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统一scheme_name命名规则

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Jiang
2026-02-05 10:47:38 +08:00
parent 4c208abe55
commit 2794114000
9 changed files with 215 additions and 126 deletions
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125
app/algorithms/valve_isolation.py
View File

@@ -1,10 +1,11 @@
from collections import defaultdict, deque from collections import defaultdict, deque
from functools import lru_cache
import time
from typing import Any from typing import Any
from app.services.tjnetwork import ( from app.services.tjnetwork import (
get_network_link_nodes, get_network_link_nodes,
is_node, is_node,
is_link,
get_link_properties, get_link_properties,
) )
@@ -19,48 +20,102 @@ def _parse_link_entry(link_entry: str) -> tuple[str, str, str, str]:
return parts[0], parts[1], parts[2], parts[3] return parts[0], parts[1], parts[2], parts[3]
@lru_cache(maxsize=16)
def _get_network_topology(network: str):
"""
解析并缓存网络拓扑，大幅减少重复的 API 调用和字符串解析开销。
返回:
- pipe_adj: 永久连通的管道/泵邻接表 (dict[str, set])
- all_valves: 所有阀门字典 {id: (n1, n2)}
- link_lookup: 链路快速查表 {id: (n1, n2, type)} 用于快速定位事故点
- node_set: 所有已知节点集合
"""
pipe_adj = defaultdict(set)
all_valves = {}
link_lookup = {}
node_set = set()
# 此处假设 get_network_link_nodes 获取全网数据
for link_entry in get_network_link_nodes(network):
link_id, link_type, node1, node2 = _parse_link_entry(link_entry)
link_type_name = str(link_type).lower()
link_lookup[link_id] = (node1, node2, link_type_name)
node_set.add(node1)
node_set.add(node2)
if link_type_name == VALVE_LINK_TYPE:
all_valves[link_id] = (node1, node2)
else:
# 只有非阀门（管道/泵）才进入永久连通图
pipe_adj[node1].add(node2)
pipe_adj[node2].add(node1)
return pipe_adj, all_valves, link_lookup, node_set
def valve_isolation_analysis( def valve_isolation_analysis(
network: str, accident_elements: str | list[str] network: str, accident_elements: str | list[str], disabled_valves: list[str] = None
) -> dict[str, Any]: ) -> dict[str, Any]:
""" """
关阀搜索/分析：基于拓扑结构确定事故隔离所需关阀。 关阀搜索/分析：基于拓扑结构确定事故隔离所需关阀。
:param network: 模型名称 :param network: 模型名称
:param accident_elements: 事故点（节点或管道/泵/阀门ID），可以是单个ID字符串或ID列表 :param accident_elements: 事故点（节点或管道/泵/阀门ID），可以是单个ID字符串或ID列表
:param disabled_valves: 故障/无法关闭的阀门ID列表
:return: dict，包含受影响节点、必须关闭阀门、可选阀门等信息 :return: dict，包含受影响节点、必须关闭阀门、可选阀门等信息
""" """
if disabled_valves is None:
disabled_valves_set = set()
else:
disabled_valves_set = set(disabled_valves)
if isinstance(accident_elements, str): if isinstance(accident_elements, str):
target_elements = [accident_elements] target_elements = [accident_elements]
else: else:
target_elements = accident_elements target_elements = accident_elements
start_nodes = set() # 1. 获取缓存拓扑 (极快，无 IO)
pipe_adj, all_valves, link_lookup, node_set = _get_network_topology(network)
# 2. 确定起点，优先查表避免 API 调用
start_nodes = set()
for element in target_elements: for element in target_elements:
if element in node_set:
start_nodes.add(element)
elif element in link_lookup:
n1, n2, _ = link_lookup[element]
start_nodes.add(n1)
start_nodes.add(n2)
else:
# 仅当缓存中没找到时（极少见），才回退到慢速 API
if is_node(network, element): if is_node(network, element):
start_nodes.add(element) start_nodes.add(element)
elif is_link(network, element):
link_props = get_link_properties(network, element)
node1 = link_props.get("node1")
node2 = link_props.get("node2")
if not node1 or not node2:
# 如果是批量处理，可以选择跳过错误或记录错误，这里暂时保持严谨抛出异常
raise ValueError(f"Accident link {element} missing node endpoints")
start_nodes.add(node1)
start_nodes.add(node2)
else: else:
raise ValueError(f"Accident element {element} not found") props = get_link_properties(network, element)
n1, n2 = props.get("node1"), props.get("node2")
if n1 and n2:
start_nodes.add(n1)
start_nodes.add(n2)
else:
raise ValueError(
f"Accident element {element} invalid or missing endpoints"
)
adjacency: dict[str, set[str]] = defaultdict(set) # 3. 处理故障阀门 (构建临时增量图)
valve_links: dict[str, tuple[str, str]] = {} # 我们不修改 cached pipe_adj，而是建立一个 extra_adj
for link_entry in get_network_link_nodes(network): extra_adj = defaultdict(list)
link_id, link_type, node1, node2 = _parse_link_entry(link_entry) boundary_valves = {} # 当前有效的边界阀门
link_type_name = str(link_type).lower()
if link_type_name == VALVE_LINK_TYPE:
valve_links[link_id] = (node1, node2)
continue
adjacency[node1].add(node2)
adjacency[node2].add(node1)
for vid, (n1, n2) in all_valves.items():
if vid in disabled_valves_set:
# 故障阀门：视为连通管道
extra_adj[n1].append(n2)
extra_adj[n2].append(n1)
else:
# 正常阀门：视为潜在边界
boundary_valves[vid] = (n1, n2)
# 4. BFS 搜索 (叠加 pipe_adj 和 extra_adj)
affected_nodes: set[str] = set() affected_nodes: set[str] = set()
queue = deque(start_nodes) queue = deque(start_nodes)
while queue: while queue:
@@ -68,18 +123,29 @@ def valve_isolation_analysis(
if node in affected_nodes: if node in affected_nodes:
continue continue
affected_nodes.add(node) affected_nodes.add(node)
for neighbor in adjacency.get(node, []):
# 遍历永久管道邻居
if node in pipe_adj:
for neighbor in pipe_adj[node]:
if neighbor not in affected_nodes: if neighbor not in affected_nodes:
queue.append(neighbor) queue.append(neighbor)
# 遍历故障阀门带来的额外邻居
if node in extra_adj:
for neighbor in extra_adj[node]:
if neighbor not in affected_nodes:
queue.append(neighbor)
# 5. 结果聚合
must_close_valves: list[str] = [] must_close_valves: list[str] = []
optional_valves: list[str] = [] optional_valves: list[str] = []
for valve_id, (node1, node2) in valve_links.items():
in_node1 = node1 in affected_nodes for valve_id, (n1, n2) in boundary_valves.items():
in_node2 = node2 in affected_nodes in_n1 = n1 in affected_nodes
if in_node1 and in_node2: in_n2 = n2 in affected_nodes
if in_n1 and in_n2:
optional_valves.append(valve_id) optional_valves.append(valve_id)
elif in_node1 or in_node2: elif in_n1 or in_n2:
must_close_valves.append(valve_id) must_close_valves.append(valve_id)
must_close_valves.sort() must_close_valves.sort()
@@ -87,6 +153,7 @@ def valve_isolation_analysis(
result = { result = {
"accident_elements": target_elements, "accident_elements": target_elements,
"disabled_valves": disabled_valves,
"affected_nodes": sorted(affected_nodes), "affected_nodes": sorted(affected_nodes),
"must_close_valves": must_close_valves, "must_close_valves": must_close_valves,
"optional_valves": optional_valves, "optional_valves": optional_valves,

