428 lines
17 KiB
Python
428 lines
17 KiB
Python
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from collections import defaultdict
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from datetime import datetime, timedelta
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import json
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def process_incident_data_gen6(test_val):
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"""
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处理事故数据，按照incident_name去重（移除原incident_time组合去重），移除idc_tickcount_ms排序，其他逻辑不变。
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Args:
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test_val (list): 原始数据列表
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Returns:
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dict: 格式化后的数据
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"""
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# 1. 仅按照 incident_name 去重（核心修改点1：移除incident_time组合）
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unique_data_dict = {}
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for item in test_val:
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name = item.get("incident_name")
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# 仅判断incident_name是否存在，不再依赖incident_time
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if name:
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# 仅用incident_name作为去重键
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key = name
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if key not in unique_data_dict:
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unique_data_dict[key] = item
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# 获取去重后的数据列表（核心修改点2：移除排序步骤）
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unique_data_list = list(unique_data_dict.values())
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# 3. 提取指定字段（原逻辑完全保留，仅将遍历对象从sorted_data改为unique_data_list）
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processed_events_data = []
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for index, item in enumerate(unique_data_list, 1):
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incident_time_str = item.get("incident_time")
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if not incident_time_str:
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continue
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try:
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# 解析新的时间格式 "2025-11-30 20:51:35.684000"
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# 先尝试直接解析完整格式
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try:
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# 移除毫秒部分，只保留到秒
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base_time_str = incident_time_str.split('.')[0]
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incident_datetime = datetime.strptime(base_time_str, "%Y-%m-%d %H:%M:%S")
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except ValueError:
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# 如果失败，尝试其他可能的格式
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try:
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incident_datetime = datetime.fromisoformat(incident_time_str.replace('Z', '+00:00'))
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except:
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# 再次失败，使用当前时间
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incident_datetime = datetime.now()
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# 格式化日期和时间
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date_str_result = incident_datetime.strftime("%Y-%m-%d")
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time_str_result = incident_datetime.strftime("%H:%M:%S")
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except (ValueError, TypeError) as e:
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print(f"Warning: Failed to parse incident_time '{incident_time_str}': {e}")
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# 使用默认值
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date_str_result = item.get("dt", "").split(' ')[0] if item.get("dt") else datetime.now().strftime("%Y-%m-%d")
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time_str_result = "00:00:00"
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# 创建新的字典
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processed_item = {
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"id": index,
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"oneid": item.get("oneid", ""),
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"date": date_str_result,
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"time": time_str_result,
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"incident_name": item.get("incident_name", ""),
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"incident_description": "",
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}
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processed_events_data.append(processed_item)
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# 4. 构建最终返回的字典
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total_count = len(processed_events_data)
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result = {"data": processed_events_data, "total": total_count}
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return result
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def aggregate_and_sort_incidents_gen6(raw_data):
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"""
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简化版本：按(incident_time + incident_name)精确聚合
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适用于同一事件的incident_time完全一致的情况
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"""
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aggregate_dict = {}
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for item in raw_data:
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incident_time = item.get("incident_time", "1970-01-01 00:00:00.000")
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incident_name = item.get("incident_name", "unknown_incident")
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aggregate_key = (incident_time, incident_name)
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if aggregate_key not in aggregate_dict:
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aggregate_dict[aggregate_key] = {
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"base_info": {
