阅读说明：本技术 业务数据处理方法、上传方法及装置、设备、存储介质 (Service data processing method, uploading method, device, equipment and storage medium ) 是由 赵勇 于 2021-08-30 设计创作，主要内容包括：本实施例涉及数字医疗技术领域,公开了一种业务数据处理方法、上传方法及装置、设备、存储介质。业务数据处理方法包括：收集医院端的业务数据以及获取监管平台的监管数据；对监管数据进行特征提取,得到监管通用标准,其中,监管通用标准包括：监管通用字段组和对应每一医院端的标准类型；对业务数据进行状态校验筛选,以得到校验数据,其中,校验数据包括完结数据；根据标准类型对校验数据进行分类处理,得到对应每一医院端的分类数据；根据监管通用字段组对分类数据进行标准化封装处理,以得到对应每一医院端的通用数据包。本申请实施例的业务数据处理方法,降低了监管平台对医疗数据的监管难度,便于监管平台对医疗数据的监管。(The embodiment relates to the technical field of digital medical treatment, and discloses a service data processing method, an uploading method, a device, equipment and a storage medium. The service data processing method comprises the following steps: collecting service data of a hospital end and acquiring supervision data of a supervision platform; carrying out feature extraction on the supervision data to obtain a supervision universal standard, wherein the supervision universal standard comprises: a supervision general field group and a standard type corresponding to each hospital end; performing state checking and screening on the service data to obtain checking data, wherein the checking data comprises finishing data; classifying the check data according to the standard type to obtain classified data corresponding to each hospital end; and carrying out standardized packaging processing on the classified data according to the supervision general field group to obtain a general data packet corresponding to each hospital end. The business data processing method reduces the supervision difficulty of the supervision platform on the medical data, and facilitates supervision of the supervision platform on the medical data.)

1. A method for processing service data is characterized by comprising the following steps:

collecting service data of a hospital end and acquiring supervision data of a supervision platform;

carrying out feature extraction on the supervision data to obtain a supervision universal standard, wherein the supervision universal standard comprises: a supervision general field group and a standard type corresponding to each hospital end;

performing state checking and screening on the service data to obtain checking data, wherein the checking data comprises finishing data;

classifying the verification data according to the standard type to obtain classification data corresponding to each hospital end;

and carrying out standardized packaging processing on the classified data according to the supervision general field group to obtain a general data packet corresponding to each hospital end.

2. The method of claim 1, wherein said performing feature extraction on said regulatory data to obtain a regulatory universal standard comprises:

semantic feature extraction is carried out on the supervision data to obtain data fields;

carrying out frequency statistics on the data fields to obtain field frequencies;

and screening the data fields according to the field frequency to obtain the supervision general field group.

3. The method of claim 1 or 2, wherein said performing feature extraction on said regulatory data to obtain a regulatory universal standard further comprises:

performing relevance feature extraction on the supervision data to obtain a relevance feature vector representing the relevance relationship between each hospital end and the supervision platform;

and matching the hospital side and the supervision data according to the associated feature vectors to obtain the standard type of each hospital side.

4. The method of claim 1 or 2, wherein the status-check screening comprises a first type of status-check screening and a second type of status-check screening;

the performing state checking and screening on the service data to obtain checking data includes:

performing first-class state check screening on the service data by using the first-class state code to obtain first-class check data;

and carrying out second type state check screening on the first type check data by using second type state codes to obtain the check data, wherein the first type state codes and the second type state codes are different state codes.

5. A service data uploading method is characterized by comprising the following steps:

acquiring a general data packet of a hospital end, wherein the general data packet is a data packet processed according to the method of any one of claims 1 to 4;

acquiring a data uploading rule of a supervision platform and a mapping rule of the supervision platform and the hospital side;

integrating the general data packet based on the mapping rule and the data uploading rule to obtain a data packet to be uploaded;

and uploading the data packet to be uploaded.

6. The method of claim 5, wherein the mapping rules include encryption rules and customized processing rules;

the integrating the general data packet based on the mapping rule to obtain a data packet to be uploaded comprises the following steps:

customizing the general data packet according to the customizing processing rule to obtain a customizing data packet;

converting the customized data packet based on the data uploading rule to obtain a recombined data packet;

and encrypting the recombined data packet according to the encryption rule to obtain a data packet to be uploaded.