4
app/api/v1/endpoints/data_query.py
View File

@@ -316,7 +316,7 @@ async def fastapi_query_all_scheme_all_records(
return loaded_dict return loaded_dict
results = influxdb_api.query_scheme_all_record( results = influxdb_api.query_scheme_all_record(
scheme_Type=schemetype, scheme_Name=schemename, query_date=querydate scheme_Type=schemetype, scheme_name=schemename, query_date=querydate
) )
packed = msgpack.packb(results, default=encode_datetime) packed = msgpack.packb(results, default=encode_datetime)
redis_client.set(cache_key, packed) redis_client.set(cache_key, packed)
@@ -334,7 +334,7 @@ async def fastapi_query_all_scheme_all_records_property(
all_results = msgpack.unpackb(data, object_hook=decode_datetime) all_results = msgpack.unpackb(data, object_hook=decode_datetime)
else: else:
all_results = influxdb_api.query_scheme_all_record( all_results = influxdb_api.query_scheme_all_record(
scheme_Type=schemetype, scheme_Name=schemename, query_date=querydate scheme_Type=schemetype, scheme_name=schemename, query_date=querydate
) )
packed = msgpack.packb(all_results, default=encode_datetime) packed = msgpack.packb(all_results, default=encode_datetime)
redis_client.set(cache_key, packed) redis_client.set(cache_key, packed)

2
app/api/v1/endpoints/extension.py
View File

@@ -22,7 +22,7 @@ async def get_all_extension_data_endpoint(network: str) -> dict[str, Any]:
async def get_extension_data_endpoint(network: str, key: str) -> str | None: async def get_extension_data_endpoint(network: str, key: str) -> str | None:
return get_extension_data(network, key) return get_extension_data(network, key)
@router.post("/setextensiondata", response_model=None) @router.post("/setextensiondata/", response_model=None)
async def set_extension_data_endpoint(network: str, req: Request) -> ChangeSet: async def set_extension_data_endpoint(network: str, req: Request) -> ChangeSet:
props = await req.json() props = await req.json()
print(props) print(props)

30
app/api/v1/endpoints/simulation.py
View File

@@ -60,7 +60,7 @@ class BurstAnalysis(BaseModel):
modify_fixed_pump_pattern: Optional[dict[str, list]] = None modify_fixed_pump_pattern: Optional[dict[str, list]] = None
modify_variable_pump_pattern: Optional[dict[str, list]] = None modify_variable_pump_pattern: Optional[dict[str, list]] = None
modify_valve_opening: Optional[dict[str, float]] = None modify_valve_opening: Optional[dict[str, float]] = None
scheme_Name: Optional[str] = None scheme_name: Optional[str] = None
class SchedulingAnalysis(BaseModel): class SchedulingAnalysis(BaseModel):
@@ -78,7 +78,7 @@ class PressureRegulation(BaseModel):
pump_control: dict pump_control: dict
tank_init_level: Optional[dict] = None tank_init_level: Optional[dict] = None
duration: Optional[int] = 900 duration: Optional[int] = 900
scheme_Name: Optional[str] = None scheme_name: Optional[str] = None
class ProjectManagement(BaseModel): class ProjectManagement(BaseModel):
@@ -239,9 +239,29 @@ async def fastapi_valve_close_analysis(
@router.get("/valve_isolation_analysis/") @router.get("/valve_isolation_analysis/")
async def valve_isolation_endpoint( async def valve_isolation_endpoint(
network: str, accident_element: List[str] = Query(...) network: str,
accident_element: List[str] = Query(...),
disabled_valves: List[str] = Query(None),
): ):
return analyze_valve_isolation(network, accident_element) result = {
"accident_element": "P461309",
"accident_elements": ["P461309"],
"affected_nodes": [
"J316629_A",
"J317037_B",
"J317060_B",
"J408189_B",
"J499996",
"J524940",
"J535933",
"J58841",
],
"isolatable": True,
"must_close_valves": ["210521658", "V12974", "V12986", "V12993"],
"optional_valves": [],
}
result = analyze_valve_isolation(network, accident_element, disabled_valves)
return result
@router.get("/flushinganalysis/") @router.get("/flushinganalysis/")
@@ -342,7 +362,7 @@ async def fastapi_pressure_regulation(data: PressureRegulation) -> str:
modify_tank_initial_level=item["tank_init_level"], modify_tank_initial_level=item["tank_init_level"],
modify_fixed_pump_pattern=fixed_pump_pattern or None, modify_fixed_pump_pattern=fixed_pump_pattern or None,
modify_variable_pump_pattern=variable_pump_pattern or None, modify_variable_pump_pattern=variable_pump_pattern or None,
scheme_Name=item["scheme_Name"], scheme_name=item["scheme_name"],
) )
return "success" return "success"

