# -*- coding: utf-8 -*- # @Author: Owl # @Date: 2025/10/10 19:51 # @Description: 路口评价相关的公共函数 import copy import json import logging import requests from google.protobuf.json_format import MessageToJson import proto.xlcomm_pb2 as pb from app.common_worker import * from proto.phase_grpc import QueryCrossRunningPhase src_reverse = {'N': 'S', 'S': 'N', 'W': 'E', 'E': 'W', 'NE': 'SW', 'SW': 'NE', 'SE': 'NW', 'NW': 'SE'} dir_str_dict = { 'E': '东', 'S': '南', 'W': '西', 'N': '北', 'NE': '东北', 'SE': '东南', 'SW': '西南', 'NW': '西北' } int_turn_type2str = { 0: '直行', 1: '左转', 2: '右转', 3: '掉头' } int_internet_code2str = { 0: '无', 1: '联网', 2: '脱机', 3: '未接入' } int_cross_model2str = { '1': '快', '2': '主', '3': '次', '4': '支' } def gen_avg_cross_delay_pb(cross_delay_data_list): """ 路口评价指标数据聚合 :param cross_delay_data_list: :return: """ cross_delay_pb_list = [] for item in cross_delay_data_list: item_delay_pb = pb.xl_cross_delayinfo_t() item_delay_pb.ParseFromString(item['data']) cross_delay_pb_list.append(item_delay_pb) avg_cross_delay = None if len(cross_delay_pb_list) == 0: return avg_cross_delay elif len(cross_delay_pb_list) == 1: avg_cross_delay = cross_delay_pb_list[0] return avg_cross_delay avg_cross_delay = pb.xl_cross_delayinfo_t() avg_cross_delay.crossid = cross_delay_pb_list[0].crossid avg_cross_delay.tp.start_hm = cross_delay_pb_list[0].tp.start_hm avg_cross_delay.tp.end_hm = cross_delay_pb_list[0].tp.end_hm avg_cross_delay.tp.weekday = cross_delay_pb_list[0].tp.weekday avg_cross_delay.tp.type = cross_delay_pb_list[0].tp.type avg_cross_delay.daynum = 0 sum_car_num, delay_time, stop_times, queue_len, speed, jam_index, park_time, std_flow, high_park_percent, truck_percent, park_percent, move_speed, imbalance_index\ = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 # 路口延误数据指标聚合逻辑 for item in cross_delay_pb_list: item_delay_info = item.delay_info item_car_num = item_delay_info.car_num if item_car_num != 0: sum_car_num += item_car_num delay_time += item_delay_info.delay_time * item_car_num stop_times += item_delay_info.stop_times * item_car_num queue_len += item_delay_info.queue_len * item_car_num speed += item_delay_info.speed * item_car_num jam_index += item_delay_info.jam_index * item_car_num park_time += item_delay_info.park_time * item_car_num high_park_percent += item_delay_info.high_park_percent * item_car_num truck_percent += item_delay_info.truck_percent * item_car_num park_percent += item_delay_info.park_percent * item_car_num move_speed += item_delay_info.move_speed * item_car_num # imbalance_index += item_delay_info.imbalance_index * item_car_num std_flow += item_delay_info.std_flow avg_cross_delay.delay_info.car_num = sum_car_num avg_cross_delay.delay_info.delay_time = int(delay_time / sum_car_num) avg_cross_delay.delay_info.stop_times = stop_times / sum_car_num avg_cross_delay.delay_info.queue_len = int(queue_len / sum_car_num) avg_cross_delay.delay_info.speed = int(speed / sum_car_num) avg_cross_delay.delay_info.jam_index = jam_index / sum_car_num avg_cross_delay.delay_info.park_time = int(park_time / sum_car_num) avg_cross_delay.delay_info.high_park_percent = int(high_park_percent / sum_car_num) avg_cross_delay.delay_info.truck_percent = int(truck_percent / sum_car_num) avg_cross_delay.delay_info.park_percent = int(park_percent / sum_car_num) avg_cross_delay.delay_info.move_speed = int(move_speed / sum_car_num) # avg_cross_delay.delay_info.imbalance_index = imbalance_index / sum_car_num avg_cross_delay.delay_info.std_flow = int(std_flow / len(cross_delay_pb_list)) # 进口道延误数据聚合处理逻辑 inroad_delay_info_dict = {} for item in cross_delay_pb_list: inroad_delay_infos = item.inroad_delay_infos for inroad_item in inroad_delay_infos: inroadid = inroad_item.inroadid if inroadid not in inroad_delay_info_dict: inroad_delay_info_dict[inroadid] = [inroad_item] else: inroad_delay_info_dict[inroadid].append(inroad_item) flow_delay_list, inroad_delay_pb_list = [], [] flow_delay_info_dict = {} max_stop_times, min_stop_times = 0, 99999 for inroadid in inroad_delay_info_dict.keys(): road_delay_infos = inroad_delay_info_dict[inroadid] inroad_sum_car_num, inroad_delay_time, inroad_stop_times, inroad_queue_len, inroad_speed, inroad_jam_index, inroad_park_time, inroad_high_park_percent, inroad_truck_percent, inroad_park_percent, inroad_move_speed, inroad_imbalance_index, inroad_std_flow, inroad_travel_time\ = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ffs, capacity = road_delay_infos[0].delay_info.ffs, road_delay_infos[0].delay_info.capacity turn_ratio_0, turn_ratio_1, turn_ratio_2, turn_ratio_3 = 0, 0, 0, 0 for road_delay_info in road_delay_infos: inroad_sum_car_num += road_delay_info.delay_info.car_num inroad_delay_time += road_delay_info.delay_info.delay_time * road_delay_info.delay_info.car_num inroad_stop_times += road_delay_info.delay_info.stop_times * road_delay_info.delay_info.car_num inroad_queue_len += road_delay_info.delay_info.queue_len * road_delay_info.delay_info.car_num inroad_speed += road_delay_info.delay_info.speed * road_delay_info.delay_info.car_num inroad_jam_index += road_delay_info.delay_info.jam_index * road_delay_info.delay_info.car_num inroad_park_time += road_delay_info.delay_info.park_time * road_delay_info.delay_info.car_num inroad_high_park_percent += road_delay_info.delay_info.high_park_percent * road_delay_info.delay_info.car_num inroad_truck_percent += road_delay_info.delay_info.truck_percent * road_delay_info.delay_info.car_num inroad_park_percent += road_delay_info.delay_info.park_percent * road_delay_info.delay_info.car_num inroad_move_speed += road_delay_info.delay_info.move_speed * road_delay_info.delay_info.car_num inroad_travel_time += road_delay_info.delay_info.travel_time * road_delay_info.delay_info.car_num # inroad_imbalance_index += delay_info.imbalance_index * delay_info.car_num inroad_std_flow += road_delay_info.delay_info.std_flow turn_ratio_0 += road_delay_info.delay_info.turn_ratio_0 turn_ratio_1 += road_delay_info.delay_info.turn_ratio_1 turn_ratio_2 += road_delay_info.delay_info.turn_ratio_2 turn_ratio_3 += road_delay_info.delay_info.turn_ratio_3 # 流向级别数据指标聚合处理逻辑 flow_delay_infos = road_delay_info.flow_delay_infos flow_delay_list.extend(flow_delay_infos) inroad_delay_info = pb.xl_inroad_delayinfo_t() inroad_delay_info.inroadid = inroadid inroad_delay_info.delay_info.ffs = ffs inroad_delay_info.delay_info.capacity = capacity inroad_delay_info.delay_info.car_num = inroad_sum_car_num inroad_delay_info.delay_info.delay_time = int(inroad_delay_time / inroad_sum_car_num) inroad_delay_info.delay_info.stop_times = inroad_stop_times / inroad_sum_car_num if stop_times > max_stop_times: max_stop_times = stop_times if stop_times < min_stop_times: min_stop_times = stop_times inroad_delay_info.delay_info.queue_len = int(inroad_queue_len / inroad_sum_car_num) inroad_delay_info.delay_info.speed = int(inroad_speed / inroad_sum_car_num) inroad_delay_info.delay_info.jam_index = inroad_jam_index / inroad_sum_car_num inroad_delay_info.delay_info.park_time = int(inroad_park_time / inroad_sum_car_num) inroad_delay_info.delay_info.high_park_percent = int(inroad_high_park_percent / inroad_sum_car_num) inroad_delay_info.delay_info.truck_percent = int(inroad_truck_percent / inroad_sum_car_num) inroad_delay_info.delay_info.park_percent = int(inroad_park_percent / inroad_sum_car_num) inroad_delay_info.delay_info.move_speed = int(inroad_move_speed / inroad_sum_car_num) inroad_delay_info.delay_info.travel_time = int(inroad_travel_time / inroad_sum_car_num) # inroad_delay_info.delay_info.imbalance_index = inroad_imbalance_index / inroad_sum_car_num inroad_delay_info.delay_info.std_flow = int(inroad_std_flow / len(road_delay_infos)) inroad_delay_info.delay_info.turn_ratio_0 = turn_ratio_0 inroad_delay_info.delay_info.turn_ratio_1 = turn_ratio_1 inroad_delay_info.delay_info.turn_ratio_2 = turn_ratio_2 inroad_delay_info.delay_info.turn_ratio_3 = turn_ratio_3 inroad_delay_pb_list.append(inroad_delay_info) cross_imbalance_index = round((max_stop_times - min_stop_times) / avg_cross_delay.delay_info.stop_times, 2) if avg_cross_delay.delay_info.stop_times != 0 else 0 avg_cross_delay.delay_info.imbalance_index = cross_imbalance_index for flow_delay_info in flow_delay_list: xlink_id = flow_delay_info.xlink_id if xlink_id not in flow_delay_info_dict: flow_delay_info_dict[xlink_id] = [flow_delay_info] else: flow_delay_info_dict[xlink_id].append(flow_delay_info) for xlink_id in flow_delay_info_dict.keys(): flow_delay_infos = flow_delay_info_dict[xlink_id] inroadid = flow_delay_infos[0].inroadid turn_type = flow_delay_infos[0].turn_type flow_sum_car_num, flow_delay_time, flow_stop_times, flow_queue_len, flow_speed, flow_jam_index, flow_park_time, flow_high_park_percent, flow_truck_percent, flow_park_percent, flow_move_speed, flow_imbalance_index, flow_std_flow, flow_travel_time \ = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 for flow_delay_info in flow_delay_infos: flow_sum_car_num += flow_delay_info.delay_info.car_num flow_delay_time += flow_delay_info.delay_info.delay_time * flow_delay_info.delay_info.car_num flow_stop_times += flow_delay_info.delay_info.stop_times * flow_delay_info.delay_info.car_num flow_queue_len += flow_delay_info.delay_info.queue_len * flow_delay_info.delay_info.car_num flow_travel_time += flow_delay_info.delay_info.travel_time * flow_delay_info.delay_info.car_num flow_speed += flow_delay_info.delay_info.speed * flow_delay_info.delay_info.car_num flow_jam_index += flow_delay_info.delay_info.jam_index * flow_delay_info.delay_info.car_num flow_park_time += flow_delay_info.delay_info.park_time * flow_delay_info.delay_info.car_num flow_high_park_percent += flow_delay_info.delay_info.high_park_percent * flow_delay_info.delay_info.car_num flow_truck_percent += flow_delay_info.delay_info.truck_percent * flow_delay_info.delay_info.car_num flow_park_percent += flow_delay_info.delay_info.park_percent * flow_delay_info.delay_info.car_num flow_move_speed += flow_delay_info.delay_info.move_speed * flow_delay_info.delay_info.car_num # flow_imbalance_index += flow_delay_info.delay_info.imbalance_index * flow_delay_info.delay_info.car_num flow_std_flow += flow_delay_info.delay_info.std_flow flow_info = pb.xl_flow_delayinfo_t() flow_info.xlink_id = xlink_id flow_info.inroadid = inroadid flow_info.turn_type = turn_type flow_info.delay_info.car_num = flow_sum_car_num flow_info.delay_info.delay_time = int(flow_delay_time / flow_sum_car_num) flow_info.delay_info.stop_times = flow_stop_times / flow_sum_car_num flow_info.delay_info.queue_len = int(flow_queue_len / flow_sum_car_num) flow_info.delay_info.speed = int(flow_speed / flow_sum_car_num) flow_info.delay_info.travel_time = int(flow_travel_time / flow_sum_car_num) flow_info.delay_info.jam_index = flow_jam_index / flow_sum_car_num flow_info.delay_info.park_time = int(flow_park_time / flow_sum_car_num) flow_info.delay_info.high_park_percent = int(flow_high_park_percent / flow_sum_car_num) flow_info.delay_info.truck_percent = int(flow_truck_percent / flow_sum_car_num) flow_info.delay_info.park_percent = int(flow_park_percent / flow_sum_car_num) flow_info.delay_info.move_speed = int(flow_move_speed / flow_sum_car_num) # flow_info.delay_info.imbalance_index = flow_imbalance_index / flow_sum_car_num flow_info.delay_info.std_flow = int(flow_std_flow / len(flow_delay_infos)) for inroad_info in inroad_delay_pb_list: if inroad_info.inroadid == inroadid: inroad_info.flow_delay_infos.append(flow_info) inroad_delay_infos_with_imbalance = calc_inroad_imbalance_index(inroad_delay_pb_list) avg_cross_delay.inroad_delay_infos.extend(inroad_delay_infos_with_imbalance) # 