7. A service data processing apparatus, comprising:

the collection module is used for collecting the business data of the hospital end;

the collection module is also used for acquiring supervision data of the supervision platform;

an extraction module, configured to perform feature extraction on the supervision data to obtain a universal supervision standard, where the universal supervision standard includes: a supervision general field group and a standard type corresponding to each hospital end;

the verification screening module is used for performing state verification screening on the service data to obtain verification data, wherein the verification data comprises finishing data;

the classification module is used for classifying the check data according to the standard type to obtain classification data corresponding to each hospital end;

and the first processing module is used for carrying out standardized packaging processing on the classified data according to the supervision general field set so as to obtain a general data packet corresponding to each hospital end.

8. A service data uploading apparatus, comprising:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module acquires a general data packet of a hospital end, and the general data packet is a data packet obtained by processing according to the method of any one of claims 1 to 4;

the second acquisition module is used for acquiring a data uploading rule of a supervision platform and a mapping rule of the supervision platform and the hospital side;

the second processing module is used for integrating the general data packet based on the mapping rule and the data uploading rule to obtain a data packet to be uploaded;

and the uploading module is used for uploading the data packet to be uploaded.

9. An electronic device, comprising:

at least one memory;

at least one processor;

at least one program;

the programs are stored in the memory, and the processor executes the at least one program to implement:

the business data processing method of any one of claims 1 to 4;

or

A method for uploading service data as claimed in any of claims 5 to 6.

10. A storage medium that is a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform:

the business data processing method of any one of claims 1 to 4;

or

A method for uploading service data as claimed in any of claims 5 to 6.

Technical Field

The present application relates to the field of digital medical technology, and in particular, to a method and an apparatus for processing and uploading service data, a device, and a storage medium.

Background

In the actual operation process of the internet hospital, a supervision platform assigned by the health service committee needs to be accepted to perform operations such as license plate acceptance and service data monitoring.

However, since the name and the code of each hospital and the supervision platform for the same kind of data are different, the supervision difficulty of the supervision platform for the medical data is increased, and the supervision platform cannot effectively supervise the medical data, which results in inaccuracy of medical status assessment.

Disclosure of Invention

The main purpose of the embodiments of the present disclosure is to provide a service data processing method, an uploading device, a device, and a storage medium, so as to reduce the difficulty of the supervision platform in supervising the medical data, and facilitate the supervision of the supervision platform on the medical data.

In order to achieve the above object, a first aspect of the embodiments of the present disclosure provides a method for processing service data, including:

collecting service data of a hospital end and acquiring supervision data of a supervision platform;

carrying out feature extraction on the supervision data to obtain a supervision universal standard, wherein the supervision universal standard comprises: a supervision general field group and a standard type corresponding to each hospital end;

performing state checking and screening on the service data to obtain checking data, wherein the checking data comprises finishing data;

classifying the check data according to the standard type to obtain classified data corresponding to each hospital end;

and carrying out standardized packaging processing on the classified data according to the supervision general field group to obtain a general data packet corresponding to each hospital end.

In some embodiments, feature extraction is performed on the regulatory data to obtain a regulatory universal standard, including:

semantic feature extraction is carried out on the supervision data to obtain data fields;

carrying out frequency statistics on the data fields to obtain field frequencies;

and screening the data fields according to the field frequency to obtain a supervision general field group.

In some embodiments, the performing feature extraction on the supervision data to obtain a supervision universal standard further includes:

performing relevance feature extraction on the supervision data to obtain a relevance feature vector representing the relevance relationship between each hospital end and the supervision platform;

and matching the hospital side and the supervision data according to the associated feature vectors to obtain the standard type of each hospital side.

In some embodiments, the status-checking screening includes a first type of status-checking screening and a second type of status-checking screening;

performing state checking and screening on the service data to obtain checking data, including:

performing first-class state check screening on the service data by using the first-class state code to obtain first-class check data;

and carrying out second type state check screening on the first type check data by using a second type state code to obtain check data, wherein the first type state code and the second type state code are different state codes.

In order to achieve the above object, a second aspect of the embodiments of the present disclosure provides a method for uploading service data, including:

acquiring a general data packet of a hospital end, wherein the general data packet is a data packet obtained by processing according to any one of the methods in the embodiments of the first aspect;

acquiring a data uploading rule of a supervision platform and a mapping rule of the supervision platform and a hospital side;

integrating the general data packet based on the mapping rule and the data uploading rule to obtain a data packet to be uploaded;

and uploading the data packet to be uploaded.

In some embodiments, the mapping rules include encryption rules and customized processing rules;

integrating the general data packet based on the mapping rule to obtain a data packet to be uploaded, comprising the following steps:

customizing the general data packet according to the customizing processing rule to obtain a customizing data packet;

converting the customized data packet based on the data uploading rule to obtain a recombined data packet;

and encrypting the data packet to be uploaded according to the encryption rule to obtain the data packet to be uploaded.

