新增节点、管道计算值按时间、属性查询的 API；新增方案中节点、管道按时间、属性、方案名查询的 API；新增计算值按时间、方案查询所有计算值；强制模拟计算方法新增 开始时间和持续时间 参数，并返回计算状态

main.py

@@ -64,7 +64,7 @@ LINK_COUNT = 2
 prjs = []
 inpDir = "C:/inpfiles/"
 tmpDir = "C:/tmpfiles/"
-
+proj_name = project_info.name
 lockedPrjs = {}
 
 if not os.path.exists(inpDir):
@@ -191,8 +191,9 @@ async def startup_db():
     logger.info("TJWater CloudService is starting...")
     logger.info("**********************************************************")
 
-    # open 'szh' by default
+    # open proj_name by default
-    open_project("szh")
+    print(project_info.name)
+    open_project(project_info.name)
 
 
 ############################################################
@@ -2835,6 +2836,69 @@ async def fastapi_query_all_records_by_time(querytime: str) -> dict[str, list]:
     return {"nodes": results[0], "links": results[1]}
 
 
+# def query_all_record_by_time_property(querytime: str, type: str, property: str, bucket: str = "realtime_simulation_result") -> tuple:
+@app.get("/queryallrecordsbytimeproperty/")
+async def fastapi_query_all_record_by_time_property(
+    querytime: str, type: str, property: str, bucket: str = "realtime_simulation_result"
+) -> dict[str, list]:
+    results: tuple = influxdb_api.query_all_record_by_time_property(
+        query_time=querytime, type=type, property=property, bucket=bucket
+    )
+    return {"results": results}
+
+
+@app.get("/queryallschemerecordsbytimeproperty/")
+async def fastapi_query_all_scheme_record_by_time_property(
+    querytime: str,
+    type: str,
+    property: str,
+    schemename: str,
+    bucket: str = "scheme_simulation_result",
+) -> dict[str, list]:
+    """
+    查询指定方案某一时刻的所有记录,查询 'node' 或 'link' 的某一属性值
+
+    :param querytime: 查询时间,格式为 '2024-11-24T17:30:00+08:00'
+    :param type: 查询类型 'node' 或 'link'
+    :param property: 查询的属性字段名
+    :param schemename: 方案名称,如 "FANGAN1761124840355"
+    :param bucket: 数据存储的bucket名称
+    :return: 包含查询结果的字典
+    """
+    results: list = influxdb_api.query_all_scheme_record_by_time_property(
+        query_time=querytime,
+        type=type,
+        property=property,
+        scheme_name=schemename,
+        bucket=bucket,
+    )
+    return {"results": results}
+
+
+@app.get("/querysimulationrecordsbyidtime/")
+async def fastapi_query_simulation_record_by_ids_time(
+    id: str, querytime: str, type: str, bucket: str = "realtime_simulation_result"
+) -> dict[str, list]:
+    results: tuple = influxdb_api.query_simulation_result_by_ID_time(
+        ID=id, type=type, query_time=querytime, bucket=bucket
+    )
+    return {"results": results}
+
+
+@app.get("/queryschemesimulationrecordsbyidtime/")
+async def fastapi_query_scheme_simulation_record_by_ids_time(
+    scheme_name: str,
+    id: str,
+    querytime: str,
+    type: str,
+    bucket: str = "scheme_simulation_result",
+) -> dict[str, list]:
+    results: tuple = influxdb_api.query_scheme_simulation_result_by_ID_time(
+        scheme_name=scheme_name, ID=id, type=type, query_time=querytime, bucket=bucket
+    )
+    return {"results": results}
+
+
 @app.get("/queryallrecordsbydate/")
 async def fastapi_query_all_records_by_date(querydate: str) -> dict:
     # 缓存查询结果提高性能
@@ -3004,7 +3068,7 @@ async def fastapi_query_scada_data_by_device_id_and_time(ids: str, querytime: st
 
 
 # 2025/05/04 DingZQ
-# 对于SCADA的曲线数据，我们需要有4 套数据值
+# 对于SCAD的曲线数据，我们需要有4 套数据值
 # 1. 原始数据
 # 2. 补充的数据 （补充前面第一步缺失的数据）
 # 3. 清洗后的数据点 （用五角星表示）
@@ -3288,25 +3352,36 @@ async def fastapi_download_history_data_manually(
 
 
 # DingZQ, 2025-05-17
+# 新增开始时间和持续时间参数
 class Run_Simulation_Manually_by_Date(BaseModel):
     """
     name：数据库名称
     simulation_date：样式如 2025-05-04
+    start_time：开始时间，样式如 08:00:00
+    duration：持续时间，单位为分钟
     """
 
     name: str
     simulation_date: str
+    start_time: str
+    duration: int
 
 
-def run_simulation_manually_by_date(network_name: str, base_date: datetime) -> None:
+def run_simulation_manually_by_date(
-    # 循环生成96个时间点（15分钟间隔）
+    network_name: str, base_date: datetime, start_time: str, duration: int
-    for i in range(96):
+) -> None:
-        # 计算当前时间偏移
+    # 解析开始时间
-        time_offset = timedelta(minutes=15 * i)
+    start_hour, start_minute, start_second = map(int, start_time.split(":"))
+    start_datetime = base_date.replace(
+        hour=start_hour, minute=start_minute, second=start_second
+    )
 
