}", "groupby": "", "minvaluecols": "价格",
}, output_var="minPrice", pos={"x": 240, "y": 1350})

node("node_max", "Bivartable_GetMaxValue", "算最高价", {
    "df": "${priceTable}", "groupby": "", "maxvaluecols": "价格",
}, output_var="maxPrice", pos={"x": 240, "y": 1470})

node("node_avg", "Bivartable_GetAvgValue", "算平均价", {
    "df": "${priceTable}", "groupby": "", "avgvaluecols": "价格",
}, output_var="avgPrice", pos={"x": 240, "y": 1590})

node("node_write_cheap", "Bivartable_DfWriteToFile", "保存低价清单 Excel", {
    "df": "${filteredTable}", "suffix": "xlsx", "filePath": OUT_XLSX, "folderPath": "C:/Users/Administrator/Desktop", "sheet": "Sheet1",
}, pos={"x": 240, "y": 1710})

# ---- 板块 C：记事本键鼠 + 截图 ----
node("node_notepad_run", "App_Run", "启动记事本", {
    "executeable": "notepad.exe", "params": "", "cwd": "", "is_admin": "0", "create_new_console": "0",
}, output_var="notepadPid", pos={"x": 240, "y": 1830})

node("node_delay_notepad", "Basic_Delay_V2", "等记事本就绪", {
    "is_random": "0", "second": "1", "min_second": "1", "max_second": "5",
}, pos={"x": 240, "y": 1950})

summary_expr = (
    '${"RPA 晨间助手报告 " + today() + "：已整理测试文件 " + len(dlFiles) + " 个；'
    '比价表筛出 3000 元以下手机 " + (len(filteredTable) - 1) + " 款；'
    '最低 " + minPrice + " 元，最高 " + maxPrice + " 元，平均 " + round(avgPrice, 1) + " 元。"}'
)
node("node_notepad_type", "Keyboard_Send_V1", "记事本输入摘要（键盘模拟）", {
    "words": summary_expr, "interval": "10", "fuctype": "0", "window_title": "记事本",
}, pos={"x": 240, "y": 2070})

node("node_shot", "Image_Screenshot", "截屏留证", {
    "pictureSource": "1", "shotRange": "1", "image_path": SHOT, "position": "", "outImageName": "",
}, pos={"x": 240, "y": 2190})

node("node_notepad_kill", "App_Kill", "关闭记事本", {
    "pid": "${notepadPid}",
}, pos={"x": 240, "y": 2310})

# ---- 板块 D：OCR + AI + 收尾 ----
node("node_ocr", "OcrOffline_GetOcrTextNew", "OCR 识别截图", {
    "image_path": SHOT,
}, output_var="ocrText", timeout=120000, pos={"x": 240, "y": 2430})

ai_prompt = (
    "这是一次 RPA 自动化的收尾总结。屏幕 OCR 识别到的内容：${ocrText}。"
    "数据统计：整理测试文件 ${len(dlFiles)} 个；低价手机 ${len(filteredTable) - 1} 款；"
    "最低价 ${minPrice} 元、最高价 ${maxPrice} 元、平均 ${round(avgPrice, 1)} 元。"
    "请用中文生成一段 100 字左右的晨间摘要，说明本次自动化完成了哪些事、结果如何，语气轻松一点。"
)
node("node_ai", "DataService_LargeModel", "AI 生成晨间摘要", {
    "hint_word": ai_prompt,
}, output_var="aiSummary", timeout=200000, pos={"x": 240, "y": 2550})

node("node_clipboard", "Clipboard_SetText", "摘要复制到剪贴板", {
    "text": "${aiSummary}",
}, pos={"x": 240, "y": 2670})

node("node_notice", "Dialog_Notice_V2", "弹窗通知完成", {
    "text": "晨间摘要已生成并复制到剪贴板：${aiSummary}", "title": "RPA 晨间助手", "time_out": "20",
}, timeout=60000, pos={"x": 240, "y": 2790})

# ---- 主链连线 ----
main_chain = [
    "node_start", "node_scan",
    "node_mkdir_zip", "node_mkdir_img", "node_mkdir_doc", "node_mkdir_other",
    "node_loop",
    "node_read_excel", "node_sort", "node_filter",
    "node_min", "node_max", "node_avg", "node_write_cheap",
    "node_notepad_run", "node_delay_notepad", "node_notepad_type", "node_shot", "node_notepad_kill",
    "node_ocr", "node_ai", "node_clipboard", "node_notice",
]
for a, b in zip(main_chain, main_chain[1:]):
    link(a, b)

flow = {
    "schemaVersion": 1,
    "id": "flow_morning_assistant",
    "name": "全家桶 · 晨间自动化助手",
    "description": "测试长流程：文件整理(IF分支+遍历+文件操作) → Excel数据加工(读取/排序/筛选/统计/落盘) → 记事本键鼠模拟+截屏 → OCR识别+AI摘要+剪贴板+弹窗通知。",
    "enabled": True,
    "trigger": {"label": "手动运行", "type": "manual"},
    "variables": [],
    "nodes": nodes,
    "edges": edges,
    "meta": {
        "createdAt": "2026-11-05T07:25:00.000Z",
        "updatedAt": "2026-11-05T07:25:00.000Z",
        "appVersion": "0.1.0",
    },
}

out = os.path.join(os.environ["APPDATA"], "rpa-studio", "flows", "flow_morning_assistant.json")
with open(out, "w", encoding="utf-8") as f:
    json.dump(flow, f, ensure_ascii=False, indent=2)
print("written:", out, "| nodes:", len(nodes), "| edges:", len(edges))