CN109558397B - Data processing method, device, server and computer storage medium - Google Patents
Data processing method, device, server and computer storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a data processing method, a device, a server and a storage medium, wherein the method comprises the following steps: acquiring initial medical data from at least one hospital information system, the initial medical data comprising medical sub-data of at least two dimensions; based on different dimensionalities corresponding to the medical sub-data, carrying out shunting processing on the initial medical data by adopting a distributed publishing and subscribing message system to obtain a plurality of pieces of tributary data; analyzing each piece of tributary data through a stream computing platform to obtain a plurality of pieces of analysis data; storing each piece of analysis data in an object-relation database in an associated mode, wherein all analysis data stored in an associated mode form target medical data corresponding to the initial medical data; when a query request sent by a client is received, the target medical data is searched in the object-relation database by adopting a streaming algorithm, and the target medical data is sent to the client, so that the processing efficiency of the medical data can be improved.
Description
Technical Field
The invention relates to the technical field of medical insurance, in particular to a data processing method, a data processing device, a server and a computer storage medium.
Background
Medical insurance is an important component of social insurance, in a medical insurance social system, the number of medical data is huge, the variety is various, the mode of processing the medical data at present mainly adopts a manual collection and arrangement method, then the arranged medical data is stored, the processing efficiency of the medical data is low by means of a manual method of processing a large amount of data, and the accuracy of the processed medical data is not high because the manual processing cannot guarantee the accurate processing of the medical data, and the mode of processing the medical data manually cannot meet the requirement of analyzing the real-time data, so that the current processing mode of the medical data does not meet the actual requirement of a hospital system.
Disclosure of Invention
The embodiment of the invention provides a data processing method, a data processing device, a server and a computer storage medium, which can improve the processing efficiency of medical data.
In a first aspect, an embodiment of the present invention provides a data processing method, including:
Acquiring initial medical data from at least one hospital information system, the initial medical data comprising medical sub-data of at least two dimensions;
based on different dimensionalities corresponding to the medical sub-data, carrying out shunting processing on the initial medical data by adopting a distributed release subscription message system to obtain a plurality of pieces of tributary data, wherein any one piece of tributary data corresponds to the medical sub-data comprising one dimensionality;
analyzing each piece of tributary data through a stream computing platform to obtain a plurality of pieces of analysis data;
storing each piece of analysis data in an object-relation database in an associated mode, wherein all analysis data stored in an associated mode form target medical data corresponding to the initial medical data;
and when a query request sent by the client is received, searching the target medical data in the object-relation database by adopting a streaming algorithm, and sending the target medical data to the client.
In one embodiment, before the initial medical data is shunted by using the distributed publish-subscribe message system to obtain a plurality of tributary data, the method further includes:
load balancing the initial medical data;
The medical sub-data after load balancing is put into a message queue;
the adoption of the distributed publish-subscribe message system to carry out the distribution processing on the initial medical data to obtain a plurality of pieces of tributary data comprises the following steps:
and carrying out shunting processing on the message queue by adopting the distributed publish-subscribe message system to obtain the plurality of pieces of tributary data.
In one embodiment, the splitting the initial medical data by using the distributed publish-subscribe message system to obtain a plurality of tributary data includes:
broadcasting each medical sub-data to at least two processing modules by adopting a distributed publishing and subscribing message system;
and carrying out splitting processing on the target medical sub-data through a target processing module to obtain tributary data containing the target medical sub-data, wherein the target processing module is any one of the at least two processing modules, and the target medical sub-data is any one of the medical sub-data with at least two dimensions.
In one embodiment, the method further comprises:
detecting whether at least two pieces of analysis data exist in the target medical data, wherein the at least two pieces of analysis data point to at least two pieces of medical sub-data of the same user from different hospital information systems in the same time period;
And if so, performing data cleaning on the at least two analysis data.
In one embodiment, the method further comprises:
acquiring each piece of analysis data constituting the target medical data;
calculating the similarity between every two pieces of analysis data;
and when the similarity is greater than a preset threshold, performing deduplication processing on the two analysis data.
