227 lines
9.4 KiB
Python
227 lines
9.4 KiB
Python
import json
|
||
import re
|
||
import string
|
||
from collections import OrderedDict
|
||
from collections import defaultdict
|
||
#传输技术参数需求的时候后处理
|
||
def extract_matching_keys(data, good_list, special_keys=None, parent_key=''):
|
||
def get_suffix(n):
|
||
"""
|
||
根据数字n返回对应的字母后缀。
|
||
1 -> 'a', 2 -> 'b', ..., 26 -> 'z', 27 -> 'aa', 28 -> 'ab', ...
|
||
"""
|
||
suffix = ''
|
||
while n > 0:
|
||
n, r = divmod(n - 1, 26)
|
||
suffix = chr(97 + r) + suffix
|
||
return suffix
|
||
|
||
def count_matching_keys(data, patterns, special_keys, counter=None):
|
||
"""递归统计匹配键的出现次数，仅统计值为列表的键"""
|
||
if counter is None:
|
||
counter = defaultdict(int)
|
||
|
||
if isinstance(data, dict):
|
||
for key, value in data.items():
|
||
if isinstance(value, list):
|
||
if key not in special_keys and any(pattern.match(key) for pattern in patterns):
|
||
counter[key] += 1
|
||
elif isinstance(value, dict):
|
||
count_matching_keys(value, patterns, special_keys, counter)
|
||
elif isinstance(data, list):
|
||
for item in data:
|
||
if isinstance(item, (dict, list)):
|
||
count_matching_keys(item, patterns, special_keys, counter)
|
||
|
||
return counter
|
||
|
||
def process_data(data, patterns, special_keys, key_counter, suffix_map, filtered_data, parent_key):
|
||
"""递归处理数据并构建结果"""
|
||
|
||
def get_suffix_label(key):
|
||
suffix_map[key] += 1
|
||
return get_suffix(suffix_map[key])
|
||
|
||
if isinstance(data, dict):
|
||
for key, value in data.items():
|
||
if isinstance(value, list):
|
||
# 处理值为列表的键
|
||
if any(pattern.match(key) for pattern in patterns):
|
||
new_key = generate_key(key, parent_key, key_counter, suffix_map, special_keys)
|
||
filtered_data[new_key] = value
|
||
elif isinstance(value, dict):
|
||
# 继续递归处理嵌套字典
|
||
new_parent_key = key if parent_key == '' else f"{parent_key}的{key}"
|
||
process_data(value, patterns, special_keys, key_counter, suffix_map,
|
||
filtered_data, new_parent_key)
|
||
elif isinstance(data, list):
|
||
for item in data:
|
||
if isinstance(item, (dict, list)):
|
||
process_data(item, patterns, special_keys, key_counter, suffix_map,
|
||
filtered_data, parent_key)
|
||
|
||
def generate_key(key, parent_key, key_counter, suffix_map, special_keys):
|
||
"""生成新的键名"""
|
||
if key in special_keys and parent_key:
|
||
return f"{parent_key}的{key}"
|
||
elif key_counter[key] > 1:
|
||
suffix = get_suffix(suffix_map[key] + 1)
|
||
suffix_map[key] += 1
|
||
return f"{key}-{suffix}"
|
||
return key
|
||
|
||
if special_keys is None:
|
||
special_keys = []
|
||
|
||
patterns = [re.compile(r'^' + re.escape(g) + r'(?:-\d+)?$') for g in good_list]
|
||
|
||
# 先统计所有匹配键的出现次数，仅统计值为列表的键
|
||
key_counter = count_matching_keys(data, patterns, special_keys)
|
||
|
||
# 初始化后缀映射
|
||
suffix_map = {key: 0 for key, count in key_counter.items() if count > 1}
|
||
|
||
# 用于存储最终结果
|
||
filtered_data = {}
|
||
|
||
# 递归处理数据
|
||
process_data(data, patterns, special_keys, key_counter, suffix_map, filtered_data, parent_key)
|
||
|
||
return filtered_data
|
||
|
||
def postprocess(data):
|
||
"""递归地转换字典中的值为列表，如果所有键对应的值都是'/', '{}' 或 '未知'"""
|
||
def convert_dict(value):
|
||
# 如果所有值是'/', '{}' 或 '未知'
|
||
if all(v in ['/', '未知', {}] for v in value.values()):
|
||
return list(value.keys())
|
||
else:
|
||
# 如果不满足条件，则递归处理嵌套的字典
|
||
return {k: convert_dict(v) if isinstance(v, dict) else v for k, v in value.items()}
|
||
|
||
# 递归处理顶层数据
|
||
return {key: convert_dict(val) if isinstance(val, dict) else val for key, val in data.items()}
|
||
|
||
|
||
def all_postprocess(data):
|
||
temp = restructure_data(data)
|
||
|
||
def recursive_process(item):
|
||
if isinstance(item, dict):
|
||
return {k: recursive_process(v) for k, v in item.items()}
|
||
elif isinstance(item, list):
|
||
return remove_common_prefixes(item)
|
||
else:
|
||
return item
|
||
|
||
processed_data = recursive_process(temp)
|
||
return processed_data
|
||
def detect_depth(data):
|
||
"""
|
||
Detects the depth of the nested dictionary.