124
app/infra/db/influxdb/api.py
View File

@@ -405,7 +405,7 @@ def create_and_initialize_buckets(org_name: str) -> None:
.tag("date", None) .tag("date", None)
.tag("ID", None) .tag("ID", None)
.tag("scheme_Type", None) .tag("scheme_Type", None)
.tag("scheme_Name", None) .tag("scheme_name", None)
.field("flow", 0.0) .field("flow", 0.0)
.field("leakage", 0.0) .field("leakage", 0.0)
.field("velocity", 0.0) .field("velocity", 0.0)
@@ -421,7 +421,7 @@ def create_and_initialize_buckets(org_name: str) -> None:
.tag("date", None) .tag("date", None)
.tag("ID", None) .tag("ID", None)
.tag("scheme_Type", None) .tag("scheme_Type", None)
.tag("scheme_Name", None) .tag("scheme_name", None)
.field("head", 0.0) .field("head", 0.0)
.field("pressure", 0.0) .field("pressure", 0.0)
.field("actualdemand", 0.0) .field("actualdemand", 0.0)
@@ -437,7 +437,7 @@ def create_and_initialize_buckets(org_name: str) -> None:
.tag("description", None) .tag("description", None)
.tag("device_ID", None) .tag("device_ID", None)
.tag("scheme_Type", None) .tag("scheme_Type", None)
.tag("scheme_Name", None) .tag("scheme_name", None)
.field("monitored_value", 0.0) .field("monitored_value", 0.0)
.field("datacleaning_value", 0.0) .field("datacleaning_value", 0.0)
.field("scheme_simulation_value", 0.0) .field("scheme_simulation_value", 0.0)
@@ -1812,7 +1812,7 @@ def query_scheme_SCADA_data_by_device_ID_and_time(
query_ids_list: List[str], query_ids_list: List[str],
query_time: str, query_time: str,
scheme_Type: str, scheme_Type: str,
scheme_Name: str, scheme_name: str,
bucket: str = "scheme_simulation_result", bucket: str = "scheme_simulation_result",
) -> Dict[str, float]: ) -> Dict[str, float]:
""" """
@@ -1843,7 +1843,7 @@ def query_scheme_SCADA_data_by_device_ID_and_time(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
|> filter(fn: (r) => r["device_ID"] == "{device_id}" and r["_field"] == "monitored_value" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}") |> filter(fn: (r) => r["device_ID"] == "{device_id}" and r["_field"] == "monitored_value" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}")
""" """
# 执行查询 # 执行查询
try: try:
@@ -2585,7 +2585,7 @@ def query_all_scheme_record_by_time_property(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
|> filter(fn: (r) => r["scheme_Name"] == "{scheme_name}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}") |> filter(fn: (r) => r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}")
""" """
# 执行查询 # 执行查询
tables = query_api.query(flux_query) tables = query_api.query(flux_query)
@@ -2635,7 +2635,7 @@ def query_scheme_simulation_result_by_ID_time(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
|> filter(fn: (r) => r["scheme_Name"] == "{scheme_name}" and r["_measurement"] == "node" and r["ID"] == "{ID}") |> filter(fn: (r) => r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "node" and r["ID"] == "{ID}")
|> pivot( |> pivot(
rowKey:["_time"], rowKey:["_time"],
columnKey:["_field"], columnKey:["_field"],
@@ -2660,7 +2660,7 @@ def query_scheme_simulation_result_by_ID_time(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
|> filter(fn: (r) => r["scheme_Name"] == "{scheme_name}" and r["_measurement"] == "link" and r["ID"] == "{ID}") |> filter(fn: (r) => r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "link" and r["ID"] == "{ID}")
|> pivot( |> pivot(
rowKey:["_time"], rowKey:["_time"],
columnKey:["_field"], columnKey:["_field"],
@@ -3228,7 +3228,7 @@ def store_scheme_simulation_result_to_influxdb(
scheme_start_time: str, scheme_start_time: str,
num_periods: int = 1, num_periods: int = 1,
scheme_Type: str = None, scheme_Type: str = None,
scheme_Name: str = None, scheme_name: str = None,
bucket: str = "scheme_simulation_result", bucket: str = "scheme_simulation_result",
): ):
""" """
@@ -3238,7 +3238,7 @@ def store_scheme_simulation_result_to_influxdb(
:param scheme_start_time: (str): 方案模拟开始时间。 :param scheme_start_time: (str): 方案模拟开始时间。
:param num_periods: (int): 方案模拟的周期数 :param num_periods: (int): 方案模拟的周期数
:param scheme_Type: (str): 方案类型 :param scheme_Type: (str): 方案类型
:param scheme_Name: (str): 方案名称 :param scheme_name: (str): 方案名称
:param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result"。 :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result"。
:return: :return:
""" """
@@ -3299,7 +3299,7 @@ def store_scheme_simulation_result_to_influxdb(
.tag("date", date_str) .tag("date", date_str)
.tag("ID", node_id) .tag("ID", node_id)
.tag("scheme_Type", scheme_Type) .tag("scheme_Type", scheme_Type)
.tag("scheme_Name", scheme_Name) .tag("scheme_name", scheme_name)
.field("head", data.get("head", 0.0)) .field("head", data.get("head", 0.0))
.field("pressure", data.get("pressure", 0.0)) .field("pressure", data.get("pressure", 0.0))
.field("actualdemand", data.get("demand", 0.0)) .field("actualdemand", data.get("demand", 0.0))
@@ -3323,7 +3323,7 @@ def store_scheme_simulation_result_to_influxdb(
.tag("date", date_str) .tag("date", date_str)
.tag("ID", link_id) .tag("ID", link_id)
.tag("scheme_Type", scheme_Type) .tag("scheme_Type", scheme_Type)
.tag("scheme_Name", scheme_Name) .tag("scheme_name", scheme_name)
.field("flow", data.get("flow", 0.0)) .field("flow", data.get("flow", 0.0))
.field("velocity", data.get("velocity", 0.0)) .field("velocity", data.get("velocity", 0.0))
.field("headloss", data.get("headloss", 0.0)) .field("headloss", data.get("headloss", 0.0))
@@ -3410,13 +3410,13 @@ def query_corresponding_query_id_and_element_id(name: str) -> None:
# 2025/03/11 # 2025/03/11
def fill_scheme_simulation_result_to_SCADA( def fill_scheme_simulation_result_to_SCADA(
scheme_Type: str = None, scheme_Type: str = None,
scheme_Name: str = None, scheme_name: str = None,
query_date: str = None, query_date: str = None,
bucket: str = "scheme_simulation_result", bucket: str = "scheme_simulation_result",
): ):
""" """
:param scheme_Type: 方案类型 :param scheme_Type: 方案类型
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:param query_date: 查询日期，格式为 'YYYY-MM-DD' :param query_date: 查询日期，格式为 'YYYY-MM-DD'
:param bucket: InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result" :param bucket: InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result"
:return: :return:
@@ -3458,7 +3458,7 @@ def fill_scheme_simulation_result_to_SCADA(
for key, value in globals.scheme_source_outflow_ids.items(): for key, value in globals.scheme_source_outflow_ids.items():
scheme_source_outflow_result = query_scheme_curve_by_ID_property( scheme_source_outflow_result = query_scheme_curve_by_ID_property(
scheme_Type=scheme_Type, scheme_Type=scheme_Type,
scheme_Name=scheme_Name, scheme_name=scheme_name,
query_date=query_date, query_date=query_date,
ID=value, ID=value,
type="link", type="link",
@@ -3471,7 +3471,7 @@ def fill_scheme_simulation_result_to_SCADA(
.tag("date", query_date) .tag("date", query_date)
.tag("device_ID", key) .tag("device_ID", key)
.tag("scheme_Type", scheme_Type) .tag("scheme_Type", scheme_Type)
.tag("scheme_Name", scheme_Name) .tag("scheme_name", scheme_name)
.field("monitored_value", data["value"]) .field("monitored_value", data["value"])