In one embodiment, the method further comprises:
acquiring each piece of analysis data constituting the target medical data;
detecting whether filling information corresponding to preset attributes exists in each piece of analysis data;
if not, acquiring the historical medical data, and performing attribute filling on the analysis data without filling information based on the historical medical data.
In one embodiment, the acquiring initial medical data includes:
transmitting an initial medical data acquisition request to the at least one hospital information system, wherein the initial medical data acquisition request comprises authority information;
and receiving initial medical data sent by the at least one hospital information system, wherein the initial medical data is determined by the at least one hospital information system according to the type and/or the number of the medical data, and the type and/or the number of the medical data are determined according to the authority information.
In a second aspect, an embodiment of the present invention provides a data processing apparatus, including:
an acquisition unit for acquiring initial medical data from at least one hospital information system, the initial medical data comprising medical sub-data of at least two dimensions;
the distribution unit is used for distributing the initial medical data by adopting a distributed publishing and subscribing message system based on different dimensionalities corresponding to the medical sub-data to obtain a plurality of pieces of branch data, wherein any branch data corresponds to the medical sub-data comprising one dimensionality;
the analysis unit is used for respectively analyzing each piece of tributary data through the stream computing platform to obtain a plurality of pieces of analysis data;
the storage unit is used for storing each piece of analysis data in an object-relation type database in an associated mode, and all analysis data stored in the associated mode form target medical data corresponding to the initial medical data;
and the searching unit is used for searching the target medical data in the object-relation type database by adopting a streaming algorithm when receiving the query request sent by the client, and sending the target medical data to the client.
In one embodiment, the apparatus further comprises: a processing unit and an insertion unit.
The processing unit is used for carrying out load balancing on the initial medical data;
the loading unit is used for loading the medical sub-data subjected to load balancing into the message queue;
the flow dividing unit is specifically used for:
and carrying out shunting processing on the message queue by adopting the distributed publish-subscribe message system to obtain the plurality of pieces of tributary data.
In one embodiment, the splitting unit is specifically configured to:
broadcasting each medical sub-data to at least two processing modules by adopting a distributed publishing and subscribing message system;
and carrying out splitting processing on the target medical sub-data through a target processing module to obtain tributary data containing the target medical sub-data, wherein the target processing module is any one of the at least two processing modules, and the target medical sub-data is any one of the medical sub-data with at least two dimensions.
In one embodiment, the apparatus further comprises: and a detection unit.
The detection unit is used for detecting whether at least two analysis data exist in the target medical data, wherein the at least two analysis data point to at least two medical sub-data of the same user from different hospital information systems in the same time period;
The processing unit is further configured to perform data cleansing on at least two pieces of analysis data if the detection unit detects that the at least two pieces of analysis data exist in the target medical data, but the at least two pieces of analysis data point to at least two pieces of medical sub-data of the same user from different hospital information systems in the same time period.
In one embodiment, the apparatus further comprises: and a calculation unit.
The acquisition unit is also used for acquiring each piece of analysis data forming the target medical data;
a calculation unit for calculating the similarity between every two pieces of analysis data;
and the processing unit is further used for performing duplicate removal processing on the two analysis data when the similarity is greater than a preset threshold value.
In one embodiment, the apparatus further comprises:
the acquisition unit is also used for acquiring each piece of analysis data forming the target medical data;
the detection unit is also used for detecting whether filling information corresponding to preset attributes exists in each piece of analysis data;
and the processing unit is further used for acquiring historical medical data and performing attribute filling on the analysis data without filling information based on the historical medical data if the detection unit detects that the filling information corresponding to the preset attribute does not exist in each analysis data.
In one embodiment, the acquiring unit is specifically configured to:
transmitting an initial medical data acquisition request to the at least one hospital information system, wherein the initial medical data acquisition request comprises authority information;
and receiving initial medical data sent by the at least one hospital information system, wherein the initial medical data is determined by the at least one hospital information system according to the type and/or the number of the medical data, and the type and/or the number of the medical data are determined according to the authority information.