In order to achieve the above object, a third aspect of the embodiments of the present disclosure provides a service data processing apparatus, including:

the collection module is used for collecting the business data of the hospital end;

the collection module is also used for acquiring supervision data of the supervision platform;

the extraction module is used for carrying out feature extraction on the supervision data to obtain a supervision general standard, wherein the supervision general standard comprises: a supervision general field group and a standard type corresponding to each hospital end;

the verification screening module is used for performing state verification screening on the service data to obtain verification data, wherein the verification data comprises finishing data;

the classification module is used for classifying the check data according to the standard type to obtain classification data corresponding to each hospital end;

and the first processing module is used for carrying out standardized packaging processing on the classified data according to the supervision general field set so as to obtain a general data packet corresponding to each hospital end.

In order to achieve the above object, a fourth aspect of the embodiments of the present disclosure provides a service data uploading apparatus, including:

a second obtaining module, configured to obtain a general data packet at a hospital end, where the general data packet is a data packet obtained by any one of the methods in the embodiments of the first aspect;

the third acquisition module is used for acquiring the data uploading rule of the supervision platform and the mapping rule of the supervision platform and the hospital side;

the second processing module is used for integrating the general data packet based on the mapping rule and the data uploading rule to obtain a data packet to be uploaded;

and the uploading module is used for uploading the data packet to be uploaded.

To achieve the above object, a fifth aspect of an embodiment of the present disclosure provides an electronic device, including:

at least one memory;

at least one processor;

at least one program;

the program is stored in the memory, and the processor executes at least one program to implement the business data processing method of the first aspect of the embodiment of the present disclosure to implement the business data uploading method of the second aspect of the embodiment of the present disclosure.

To achieve the above object, a sixth aspect of embodiments of the present disclosure proposes a storage medium which is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform:

the service data processing method according to the first aspect and the service data uploading method according to the second aspect are described above.

The service data processing method, the uploading device, the equipment and the storage medium provided by the embodiment of the disclosure are characterized in that the supervision data of the supervision platform is subjected to feature extraction to obtain a supervision general field group and a standard type of each hospital end, the service data is subjected to state verification screening to obtain check data, then the check data is classified according to the standard type to obtain classified data corresponding to each hospital end, then the classified data is subjected to standardized encapsulation processing according to the supervision general field, and the data of each hospital end is encapsulated into a general data packet, so that the supervision platform supervises the general data packet, the supervision difficulty of the supervision platform on medical data is reduced, and the supervision platform supervises the medical data conveniently.

Drawings

Fig. 1 is a flowchart of a service data processing method provided in an embodiment of the present application.

Fig. 2 is a flowchart of step S200 in fig. 1.

Fig. 3 is another flowchart of step S200 in fig. 1.

Fig. 4 is a flowchart of step S300 in fig. 1.

Fig. 5 is a flowchart of a service data uploading method provided in an embodiment of the present application.

Fig. 6 is a flowchart of step S800 in fig. 5.

Fig. 7 is a block diagram of a service data processing apparatus according to an embodiment of the present application.

Fig. 8 is a block diagram of a service data uploading apparatus according to an embodiment of the present application.

Fig. 9 is a schematic diagram of a hardware structure of an electronic device provided in an embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.

First, several terms referred to in the present application are resolved:

artificial Intelligence (AI): is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produces a new intelligent machine that can react in a manner similar to human intelligence, and research in this field includes robotics, language recognition, image recognition, natural language processing, and expert systems, among others. The artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.

Natural Language Processing (NLP): NLP uses computer to process, understand and use human language (such as chinese, english, etc.), and belongs to a branch of artificial intelligence, which is a cross discipline between computer science and linguistics, also commonly called computational linguistics. Natural language processing includes parsing, semantic analysis, discourse understanding, and the like. Natural language processing is commonly used in the technical fields of machine translation, character recognition of handwriting and print, speech recognition and text-to-speech conversion, information retrieval, information extraction and filtering, text classification and clustering, public opinion analysis and opinion mining, and relates to data mining, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, linguistic research related to language calculation, and the like, which are related to language processing.

The Medical cloud is a Medical cloud platform which is created by using cloud computing on the basis of new technologies such as cloud computing, mobile technology, multimedia, 4G communication, big data, internet of things and the like and combining Medical technology, and Medical resources are shared and the Medical scope is expanded. Due to the combination of the cloud computing technology, the medical cloud improves the efficiency of medical institutions and brings convenience to residents to see medical advice. Like the appointment register, the electronic medical record, the medical insurance and the like of the existing hospital are all products combining cloud computing and the medical field, and the medical cloud also has the advantages of data security, information sharing, dynamic expansion and overall layout.