-        # 生成完整时间对象
+    # 计算结束时间
-        current_time = base_date + time_offset
+    end_datetime = start_datetime + timedelta(minutes=duration)
 
+    # 生成时间点，每15分钟一个
+    current_time = start_datetime
+    while current_time < end_datetime:
         # 格式化成ISO8601带时区格式
         iso_time = current_time.strftime("%Y-%m-%dT%H:%M:%S") + "+08:00"
 
@@ -3317,11 +3392,14 @@ def run_simulation_manually_by_date(network_name: str, base_date: datetime) -> N
             modify_pattern_start_time=iso_time,
         )
 
+        # 增加15分钟
+        current_time += timedelta(minutes=15)
+
 
 @app.post("/runsimulationmanuallybydate/")
 async def fastapi_run_simulation_manually_by_date(
     data: Run_Simulation_Manually_by_Date,
-) -> None:
+) -> dict[str, str]:
     item = data.dict()
     print(f"item: {item}")
 
@@ -3332,40 +3410,50 @@ async def fastapi_run_simulation_manually_by_date(
         raise HTTPException(status_code=409, detail="is in simulation")
     else:
         print("file doesnt exists")
-        simulation.query_corresponding_element_id_and_query_id(item["name"])
+        try:
-        simulation.query_corresponding_pattern_id_and_query_id(item["name"])
+            simulation.query_corresponding_element_id_and_query_id(item["name"])
-        region_result = simulation.query_non_realtime_region(item["name"])
+            simulation.query_corresponding_pattern_id_and_query_id(item["name"])
+            region_result = simulation.query_non_realtime_region(item["name"])
 
-        globals.source_outflow_region_id = simulation.get_source_outflow_region_id(
+            globals.source_outflow_region_id = simulation.get_source_outflow_region_id(
-            item["name"], region_result
-        )
-        globals.realtime_region_pipe_flow_and_demand_id = (
-            simulation.query_realtime_region_pipe_flow_and_demand_id(
                 item["name"], region_result
             )
-        )
+            globals.realtime_region_pipe_flow_and_demand_id = (
-        globals.pipe_flow_region_patterns = simulation.query_pipe_flow_region_patterns(
+                simulation.query_realtime_region_pipe_flow_and_demand_id(
-            item["name"]
+                    item["name"], region_result
-        )
+                )
+            )
+            globals.pipe_flow_region_patterns = (
+                simulation.query_pipe_flow_region_patterns(item["name"])
+            )
 
-        globals.non_realtime_region_patterns = (
+            globals.non_realtime_region_patterns = (
-            simulation.query_non_realtime_region_patterns(item["name"], region_result)
+                simulation.query_non_realtime_region_patterns(
-        )
+                    item["name"], region_result
-        (
+                )
-            globals.source_outflow_region_patterns,
+            )
-            globals.realtime_region_pipe_flow_and_demand_patterns,
+            (
-        ) = simulation.get_realtime_region_patterns(
+                globals.source_outflow_region_patterns,
-            item["name"],
+                globals.realtime_region_pipe_flow_and_demand_patterns,
-            globals.source_outflow_region_id,
+            ) = simulation.get_realtime_region_patterns(
-            globals.realtime_region_pipe_flow_and_demand_id,
+                item["name"],
-        )
+                globals.source_outflow_region_id,
+                globals.realtime_region_pipe_flow_and_demand_id,
+            )
 
-        base_date = datetime.strptime(item["simulation_date"], "%Y-%m-%d")
+            base_date = datetime.strptime(item["simulation_date"], "%Y-%m-%d")
 
-        thread = threading.Thread(
+            thread = threading.Thread(
-            target=lambda: run_simulation_manually_by_date(item["name"], base_date)
+                target=lambda: run_simulation_manually_by_date(
-        )
+                    item["name"], base_date, item["start_time"], item["duration"]
-        thread.start()
+                )
+            )
+            thread.start()
+            thread.join()  # 等待线程完成
+
+            return {"status": "success"}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
 
         # thread.join()
         # DingZQ 08152025
@@ -3941,7 +4029,10 @@ async def get_dict(item: Item):
 if __name__ == "__main__":
     # uvicorn.run(app, host="0.0.0.0", port=8000)
     # url='http://127.0.0.1:8000/valve_close_analysis?network=beibeizone&start_time=2024-04-01T08:00:00Z&valve_IDs=GSD2307192058577780A3287D78&valve_IDs=GSD2307192058572E953B707226(S2)&duration=1800'
-    url = "http://127.0.0.1:8000/burst_analysis?network=beibeizone&start_time=2024-04-01T08:00:00Z&burst_ID=ZBBGXSZW000001&duration=1800"
+    # url='http://127.0.0.1:8000/burst_analysis?network=beibeizone&start_time=2024-04-01T08:00:00Z&burst_ID=ZBBGXSZW000001&duration=1800'
-    Request.get(
+    # url = "http://192.168.1.36:8000/queryallschemeallrecords/?schemename=Fangan0817114448&querydate=2025-08-13&schemetype=burst_Analysis"
-        url,
+    # response = Request.get(url)
-    )
+
+    # import requests
+
+    # response = requests.get(url)