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"oneid": item.get("oneid"),
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"incident_time": incident_time,
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"speed": item.get("speed"),
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"gps_heading": item.get("gps_heading"),
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"incident_name": incident_name,
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"latitude": item.get("latitude"),
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"longitude": item.get("longitude"),
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"odometer": item.get("odometer"),
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"sw_version": item.get("sw_version"),
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"carmodel": item.get("carmodel"),
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"session_id": item.get("session_id"),
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"dt": item.get("dt")
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},
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"signal_data": {}
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}
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signal_key = item.get("key", "unknown_key")
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aggregate_dict[aggregate_key]["signal_data"][signal_key] = {
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'signal_value': item.get('signal_value', '0.0'),
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"odometer": item.get('odometer', '0.0')
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}
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# 按incident_time排序
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sorted_result = sorted(
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aggregate_dict.values(),
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key=lambda x: x["base_info"]["incident_time"]
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)
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return sorted_result
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def aggregate_line_list_gen6(map_list):
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"""
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按incident_time聚合事件数据，生成时间线列表
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改进点：
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1. 使用正确的时间字段名
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2. 支持精确的时间排序
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3. 处理同一时间点的多条记录
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4. 添加健壮的错误处理
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"""
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aggregated_data = {}
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# 遍历所有事件
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for item in map_list:
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try:
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base_info = item['base_info']
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# 提取需要的字段
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incident_time_str = base_info.get('incident_time', '1970-01-01 00:00:00.000')
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speed = base_info.get('speed', '0')
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oneid = base_info.get('oneid', '')
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latitude = base_info.get('latitude', '0')
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longitude = base_info.get('longitude', '0')
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incident_name = base_info.get('incident_name', 'Unknown Incident')
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# 创建聚合键（使用完整时间字符串）
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aggregate_key = incident_time_str
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# 聚合处理
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if aggregate_key not in aggregated_data:
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# 新的时间点，初始化记录
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aggregated_data[aggregate_key] = {
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'incident_time': incident_time_str, # 使用正确的字段名
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'speed': speed,
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'oneid': oneid,
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'latitude': latitude,
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'longitude': longitude,
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'incident_names': [incident_name], # 使用复数形式，表示可能有多个
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'record_count': 1 # 记录该时间点的记录数
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}
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else:
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# 已存在的时间点
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existing = aggregated_data[aggregate_key]
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# 可选：处理同一时间点不同记录的字段冲突
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# 这里选择保留第一条的速度，但如果需要可以取平均值或最新值
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# existing['speed'] = str((float(existing['speed']) + float(speed)) / 2)
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# 添加事件名称（去重）
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if incident_name not in existing['incident_names']:
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existing['incident_names'].append(incident_name)
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existing['record_count'] += 1
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except (KeyError, TypeError) as e:
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print(f"Warning: Skipping item due to missing field: {e}")
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continue
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# 转换为列表形式，并按时间正序排序
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def get_sort_time(record):
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try:
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time_str = record['incident_time']
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if '.' in time_str:
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# 处理带毫秒的时间
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return datetime.strptime(time_str, "%Y-%m-%d %H:%M:%S.%f")