Convolutional Neural Networks (CNN) are a class of feed Forward Neural Networks (FNN) that contain convolution computations and have a deep structure, and are one of the representative algorithms for deep learning (deep learning). Since convolutional Neural Networks are capable of Shift-Invariant classification, they are also referred to as "Shift-Invariant Artificial Neural Networks (SIANN)". The artificial neurons of the convolutional neural network can respond to a part of the surrounding cells within the coverage range, and have excellent performance on large-scale image processing. With the proposal of deep learning theory and the improvement of numerical computation equipment, the convolutional neural network is rapidly developed and applied to the fields of computer vision, natural language processing and the like.

Dubbo: the Dubbo is an open-source high-performance service framework, and the application can realize the output and input functions of the service through a high-performance Remote Procedure Call (RPC), and can be seamlessly integrated with the Spring framework. The remote procedure call system mainly comprises Remoting, RPC and a registration service center (registration), wherein the Remoting is a network communication framework and is used for realizing a synchronization-over-async (sync-async) and message reply (request-response) mechanism, the RPC is an abstraction of remote procedure call and supports load balancing, disaster tolerance and cluster functions, and the registration service center (registration) is used for registration of services and service time publishing and subscribing.

A Message Queue (MQ) is a "first-in first-out" data structure in an underlying data structure. The method is generally used for solving the problems of application decoupling, asynchronous messages, flow peak clipping and the like, and realizes a high-performance, high-availability, scalable and final consistency framework. An mq (message queue) message queue is a data structure of "first-in first-out" in an underlying data structure. Refers to placing data (messages) to be transmitted in a queue, using a queue mechanism to effect message delivery-a producer generates and places messages in a queue, which are then processed by a consumer. The consumer can pull the message to the designated queue or subscribe to the corresponding queue, and the MQ server side pushes the message to the corresponding queue.

Extensible Markup Language (XML) is a simple data storage Language. Data is described using a series of simple tags which can be created in a convenient manner, and extensible markup language is extremely simple to master and use, although it takes up more space than binary data.

Java Object Notation (JSON) is a lightweight data interchange format. It stores and represents data in a text format that is completely independent of the programming language, based on a subset of ECMAScript (js specification set by the european computer association). The compact and clear hierarchy makes JSON an ideal data exchange language. The network transmission method is easy to read and write by people, is easy to analyze and generate by machines, and effectively improves the network transmission efficiency.

The Bean is a software model for describing JAVA, and the Bean is injected into the interceptor, so that the performance and the robustness of the original system can be ensured when data is processed.

fastjson is a JSON parser and generator developed by the arieba engineer based on JAVA that can be used to convert JAVA objects into their JSON representation. It can also be used to convert JSON strings to equivalent Java objects. fastjson can handle any Java object, including pre-existing objects that do not have source code.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Based on this, the embodiment of the application provides a service data processing method, an uploading method and a corresponding device thereof, so as to reduce the supervision difficulty of the supervision platform on the medical data and facilitate the supervision of the supervision platform on the medical data.

The embodiment of the application provides a business data processing method and an uploading method, and relates to the technical field of artificial intelligence. The service data processing method and the service data uploading method provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, smart watch, or the like; the server side can be configured as an independent physical server, or a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN) and a big data and artificial intelligence platform; the software may be an application that implements a service data processing method, an upload method, etc., but is not limited to the above form.

Referring to fig. 1, a detailed description is provided for a specific processing procedure of the service data processing method in the present application.

As shown in fig. 1, in a first aspect, some embodiments of the present application provide a service data processing method, including step S100, step S200, step S300, step S400, and step S500. These five steps are described in detail below. It should be understood that the service data processing method of the present application includes, but is not limited to, step 100 to step S500.

Step S100: collecting service data of a hospital end and acquiring supervision data of a supervision platform;

specifically, in step S100, the business data includes various prescription data, registration data, filing data, various appointment data (e.g., registration appointment, physician appointment, etc.), inquiry data, charging data, and the like. The supervision platform is provided with a plurality of supervision platforms, and the supervision data comprises the data type to be supervised by each supervision platform and the code and the field of each supervision platform for certain service data, such as: for prescription data, administration platform a encodes 0001 for prescription data, fields are prescriptions, administration platform B encodes 002 for prescription data, and fields are prescriptions. The service data and the supervision data may be obtained through Message Queue (MQ) message monitoring or a Dubbo service framework, may be obtained directly from the medical cloud server, or may be obtained by other means, and the present application is not particularly limited.