In a third aspect, an embodiment of the present invention provides a server, including a processor and a storage device, where the processor and the storage device are connected to each other, and the storage device is configured to store computer program instructions, and the processor is configured to execute the program instructions to implement a method according to the first aspect.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method according to the first aspect.
In the embodiment of the invention, the server obtains the initial medical data from at least one hospital information system, processes the initial medical data through the distributed publishing and subscribing information system, and then obtains a plurality of pieces of tributary data, so that the plurality of pieces of analysis data obtained by analyzing and processing the tributary data including medical sub-data with different dimensions through the streaming computing platform can be stored in the object-relational database in an associated manner, wherein all analysis data stored in the association form target medical data corresponding to the initial medical data, so that the target medical data can be searched in the object-relational database by adopting a streaming algorithm, and the target medical data is sent to the client, thereby realizing real-time processing of a large amount of initial medical data, simultaneously ensuring the processing accuracy of the initial medical data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a data processing method according to another embodiment of the present invention;
FIG. 3 is a schematic block diagram of a data processing apparatus provided by an embodiment of the present invention;
fig. 4 is a schematic block diagram of a server according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
After a patient (user) in a doctor takes a doctor, each item of medical data of the patient is uploaded to a hospital information system (Hospital Information System, HIS), and the medical data can be divided into at least two dimensions, namely an identity dimension (including a name, an identity card number and the like) and a medicine purchasing dimension (such as prescription data and the like), and the number of users received by each hospital every day is huge, so that the generated medical data is too large, if a traditional mode of manually processing the medical data is adopted, a great deal of manpower and material resources are consumed, the accuracy is not high, and therefore, when the medical data in the HIS system is extracted later, the accuracy of the data cannot be guaranteed, and the availability of the extracted data cannot be guaranteed.
Based on this, the present invention proposes a data processing method, which is used for processing a large amount of medical data in an HIS system, firstly, the medical data acquired from the HIS system can be used as initial medical data, then the initial medical data is put into a message queue, a distributed publish-subscribe message system kafka is adopted to perform a shunting process on the initial medical data, then the data obtained by shunting is subjected to an parsing process to obtain parsed data, and the parsed data is stored in an object-relational database, which may be a PG database, for example, and the data in the PG database is grasped in real time by using a streaming technology (e.g., pipeline streaming algorithm), so that the efficiency of grasping the medical data in real time is higher.
In one embodiment, the publish-subscribe messaging system kafka may broadcast a message to all receiving ends (i.e., processing ends), which may be processed by the receiving end receiving the message; the pipeline flow type technology can connect tasks which are independently operated on a plurality of single or multiple nodes, so that a complex release process which is difficult to complete by a single task is realized, the pipeline flow type technology can not cause pressure on a PG database when the data is grabbed, and the processing efficiency of the data can be improved, wherein the PG database is an object database management system.
Referring to fig. 1, a schematic flow chart of a data processing method according to an embodiment of the invention is shown in fig. 1, where the method may include:
s101, the server acquires initial medical data from at least one hospital information system, wherein the initial medical data comprises medical sub-data with at least two dimensions.
Because the hospital information system (i.e., the HIS system) is mainly used for running internal services of a hospital, such as registering, checking, receiving initial medical data, and the like, and because the data processing amount carried by the HIS system itself is relatively large, when processing the initial medical data stored in the HIS system, in order to not burden the data processing burden of the HIS system, the processing server may be pre-connected with the HIS system, so that the initial medical data may be derived from the HIS system for data processing.
In one embodiment, the initial medical data stored in the HIS system is uploaded and generated by a plurality of terminals that establish a communication connection with the HIS system, for example, the initial medical data about the patient is generated according to patient basic data (such as name, identification card number, etc.) uploaded by the patient user, case data and purchase data of the patient user uploaded by the nurse terminal and/or doctor terminal, and after receiving the patient basic data uploaded by the patient user, the case data and purchase data of the patient user uploaded by the nurse terminal and/or doctor terminal, the HIS system may generate initial medical data about the patient according to the patient basic data, the case data and purchase data, for example, by establishing an association relationship between the basic data, the case data and the purchase data.