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else:
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# 处理不带毫秒的时间
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return datetime.strptime(time_str, "%Y-%m-%d %H:%M:%S")
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except (ValueError, TypeError) as e:
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print(f"Warning: Failed to parse time '{record['incident_time']}' for sorting: {e}")
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return datetime(1970, 1, 1) # 返回默认时间
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# 按incident_time正序排序（从小到大）
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result = sorted(aggregated_data.values(), key=get_sort_time)
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return result
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def get_logging_list_gen6(map_list):
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"""
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处理map_list数据，生成logging列表，并按incident_time正序排序
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同时对每个记录的signals按name字段进行字符顺序排序
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"""
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# 1. 首先对map_list按incident_time进行排序
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def get_sort_key(item):
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try:
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incident_time_str = item['base_info']['incident_time']
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# 处理时间字符串，统一格式
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if '.' in incident_time_str:
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# 保留到毫秒级别进行排序
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base_time_str = incident_time_str.split('.')[0]
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milliseconds = incident_time_str.split('.')[1][:6] # 取最多6位毫秒
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full_time_str = f"{base_time_str}.{milliseconds}"
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return datetime.strptime(full_time_str, "%Y-%m-%d %H:%M:%S.%f")
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else:
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return datetime.strptime(incident_time_str, "%Y-%m-%d %H:%M:%S")
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except (ValueError, TypeError, KeyError) as e:
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print(f"Warning: Failed to parse incident_time for sorting: {e}")
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# 解析失败时返回一个很早的时间，确保这些记录排在最后
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return datetime(1970, 1, 1)
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# 按incident_time正序排序（从小到大）
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sorted_map_list = sorted(map_list, key=get_sort_key)
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# 2. 处理排序后的数据
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result = []
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id = 0
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for item in sorted_map_list:
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id += 1
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row_data = {}
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row_data['id'] = id
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base_info = item['base_info']
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# 处理时间格式 - 新格式为 "2025-11-30 20:23:05.394000"
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incident_time_str = base_info['incident_time']
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try:
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# 尝试解析新格式的时间
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if '.' in incident_time_str:
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# 移除毫秒部分，只保留到秒
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base_time_str = incident_time_str.split('.')[0]
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dt = datetime.strptime(base_time_str, "%Y-%m-%d %H:%M:%S")
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else:
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dt = datetime.strptime(incident_time_str, "%Y-%m-%d %H:%M:%S")
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row_data['time'] = dt.strftime("%H:%M:%S")
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except (ValueError, TypeError) as e:
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print(f"Warning: Failed to parse incident_time '{incident_time_str}': {e}")
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# 使用默认时间
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row_data['time'] = "00:00:00"
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# 处理经纬度 - 可能为 "NULL" 字符串
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longitude = str(base_info.get('longitude', '')).strip()
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latitude = str(base_info.get('latitude', '')).strip()
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# 处理 "NULL" 字符串
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if longitude.lower() == "null" or longitude == "":
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longitude = "N/A"
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if latitude.lower() == "null" or latitude == "":
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latitude = "N/A"
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# 格式化坐标显示
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row_data['coordinates'] = f"{longitude[:12]}\n{latitude[:12]}"
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# 处理idc_tickcount_ms
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row_data['idc_tickcount_ms'] = base_info.get('idc_tickcount_ms_int', 0)
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# 处理速度 - 可能为 "NULL" 字符串
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speed = str(base_info.get('speed', '')).strip()
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if speed.lower() == "null" or speed == "":
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speed = "0"
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row_data['speed'] = speed
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# 处理incident信息 - 新格式没有Incident__description
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row_data['incident'] = {
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"name": base_info.get('incident_name', 'Unknown Incident'),