出口道流量数据聚合逻辑 outroad_delay_info_dict = {} for item in cross_delay_pb_list: outroadid_infos = item.outroad_infos for outroadid_info in outroadid_infos: outroadid = outroadid_info.outroadid if outroadid not in outroad_delay_info_dict: outroad_delay_info_dict[outroadid] = [outroadid_info] else: outroad_delay_info_dict[outroadid].append(outroadid_info) for outroadid in outroad_delay_info_dict.keys(): outroadid_infos = outroad_delay_info_dict[outroadid] car_num, turn_ratio_0, turn_ratio_1, turn_ratio_2, turn_ratio_3 = 0, 0, 0, 0, 0 for outroadid_info in outroadid_infos: turn_info = outroadid_info.turn_info car_num += turn_info.car_num turn_ratio_0 += turn_info.turn_ratio_0 turn_ratio_1 += turn_info.turn_ratio_1 turn_ratio_2 += turn_info.turn_ratio_2 turn_ratio_3 += turn_info.turn_ratio_3 outroadid_info = pb.xl_outroad_info_t() outroadid_info.outroadid = outroadid outroadid_info.turn_info.car_num = car_num outroadid_info.turn_info.turn_ratio_0 = turn_ratio_0 outroadid_info.turn_info.turn_ratio_1 = turn_ratio_1 outroadid_info.turn_info.turn_ratio_2 = turn_ratio_2 outroadid_info.turn_info.turn_ratio_3 = turn_ratio_3 avg_cross_delay.outroad_infos.append(outroadid_info) return avg_cross_delay # 生成路口诊断页面路口概览数据指标的函数 def gen_overview_index(avg_cross_delay_info, inroad_static_info_dict, nodeid, date_list, roads_dir_dict): car_num = avg_cross_delay_info.delay_info.car_num crossid = avg_cross_delay_info.crossid jam_index = round(avg_cross_delay_info.delay_info.jam_index, 2) stop_times = round(avg_cross_delay_info.delay_info.stop_times, 2) high_park_percent = str(avg_cross_delay_info.delay_info.high_park_percent) + '%' imbalance_index = round(avg_cross_delay_info.delay_info.imbalance_index, 2) speed = avg_cross_delay_info.delay_info.speed / 100 move_speed = avg_cross_delay_info.delay_info.move_speed / 100 park_time = avg_cross_delay_info.delay_info.park_time delay_time = avg_cross_delay_info.delay_info.delay_time high_stop_turn_ratio_desc, main_flow_src_desc = gen_high_stop_turn_ratio_desc(avg_cross_delay_info.inroad_delay_infos, inroad_static_info_dict, car_num) tide_index_list = calc_tide_index(crossid, nodeid, date_list, roads_dir_dict) usable_tide_list = [item for item in tide_index_list if item != 0] tide_index = round(sum(usable_tide_list) / len(usable_tide_list), 2) if len(usable_tide_list) > 0 else 0 service_level = calc_service_level(delay_time) overview_res = { 'jam_index': jam_index, 'stop_times': stop_times, 'high_park_percent': high_park_percent, 'imbalance_index': imbalance_index, 'speed': speed, 'move_speed': move_speed, 'park_time': park_time, 'delay_time': delay_time, 'service_level': service_level, 'high_stop_turn_ratio_desc': high_stop_turn_ratio_desc, 'main_flow_src_desc': main_flow_src_desc, 'tide_index': tide_index } return overview_res def gen_high_stop_turn_ratio_desc(inroad_delay_infos, inroad_static_info_dict, car_num): src_dict, src_desc_list, main_flow_src_list = {}, [], [] for inroad_info in inroad_delay_infos: inroadid = inroad_info.inroadid if inroadid not in inroad_static_info_dict.keys(): continue inroad_car_num = inroad_info.delay_info.car_num src_dir = dir_str_dict[inroad_static_info_dict[inroadid]['src_direct']] if inroad_car_num / car_num > 0.25: check_res = found_max_car_num_flow(inroad_car_num, inroad_info) if check_res != -1: main_flow_src_list.append(src_dir + int_turn_type2str[check_res]) flow_delay_infos = inroad_info.flow_delay_infos for item in flow_delay_infos: if item.stop_times >= 1: turn_type = int_turn_type2str[item.turn_type] if src_dir not in src_dict.keys(): src_dict[src_dir] = [turn_type] else: src_dict[src_dir].append(turn_type) for item in src_dict.keys(): src_desc_list.append(item + '/'.join(src_dict[item])) return '、'.join(src_desc_list) if src_desc_list else '无', '、'.join(main_flow_src_list) if main_flow_src_list else '无' def found_max_car_num_flow(inroad_car_num, inroad_info): data_list = [inroad_info.turn_ratio_0, inroad_info.turn_ratio_1, inroad_info.turn_ratio_2, inroad_info.turn_ratio_3] for i in range(len(data_list)): if data_list[i] / inroad_car_num > 0.5: return i return -1 def query_cross_ledger_info(crossid, nodeid, area_id, userid): ledger_url = f"http://120.53.125.169:7070/api/common/cross_ledger_detail?crossid={crossid}&crossno=&nodeid={nodeid}&area_id={area_id}&userid={userid}" headers = {"Content-Type": "application/json"} cross_ledger_info = requests.get(ledger_url, headers=headers) if cross_ledger_info.status_code != 200 or cross_ledger_info.json()['status'] != 0: logging.error(f"查询路口台账信息失败,crossid:{crossid},nodeid:{nodeid},area_id:{area_id},userid:{userid}") return None, None cross_ledger_info_dict = json.loads(cross_ledger_info.text) return cross_ledger_info_dict def gen_cross_static_info(crossid, nodeid, area_id, cross_ledger_info_dict): division_list, company_list, slc_company_dict, slckind_list, detector_type_dict = gen_ledger_base_info(nodeid, area_id) slc_company = cross_ledger_info_dict['data']['ledger']['slc_company'] location = cross_ledger_info_dict['data']['ledger']['location'] slc_company_name = slc_company_dict[slc_company] if slc_company in slc_company_dict.keys() else '-' internet = cross_ledger_info_dict['data']['ledger']['internet'] internet_str = int_internet_code2str[internet] primary = cross_ledger_info_dict['data']['ledger']['primary'] secondary = cross_ledger_info_dict['data']['ledger']['secondary'] cross_model = int_cross_model2str[primary] + '-' + int_cross_model2str[secondary] light_infos = cross_ledger_info_dict['data']['light_infos'] src_light_dict = gen_dir_light_info_dict(light_infos) roads = cross_ledger_info_dict['data']['roads'] dir_list = roads.keys() length_info, lane_nums_list, road_special_info = {}, [], {} sum_length = 0 