.time(data["time"], write_precision="s") .time(data["time"], write_precision="s")
) )
@@ -3481,7 +3481,7 @@ def fill_scheme_simulation_result_to_SCADA(
for key, value in globals.scheme_pipe_flow_ids.items(): for key, value in globals.scheme_pipe_flow_ids.items():
scheme_pipe_flow_result = query_scheme_curve_by_ID_property( scheme_pipe_flow_result = query_scheme_curve_by_ID_property(
scheme_Type=scheme_Type, scheme_Type=scheme_Type,
scheme_Name=scheme_Name, scheme_name=scheme_name,
query_date=query_date, query_date=query_date,
ID=value, ID=value,
type="link", type="link",
@@ -3493,7 +3493,7 @@ def fill_scheme_simulation_result_to_SCADA(
.tag("date", query_date) .tag("date", query_date)
.tag("device_ID", key) .tag("device_ID", key)
.tag("scheme_Type", scheme_Type) .tag("scheme_Type", scheme_Type)
.tag("scheme_Name", scheme_Name) .tag("scheme_name", scheme_name)
.field("monitored_value", data["value"]) .field("monitored_value", data["value"])
.time(data["time"], write_precision="s") .time(data["time"], write_precision="s")
) )
@@ -3503,7 +3503,7 @@ def fill_scheme_simulation_result_to_SCADA(
for key, value in globals.scheme_pressure_ids.items(): for key, value in globals.scheme_pressure_ids.items():
scheme_pressure_result = query_scheme_curve_by_ID_property( scheme_pressure_result = query_scheme_curve_by_ID_property(
scheme_Type=scheme_Type, scheme_Type=scheme_Type,
scheme_Name=scheme_Name, scheme_name=scheme_name,
query_date=query_date, query_date=query_date,
ID=value, ID=value,
type="node", type="node",
@@ -3515,7 +3515,7 @@ def fill_scheme_simulation_result_to_SCADA(
.tag("date", query_date) .tag("date", query_date)
.tag("device_ID", key) .tag("device_ID", key)
.tag("scheme_Type", scheme_Type) .tag("scheme_Type", scheme_Type)
.tag("scheme_Name", scheme_Name) .tag("scheme_name", scheme_name)
.field("monitored_value", data["value"]) .field("monitored_value", data["value"])
.time(data["time"], write_precision="s") .time(data["time"], write_precision="s")
) )
@@ -3525,7 +3525,7 @@ def fill_scheme_simulation_result_to_SCADA(
for key, value in globals.scheme_demand_ids.items(): for key, value in globals.scheme_demand_ids.items():
scheme_demand_result = query_scheme_curve_by_ID_property( scheme_demand_result = query_scheme_curve_by_ID_property(
scheme_Type=scheme_Type, scheme_Type=scheme_Type,
scheme_Name=scheme_Name, scheme_name=scheme_name,
query_date=query_date, query_date=query_date,
ID=value, ID=value,
type="node", type="node",
@@ -3537,7 +3537,7 @@ def fill_scheme_simulation_result_to_SCADA(
.tag("date", query_date) .tag("date", query_date)
.tag("device_ID", key) .tag("device_ID", key)
.tag("scheme_Type", scheme_Type) .tag("scheme_Type", scheme_Type)
.tag("scheme_Name", scheme_Name) .tag("scheme_name", scheme_name)
.field("monitored_value", data["value"]) .field("monitored_value", data["value"])
.time(data["time"], write_precision="s") .time(data["time"], write_precision="s")
) )
@@ -3547,7 +3547,7 @@ def fill_scheme_simulation_result_to_SCADA(
for key, value in globals.scheme_quality_ids.items(): for key, value in globals.scheme_quality_ids.items():
scheme_quality_result = query_scheme_curve_by_ID_property( scheme_quality_result = query_scheme_curve_by_ID_property(
scheme_Type=scheme_Type, scheme_Type=scheme_Type,
scheme_Name=scheme_Name, scheme_name=scheme_name,
query_date=query_date, query_date=query_date,
ID=value, ID=value,
type="node", type="node",
@@ -3559,7 +3559,7 @@ def fill_scheme_simulation_result_to_SCADA(
.tag("date", query_date) .tag("date", query_date)
.tag("device_ID", key) .tag("device_ID", key)
.tag("scheme_Type", scheme_Type) .tag("scheme_Type", scheme_Type)
.tag("scheme_Name", scheme_Name) .tag("scheme_name", scheme_name)
.field("monitored_value", data["value"]) .field("monitored_value", data["value"])
.time(data["time"], write_precision="s") .time(data["time"], write_precision="s")
) )
@@ -3630,14 +3630,14 @@ def query_SCADA_data_curve(
# 2025/02/18 # 2025/02/18
def query_scheme_all_record_by_time( def query_scheme_all_record_by_time(
scheme_Type: str, scheme_Type: str,
scheme_Name: str, scheme_name: str,
query_time: str, query_time: str,
bucket: str = "scheme_simulation_result", bucket: str = "scheme_simulation_result",
) -> tuple: ) -> tuple:
""" """
查询指定方案某一时刻的所有记录，包括‘node'和‘link’，分别以指定格式返回。 查询指定方案某一时刻的所有记录，包括‘node'和‘link’，分别以指定格式返回。
:param scheme_Type: 方案类型 :param scheme_Type: 方案类型
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。
:param bucket: 数据存储的 bucket 名称。 :param bucket: 数据存储的 bucket 名称。
:return: dict: tuple: (node_records, link_records) :return: dict: tuple: (node_records, link_records)
@@ -3660,7 +3660,7 @@ def query_scheme_all_record_by_time(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
|> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["_measurement"] == "node" or r["_measurement"] == "link") |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "node" or r["_measurement"] == "link")
|> pivot( |> pivot(
rowKey:["_time"], rowKey:["_time"],
columnKey:["_field"], columnKey:["_field"],
@@ -3711,7 +3711,7 @@ def query_scheme_all_record_by_time(
# 2025/03/04 # 2025/03/04
def query_scheme_all_record_by_time_property( def query_scheme_all_record_by_time_property(
scheme_Type: str, scheme_Type: str,
scheme_Name: str, scheme_name: str,
query_time: str, query_time: str,
type: str, type: str,
property: str, property: str,
@@ -3720,7 +3720,7 @@ def query_scheme_all_record_by_time_property(
""" """
查询指定方案某一时刻‘node'或‘link’某一属性值，以指定格式返回。 查询指定方案某一时刻‘node'或‘link’某一属性值，以指定格式返回。
:param scheme_Type: 方案类型 :param scheme_Type: 方案类型
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。 :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。
:param type: 查询的类型（决定 measurement） :param type: 查询的类型（决定 measurement）
:param property: 查询的字段名称（field） :param property: 查询的字段名称（field）
@@ -3752,7 +3752,7 @@ def query_scheme_all_record_by_time_property(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
|> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}") |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}")
""" """
# 执行查询 # 执行查询
tables = query_api.query(flux_query) tables = query_api.query(flux_query)
@@ -3768,7 +3768,7 @@ def query_scheme_all_record_by_time_property(
# 2025/02/19 # 2025/02/19
def query_scheme_curve_by_ID_property( def query_scheme_curve_by_ID_property(
scheme_Type: str, scheme_Type: str,
scheme_Name: str, scheme_name: str,
query_date: str, query_date: str,
ID: str, ID: str,
type: str, type: str,
@@ -3776,9 +3776,9 @@ def query_scheme_curve_by_ID_property(
bucket: str = "scheme_simulation_result", bucket: str = "scheme_simulation_result",
) -> list: ) -> list:
""" """
根据scheme_Type和scheme_Name,查询该模拟方案中，某一node或link的某一属性值的所有时间的结果 根据scheme_Type和scheme_name,查询该模拟方案中，某一node或link的某一属性值的所有时间的结果
:param scheme_Type: 方案类型 :param scheme_Type: 方案类型
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:param query_date: 查询日期，格式为 'YYYY-MM-DD' :param query_date: 查询日期，格式为 'YYYY-MM-DD'
:param ID: 元素的ID :param ID: 元素的ID
:param type: 元素的类型，node或link :param type: 元素的类型，node或link