Step S200: carrying out feature extraction on the supervision data to obtain a supervision universal standard, wherein the supervision universal standard comprises: a supervision general field group and a standard type corresponding to each hospital end;

in step S200, the universal supervision standard refers to a supervision standard applicable to all supervision platforms and a data type to be supervised by each hospital, and may be obtained by extracting commonalities of all supervision platforms, or may be modified by specifying the supervision standard of a certain supervision platform C as the universal supervision standard, and the other supervision platforms are modified according to the supervision standard of the supervision platform C, which is not specifically limited in this application.

Step S300: performing state checking and screening on the service data to obtain checking data, wherein the checking data comprises finishing data;

step S400: classifying the check data according to the standard type to obtain classified data corresponding to each hospital end;

in step S400, the standard type refers to which types of business data need to be monitored in the hospital. For example: the registration data of hospital A does not need to be supervised, but the registration data of hospital B needs to be supervised, and different managed data types of hospitals are different. And classifying, screening and processing the verification data through the standard types to obtain classification data corresponding to each hospital end.

Step S500: and carrying out standardized packaging processing on the classified data according to the supervision general field group to obtain a general data packet corresponding to each hospital end.

In step S500, the standardized encapsulation process includes a code matching process and a padding process, where the code matching process refers to matching and encoding all the service data in the supervision general field set, and encoding the service data into a general encoding code number in the supervision general field set. Filling refers to subjecting the mandatory item to filling processing, such as: the registration data of the hospital a does not need to be supervised, but the registration data of the hospital B needs to be supervised, so when the universal data packet is obtained through standardized encapsulation processing, because the registration data of the hospital B needs to be supervised, the registration data is a necessary item in the universal data packet, the registration data of the hospital a needs to be filled, and the registration data can be filled as an unrelated data, for example, the filling is 0.

In the related art, a supervision platform assigned by a health service committee needs to be accepted by an internet hospital in the actual operation process to perform operations such as license plate acceptance and service data monitoring. However, because the codes and names of the same type of data of each hospital are different, the types of the monitored data to be monitored of different hospitals are different, and the codes and names of the same type of data of different monitoring platforms are different, no universal monitoring platform is provided to realize the uniform monitoring of the hospital service data, and the arrangement of a plurality of monitoring platforms causes resource waste and increase of operation cost. Based on this, the embodiment of the application provides a service data processing method, status check screening is performed on service data to obtain check data, then classification processing is performed on the check data according to standard types to obtain classification data corresponding to each hospital end, then standardized encapsulation processing is performed on the classification data according to a supervision general field, and the data of each hospital end is encapsulated into a general data packet, so that the supervision platform supervises the general data packet, the supervision difficulty of the supervision platform on medical data is reduced, the supervision platform supervises the medical data, the waste of resources is avoided, the operation cost is reduced, and the supervision efficiency is improved.

The process of determining the supervision general field set in S200 described above is described in detail below with reference to fig. 2.

As shown in fig. 2, step S200 includes step S210, step S220, and step S230. These three steps are described in detail below. It should be understood that step S200 in the present application includes, but is not limited to, step S210, step S220, and step S230.

Step S210: semantic feature extraction is carried out on the supervision data to obtain data fields;

specifically, in step S210, semantic feature extraction may be performed on the supervision data through a neural network (e.g., a convolutional neural network CNN, a recurrent neural network RNN, or a transform layer), so as to obtain a data field. The semantic features of the supervision data may also be extracted by means of manual screening and comparison to obtain data fields, which is not specifically limited in the present application. The data field refers to the name or code of the same service data in the supervision data. For example: for a certain service data a, both names or codes a1, a2 may represent the service data a, and then a1, a2 are different data fields of the service data a. Through semantic feature extraction of the supervision data, names or codes representing the same service data in the supervision data are extracted.

Step S220: carrying out frequency statistics on the data fields to obtain field frequencies;

specifically, in step S220, frequency statistics is performed on the data field obtained in step S210 to obtain a field frequency. Wherein field frequency refers to the frequency of occurrence of each data field at a different regulatory platform. For example: for a certain service data a, both names or codes a1, a2 may represent the service data a, and then a1, a2 are different data fields of the service data a. In the supervision platform a, the name or code of the service data is a1, in the supervision platform b, the name or code of the service data is a2, in the supervision platform c, the name or code of the service data is a2, and frequency statistics is performed on the appearing data fields to obtain that the field frequency of a1 is 1/3, and the field frequency of a2 is 2/3. By performing frequency statistics on the data fields, the frequency of occurrence of different data fields can be obtained.

Step S230: and screening the data fields according to the field frequency to obtain a supervision general field group.

