更新OCR版本，Bata版，还不能上线
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@@ -2,9 +2,9 @@ import jieba
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from paddlenlp import Taskflow
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from paddleocr import PaddleOCR
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'''
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"""
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项目配置
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'''
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"""
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# 每次从数据库获取的案子数量
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PHHD_BATCH_SIZE = 10
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# 没有查询到案子的等待时间(分钟)
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@@ -18,35 +18,35 @@ LAYOUT_ANALYSIS = False
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信息抽取关键词配置
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"""
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# 患者姓名
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PATIENT_NAME = ['患者姓名']
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PATIENT_NAME = ["患者姓名"]
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# 入院日期
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ADMISSION_DATE = ['入院日期']
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ADMISSION_DATE = ["入院日期"]
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# 出院日期
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DISCHARGE_DATE = ['出院日期']
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DISCHARGE_DATE = ["出院日期"]
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# 发生医疗费
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MEDICAL_EXPENSES = ['费用总额']
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MEDICAL_EXPENSES = ["费用总额"]
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# 个人现金支付
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PERSONAL_CASH_PAYMENT = ['个人现金支付']
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PERSONAL_CASH_PAYMENT = ["个人现金支付"]
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# 个人账户支付
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PERSONAL_ACCOUNT_PAYMENT = ['个人账户支付']
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PERSONAL_ACCOUNT_PAYMENT = ["个人账户支付"]
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# 个人自费金额
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PERSONAL_FUNDED_AMOUNT = ['自费金额', '个人自费']
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PERSONAL_FUNDED_AMOUNT = ["自费金额", "个人自费"]
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# 医保类别
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MEDICAL_INSURANCE_TYPE = ['医保类型']
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MEDICAL_INSURANCE_TYPE = ["医保类型"]
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# 就诊医院
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HOSPITAL = ['医院']
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HOSPITAL = ["医院"]
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# 就诊科室
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DEPARTMENT = ['科室']
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DEPARTMENT = ["科室"]
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# 主治医生
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DOCTOR = ['主治医生']
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DOCTOR = ["主治医生"]
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# 住院号
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ADMISSION_ID = ['住院号']
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ADMISSION_ID = ["住院号"]
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# 医保结算单号码
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SETTLEMENT_ID = ['医保结算单号码']
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SETTLEMENT_ID = ["医保结算单号码"]
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# 年龄
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AGE = ['年龄']
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AGE = ["年龄"]
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# 大写总额
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UPPERCASE_MEDICAL_EXPENSES = ['大写总额']
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UPPERCASE_MEDICAL_EXPENSES = ["大写总额"]
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SETTLEMENT_LIST_SCHEMA = \
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(PATIENT_NAME + ADMISSION_DATE + DISCHARGE_DATE + MEDICAL_EXPENSES + PERSONAL_CASH_PAYMENT
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@@ -58,57 +58,55 @@ DISCHARGE_RECORD_SCHEMA = \
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COST_LIST_SCHEMA = PATIENT_NAME + ADMISSION_DATE + DISCHARGE_DATE + MEDICAL_EXPENSES
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'''
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"""
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别名配置
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'''
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"""
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# 使用别名中的value替换key。考虑到效率问题，只会替换第一个匹配到的key。
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HOSPITAL_ALIAS = {
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'沐阳': ['沭阳'],
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'连水': ['涟水'],
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'唯宁': ['睢宁'], # 雕宁
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'九〇四': ['904'],
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'漂水': ['溧水'],
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"沐阳": ["沭阳"],
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"连水": ["涟水"],
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"唯宁": ["睢宁"], # 雕宁
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"九〇四": ["904"],
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"漂水": ["溧水"],
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}
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DEPARTMENT_ALIAS = {
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'耳鼻喉': ['耳鼻咽喉'],
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'急症': ['急诊'],
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"耳鼻喉": ["耳鼻咽喉"],
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"急症": ["急诊"],
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}
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'''
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"""
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搜索过滤配置
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'''
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"""
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# 默认会过滤单字
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HOSPITAL_FILTER = ['医院', '人民', '第一', '第二', '第三', '大学', '附属']
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HOSPITAL_FILTER = ["医院", "人民", "第一", "第二", "第三", "大学", "附属"]
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DEPARTMENT_FILTER = ['医', '伤', '西', '新']
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DEPARTMENT_FILTER = ["医", "伤", "西", "新"]
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'''
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"""
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分词配置
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'''
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jieba.suggest_freq(('肿瘤', '医院'), True)
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jieba.suggest_freq(('骨', '伤'), True)
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jieba.suggest_freq(('感染', '性'), True)
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jieba.suggest_freq(('胆', '道'), True)
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jieba.suggest_freq(('脾', '胃'), True)
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"""
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jieba.suggest_freq(("肿瘤", "医院"), True)
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jieba.suggest_freq(("骨", "伤"), True)
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jieba.suggest_freq(("感染", "性"), True)
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jieba.suggest_freq(("胆", "道"), True)
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jieba.suggest_freq(("脾", "胃"), True)
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'''
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"""
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模型配置
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'''
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SETTLEMENT_IE = Taskflow('information_extraction', schema=SETTLEMENT_LIST_SCHEMA, model='uie-x-base',
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task_path='model/settlement_list_model', layout_analysis=LAYOUT_ANALYSIS, precision='fp16')
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DISCHARGE_IE = Taskflow('information_extraction', schema=DISCHARGE_RECORD_SCHEMA, model='uie-x-base',
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task_path='model/discharge_record_model', layout_analysis=LAYOUT_ANALYSIS, precision='fp16')
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COST_IE = Taskflow('information_extraction', schema=COST_LIST_SCHEMA, model='uie-x-base', device_id=1,
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task_path='model/cost_list_model', layout_analysis=LAYOUT_ANALYSIS, precision='fp16')
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"""
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SETTLEMENT_IE = Taskflow("information_extraction", schema=SETTLEMENT_LIST_SCHEMA, model="uie-x-base",
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task_path="model/settlement_list_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16")
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DISCHARGE_IE = Taskflow("information_extraction", schema=DISCHARGE_RECORD_SCHEMA, model="uie-x-base",
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task_path="model/discharge_record_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16")
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COST_IE = Taskflow("information_extraction", schema=COST_LIST_SCHEMA, model="uie-x-base", device_id=1,
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task_path="model/cost_list_model", layout_analysis=LAYOUT_ANALYSIS, precision="fp16")
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OCR = PaddleOCR(
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gpu_id=1,
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use_angle_cls=False,
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show_log=False,
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det_db_thresh=0.1,
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det_db_box_thresh=0.3,
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det_limit_side_len=1248,
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drop_score=0.3,
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rec_model_dir='model/ocr/openatom_rec_repsvtr_ch_infer',
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rec_algorithm='SVTR_LCNet',
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)
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device="gpu:0",
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ocr_version="PP-OCRv4",
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use_textline_orientation=False,
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# 检测像素阈值，输出的概率图中，得分大于该阈值的像素点才会被认为是文字像素点
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text_det_thresh=0.1,
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# 检测框阈值，检测结果边框内，所有像素点的平均得分大于该阈值时，该结果会被认为是文字区域
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text_det_box_thresh=0.3,
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)
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