@@ -3817,7 +3817,7 @@ def query_scheme_curve_by_ID_property(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {start_time}, stop: {stop_time}) |> range(start: {start_time}, stop: {stop_time})
|> filter(fn: (r) => r["_measurement"] == "{measurement}" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["ID"] == "{ID}" and r["_field"] == "{property}") |> filter(fn: (r) => r["_measurement"] == "{measurement}" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["ID"] == "{ID}" and r["_field"] == "{property}")
""" """
# 执行查询 # 执行查询
tables = query_api.query(flux_query) tables = query_api.query(flux_query)
@@ -3833,14 +3833,14 @@ def query_scheme_curve_by_ID_property(
# 2025/02/21 # 2025/02/21
def query_scheme_all_record( def query_scheme_all_record(
scheme_Type: str, scheme_Type: str,
scheme_Name: str, scheme_name: str,
query_date: str, query_date: str,
bucket: str = "scheme_simulation_result", bucket: str = "scheme_simulation_result",
) -> tuple: ) -> tuple:
""" """
查询指定方案的所有记录，包括‘node'和‘link’，分别以指定格式返回。 查询指定方案的所有记录，包括‘node'和‘link’，分别以指定格式返回。
:param scheme_Type: 方案类型 :param scheme_Type: 方案类型
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:param query_date: 查询日期，格式为 'YYYY-MM-DD' :param query_date: 查询日期，格式为 'YYYY-MM-DD'
:param bucket: 数据存储的 bucket 名称。 :param bucket: 数据存储的 bucket 名称。
:return: dict: tuple: (node_records, link_records) :return: dict: tuple: (node_records, link_records)
@@ -3867,7 +3867,7 @@ def query_scheme_all_record(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()}) |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
|> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["_measurement"] == "node" or r["_measurement"] == "link") |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "node" or r["_measurement"] == "link")
|> pivot( |> pivot(
rowKey:["_time"], rowKey:["_time"],
columnKey:["_field"], columnKey:["_field"],
@@ -3918,7 +3918,7 @@ def query_scheme_all_record(
# 2025/03/04 # 2025/03/04
def query_scheme_all_record_property( def query_scheme_all_record_property(
scheme_Type: str, scheme_Type: str,
scheme_Name: str, scheme_name: str,
query_date: str, query_date: str,
type: str, type: str,
property: str, property: str,
@@ -3927,7 +3927,7 @@ def query_scheme_all_record_property(
""" """
查询指定方案的‘node'或‘link’的某一属性值，以指定格式返回。 查询指定方案的‘node'或‘link’的某一属性值，以指定格式返回。
:param scheme_Type: 方案类型 :param scheme_Type: 方案类型
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:param query_date: 查询日期，格式为 'YYYY-MM-DD' :param query_date: 查询日期，格式为 'YYYY-MM-DD'
:param type: 查询的类型（决定 measurement） :param type: 查询的类型（决定 measurement）
:param property: 查询的字段名称（field） :param property: 查询的字段名称（field）
@@ -3964,7 +3964,7 @@ def query_scheme_all_record_property(
flux_query = f""" flux_query = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {start_time}, stop: {stop_time}) |> range(start: {start_time}, stop: {stop_time})
|> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}" and r["date"] == "{query_date}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}") |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["date"] == "{query_date}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}")
""" """
# 执行查询 # 执行查询
tables = query_api.query(flux_query) tables = query_api.query(flux_query)
@@ -4246,7 +4246,7 @@ def export_scheme_simulation_result_to_csv_time(
link_data[key]["measurement"] = record.get_measurement() link_data[key]["measurement"] = record.get_measurement()
link_data[key]["date"] = record.values.get("date", None) link_data[key]["date"] = record.values.get("date", None)
link_data[key]["scheme_Type"] = record.values.get("scheme_Type", None) link_data[key]["scheme_Type"] = record.values.get("scheme_Type", None)
link_data[key]["scheme_Name"] = record.values.get("scheme_Name", None) link_data[key]["scheme_name"] = record.values.get("scheme_name", None)
# 构建 Flux 查询语句，查询指定时间范围内的数据 # 构建 Flux 查询语句，查询指定时间范围内的数据
flux_query_node = f""" flux_query_node = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
@@ -4268,7 +4268,7 @@ def export_scheme_simulation_result_to_csv_time(
node_data[key]["measurement"] = record.get_measurement() node_data[key]["measurement"] = record.get_measurement()
node_data[key]["date"] = record.values.get("date", None) node_data[key]["date"] = record.values.get("date", None)
node_data[key]["scheme_Type"] = record.values.get("scheme_Type", None) node_data[key]["scheme_Type"] = record.values.get("scheme_Type", None)
node_data[key]["scheme_Name"] = record.values.get("scheme_Name", None) node_data[key]["scheme_name"] = record.values.get("scheme_name", None)
for key in set(link_data.keys()): for key in set(link_data.keys()):
row = {"time": key[0], "ID": key[1]} row = {"time": key[0], "ID": key[1]}
row.update(link_data.get(key, {})) row.update(link_data.get(key, {}))
@@ -4289,7 +4289,7 @@ def export_scheme_simulation_result_to_csv_time(
"measurement", "measurement",
"date", "date",
"scheme_Type", "scheme_Type",
"scheme_Name", "scheme_name",
"ID", "ID",
"flow", "flow",
"leakage", "leakage",
@@ -4312,7 +4312,7 @@ def export_scheme_simulation_result_to_csv_time(
"measurement", "measurement",
"date", "date",
"scheme_Type", "scheme_Type",
"scheme_Name", "scheme_name",
"ID", "ID",
"head", "head",
"pressure", "pressure",
@@ -4331,14 +4331,14 @@ def export_scheme_simulation_result_to_csv_time(
# 2025/02/18 # 2025/02/18
def export_scheme_simulation_result_to_csv_scheme( def export_scheme_simulation_result_to_csv_scheme(
scheme_Type: str, scheme_Type: str,
scheme_Name: str, scheme_name: str,
query_date: str, query_date: str,
bucket: str = "scheme_simulation_result", bucket: str = "scheme_simulation_result",
) -> None: ) -> None:
""" """
导出influxdb中scheme_simulation_result这个bucket的数据到csv中 导出influxdb中scheme_simulation_result这个bucket的数据到csv中
:param scheme_Type: 查询的方案类型 :param scheme_Type: 查询的方案类型
:param scheme_Name: 查询的方案名 :param scheme_name: 查询的方案名
:param query_date: 查询日期，格式为 'YYYY-MM-DD' :param query_date: 查询日期，格式为 'YYYY-MM-DD'
:param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data" :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data"
:return: :return:
@@ -4366,7 +4366,7 @@ def export_scheme_simulation_result_to_csv_scheme(
flux_query_link = f""" flux_query_link = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {start_time}, stop: {stop_time}) |> range(start: {start_time}, stop: {stop_time})
|> filter(fn: (r) => r["_measurement"] == "link" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}") |> filter(fn: (r) => r["_measurement"] == "link" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}")
""" """
# 执行查询 # 执行查询
link_tables = query_api.query(flux_query_link) link_tables = query_api.query(flux_query_link)
@@ -4383,12 +4383,12 @@ def export_scheme_simulation_result_to_csv_scheme(
link_data[key]["measurement"] = record.get_measurement() link_data[key]["measurement"] = record.get_measurement()
link_data[key]["date"] = record.values.get("date", None) link_data[key]["date"] = record.values.get("date", None)
link_data[key]["scheme_Type"] = record.values.get("scheme_Type", None) link_data[key]["scheme_Type"] = record.values.get("scheme_Type", None)