Specifically, in step S230, the data field obtained in step S210 is screened according to the field frequency obtained in step S220, so as to obtain a supervision general field set. And selecting a data field from different data fields of the same service data as a data field common to all supervision platforms and hospital terminals to obtain a supervision common field group. The data field with the highest field frequency may be selected as a general data field, and the data field with the highest field frequency may also be selected as a general data field. For example: for a certain service data a, both names or codes a1, a2 may represent the service data a, and then a1, a2 are different data fields of the service data a. In the supervision platform a, the name or code of the service data is a1, in the supervision platform b, the name or code of the service data is a2, in the supervision platform c, the name or code of the service data is a2, and frequency statistics is performed on the appearing data fields to obtain that the field frequency of a1 is 1/3, and the field frequency of a2 is 2/3. In the present embodiment, a2 is selected as the general data field of the service data a, and the general data fields of a plurality of different service data form a supervision general field group.

The process of determining the standard type in step S200 described above will be described in detail with reference to fig. 3.

As shown in fig. 3, step S200 includes, but is not limited to, step S240 and step S250. These two steps are described in detail below. It should be understood that, in the present application, step S200 includes, but is not limited to, step S240 and step S250.

Step S240: performing relevance feature extraction on the supervision data to obtain a relevance feature vector representing the relevance relationship between each hospital end and the supervision platform;

specifically, in step S240, relevance feature extraction may be performed on the supervision data through a neural network (e.g., a convolutional neural network CNN and a recurrent neural network RNN) to obtain a relevance feature vector, or relevance feature extraction may be performed on the supervision data through a manual screening method to obtain a relevance feature vector. The relevance feature vector represents a relevance relationship between each hospital end and the supervision platform, and the relevance feature vector may be represented by an M × N feature matrix or in other forms, which is not limited in this application. For example: four service data at a hospital end need to be supervised by three different supervision platforms, wherein the service data a and the service data B are supervised by a supervision platform a, the service data C is supervised by a supervision platform B, and the service data D is supervised by a supervision platform C, correlation feature extraction is performed on the supervision data of all the supervision platforms to obtain correlation feature vectors, in this embodiment, the correlation feature vectors are represented by M × N feature matrices, and then the correlation feature vectors can be represented as 4 × 3 feature matrices. The association feature vector characterizes the association relationship between the hospital side and the supervision platform.

Step S250: and matching the hospital side and the supervision data according to the associated feature vectors to obtain the standard type of each hospital side.

Specifically, in step S250, the hospital side and the supervision data are matched according to the associated feature vector obtained in step S240, so as to obtain a standard type of each hospital side. The standard type refers to the type of the hospital end which needs to be managed, and the data type of the hospital end which needs to be managed and the supervision standard of the corresponding supervision platform. For example: the business data D of a certain hospital end comprises data D1, D2 and D3, the business data D is supervised by a supervision platform k, however, in the supervision platform k, only data D1 and D2 are supervised, and for data D3, the standard type of the hospital end is determined. And matching the hospital side and the supervision data according to the associated feature vectors to obtain the standard type of the hospital side.

The verification process of the status verification screening of step S300 is described in detail below with reference to fig. 4.

As shown in fig. 4, the status check screening includes a first type status check screening and a second type status check screening, and step S300 includes step S310 and step S320. These two steps are described in detail below, and it should be understood that step S300 includes, but is not limited to, step S310 and step S320 in the present application.

Step S310: performing first-class state check screening on the service data by using the first-class state code to obtain first-class check data;

step S320: and carrying out second type state check screening on the first type check data by using a second type state code to obtain check data, wherein the first type state code and the second type state code are different state codes.

Specifically, in the present application, the first type of status checking and screening is service checking and screening, the second type of status checking and screening is process checking and screening, one process includes a plurality of service data, each service data is provided with a service status completion code, that is, a first type of status code, and each process is provided with a process status completion code, that is, a second type of status code. And filtering the service data which is not terminated through the unique service state ending code of each service data, and judging whether the whole process is completely terminated or not by using the unique process state ending code of the process. For example: in the inquiry process, the inquiry process includes an inquiry service, a doctor visit service, a prescription making service, a payment service, and the like. Each service is provided with a service state ending code to judge whether the service is ended, for example, a doctor signature is used as the service state ending code for the doctor to see the service. In the inquiry flow, the payment service can be used as the flow state ending code, and the doctor signature can also be used as the flow state ending code. And checking and screening the service data through the service state checking and screening and the process state checking and screening to obtain checking data. The verification data comprises the finalization data and the to-be-finalized data, and screening and classification of the service data are achieved, so that subsequent encapsulation processing of the service data is facilitated. The data to be finished means that single service data in the whole flow is in a finished state, but some single services are not in a finished state.