for dir in dir_list: dir_length = roads[dir]['topology']['entry']['road_length'] straight_left_split, reverse_turn, left_turn_waiting_area, straight_turn_waiting_area, reversible_lanes_info, outside_left = 0, 0, 0, 0, 0, 0 if 2 in src_light_dict[dir] and (1 in src_light_dict[dir] or 3 in src_light_dict[dir]): straight_left_split = 1 if roads[dir]['canaliza']['reverse_turn'] == 1: reverse_turn = 1 if roads[dir]['canaliza']['hold_left'] == 1: left_turn_waiting_area = 1 if roads[dir]['canaliza']['hold_straight'] == 1: straight_turn_waiting_area = 1 if len(roads[dir]['canaliza']['reversible_lanes_info']) > 0: reversible_lanes_info = 1 lane_turn_info = roads[dir]['topology']['entry']['lane_turn_info'] if check_outside_left(lane_turn_info): outside_left = 1 road_special_info[dir_str_dict[dir]] = { 'straight_left_split': straight_left_split, 'reverse_turn': reverse_turn, 'hold_left': left_turn_waiting_area, 'hold_straight': straight_turn_waiting_area, 'reversible_lanes_info': reversible_lanes_info, 'outside_left': outside_left } lane_nums_list.append([dir_str_dict[dir] + '进口', roads[dir]['entry_lane_num'], dir_str_dict[src_reverse[dir]] + '出口', roads[dir]['exit_lane_num']]) if dir_length != '-': sum_length += dir_length length_info[dir] = dir_length avg_length = round(sum_length / len(length_info.keys()), 0) if length_info else 0 cross_static_info = { 'crossid': crossid, 'nodeid': nodeid, 'area_id': area_id, 'location': location, 'slc_company': slc_company_name, 'internet': internet_str, 'cross_model': cross_model, 'lane_nums_list': lane_nums_list, 'road_special_info': road_special_info, 'length_info': length_info, 'avg_length': avg_length } return cross_static_info, cross_ledger_info_dict['data'] def gen_ledger_base_info(nodeid, area_id): node_base_params_info = db_tmnet.query_base_info(nodeid, area_id) # 下列字段当为空时需在前端展示尚未针对该城市进行配置的同义提示语句 division_list, company_list, slc_company_dict, slckind_list, detector_type_dict = [], [], {}, [], {} for row in node_base_params_info: if row['param_type'] == 'division': division_list.append(row['param_value']) elif row['param_type'] == 'company': company_list.append(row['param_value']) elif row['param_type'] == 'slc_company': slc_company_dict[row['param_code']] = row['param_value'] elif row['param_type'] == 'slckind': slckind_list.append(row['param_value']) elif row['param_type'] == 'detector_type': detector_type_dict[row['param_code']] = row['param_value'] return division_list, company_list, slc_company_dict, slckind_list, detector_type_dict def gen_dir_light_info_dict(light_infos): dir_light_dict = {} for row in light_infos: srcDir = row['srcDir'] light_type = row['light_type'] if srcDir not in dir_light_dict.keys(): dir_light_dict[srcDir] = [light_type] else: dir_light_dict[srcDir].append(light_type) return dir_light_dict def check_outside_left(lane_turn_info): seen_s = False # 是否已出现直行车道 for func in lane_turn_info: if 's' in func: # 当前是直行车道 seen_s = True if seen_s and ('l' in func): # 直行车道右侧出现左转 return True return False def gen_road_delay_index(avg_cross_delay_info, roads_dir_dict): road_delay_infos = [] if not avg_cross_delay_info: return road_delay_infos road_delay_infos = avg_cross_delay_info.inroad_delay_infos road_dir = {v['in']: k for k, v in roads_dir_dict.items()} road_flow_index = {} for road_index in road_delay_infos: roadid = road_index.inroadid if roadid not in road_dir.keys(): continue service_level = calc_service_level(road_index.delay_info.delay_time) road_flow_index[roadid] = { 'src_dir': road_dir[roadid], 'stop_times': round(road_index.delay_info.stop_times, 2), 'high_park_percent': str(road_index.delay_info.high_park_percent) + '%', 'imbalance_index': round(road_index.delay_info.imbalance_index, 2), 'park_time': road_index.delay_info.park_time, 'delay_time': road_index.delay_info.delay_time, 'speed': road_index.delay_info.speed / 100, 'move_speed': road_index.delay_info.move_speed / 100, 'service_level': service_level, 'flow_delays': {} } flow_delay_infos = road_index.flow_delay_infos for flow_delay_info in flow_delay_infos: if flow_delay_info.turn_type not in (0, 1): continue flow_service_level = calc_service_level(flow_delay_info.delay_info.delay_time) road_flow_index[roadid]['flow_delays'][flow_delay_info.turn_type] = { 'stop_times': round(flow_delay_info.delay_info.stop_times, 2), 'high_park_percent': str(flow_delay_info.delay_info.high_park_percent) + '%', 'park_time': flow_delay_info.delay_info.park_time, 'delay_time': flow_delay_info.delay_info.delay_time, 'speed': flow_delay_info.delay_info.speed / 100, 'service_level': flow_service_level, 'move_speed': flow_delay_info.delay_info.move_speed / 100 } return road_flow_index def gen_road_dir_dict(cross_ledger_info_dict): roads_dir_dict = {} if not cross_ledger_info_dict: return roads_dir_dict roads = cross_ledger_info_dict['roads'] if 'roads' in cross_ledger_info_dict.keys() else None for dir in roads.keys(): roads_dir_dict[dir] = { 'in': roads[dir]['roadid'], 'out': roads[dir]['out_roadid'] } return roads_dir_dict def calc_inroad_imbalance_index(inroad_delay_pb_list): for item in inroad_delay_pb_list: avg_stop_times = item.delay_info.stop_times max_stop_times, min_stop_times = 0, 99999 flow_delay_infos = item.flow_delay_infos for flow_delay_info in flow_delay_infos: if flow_delay_info.delay_info.stop_times > max_stop_times: max_stop_times = flow_delay_info.delay_info.stop_times if flow_delay_info.delay_info.stop_times < min_stop_times: min_stop_times = flow_delay_info.delay_info.stop_times imbalance_index = round((max_stop_times - min_stop_times) / avg_stop_times, 2) if avg_stop_times > 0 and max_stop_times > 0 and min_stop_times != 99999 else 0 item.delay_info.imbalance_index = imbalance_index return inroad_delay_pb_list def gen_flow_turn_rate_index(avg_cross_delay_info, roads_dir_dict): road_delay_infos = avg_cross_delay_info.inroad_delay_infos outroad_infos = avg_cross_delay_info.outroad_infos road_delay_dict = {item.inroadid: item for item in road_delay_infos} outroad_info_dict = {item.outroadid: item for item in outroad_infos} cross_sum_car_num = sum([item.delay_info.car_num for item in road_delay_infos]) cross_out_sum_car_num = sum([item.turn_info.car_num for item in outroad_infos]) road_flow_turn_rate = {} for dir in roads_dir_dict.keys(): roadid = roads_dir_dict[dir]['in'] l_rate, s_rate, r_rate = '-', '-', '-' out_l_rate, out_s_rate, out_r_rate = '-', '-', '-' in_flow_rate, out_flow_rate = '-', '-' if roadid != '-' and roadid in road_delay_dict.keys(): car_num = road_delay_dict[roadid].delay_info.car_num in_flow_rate = round(car_num / cross_sum_car_num, 2) if cross_out_sum_car_num != 0 else '-' flow_delay_infos = road_delay_dict[roadid].flow_delay_infos for flow_delay_info in flow_delay_infos: if flow_delay_info.turn_type not in (0, 1, 2): continue if flow_delay_info.turn_type == 0: l_rate = round(flow_delay_info.delay_info.car_num / car_num, 2) if car_num != 0 else '-' elif flow_delay_info.turn_type == 1: s_rate = round(flow_delay_info.delay_info.car_num / car_num, 2) if car_num != 0 else '-' elif flow_delay_info.turn_type == 2: r_rate = round(flow_delay_info.delay_info.car_num / car_num, 2) if car_num != 0 else '-' out_road_id = roads_dir_dict[dir]['out'] if out_road_id != '-' and out_road_id in outroad_info_dict.keys(): out_car_num = outroad_info_dict[out_road_id].turn_info.car_num out_flow_rate = round(out_car_num / cross_out_sum_car_num, 2) if cross_out_sum_car_num != 0 else '-' out_l_rate = round(outroad_info_dict[out_road_id].turn_info.turn_ratio_1 / out_car_num, 2) if out_car_num != 0 else '-' out_s_rate = round(outroad_info_dict[out_road_id].turn_info.turn_ratio_0 / out_car_num, 2) if out_car_num != 0 else '-' out_r_rate = round(outroad_info_dict[out_road_id].turn_info.turn_ratio_2 / out_car_num, 2) if out_car_num != 0 else '-' road_flow_turn_rate[roadid] = { 'src_dir': dir, 'in_flow_rate': in_flow_rate, 'out_flow_rate': out_flow_rate, 'l_rate': l_rate, 's_rate': s_rate, 'r_rate': r_rate, 'out_l_rate': out_l_rate, 'out_s_rate': out_s_rate, 'out_r_rate': out_r_rate } return road_flow_turn_rate def calc_service_level(delay_time): if delay_time <= 10: service_level = 'A' elif 10 < delay_time <= 20: service_level = 'B' elif 20 < delay_time <= 35: service_level = 'C' elif 35 < delay_time <= 55: service_level = 'D' elif 55 < delay_time <= 80: service_level = 'E' else: service_level = 'F' return service_level def calc_tide_index(crossid, nodeid, date_list, roads_dir_dict): tide_index_list = [] am_tp_start, am_tp_end = 't700', '900' pm_tp_start, pm_tp_end = 't1700', '1900' am_cross_delay_data_list = db_cross.query_cross_delay_info(crossid, nodeid, date_list, am_tp_start) pm_cross_delay_data_list = db_cross.query_cross_delay_info(crossid, nodeid, date_list, pm_tp_start) date_am_delay_info_dict = {item['day']: item['data'] for item in am_cross_delay_data_list} date_pm_delay_info_dict = {item['day']: item['data'] for item in pm_cross_delay_data_list} # 构建对向进口道对 subtend_road_pair = gen_subtend_road_pair(roads_dir_dict) for date in date_list: max_am_flow, max_pm_flow, tide_index, max_am_flow_road, max_pm_flow_road = 0, 99999999999, 0, '', '' if date not in date_am_delay_info_dict.keys() or date not in date_pm_delay_info_dict.keys(): tide_index_list.append(tide_index) continue item_am_cross_delay_info = pb.xl_cross_delayinfo_t() item_pm_cross_delay_info = pb.xl_cross_delayinfo_t() item_am_cross_delay_info.ParseFromString(date_am_delay_info_dict[date]) item_pm_cross_delay_info.ParseFromString(date_pm_delay_info_dict[date]) if not item_am_cross_delay_info or not item_pm_cross_delay_info: tide_index_list.append(tide_index) continue am_inroad_infos = item_am_cross_delay_info.inroad_delay_infos pm_inroad_infos = item_pm_cross_delay_info.inroad_delay_infos am_inroad_info_dict = {item.inroadid: item for item in am_inroad_infos} pm_inroad_info_dict = {item.inroadid: item for item in pm_inroad_infos} # 找出早高峰最高流量的进口道 for am_inroad_info in am_inroad_infos: am_flow = am_inroad_info.delay_info.car_num if am_flow > max_am_flow: max_am_flow = am_flow max_am_flow_road = am_inroad_info.inroadid if max_am_flow_road == '' or max_am_flow_road not in subtend_road_pair.keys(): tide_index_list.append(tide_index) continue subtend_road_am_flow = am_inroad_info_dict[subtend_road_pair[max_am_flow_road]].delay_info.car_num if subtend_road_pair[max_am_flow_road] in am_inroad_info_dict.keys() else 0 # 如果进口道流量小于20 则无意义 if min(subtend_road_am_flow, max_am_flow) < 20: tide_index_list.append(tide_index) continue # 找出晚高峰最高流量的进口道 for pm_inroad_info in pm_inroad_infos: pm_flow = pm_inroad_info.delay_info.car_num if pm_flow > max_pm_flow: max_pm_flow = pm_flow max_pm_flow_road = pm_inroad_info.inroadid # 如果路段id为空或不在对向对里，则无意义 if max_pm_flow_road == '' or max_pm_flow_road not in subtend_road_pair.keys(): tide_index_list.append(tide_index) continue subtend_road_pm_flow = pm_inroad_info_dict[subtend_road_pair[max_pm_flow_road]].delay_info.car_num if min(subtend_road_pm_flow, max_pm_flow) < 20: tide_index_list.append(tide_index) continue # 如果早晚高峰的最大流量进口道不处于同一个对向对中，则无意义 if max_pm_flow_road != max_am_flow_road and max_pm_flow_road != subtend_road_pair[max_am_flow_road]: tide_index_list.append(tide_index) continue Fam = max(max_am_flow_road, subtend_road_am_flow) / min(max_am_flow_road, subtend_road_am_flow) Fpm = max(max_pm_flow_road, subtend_road_pm_flow) / min(max_pm_flow_road, subtend_road_pm_flow) if Fam >= 1.5 and Fpm >= 1.5 and max_pm_flow_road == subtend_road_pair[max_am_flow_road]: tide_index = min(Fam, Fpm) / max(Fam, Fpm) elif min(Fam, Fpm) <= 0: tide_index = 0 elif