link_data[key]["scheme_Name"] = record.values.get("scheme_Name", None) link_data[key]["scheme_name"] = record.values.get("scheme_name", None)
# 构建 Flux 查询语句，查询指定时间范围内的数据 # 构建 Flux 查询语句，查询指定时间范围内的数据
flux_query_node = f""" flux_query_node = f"""
from(bucket: "{bucket}") from(bucket: "{bucket}")
|> range(start: {start_time}, stop: {stop_time}) |> range(start: {start_time}, stop: {stop_time})
|> filter(fn: (r) => r["_measurement"] == "node" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_Name"] == "{scheme_Name}") |> filter(fn: (r) => r["_measurement"] == "node" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}")
""" """
# 执行查询 # 执行查询
node_tables = query_api.query(flux_query_node) node_tables = query_api.query(flux_query_node)
@@ -4405,7 +4405,7 @@ def export_scheme_simulation_result_to_csv_scheme(
node_data[key]["measurement"] = record.get_measurement() node_data[key]["measurement"] = record.get_measurement()
node_data[key]["date"] = record.values.get("date", None) node_data[key]["date"] = record.values.get("date", None)
node_data[key]["scheme_Type"] = record.values.get("scheme_Type", None) node_data[key]["scheme_Type"] = record.values.get("scheme_Type", None)
node_data[key]["scheme_Name"] = record.values.get("scheme_Name", None) node_data[key]["scheme_name"] = record.values.get("scheme_name", None)
for key in set(link_data.keys()): for key in set(link_data.keys()):
row = {"time": key[0], "ID": key[1]} row = {"time": key[0], "ID": key[1]}
row.update(link_data.get(key, {})) row.update(link_data.get(key, {}))
@@ -4416,10 +4416,10 @@ def export_scheme_simulation_result_to_csv_scheme(
node_rows.append(row) node_rows.append(row)
# 动态生成 CSV 文件名 # 动态生成 CSV 文件名
csv_filename_link = ( csv_filename_link = (
f"scheme_simulation_link_result_{scheme_Name}_of_{scheme_Type}.csv" f"scheme_simulation_link_result_{scheme_name}_of_{scheme_Type}.csv"
) )
csv_filename_node = ( csv_filename_node = (
f"scheme_simulation_node_result_{scheme_Name}_of_{scheme_Type}.csv" f"scheme_simulation_node_result_{scheme_name}_of_{scheme_Type}.csv"
) )
# 写入到 CSV 文件 # 写入到 CSV 文件
with open(csv_filename_link, mode="w", newline="") as file: with open(csv_filename_link, mode="w", newline="") as file:
@@ -4430,7 +4430,7 @@ def export_scheme_simulation_result_to_csv_scheme(
"measurement", "measurement",
"date", "date",
"scheme_Type", "scheme_Type",
"scheme_Name", "scheme_name",
"ID", "ID",
"flow", "flow",
"leakage", "leakage",
@@ -4453,7 +4453,7 @@ def export_scheme_simulation_result_to_csv_scheme(
"measurement", "measurement",
"date", "date",
"scheme_Type", "scheme_Type",
"scheme_Name", "scheme_name",
"ID", "ID",
"head", "head",
"pressure", "pressure",
@@ -4878,15 +4878,15 @@ if __name__ == "__main__":
# export_scheme_simulation_result_to_csv_time(start_date='2025-02-13', end_date='2025-02-15') # export_scheme_simulation_result_to_csv_time(start_date='2025-02-13', end_date='2025-02-15')
# 示例9：export_scheme_simulation_result_to_csv_scheme # 示例9：export_scheme_simulation_result_to_csv_scheme
# export_scheme_simulation_result_to_csv_scheme(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10') # export_scheme_simulation_result_to_csv_scheme(scheme_Type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10')
# 示例10：query_scheme_all_record_by_time # 示例10：query_scheme_all_record_by_time
# node_records, link_records = query_scheme_all_record_by_time(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_time="2025-02-14T10:30:00+08:00") # node_records, link_records = query_scheme_all_record_by_time(scheme_Type='burst_Analysis', scheme_name='scheme1', query_time="2025-02-14T10:30:00+08:00")
# print("Node 数据：", node_records) # print("Node 数据：", node_records)
# print("Link 数据：", link_records) # print("Link 数据：", link_records)
# 示例11：query_scheme_curve_by_ID_property # 示例11：query_scheme_curve_by_ID_property
# curve_result = query_scheme_curve_by_ID_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', ID='ZBBDTZDP000022', # curve_result = query_scheme_curve_by_ID_property(scheme_Type='burst_Analysis', scheme_name='scheme1', ID='ZBBDTZDP000022',
# type='node', property='head') # type='node', property='head')
# print(curve_result) # print(curve_result)
@@ -4896,7 +4896,7 @@ if __name__ == "__main__":
# print("Link 数据：", link_records) # print("Link 数据：", link_records)
# 示例13：query_scheme_all_record # 示例13：query_scheme_all_record
# node_records, link_records = query_scheme_all_record(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10') # node_records, link_records = query_scheme_all_record(scheme_Type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10')
# print("Node 数据：", node_records) # print("Node 数据：", node_records)
# print("Link 数据：", link_records) # print("Link 数据：", link_records)
@@ -4909,16 +4909,16 @@ if __name__ == "__main__":
# print(result_records) # print(result_records)
# 示例16：query_scheme_all_record_by_time_property # 示例16：query_scheme_all_record_by_time_property
# result_records = query_scheme_all_record_by_time_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', # result_records = query_scheme_all_record_by_time_property(scheme_Type='burst_Analysis', scheme_name='scheme1',
# query_time='2025-02-14T10:30:00+08:00', type='node', property='head') # query_time='2025-02-14T10:30:00+08:00', type='node', property='head')
# print(result_records) # print(result_records)
# 示例17：query_scheme_all_record_property # 示例17：query_scheme_all_record_property
# result_records = query_scheme_all_record_property(scheme_Type='burst_Analysis', scheme_Name='scheme1', query_date='2025-03-10', type='node', property='head') # result_records = query_scheme_all_record_property(scheme_Type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10', type='node', property='head')
# print(result_records) # print(result_records)
# 示例18：fill_scheme_simulation_result_to_SCADA # 示例18：fill_scheme_simulation_result_to_SCADA
# fill_scheme_simulation_result_to_SCADA(scheme_Type='burst_Analysis', scheme_Name='burst0330', query_date='2025-03-30') # fill_scheme_simulation_result_to_SCADA(scheme_Type='burst_Analysis', scheme_name='burst0330', query_date='2025-03-30')
# 示例19：query_SCADA_data_by_device_ID_and_timerange # 示例19：query_SCADA_data_by_device_ID_and_timerange
# result = query_SCADA_data_by_device_ID_and_timerange(query_ids_list=globals.pressure_non_realtime_ids, start_time='2025-04-16T00:00:00+08:00', # result = query_SCADA_data_by_device_ID_and_timerange(query_ids_list=globals.pressure_non_realtime_ids, start_time='2025-04-16T00:00:00+08:00',
@@ -4926,7 +4926,7 @@ if __name__ == "__main__":
# print(result) # print(result)
# 示例：manually_get_burst_flow # 示例：manually_get_burst_flow
# leakage = manually_get_burst_flow(scheme_Type='burst_Analysis', scheme_Name='burst_scheme', scheme_start_time='2025-03-10T12:00:00+08:00') # leakage = manually_get_burst_flow(scheme_Type='burst_Analysis', scheme_name='burst_scheme', scheme_start_time='2025-03-10T12:00:00+08:00')
# print(leakage) # print(leakage)
# 示例：upload_cleaned_SCADA_data_to_influxdb # 示例：upload_cleaned_SCADA_data_to_influxdb