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"description": "" # 使用incident_name作为描述
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}
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# 处理信号数据
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row_data['signals'] = []
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for key, value in item["signal_data"].items():
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signals_name_value = {}
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signals_name_value["name"] = key
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# 新格式使用signal_value字段
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signal_value = value.get('signal_value', '')
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if signal_value is None or signal_value == "":
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signal_value = "N/A"
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signals_name_value["value"] = signal_value
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# 匹配到meanning的值
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meaning_result=match_meaning(key,signal_value)
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# 新格式没有Meaning字段，使用空字符串
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signals_name_value["meaning"] = meaning_result
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row_data['signals'].append(signals_name_value)
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# === 新增：对signals列表按name字段进行字符顺序排序 ===
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row_data['signals'] = sorted(row_data['signals'], key=lambda x: x['name'])
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result.append(row_data)
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return result
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def convert_to_histro_data(raw_data):
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"""
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转换原始数据为前端折线图格式（time保留%Y-%m-%d %H:%M:%S.%f格式）
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:param raw_data: 原始字典列表
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:return: 前端所需格式的列表
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"""
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# 步骤1：按signal分组
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|||
|
|
signal_groups = defaultdict(list)
|
|||
|
|
for item in raw_data:
|
|||
|
|
signal_groups[item["signal"]].append(item)
|
|||
|
|
|
|||
|
|
# 步骤2：处理每个分组，构造最终数据
|
|||
|
|
chart_data = []
|
|||
|
|
for signal_name, items in signal_groups.items():
|
|||
|
|
# 修复1：统一使用start_ts字段进行排序（因为这是时间轴数据）
|
|||
|
|
def sort_by_start_ts(item):
|
|||
|
|
return datetime.strptime(item["time"], "%Y-%m-%d %H:%M:%S.%f")
|
|||
|
|
|
|||
|
|
sorted_items = sorted(items, key=sort_by_start_ts)
|
|||
|
|
|
|||
|
|
# 修复3：统一使用time作为X轴时间数据（与排序字段一致）
|
|||
|
|
time_list = [item["time"] for item in sorted_items] # 使用start_ts作为时间轴
|
|||
|
|
values_list = [float(item["value"]) for item in sorted_items] # value转数值
|
|||
|
|
|
|||
|
|
# 子步骤3：构造当前signal的折线图数据
|
|||
|
|
chart_item = {
|
|||
|
|
"signalName": signal_name,
|
|||
|
|
"time": time_list, # X轴时间数据
|
|||
|
|
"values": values_list # Y轴数值数据
|
|||
|
|
}
|
|||
|
|
chart_data.append(chart_item)
|
|||
|
|
|
|||
|
|
return chart_data
|
|||
|
|
|
|||
|
|
|
|||
|
|
def convert_to_histro_data_v2(raw_data):
|
|||
|
|
"""
|
|||
|
|
转换原始数据为前端图表格式 {x: 时间, y: 数值, value: 数值}
|
|||
|
|
:param raw_data: 原始字典列表，需包含 "signal"、"time"、"value" 字段
|
|||
|
|
:return: 字典（key=signal名称，value=对应{x,y,value}格式的列表）；
|
|||
|
|
若需合并所有signal为单列表，可取消注释对应逻辑
|
|||
|
|
"""
|
|||
|
|
# 步骤1：按signal字段分组
|
|||
|
|
signal_groups = defaultdict(list)
|
|||
|
|
for item in raw_data:
|
|||
|
|
signal_groups[item["signal"]].append(item)
|
|||
|
|
|
|||
|
|
# 步骤2：处理每个分组，构造目标格式数据
|
|||
|
|
chart_data = {}
|
|||
|
|
for signal_name, items in signal_groups.items():
|
|||
|
|
# 按time字段排序（保证时间轴顺序）
|
|||
|
|
def sort_by_time(item):
|
|||
|
|
return datetime.strptime(item["time"], "%Y-%m-%d %H:%M:%S.%f")
|
|||
|
|
|
|||
|
|
sorted_items = sorted(items, key=sort_by_time)
|
|||
|
|
|
|||
|
|
# 构造 {x: 时间, y: 数值, value: 数值} 格式的列表
|
|||
|
|
signal_item_list = []
|
|||
|
|
for item in sorted_items:
|
|||
|
|
val = float(item["value"]) # 确保数值类型为浮点数
|
|||
|
|
signal_item_list.append({
|
|||
|
|
"x": item["time"], # x轴：时间字符串（对应示例中的"销量4"类标签）
|
|||
|
|
"y": item['valueExplanation'], # y轴：原始value数值
|
|||
|
|
"value": val # value字段：与y轴数值一致（匹配示例格式）
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
# ========== 新增逻辑开始 ==========
|
|||
|
|
# 获取排序后第一条原始数据
|
|||
|
|
first_raw_item = sorted_items[0]
|
|||
|
|
# 判断is_pre_value_needed是否非空（处理空字符串、None、"NULL"等情况）
|
|||
|
|
is_needed = first_raw_item.get("is_pre_value_needed", "")
|
|||
|
|
if is_needed and is_needed.strip() and is_needed != "NULL":
|
|||
|
|
try:
|
|||
|
|
# 提取pre相关值并转换类型
|
|||
|
|
pre_val = float(first_raw_item["pre_value"])
|
|||
|
|
pre_explanation = first_raw_item["pre_valueExplanation"]
|
|||
|
|
first_time = first_raw_item["time"] # 在第一条数据的时间基础上减10秒
|
|||
|
|
first_time_dt = datetime.strptime(first_time, "%Y-%m-%d %H:%M:%S.%f")
|
|||
|
|
pre_time_dt = first_time_dt - timedelta(seconds=10)
|
|||
|
|
pre_time_str = pre_time_dt.strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
|
|||
|
|
# 构造前置数据项
|
|||
|
|
pre_item = {
|
|||
|
|
"x": '', # x轴：第一条数据的时间
|
|||
|
|
"y": pre_explanation, # y轴：pre_value对应的说明
|
|||
|
|
"value": pre_val, # value字段：pre_value的数值
|
|||
|
|
"show":1
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
# 插入到列表最前面
|
|||
|
|
signal_item_list.insert(0, pre_item)
|
|||
|
|
except KeyError as e:
|
|||
|
|
print(f"警告：第一条数据缺少{e}字段，跳过前置数据插入")
|
|||
|
|
except ValueError as e:
|
|||
|
|
print(f"警告：pre_value转换浮点数失败({e})，跳过前置数据插入")
|
|||
|
|
# ========== 新增逻辑结束 ==========
|
|||
|
|
|
|||
|
|
# 按signal名称存储结果
|
|||
|
|
chart_data[signal_name] = signal_item_list
|
|||
|
|
|
|||
|
|
return chart_data
|
|||
|
|
|
|||
|
|
|
|||
|
|
def match_meaning(singla_name,value):
|
|||
|
|
import os
|
|||
|
|
current_dir = os.path.dirname(os.path.abspath(__file__))
|
|||
|
|
json_path = os.path.join(current_dir, "singal_value.json")
|
|||
|
|
with open(json_path, "r", encoding="utf-8") as f:
|
|||
|
|
mean_data = json.load(f)
|
|||
|
|
|
|||
|
|
singla=singla_name.split('.')[-1]
|
|||
|
|
meaning_state=mean_data.get(singla,None)
|
|||
|
|
if meaning_state:
|
|||
|
|
return meaning_state[value] if meaning_state[value] else ''
|
|||
|
|
else:
|
|||
|
|
return ''
|
|||
|
|
|
|||
|
|
|