The initial medical data at least comprises two-dimensional medical sub-data, and the medical sub-data can be identity sub-data for determining the identity dimension of a patient by a user and medicine purchasing sub-data for determining the medicine purchasing behavior of the patient.
In one embodiment, the server may obtain initial medical data from one or more HIS systems in communication with the server at the same time to enable simultaneous real-time analysis of a large number of initial medical data. Upon acquiring initial medical data from the HIS system, the steps may be specifically performed:
s11, the server receives the acquisition request from the client and determines verification information included in the acquisition request;
and s12, the server determines authentication information, verifies the verification information based on the authentication information, and responds to the acquisition request and triggers the acquisition of initial medical data from the HIS system when the verification is passed, wherein the authentication information is uploaded and stored by the server when the server establishes communication connection with the HIS system.
S102, the server adopts a distributed publishing and subscribing information system to conduct distribution processing on the initial medical data based on different dimensionalities corresponding to the medical sub-data to obtain a plurality of pieces of tributary data, and any one piece of tributary data corresponds to the medical sub-data comprising one dimensionality.
The distributed publish-subscribe message system, namely the kafka system has the characteristics of high throughput and low delay, can process hundreds of thousands of messages per second, has the lowest delay of a minimum of a few milliseconds, has fault tolerance, allows the nodes of the emergency masses to fail (if the number of copies is n, n-1 nodes are allowed to fail), has high concurrency, and can support thousands of clients to read and write simultaneously, and can process a message queue quickly.
After the server acquires the initial medical data from at least one HIS system, based on the above characteristics of the kafka system, the acquired initial medical data including multiple dimensions may be subjected to a splitting process, and any tributary includes medical sub-data of one dimension (i.e., one dimension), so that the multiple-dimensional initial medical data may be distributed to different processing modules (or units) of the server for processing, so that the multiple-dimensional initial medical data may be processed simultaneously. Furthermore, it is avoided that the initial medical data including a plurality of dimensions is distributed to the same processing module, so that the processing speed of the processing module is reduced.
For example, if the initial medical data acquired from at least one HIS system includes identity sub-data for determining the identity dimension of the user and medicine purchasing sub-data for determining the medicine purchasing behavior of the user, the initial medical data is split by using kafka, so that 2 pieces of tributary data can be obtained, where the tributary data included in the tributary 1 may be the identity sub-data, and the tributary data included in the corresponding tributary 2 may be the medicine purchasing sub-data; alternatively, the identity sub-data may be included in tributary 2, and the corresponding purchase sub-data may be included in tributary 1.
And S103, the server analyzes each piece of tributary data through the stream computing platform to obtain a plurality of pieces of analysis data.
The streaming computing platform, such as the Jstorm, is a message-based pipeline processing model, can process high concurrent computing tasks, has no dependency relationship among data streams, has huge initial medical data volume of HIS systems, and can improve the processing efficiency of data by adopting the Jstorm platform because the initial medical data stored by each HIS system are independent of each other and have no influence on each other. Meanwhile, the jstorem platform can operate according to 7 x 24 hours, and once an unexpected fault occurs in one processing module in the middle, the scheduler can immediately allocate a new processing module to replace the effective module, so that the stored initial medical data can be calculated in real time.
Based on the characteristics of the Jstorm on data processing, the Jstorm platform can be adopted to analyze the plurality of pieces of tributary data obtained by kafka so as to obtain analysis data of each piece of tributary data, wherein the analysis data is real-time medical data comprising summarized data for real-time analysis of medical conditions of users.