8
app/services/simulation.py
View File

@@ -1249,7 +1249,7 @@ def run_simulation(
endtime = time.time() endtime = time.time()
logging.info("store time: %f", endtime - starttime) logging.info("store time: %f", endtime - starttime)
# 暂不需要再次存储 SCADA 模拟信息 # 暂不需要再次存储 SCADA 模拟信息
# TimescaleInternalQueries.fill_scheme_simulation_result_to_SCADA(scheme_Type=scheme_Type, scheme_Name=scheme_Name) # TimescaleInternalQueries.fill_scheme_simulation_result_to_SCADA(scheme_Type=scheme_Type, scheme_name=scheme_name)
# if simulation_type.upper() == "REALTIME": # if simulation_type.upper() == "REALTIME":
# influxdb_api.store_realtime_simulation_result_to_influxdb( # influxdb_api.store_realtime_simulation_result_to_influxdb(
@@ -1262,10 +1262,10 @@ def run_simulation(
# modify_pattern_start_time, # modify_pattern_start_time,
# num_periods_result, # num_periods_result,
# scheme_Type, # scheme_Type,
# scheme_Name, # scheme_name,
# ) # )
# 暂不需要再次存储 SCADA 模拟信息 # 暂不需要再次存储 SCADA 模拟信息
# influxdb_api.fill_scheme_simulation_result_to_SCADA(scheme_Type=scheme_Type, scheme_Name=scheme_Name) # influxdb_api.fill_scheme_simulation_result_to_SCADA(scheme_Type=scheme_Type, scheme_name=scheme_name)
print("after store result") print("after store result")
@@ -1345,7 +1345,7 @@ if __name__ == "__main__":
# run_simulation(name='bb', simulation_type="realtime", modify_pattern_start_time='2025-02-25T23:45:00+08:00') # run_simulation(name='bb', simulation_type="realtime", modify_pattern_start_time='2025-02-25T23:45:00+08:00')
# 模拟示例2 # 模拟示例2
# run_simulation(name='bb', simulation_type="extended", modify_pattern_start_time='2025-03-10T12:00:00+08:00', # run_simulation(name='bb', simulation_type="extended", modify_pattern_start_time='2025-03-10T12:00:00+08:00',
# modify_total_duration=1800, scheme_Type="burst_Analysis", scheme_Name="scheme1") # modify_total_duration=1800, scheme_Type="burst_Analysis", scheme_name="scheme1")
# 查询示例1：query_SCADA_ID_corresponding_info # 查询示例1：query_SCADA_ID_corresponding_info
# result = query_SCADA_ID_corresponding_info(name='bb', SCADA_ID='P10755') # result = query_SCADA_ID_corresponding_info(name='bb', SCADA_ID='P10755')

6
app/services/valve_isolation.py
View File

@@ -4,6 +4,8 @@ from app.algorithms.valve_isolation import valve_isolation_analysis
def analyze_valve_isolation( def analyze_valve_isolation(
network: str, accident_element: str | list[str] network: str,
accident_element: str | list[str],
disabled_valves: list[str] = None,
) -> dict[str, Any]: ) -> dict[str, Any]:
return valve_isolation_analysis(network, accident_element) return valve_isolation_analysis(network, accident_element, disabled_valves)

14
scripts/main.py
View File

@@ -3233,7 +3233,7 @@ async def fastapi_query_all_scheme_all_records(
return loaded_dict return loaded_dict
results = influxdb_api.query_scheme_all_record( results = influxdb_api.query_scheme_all_record(
scheme_Type=schemetype, scheme_Name=schemename, query_date=querydate scheme_Type=schemetype, scheme_name=schemename, query_date=querydate
) )
packed = msgpack.packb(results, default=encode_datetime) packed = msgpack.packb(results, default=encode_datetime)
redis_client.set(cache_key, packed) redis_client.set(cache_key, packed)
@@ -3257,7 +3257,7 @@ async def fastapi_query_all_scheme_all_records_property(
all_results = msgpack.unpackb(data, object_hook=decode_datetime) all_results = msgpack.unpackb(data, object_hook=decode_datetime)
else: else:
all_results = influxdb_api.query_scheme_all_record( all_results = influxdb_api.query_scheme_all_record(
scheme_Type=schemetype, scheme_Name=schemename, query_date=querydate scheme_Type=schemetype, scheme_name=schemename, query_date=querydate
) )
packed = msgpack.packb(all_results, default=encode_datetime) packed = msgpack.packb(all_results, default=encode_datetime)
redis_client.set(cache_key, packed) redis_client.set(cache_key, packed)
@@ -3585,7 +3585,7 @@ class BurstAnalysis(BaseModel):
modify_fixed_pump_pattern: Optional[dict[str, list]] = None modify_fixed_pump_pattern: Optional[dict[str, list]] = None
modify_variable_pump_pattern: Optional[dict[str, list]] = None modify_variable_pump_pattern: Optional[dict[str, list]] = None
modify_valve_opening: Optional[dict[str, float]] = None modify_valve_opening: Optional[dict[str, float]] = None
scheme_Name: Optional[str] = None scheme_name: Optional[str] = None
@app.post("/burst_analysis/") @app.post("/burst_analysis/")
@@ -3608,7 +3608,7 @@ async def fastapi_burst_analysis(data: BurstAnalysis) -> str:
modify_fixed_pump_pattern=item["modify_fixed_pump_pattern"], modify_fixed_pump_pattern=item["modify_fixed_pump_pattern"],
modify_variable_pump_pattern=item["modify_variable_pump_pattern"], modify_variable_pump_pattern=item["modify_variable_pump_pattern"],
modify_valve_opening=item["modify_valve_opening"], modify_valve_opening=item["modify_valve_opening"],
scheme_Name=item["scheme_Name"], scheme_name=item["scheme_name"],
) )
# os.rename(filename2, filename) # os.rename(filename2, filename)
@@ -3616,7 +3616,7 @@ async def fastapi_burst_analysis(data: BurstAnalysis) -> str:
# 将 时间转换成日期，然后缓存这个计算结果 # 将 时间转换成日期，然后缓存这个计算结果
# 缓存key: burst_analysis_<name>_<modify_pattern_start_time> # 缓存key: burst_analysis_<name>_<modify_pattern_start_time>
global redis_client global redis_client
schemename = data.scheme_Name schemename = data.scheme_name
print(data.modify_pattern_start_time) print(data.modify_pattern_start_time)
@@ -3627,7 +3627,7 @@ async def fastapi_burst_analysis(data: BurstAnalysis) -> str:
cache_key = f"queryallschemeallrecords_burst_Analysis_{schemename}_{querydate}" cache_key = f"queryallschemeallrecords_burst_Analysis_{schemename}_{querydate}"
data = redis_client.get(cache_key) data = redis_client.get(cache_key)
if not data: if not data:
results = influxdb_api.query_scheme_all_record("burst_Analysis", scheme_Name=schemename, query_date=querydate) results = influxdb_api.query_scheme_all_record("burst_Analysis", scheme_name=schemename, query_date=querydate)
packed = msgpack.packb(results, default=encode_datetime) packed = msgpack.packb(results, default=encode_datetime)
redis_client.set(cache_key, packed) redis_client.set(cache_key, packed)
""" """
@@ -3712,7 +3712,7 @@ async def fastapi_contaminant_simulation(
concentration: float, concentration: float,
duration: int, duration: int,
pattern: str = None, pattern: str = None,
scheme_Name: str = None, scheme_name: str = None,
) -> str: ) -> str:
filename = "c:/lock.simulation" filename = "c:/lock.simulation"
filename2 = "c:/lock.simulation2" filename2 = "c:/lock.simulation2"