Referring to fig. 5, in a second aspect, an embodiment of the present application further provides a service data uploading method, including step S600, step S700, step S800, and step S900. The four steps are described in detail below, and it should be understood that the service data uploading method of the present application includes, but is not limited to, step S600, step S700, step S800, and step S900.

Step S600: acquiring a general data packet of a hospital end, wherein the general data packet is obtained by processing according to the service data processing method of any one of the embodiments of the first aspect;

in step S600, a general data packet obtained by the service data processing method of the first aspect is obtained, and the general data packet is applicable to the supervision standards of all supervision platforms.

Step S700: acquiring a data uploading rule of a supervision platform and a mapping rule of the supervision platform and a hospital side;

specifically, in step S700, since each supervision platform and the region where the hospital end is located have differences, and the policy related to each region is different, the data upload rule of the supervision platform and the mapping rule of the hospital end are different, and even though the general data packet is applicable to the supervision standards of all supervision platforms, the general data packet cannot be directly uploaded to the supervision platform, and the data upload rule of the supervision platform and the mapping rule of the hospital end need to be acquired.

Step S800: integrating the general data packet based on the mapping rule and the data uploading rule to obtain a data packet to be uploaded;

step S900: and uploading the data packet to be uploaded.

In step S900, the data packet to be uploaded at the hospital end is uploaded to the corresponding monitoring platform, so that the monitoring platform can monitor the medical data at the hospital end. After the data packet to be uploaded is uploaded, the data packet to be uploaded can be subjected to persistent reservation. For example: the data packets to be uploaded are uploaded to the medical cloud server, the data packets to be uploaded are continuously reserved in the medical cloud server, and the data packets to be uploaded can also be reserved locally, so that data can be inquired, managed, analyzed, monitored, counted and the like in the later period conveniently.

The service data uploading method of the embodiment of the application not only uses the universal data packet suitable for all supervision standards of the supervision platform, but also considers the data uploading rules of the supervision platforms in different regions and the mapping rules of the supervision platforms and the hospital side on the basis, is convenient for supervision of medical data, and reduces the supervision difficulty of the supervision platforms on the medical data.

The integrated processing procedure of the above step S800 will be described in detail with reference to fig. 6.

As shown in fig. 6, the mapping rule includes an encryption rule and a customized processing rule, and step S800 includes step S810, step S820, and step S830. These three steps are described in detail below, and it should be understood that, in the present application, step S800 includes, but is not limited to, step S810, step S820, and step S830.

Step S810: customizing the general data packet according to the customizing processing rule to obtain a customizing data packet;

in step S810, since there are differences in geographical areas, policies of hospital sides are different in different geographical areas, and the supervision policy of the supervision platform for the hospital sides is also different, it is necessary to process the general data packet according to the policies. The customized processing rules refer to different policies of the hospital side and the supervision policies of the supervision platform. Through the customization processing of the general data packet, the customized data packet suitable for the policy is obtained, the supervision of the supervision platform on the medical data is facilitated, and the supervision difficulty is reduced. Specifically, the generic data packet may be customized according to the customized interceptor bean from the perspective of the hospital according to the customized processing rule.

Step S820: converting the customized data packet based on the data uploading rule to obtain a recombined data packet;

specifically, in step S820, the supervision platforms in different regions have differences in the uploading rules of the customized data packets, and some general data packets are subjected to the customization processing, so that the general data packets may not conform to the format type of the uploading, and the like. Therefore, the customized data package needs to be subjected to conversion processing according to the data uploading rule, and the conversion processing includes, but is not limited to, changing the file type, adding or deleting a special suffix (such as a CA signature), changing the data form, and the like. For example: some supervision platforms need to upload data packets with CA signature information, and at this time, the data packets without CA signature information need to be processed to increase CA signature information. For example, the form of the customized data packet is bean + Json form, but some supervision platforms may not be applicable, and the customized data packet is disassembled and reassembled to convert the customized data packet into XML format or fast + Json message mode, so as to be compatible with supervision requirements of different supervision platforms.

Step S830: and encrypting the data packet to be uploaded according to the encryption rule to obtain the data packet to be uploaded.

Specifically, in step S830, the encryption rules include, but are not limited to, different encryption manners such as symmetric encryption, asymmetric encryption, token authentication, and the like. In addition, in the encryption process, the secret key, the entrance IP or token and the like are dynamically configured according to the hospital side, and the secret keys and the like of different hospitals are different, so that the security of the data packet to be uploaded is enhanced.