|
||
"""
|
||
if isinstance(data, dict):
|
||
return 1 + max((detect_depth(v) for v in data.values()), default=0)
|
||
elif isinstance(data, list):
|
||
return 1 # Lists are considered the terminal layer
|
||
else:
|
||
return 0 # Base case for non-nested elements
|
||
|
||
|
||
def restructure_data(data):
|
||
"""
|
||
重构数据以标准化嵌套层级。
|
||
如果整个数据都是2层结构，则保持原样返回。
|
||
如果同时存在2层和3层结构，则将所有数据重构为3层格式。
|
||
如果存在3层以上，统一处理为3层；取内层，舍弃外层
|
||
"""
|
||
# 检查数据是否包含混合的2层和3层结构
|
||
has_two_layers = False
|
||
has_three_layers = False
|
||
|
||
for key, value in data.items():
|
||
if isinstance(value, dict):
|
||
has_three_layers = True
|
||
elif isinstance(value, list):
|
||
has_two_layers = True
|
||
else:
|
||
raise ValueError(f"键'{key}'的数据格式异常: {type(value)}")
|
||
|
||
# 如果只有2层结构，直接返回原数据
|
||
if has_two_layers and not has_three_layers:
|
||
return data
|
||
|
||
# 如果是混合结构或仅有3层结构，统一标准化为3层
|
||
structured_data = {}
|
||
for key, value in data.items():
|
||
if isinstance(value, dict):
|
||
# 已经是3层结构，保持不变
|
||
structured_data[key] = value
|
||
elif isinstance(value, list):
|
||
# 将2层结构转换为3层结构
|
||
structured_data[key] = {key: value}
|
||
|
||
return structured_data
|
||
|
||
# 定义获取所有以'：'结尾的前缀的函数
|
||
def get_prefixes(s):
|
||
prefixes = []
|
||
for i in range(len(s)):
|
||
if s[i] == '：':
|
||
prefixes.append(s[:i+1])
|
||
return prefixes
|
||
|
||
# 定义删除公共前缀的函数
|
||
def remove_common_prefixes(string_list):
|
||
# 构建前缀到字符串集合的映射
|
||
prefix_to_strings = {}
|
||
for s in string_list:
|
||
prefixes = get_prefixes(s)
|
||
unique_prefixes = set(prefixes)
|
||
for prefix in unique_prefixes:
|
||
if prefix not in prefix_to_strings:
|
||
prefix_to_strings[prefix] = set()
|
||
prefix_to_strings[prefix].add(s)
|
||
# 找出至少在两个字符串中出现的前缀
|
||
prefixes_occuring_in_multiple_strings = [prefix for prefix, strings in prefix_to_strings.items() if len(strings) >=2]
|
||
# 对每个字符串，找到其匹配的最长前缀并删除
|
||
new_string_list = []
|
||
for s in string_list:
|
||
applicable_prefixes = [prefix for prefix in prefixes_occuring_in_multiple_strings if s.startswith(prefix)]
|
||
if applicable_prefixes:
|
||
# 找到最长的前缀
|
||
longest_prefix = max(applicable_prefixes, key=len)
|
||
# 删除前缀
|
||
new_s = s[len(longest_prefix):]
|
||
new_string_list.append(new_s)
|
||
else:
|
||
new_string_list.append(s)
|
||
return new_string_list
|
||
|
||
if __name__ == "__main__":
|
||
# 示例数据
|
||
sample_data = {
|
||
"交通信号机": [
|
||
"★应采用区域控制信号机，并应与广水市交通信号控制系统兼容，信号机能接入已有系统平台，实现联网优化功能。",
|
||
"1、控制功能：（1）区域协调控制：可对单个孤立交叉口、干道多个交叉口和关联性较强的交叉口群进行综合性地信号控制。",
|
||
"1、控制功能：（2）线性协调控制：可对干道多个相邻交叉口进行协调控制。",
|
||
"1、控制功能：（3）多时段控制：可根据交叉口的交通状况，将每天划分为多个不同的时段，每个时段配置不同的控制方案，能设置至少 10个时段、10种以上不同控制方案，能根据不同周日类型对方案进行调整。信号机能够根据内置时钟选择各个时段的控制方案，实现交叉口的合理控制。",
|
||
"2、采集功能：（1）信号机支持接入线圈、地磁、视频、微波、超声波检测器、RFID等多种检测方式。",
|
||
"2、采集功能：（2）信号机支持交通信息采集与统计,并支持交通流量共享。",
|
||
"3、运维功能：（1）信号机能够自动检测地磁故障，若故障，能够自动上传故障信息至监控中心。"
|
||
],
|
||
"高清视频抓拍像机": [
|
||
"1：摄像机：有效像素：≥900W像素",
|
||
"1：摄像机：最低照度：彩色≤0.001lx",
|
||
"1：摄像机：传感器类型：≥1英寸全局曝光 COMS/GMOS/GS COMS",
|
||
"1：摄像机：电子快门：至少满足 1/25s至 1/100,000s，可调",
|
||
"2：视频图像：视频压缩标准：至少支持 H264、H265等",
|
||
"2：视频图像：视频分辨率：≥4096×2160，向下可设置",
|
||
],
|
||
}
|
||
# 处理数据
|
||
result = all_postprocess(sample_data)
|
||
# 输出处理结果
|
||
print(json.dumps(result,ensure_ascii=False,indent=4))
|
||
|