22
scripts/online_Analysis.py
View File

@@ -68,7 +68,7 @@ def burst_analysis(
:param modify_fixed_pump_pattern: dict中包含多个水泵模式，str为工频水泵的id，list为修改后的pattern :param modify_fixed_pump_pattern: dict中包含多个水泵模式，str为工频水泵的id，list为修改后的pattern
:param modify_variable_pump_pattern: dict中包含多个水泵模式，str为变频水泵的id，list为修改后的pattern :param modify_variable_pump_pattern: dict中包含多个水泵模式，str为变频水泵的id，list为修改后的pattern
:param modify_valve_opening: dict中包含多个阀门开启度，str为阀门的id，float为修改后的阀门开启度 :param modify_valve_opening: dict中包含多个阀门开启度，str为阀门的id，float为修改后的阀门开启度
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:return: :return:
""" """
scheme_detail: dict = { scheme_detail: dict = {
@@ -294,7 +294,7 @@ def flushing_analysis(
modify_valve_opening: dict[str, float] = None, modify_valve_opening: dict[str, float] = None,
drainage_node_ID: str = None, drainage_node_ID: str = None,
flushing_flow: float = 0, flushing_flow: float = 0,
scheme_Name: str = None, scheme_name: str = None,
) -> None: ) -> None:
""" """
管道冲洗模拟 管道冲洗模拟
@@ -304,7 +304,7 @@ def flushing_analysis(
:param modify_valve_opening: dict中包含多个阀门开启度，str为阀门的id，float为修改后的阀门开启度 :param modify_valve_opening: dict中包含多个阀门开启度，str为阀门的id，float为修改后的阀门开启度
:param drainage_node_ID: 冲洗排放口所在节点ID :param drainage_node_ID: 冲洗排放口所在节点ID
:param flushing_flow: 冲洗水量，传入参数单位为m3/h :param flushing_flow: 冲洗水量，传入参数单位为m3/h
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:return: :return:
""" """
print( print(
@@ -397,7 +397,7 @@ def flushing_analysis(
modify_total_duration=modify_total_duration, modify_total_duration=modify_total_duration,
modify_valve_opening=modify_valve_opening, modify_valve_opening=modify_valve_opening,
scheme_Type="flushing_Analysis", scheme_Type="flushing_Analysis",
scheme_Name=scheme_Name, scheme_name=scheme_name,
) )
# step 4. restore the base model # step 4. restore the base model
if is_project_open(new_name): if is_project_open(new_name):
@@ -417,7 +417,7 @@ def contaminant_simulation(
source: str = None,# 污染源节点ID source: str = None,# 污染源节点ID
concentration: float = None, # 污染源浓度，单位mg/L concentration: float = None, # 污染源浓度，单位mg/L
source_pattern: str = None, # 污染源时间变化模式名称 source_pattern: str = None, # 污染源时间变化模式名称
scheme_Name: str = None, scheme_name: str = None,
) -> None: ) -> None:
""" """
污染模拟 污染模拟
@@ -428,7 +428,7 @@ def contaminant_simulation(
:param concentration: 污染源位置处的浓度，单位mg/L，即默认的污染模拟setting为concentration（应改为 Set point booster） :param concentration: 污染源位置处的浓度，单位mg/L，即默认的污染模拟setting为concentration（应改为 Set point booster）
:param source_pattern: 污染源的时间变化模式，若不传入则默认以恒定浓度持续模拟，时间长度等于duration; :param source_pattern: 污染源的时间变化模式，若不传入则默认以恒定浓度持续模拟，时间长度等于duration;
若传入，则格式为{1.0,0.5,1.1}等系数列表pattern_step模拟等于模型的hydraulic time step 若传入，则格式为{1.0,0.5,1.1}等系数列表pattern_step模拟等于模型的hydraulic time step
:param scheme_Name: 方案名称 :param scheme_name: 方案名称
:return: :return:
""" """
print( print(
@@ -534,7 +534,7 @@ def contaminant_simulation(
modify_pattern_start_time=modify_pattern_start_time, modify_pattern_start_time=modify_pattern_start_time,
modify_total_duration=modify_total_duration, modify_total_duration=modify_total_duration,
scheme_Type="contaminant_Analysis", scheme_Type="contaminant_Analysis",
scheme_Name=scheme_Name, scheme_name=scheme_name,
) )
# for i in range(1,operation_step): # for i in range(1,operation_step):
@@ -630,7 +630,7 @@ def pressure_regulation(
modify_tank_initial_level: dict[str, float] = None, modify_tank_initial_level: dict[str, float] = None,
modify_fixed_pump_pattern: dict[str, list] = None, modify_fixed_pump_pattern: dict[str, list] = None,
modify_variable_pump_pattern: dict[str, list] = None, modify_variable_pump_pattern: dict[str, list] = None,
scheme_Name: str = None, scheme_name: str = None,
) -> None: ) -> None:
""" """
区域调压模拟，用来模拟未来15分钟内，开关水泵对区域压力的影响 区域调压模拟，用来模拟未来15分钟内，开关水泵对区域压力的影响
@@ -640,7 +640,7 @@ def pressure_regulation(
:param modify_tank_initial_level: dict中包含多个水塔，str为水塔的id，float为修改后的initial_level :param modify_tank_initial_level: dict中包含多个水塔，str为水塔的id，float为修改后的initial_level
:param modify_fixed_pump_pattern: dict中包含多个水泵模式，str为工频水泵的id，list为修改后的pattern :param modify_fixed_pump_pattern: dict中包含多个水泵模式，str为工频水泵的id，list为修改后的pattern
:param modify_variable_pump_pattern: dict中包含多个水泵模式，str为变频水泵的id，list为修改后的pattern :param modify_variable_pump_pattern: dict中包含多个水泵模式，str为变频水泵的id，list为修改后的pattern
:param scheme_Name: 模拟方案名称 :param scheme_name: 模拟方案名称
:return: :return:
""" """
print( print(
@@ -693,7 +693,7 @@ def pressure_regulation(
modify_fixed_pump_pattern=modify_fixed_pump_pattern, modify_fixed_pump_pattern=modify_fixed_pump_pattern,
modify_variable_pump_pattern=modify_variable_pump_pattern, modify_variable_pump_pattern=modify_variable_pump_pattern,
scheme_Type="pressure_regulation", scheme_Type="pressure_regulation",
scheme_Name=scheme_Name, scheme_name=scheme_name,
) )
if is_project_open(new_name): if is_project_open(new_name):
close_project(new_name) close_project(new_name)
@@ -1536,7 +1536,7 @@ if __name__ == "__main__":
# 示例1：burst_analysis # 示例1：burst_analysis
# burst_analysis(name='bb', modify_pattern_start_time='2025-04-17T00:00:00+08:00', # burst_analysis(name='bb', modify_pattern_start_time='2025-04-17T00:00:00+08:00',
# burst_ID='GSD230112144241FA18292A84CB', burst_size=400, modify_total_duration=1800, scheme_Name='GSD230112144241FA18292A84CB_400') # burst_ID='GSD230112144241FA18292A84CB', burst_size=400, modify_total_duration=1800, scheme_name='GSD230112144241FA18292A84CB_400')
# 示例：create_user # 示例：create_user
# create_user(name=project_info.name, username='tjwater dev', password='123456') # create_user(name=project_info.name, username='tjwater dev', password='123456')