For example, if the tributaries subjected to the parsing process by the jston platform include the tributaries 1 and 2, where the tributary data included in the tributary 1 is identity sub-data and the tributary data included in the tributary 2 is purchasing sub-data, the jston platform is used to parse the identity sub-data to obtain summary data about the identity sub-data, such as the total number of the identity sub-data obtained from the HIS system, etc., and parse the purchasing sub-data to obtain summary data about the purchasing sub-data, such as the total number of purchasing for each user, the total number of purchasing for each period (such as one month), etc. After processing each tributary data by the Jstorm platform to obtain the resolved data, step S104 may be performed instead.
And S104, the server stores each piece of analysis data in an object-relation type database in an associated mode, and all analysis data stored in an associated mode form target medical data corresponding to the initial medical data.
After the server analyzes each piece of tributary data through the Jstorm platform to obtain a plurality of pieces of analysis data, the server can perform association storage on a plurality of pieces of analysis data belonging to the same initial medical data, the analysis data obtained after each piece of association storage is target medical data corresponding to the initial medical data, in one embodiment, the analysis data which are subjected to association storage can be associated and stored in an object-relational database, and in one embodiment, the object-relational database can be a PG database, for example, so that related target medical data can be captured from the PG database by adopting a pipeline streaming technology.
In one embodiment, the server may store each piece of analysis data in association by adding the same tag, or store the analysis data to be stored in association in the same storage unit of the PG database, or store the analysis data in association in the same data table. By storing the various pieces of analysis data of the initial medical data in an associated manner after analysis, when the target medical data is grabbed by adopting a pipeline streaming algorithm, one piece of analysis data can be grabbed from the PG database, and a complete piece of target medical data can be found by searching and associating data among the grabbed analysis data.
S105, when a query request sent by the client is received, the server searches the object-relation type database for the target medical data by adopting a streaming algorithm, and sends the target medical data to the client.
In one embodiment, when the server stores each piece of analysis data in association with an object-relational database (such as the PG database described above), and generates target medical data corresponding to the initial medical data, the generated target medical data may be pushed to the client in real time, so as to analyze the target medical data, or the target medical data may be stored in a storage module, such as the PG database, and when an acquisition request from the client is received, the stored target medical data may be sent to the client.
The server can push the target medical data to the client in real time, or can send the target medical data to the client when receiving a request from the client, and then can search (grab) the target medical data from the PG database by adopting a streaming algorithm (such as the pipeline streaming algorithm) when sending the target medical data to the client, so that the target medical data is sent to the client, the real-time processing and the real-time sending of the target medical data are realized, the target medical data is sent according to the requirement, and the current acquisition requirement of the target medical data is met. The client can be a terminal or a server for analyzing medical data in a hospital, so that the client can obtain target medical data quickly.
In the embodiment of the invention, the server obtains the initial medical data from at least one hospital information system, processes the initial medical data through the distributed publishing and subscribing information system, and then obtains a plurality of pieces of tributary data, so that a plurality of pieces of analysis data obtained by respectively analyzing and processing medical sub-data with different dimensions of each piece of tributary data through the streaming computing platform can be stored in the object-relational database in an associated manner, wherein all analysis data stored in the association form target medical data corresponding to the initial medical data, so that the target medical data can be searched in the object-relational database by adopting a streaming algorithm, and the target medical data is sent to the client, thereby realizing real-time processing of a large amount of initial medical data, simultaneously ensuring the processing accuracy of the initial medical data.
Referring to fig. 2, a schematic flow chart of a data processing method according to another embodiment of the invention is shown in fig. 2, and the method may include:
s201, the server obtains initial medical data from at least one hospital information system, the initial medical data including medical sub-data of at least two dimensions.
When the server acquires initial medical data from at least one hospital information system (i.e. HIS system), the steps are specifically executed:
s21, sending an initial medical data acquisition request to the at least one hospital information system, wherein the initial medical data acquisition request comprises authority information;
s22, receiving initial medical data sent by the at least one hospital information system, wherein the initial medical data is determined by the at least one hospital information system according to the type and/or the number of the medical data, and the type and/or the number of the medical data are determined according to the authority information.