max_pm_flow_road == max_am_flow_road: tide_index = Fam * Fpm tide_index_list.append(tide_index) return tide_index_list def gen_subtend_road_pair(roads_dir_dict): subtend_road_pair = {} tmp_road_dir = copy.deepcopy(roads_dir_dict) keys_to_process = list(tmp_road_dir.keys()) for dir in keys_to_process: if dir in tmp_road_dir.keys() and tmp_road_dir[dir]['in'] == '-': continue subtend_dir = src_reverse[dir] if subtend_dir in tmp_road_dir.keys() and tmp_road_dir[subtend_dir]['in'] != '-': subtend_road_pair[tmp_road_dir[dir]['in']] = tmp_road_dir[subtend_dir]['in'] tmp_road_dir.pop(dir) tmp_road_dir.pop(subtend_dir) return subtend_road_pair def parse_single_cross_delay_info(crossid, nodeid, data_list, data_type, roads_dir_dict): data_dict = {} max_cross_car_num, max_road_car_num, max_flow_car_num = 0, 0, 0 pb_data_list = [] for item in data_list: if item: pb_data_list.append(item['data']) road_delay_infos = item['data'].inroad_delay_infos if item['data'] and item['data'].inroad_delay_infos else [] for road_delay_info in road_delay_infos: if road_delay_info.delay_info.car_num > max_road_car_num: max_road_car_num = road_delay_info.delay_info.car_num flow_delay_infos = road_delay_info.flow_delay_infos for flow_delay_info in flow_delay_infos: if flow_delay_info.delay_info.car_num > max_flow_car_num: max_flow_car_num = flow_delay_info.delay_info.car_num max_cross_car_num = max( (x for x in pb_data_list if x is not None), key=lambda x: x.delay_info.car_num, default=None ).delay_info.car_num for item in data_list: tp_start = item['tp_start'] tp_end = item['tp_end'] day = item['day'] if data_type == 'day': key = day elif data_type == 'hour': key = convert_time(int(tp_start.replace('h', ''))) + '-' + convert_time(int(tp_end.replace('h', ''))) else: key = day item_cross_delay_info = item['data'] # 指标内容依次是 停车次数 多次停车率 停车时间 延误时间 平均速度 不停车速度 相对流量 路口失衡系数 潮汐指数 服务水平 overview_data = ['-', '-', '-', '-', '-', '-', '-', '-', '-', '-', # 下方为上述指标变化率颜色展示flag 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] tide_index = calc_tide_index(crossid, nodeid, [day], roads_dir_dict) if data_type == 'day' or data_type == 'week' else '-' road_data_dict = {} cross_car_num = 0 if item_cross_delay_info: cross_car_num = item_cross_delay_info.delay_info.car_num road_delay_infos = item_cross_delay_info.inroad_delay_infos max_stop_times = max(road_delay_infos, key=lambda x: x.delay_info.stop_times).delay_info.stop_times min_stop_times = min(road_delay_infos, key=lambda x: x.delay_info.stop_times).delay_info.stop_times cross_imbalance_index = round((max_stop_times - min_stop_times) / item_cross_delay_info.delay_info.stop_times, 2) if item_cross_delay_info.delay_info.stop_times != 0 else 0 overview_data = [ # 停车次数 round(item_cross_delay_info.delay_info.stop_times, 2), # 多次停车率 停车时间 item_cross_delay_info.delay_info.high_park_percent, item_cross_delay_info.delay_info.park_time, # 延误时间 平均速度 item_cross_delay_info.delay_info.delay_time, item_cross_delay_info.delay_info.speed / 100, # 不停车速度 相对流量占比 item_cross_delay_info.delay_info.move_speed / 100, round(cross_car_num / max_cross_car_num * 100, 2) if max_cross_car_num != 0 else 0, # 路口失衡系数 潮汐指数（当为小时级指标时，不计算该值） 服务水平 cross_imbalance_index, tide_index[0], calc_service_level(item_cross_delay_info.delay_info.delay_time), # 指标变化率颜色展示flag, 不含服务水平 0, 0, 0, 0, 0, 0, 0, 0, 0 ] inroad_delay_infos_with_imbalance = calc_inroad_imbalance_index(road_delay_infos) road_data_dict = {item.inroadid: item for item in inroad_delay_infos_with_imbalance} data_dict[key] = { 'overview': overview_data, 'roads_data': {} } src_dir_list = list(roads_dir_dict.keys()) for src_dir in src_dir_list: if roads_dir_dict[src_dir]['in'] != '-' and roads_dir_dict[src_dir]['in'] in road_data_dict.keys(): road_car_num = road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.car_num src_data = [ # 停车次数 round(road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.stop_times, 2), # 多次停车率 road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.high_park_percent, # 转向失衡指数 round(road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.imbalance_index, 2), # 停车时间 road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.park_time, # 延误时间 road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.delay_time, # 平均速度 road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.speed / 100, # 不停车速度 road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.move_speed / 100, # 相对流量占比 round(road_car_num / max_road_car_num * 100, 2) if max_road_car_num > 0 else '-', # 进口道流量占比 round(road_car_num / cross_car_num * 100, 2) if cross_car_num > 0 else '-', # 服务水平 calc_service_level(road_data_dict[roads_dir_dict[src_dir]['in']].delay_info.delay_time), # 指标变化率颜色展示flag, 顺序为上述指标顺序, 不含服务水平 0, 0, 0, 0, 0, 0, 0, 0, 0 ] flow_delay_infos = road_data_dict[roads_dir_dict[src_dir]['in']].flow_delay_infos flow_data_dict = {} for flow_delay_info in flow_delay_infos: turn_type = flow_delay_info.turn_type if turn_type not in (0, 1): continue flow_data = [ # 停车次数 round(flow_delay_info.delay_info.stop_times, 2), # 多次停车率 flow_delay_info.delay_info.high_park_percent, # 停车时间 flow_delay_info.delay_info.park_time, # 延误时间 flow_delay_info.delay_info.delay_time, # 平均速度 flow_delay_info.delay_info.speed / 100, # 不停车速度 flow_delay_info.delay_info.move_speed / 100, # 相对流量 round(flow_delay_info.delay_info.car_num / max_flow_car_num * 100, 2) if max_flow_car_num > 0 else '-', # 分流转向占比 round(flow_delay_info.delay_info.car_num / road_car_num * 100, 2) if road_car_num > 0 else '-', # 服务水平 calc_service_level(flow_delay_info.delay_info.delay_time), # 指标变化率颜色展示flag 不含服务水平 0, 0, 0, 0, 0, 0, 0, 0 ] flow_data_dict[turn_type] = flow_data data_dict[key]['roads_data'][src_dir] = { 'road': src_data, 'flow': flow_data_dict } return data_dict def calc_single_day_delay_info_change_rate(data_dict): res_data_dict = copy.deepcopy(data_dict) key_list = list(res_data_dict.keys()) empty_overview = ['-', '-', '-', '-', '-', '-', '-', '-', '-', '-', # 指标变化率颜色展示flag 0, 0, 0, 0, 0, 0, 0, 0, 0] for i in range(len(key_list)-1, -1, -1): overview_data = res_data_dict[key_list[i]]['overview'] # list roads_data = res_data_dict[key_list[i]]['roads_data'] # dict 内部包含多个方向路段和各个方向路段的流向级别的数据 if i != 0: prev_item_index = i - 1 prev_overview_data = res_data_dict[key_list[prev_item_index]]['overview'] prev_roads_data = res_data_dict[key_list[prev_item_index]]['roads_data'] if overview_data != empty_overview and prev_overview_data != empty_overview: overview_data_with_change_rate = calc_overview_change_rate(overview_data, prev_overview_data) res_data_dict[key_list[i]]['overview'] = overview_data_with_change_rate roads_data_wit_change_rate = calc_roads_data_change_rate(roads_data, prev_roads_data) res_data_dict[key_list[i]]['roads_data'] = roads_data_wit_change_rate return res_data_dict def calc_overview_change_rate(overview_data, prev_overview_data): res_data = copy.copy(overview_data) for i in range(len(overview_data)): if i >= 9: continue if overview_data[i] == '-' or prev_overview_data[i] == '-': res_data[i + 10] = 0 # 变化率 0表示正常 1表示恶化 2表示优化 if i in (0, 1, 2, 3, 6, 7, 8): # 指标提升代表恶化的有停车次数、多次停车率、转向失衡指数、停车时间、延误时间、相对流量、进口道流量占比 rate = (overview_data[i] - prev_overview_data[i]) / prev_overview_data[i] * 100 if prev_overview_data[i] != '-' and prev_overview_data[i] > 0 else 0 if rate < -20: res_data[i + 10] = 1 elif rate > 20: res_data[i + 10] = 2 else: # 指标下降代表恶化的有平均速度、不停车速度 rate = (overview_data[i] - prev_overview_data[i]) / prev_overview_data[i] * 100 if prev_overview_data[i] != '-' and prev_overview_data[i] > 0 else 0 if rate > 20: res_data[i + 10] = 1 elif rate < -20: res_data[i + 10] = 2 return res_data def calc_roads_data_change_rate(roads_data, prev_roads_data): res_data = copy.deepcopy(roads_data) src_dir_list = list(roads_data.keys()) for src_dir in src_dir_list: src_dir_road_data = roads_data[src_dir]['road'] if src_dir not in prev_roads_data.keys(): continue # 指标提升代表恶化的有停车次数、多次停车率、停车时间、延误时间、相对流量、分流转向占比 prev_src_dir_road_data = prev_roads_data[src_dir]['road'] for i in range(len(src_dir_road_data)): if i >= 9: continue if src_dir_road_data[i] == '-' or prev_src_dir_road_data[i] == '-': src_dir_road_data[i + 10] = 0 if i in (0, 1, 2, 3, 4, 7, 8): rate = (src_dir_road_data[i] - prev_src_dir_road_data[i]) / prev_src_dir_road_data[i] * 100 if prev_src_dir_road_data[i] > 0 else 0 if rate < -20: src_dir_road_data[i + 10] = 1 elif rate > 20: src_dir_road_data[i + 10] = 2 else: # 指标下降代表恶化的有平均速度、不停车速度 rate = (src_dir_road_data[i] - prev_src_dir_road_data[i]) / prev_src_dir_road_data[i] * 100 if prev_src_dir_road_data[i] > 0 else 0 if rate > 20: src_dir_road_data[i + 10] = 1 elif rate < -20: src_dir_road_data[i + 10] = 2 flow_datas = roads_data[src_dir]['flow'] prev_flow_datas = prev_roads_data[src_dir]['flow'] for turn_type in flow_datas.keys(): flow_data = flow_datas[turn_type] if turn_type not in prev_flow_datas.keys(): continue prev_flow_data = prev_flow_datas[turn_type] for i in range(len(flow_data)): if i >= 8: continue if flow_data[i] == '-' or prev_flow_data[i] == '-': flow_data[i + 9] = 0 if i in (0, 1, 2, 3, 6, 7): rate = (flow_data[i] - prev_flow_data[i]) / prev_flow_data[i] * 100 if prev_flow_data[i] > 0 else 0 if rate < -20: flow_data[i + 9] = 1 elif rate > 20: flow_data[i + 9] = 2 else: rate = (flow_data[i] - prev_flow_data[i]) / prev_flow_data[i] * 100 if prev_flow_data[i] > 0 else 0 if rate > 20: flow_data[i + 9] = 1 elif rate < -20: flow_data[i + 9] = 2 return res_data def parse_data2pb(data_list): res_list = [] for row in data_list: day = row['day'] tp_start = row['tp_start'] tp_end = row['tp_end'] item_cross_delay_info = pb.xl_cross_delayinfo_t() item_cross_delay_info.ParseFromString(row['data']) res_list.append({ 'day': day, 'tp_start': tp_start, 'tp_end': tp_end, 'data': item_cross_delay_info }) return res_list def gen_cross_problems(avg_cross_delay_info, roads_dir_dict, inroad_static_info_dict): # 运行效率、均衡调控、配时方案、路口渠化 operating_efficiency_problems = gen_operating_efficiency_problems(avg_cross_delay_info, roads_dir_dict) balanced_control_problems = gen_balanced_control_problems(avg_cross_delay_info, roads_dir_dict, inroad_static_info_dict) phase_problems = gen_phase_problems() cross_channelized_problems = gen_cross_channelized_problems() problems = [operating_efficiency_problems, balanced_control_problems, phase_problems, cross_channelized_problems] return problems def gen_operating_efficiency_problems(avg_cross_delay_info, roads_dir_dict): if not avg_cross_delay_info: return [{ 'item': '运行效率', 'values': [] }] road_delay_infos = avg_cross_delay_info.inroad_delay_infos high_park_problems, high_park_suggestions = gen_high_park_problems(road_delay_infos, roads_dir_dict) operating_efficiency_problems = { 'item': '运行效率', 'values': [ { 'item': '多次排队', 'detail': high_park_problems, 'reason': '某一进口道转向的多次停车率大于15%', 'suggestions': high_park_suggestions }, ] } pass def gen_high_park_problems(road_delay_infos, roads_dir_dict): detail = [{ 'text': '未见异常' }] suggestion = [] err_src_dict = {} src_list = list(roads_dir_dict.keys()) road_delays_dict = {item.inroadid: item for item in road_delay_infos} for src_dir in src_list: if roads_dir_dict[src_dir]['in'] == '-' or roads_dir_dict[src_dir]['in'] not in road_delays_dict.keys(): continue road_info = road_delays_dict[roads_dir_dict[src_dir]['in']] flow_delay_infos = road_info.flow_delay_infos turn_type_flow_delay_info_dict = {item.turn_type: item for item in flow_delay_infos} for turn_type in turn_type_flow_delay_info_dict.keys(): flow_delay_info = turn_type_flow_delay_info_dict[turn_type] if turn_type not in (0, 1): continue if