Referring to fig. 7, in a third aspect, some embodiments of the present application further provide a service data processing apparatus, which includes a collecting module 701, an extracting module 702, a checking and screening module 703, a classifying module 704, and a first processing module 705. Wherein: a collecting module 701, configured to collect service data of a hospital side, and further configured to obtain supervision data of a supervision platform; an extracting module 702, configured to perform feature extraction on the supervision data to obtain a universal supervision standard, where the universal supervision standard includes: a supervision general field group and a standard type corresponding to each hospital end; the verification screening module 703 is configured to perform status verification screening on the service data to obtain verification data, where the verification data includes the finalization data; the classification module 704 is used for classifying the check data according to the standard type to obtain classification data corresponding to each hospital end; the first processing module 705 is configured to perform standardized encapsulation processing on the classified data according to the supervision general field set to obtain a general data packet corresponding to each hospital end.

The embodiment of the application provides a business data processing apparatus, through carrying out the state check screening to business data, in order to obtain the check-up data, then carry out classification with standard type to the check-up data, obtain the classification data that corresponds each hospital end, carry out standardized encapsulation processing to classification data according to supervision general field again, become the universal data package with the data encapsulation of each hospital end, so that supervision platform supervises the universal data package, supervision platform to medical data's the supervision degree of difficulty has been reduced, be convenient for supervision platform to medical data's supervision, the waste of resource has been avoided, the operation cost is reduced, supervision efficiency has been improved.

It should be noted that, a processing procedure of the service data processing apparatus in the embodiment of the present application is consistent with the processing method in the foregoing first aspect, and for a specific processing procedure, reference is made to the foregoing processing method, which is not described herein again.

Referring to fig. 8, in a fourth aspect, an embodiment of the present application further provides a service data uploading apparatus, which includes a first obtaining module 801, a second obtaining module 802, a second processing module 803, and an uploading module 804. Wherein: a first obtaining module 801, configured to obtain a general data packet at a hospital end, where the general data packet is a data packet obtained by processing according to any one of the methods in the first aspect; a second obtaining module 802, configured to obtain a data uploading rule of the monitoring platform and a mapping rule of the monitoring platform and the hospital side; the second processing module 803 is configured to perform integration processing on the general data packet based on the mapping rule and the data upload rule to obtain a data packet to be uploaded; an upload module 804 is configured to upload a data packet to be uploaded.

The service data uploading device provided by the embodiment of the application not only uses the universal data packet applicable to all supervision standards of the supervision platform, but also considers the data uploading rules of the supervision platforms in different regions and the mapping rules of the supervision platforms and the hospital side on the basis, so that the medical data can be supervised conveniently, and the supervision difficulty of the supervision platforms on the medical data is reduced.

It should be noted that, a processing procedure of the service data processing apparatus in the embodiment of the present application is consistent with the data uploading method in the second aspect, and for a specific processing procedure, the foregoing data uploading method is referred to, and details are not described herein again.

An embodiment of the present disclosure further provides an electronic device, including:

at least one memory;

at least one processor;

at least one program;

programs are stored in the memory, and the processor executes at least one program to implement the present disclosure implementing the service data processing method and the service data uploading method described above. The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a vehicle-mounted computer, and the like.

The electronic device according to the embodiment of the present application will be described in detail with reference to fig. 9.

As shown in fig. 9, fig. 9 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:

the processor 901 may be implemented by a general Central Processing Unit (CPU), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is configured to execute a relevant program to implement the technical solution provided by the embodiment of the present disclosure;

the memory 902 may be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a Random Access Memory (RAM). The memory 902 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present disclosure is implemented by software or firmware, the relevant program codes are stored in the memory 902 and the processor 901 calls the service data processing method and the service data uploading method for executing the embodiments of the present disclosure;

an input/output interface 903 for implementing information input and output;

a communication interface 904, configured to implement communication interaction between the device and another device, where communication may be implemented in a wired manner (e.g., USB, network cable, etc.), or in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.); and

a bus 905 that transfers information between various components of the device (e.g., the processor 901, the memory 902, the input/output interface 903, and the communication interface 904);

wherein the processor 901, the memory 902, the input/output interface 903 and the communication interface 904 enable a communication connection within the device with each other through a bus 905.

The embodiment of the present disclosure also provides a storage medium, which is a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the service data processing method and the service data uploading method.

The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided in the embodiments of the present disclosure, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present disclosure are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.

It will be appreciated by those skilled in the art that the solutions shown in fig. 1-6 are not limiting of the embodiments of the present disclosure, and may include more or fewer steps than those shown, or some of the steps may be combined, or different steps.

The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.

The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and therefore do not limit the scope of the claims of the embodiments of the present disclosure. Any modifications, equivalents and improvements within the scope and spirit of the embodiments of the present disclosure should be considered within the scope of the claims of the embodiments of the present disclosure by those skilled in the art.