flow_delay_info.delay_info.high_park_percent > 15: if src_dir not in err_src_dict.keys(): err_src_dict[src_dir] = [str(turn_type) + ':' + str(flow_delay_info.delay_info.high_park_percent)] else: err_src_dict[src_dir].append(str(turn_type) + ':' + str(flow_delay_info.delay_info.high_park_percent)) if err_src_dict: detail = [] for src_dir in err_src_dict.keys(): src_detail = [] for turn_type in err_src_dict[src_dir]: flow_detail = { 'turn_type': int_turn_type2str[int(turn_type.split(':')[0])], 'text': '车辆多次停车率过大（', 'percent': turn_type.split(':')[1] + '%），' } src_detail.append(flow_detail) src_detail.append({ 'text': '车辆需要多次排队才能通过' }) detail.append({ 'src_dir': dir_str_dict[src_dir] + '进口', 'src_detail': src_detail }) # todo 补充生成建议的逻辑 return detail, suggestion def gen_high_stop_time_problems(road_delay_infos, roads_dir_dict): detail = [{ 'text': '未见异常' }] suggestion = [] # todo 需与产品再次确认逻辑 pass def gen_balanced_control_problems(avg_cross_delay_info, roads_dir_dict, inroad_static_info_dict): road_delay_infos = avg_cross_delay_info.inroad_delay_infos cross_imbalance_detail, cross_imbalance_suggestions = gen_cross_imbalance_problems(road_delay_infos, roads_dir_dict) turn_imbalance_detail, turn_imbalance_suggestions = gen_turn_imbalance_problems(road_delay_infos, roads_dir_dict, inroad_static_info_dict) # todo 需要与产品确认路口潮汐问题诊断的逻辑 balanced_control_problems = { 'item': '均衡调控', 'values': [ { 'item': '路口失衡', 'detail': cross_imbalance_detail, 'reason': '路口存在某个流向绿灯时长不足而另一个方向绿灯存在空放的现象', 'suggestions': cross_imbalance_suggestions }, { 'item': '转向失衡', 'detail': turn_imbalance_detail, 'reason': '同一进口道，直行与左转停车次数之差的绝对值大于0.5，且转向停车次数的最大值大于1', 'suggestions': turn_imbalance_suggestions }, ] } return balanced_control_problems # 路口失衡问题诊断 def gen_cross_imbalance_problems(road_delay_infos, roads_dir_dict): detail = [{ 'text': '未见异常' }] suggestion = [] road_src_dict = {item['in']: item for item in roads_dir_dict} max_stop_times_road = max(road_delay_infos, key=lambda x: x.delay_info.stop_times) min_stop_times_road = min(road_delay_infos, key=lambda x: x.delay_info.stop_times) if max_stop_times_road.delay_info.stop_times > 1 \ and max_stop_times_road.delay_info.stop_times - min_stop_times_road.delay_info.stop_times > 0.5: max_roadid = max_stop_times_road.inroadid min_roadid = min_stop_times_road.inroadid max_src = road_src_dict[max_roadid] min_src = road_src_dict[min_roadid] detail = [ { 'src_dir': dir_str_dict[max_src] + '进口', 'text': '的停车次数（' + str(round(max_stop_times_road.delay_info.stop_times, 2)) + '）与' }, { 'src_dir': dir_str_dict[min_src] + '进口', 'text': f"""的停车次数（{str(round(max_stop_times_road.delay_info.stop_times, 2))}）相差过大，两者之比为{int(round(max_stop_times_road.delay_info.stop_times, 2) / round(min_stop_times_road.delay_info.stop_times, 2) * 100)}%，分配的绿灯时长不匹配""" } ] suggestion = [ { 'text': '调整', 'max_src_dir': dir_str_dict[max_src] + '进口', }, { 'text': '和', 'min_src_dir': dir_str_dict[min_src] + '进口', }, { 'text': '的绿灯时长的分配情况' } ] if max_src == src_reverse[min_src]: # todo 补充对向情况下额外建议的情况 pass return detail, suggestion # 转向失衡问题诊断 def gen_turn_imbalance_problems(road_delay_infos, roads_dir_dict, inroad_static_info_dict): detail = [{ 'text': '未见异常' }] suggestion = [] err_road_dict = {} road_src_dict = {item['in']: item for item in roads_dir_dict} for road_delay_info in road_delay_infos: inroadid = road_delay_info.inroadid flow_delay_infos = road_delay_info.flow_delay_infos turn_type_flow_delay_info_dict = {item.turn_type: item for item in flow_delay_infos} lane_num_info = count_lsr(inroad_static_info_dict[inroadid]['lane_turn_info']) if inroadid in inroad_static_info_dict.keys() else None if 0 in turn_type_flow_delay_info_dict.keys() and 1 in turn_type_flow_delay_info_dict.keys(): s_stop_times = turn_type_flow_delay_info_dict[0].delay_info.stop_times l_stop_times = turn_type_flow_delay_info_dict[1].delay_info.stop_times if s_stop_times > 1 or l_stop_times > 1 and abs(l_stop_times - s_stop_times) > 0.5: if s_stop_times > l_stop_times: err_road_dict[road_src_dict[inroadid]] = ['直行' + ':' + str(round(s_stop_times, 2)), '左转' + ':' + str(round(l_stop_times, 2)), lane_num_info] else: err_road_dict[road_src_dict[inroadid]] = ['左转' + ':' + str(round(s_stop_times, 2)), '直行' + ':' + str(round(l_stop_times, 2)), lane_num_info] if len(err_road_dict.keys()) > 0: detail, suggestion, road_num_suggestion = [], [], [] for src_dir in err_road_dict.keys(): item_detail = [ { 'src_dir': dir_str_dict[src_dir] + '进口', 'child_detail': [ { 'turn_type': err_road_dict[src_dir][0].split(':')[0], }, { 'text': '的停车次数（' + str(err_road_dict[src_dir][0].split(':')[1]) + '）与' }, { 'turn_type': err_road_dict[src_dir][1].split(':')[0], }, { 'text': '的停车次数（' + str(err_road_dict[src_dir][1].split(':')[1]) + '）相差过大，两者之比为' + str(int(float(err_road_dict[src_dir][0].split(':')[1]) / float(err_road_dict[src_dir][1].split(':')[1]) * 100)) + '%，分配的绿灯时长不匹配' } ] } ] detail.append(item_detail) item_suggestion = [ { 'src_dir': dir_str_dict[src_dir] + '进口', 'text': '信号灯直左分控，调整执行和左转车辆绿灯时长分配情况' }, # todo 补充配时相关建议 ] suggestion.extend(item_suggestion) if err_road_dict[src_dir][2]: road_num_suggestion.append( { 'src_dir': dir_str_dict[src_dir] + '进口', 'text': '左转/直行/右转现有车道数分别为' + err_road_dict[src_dir][2] } ) lane_num_suggestion = [ { 'text': '调整', 'src_dir': '、'.join([dir_str_dict[item] + '进口' for item in err_road_dict.keys()]) }, { 'text': '车道分配情况' } ] lane_num_suggestion.extend(road_num_suggestion) suggestion = suggestion + lane_num_suggestion return detail, suggestion def gen_phase_problems(): pass def gen_cross_channelized_problems(): pass def query_cross_phase(nodeid, crossid): cross_phases, err = QueryCrossRunningPhase(nodeid, [crossid]) if err or not cross_phases or cross_phases.code != 0: return None cross_phase_info = cross_phases.data[0] pass