In one embodiment, when the server acquires initial medical data from the hospital information system (i.e., the HIS system), an acquisition request for acquiring the initial medical data may be transmitted to at least one HIS system, the acquisition request for acquiring the initial medical data including authority information, the HIS system determining a corresponding authority level according to the authority information, and further determining a type and/or number of available medical data according to the authority level, so that the determined initial medical data corresponding to the type and/or number of available medical data of the server may be transmitted to the server. The HIS system establishes the quantity and/or the type of the acquired initial medical data corresponding to different authority information in advance, wherein different identification marks can be adopted by different authority information sent to the HIS system by the server, so that the HIS system can determine the authority level corresponding to each authority information according to the different identification marks, and further the safety of information viewing in the HIS system is improved according to the type and/or the data of the medical data acquired by the server according to the authority level.
The request for acquiring the initial medical data sent by the server to at least one HIS system may be triggered to be generated and sent to the HIS system after the server receives the request for acquiring the target medical data corresponding to the initial medical data from the client, or may be automatically generated and sent to the HIS system by the server in a certain period of time (for example, one day, one week, one month, etc.).
After the initial medical data is acquired, in order to facilitate the server to perform the splitting process on the initial medical data, the steps may be performed in advance:
s31, load balancing the initial medical data;
s32, placing the medical sub-data subjected to load balancing into a message queue;
the method of load balancing is adopted to pre-process the initial medical data, medical sub-data of each dimension in the initial medical data can be shared into different operation units (processing units), the problems of overlarge data flow and overlarge network load are effectively solved, the server can be effectively utilized, the loss of data flow caused by single-point faults is avoided, after the message queue is pre-obtained by adopting the load balancing to the initial medical data, the message queue can be further subjected to split processing by adopting kafak, and a plurality of tributary data comprising the medical sub-data of different dimensions are obtained.
For example, if the initial medical data includes 2 dimensions of medical sub-data, namely identity sub-data and medicine purchasing sub-data, the identity sub-data and the medicine purchasing sub-data can be put into the message queue according to a certain sequence through load balancing, so that the message queue can be split in sequence.
S202, the server broadcasts each medical sub-data to at least two processing modules using the distributed publish-subscribe message system.
In one embodiment, based on the characteristic that the distributed publish-subscribe message system, such as the kafka system, has high concurrency, each medical sub-data in the message sequence obtained after load balancing is performed on the initial medical data can be broadcasted to at least two processing modules, and after the medical sub-data is received, the at least two processing modules can determine whether to respond to the received medical sub-data according to the current data processing capacity and the processing capacity of the data, if not, the received medical sub-data is discarded, and if so, the responding module (i.e. the target processing module) performs shunting processing on the medical sub-data.
For example, if the initial medical data a includes 3-dimensional medical sub-data, namely, medical sub-data x, medical sub-data y and medical sub-data z, and the number of processing modules available for broadcasting is 2 (respectively, processing module M and processing module N) by using kafka, after acquiring the initial medical data a, the server may broadcast the medical sub-data x to the processing module M and the processing module N, broadcast the medical sub-data y to the processing module M and the processing module N, and broadcast the medical sub-data z to the processing module M and the processing module N, and after receiving the broadcast medical sub-data x, the processing module M and the processing module N may determine the processed medical sub-data according to their own data processing amounts and data processing capacities, and if the processing module M determines to process the medical sub-data x, the processing module N may discard the broadcast received medical sub-data y and the medical sub-data z, and discard the broadcast medical sub-data x.
And S203, the server performs distribution processing on the target medical sub-data through the target processing module to obtain tributary data containing the target medical sub-data.
In one embodiment, after broadcasting the medical sub-data into at least two processing modules, the target medical sub-data is split by receiving response information of the target processing module to the target medical sub-data, wherein the target processing module is any one of the at least two processing modules and the target medical sub-data is any one of the at least two dimensions of medical sub-data. For example, the target medical sub-data is medical sub-data x, and after receiving the response information of the processing module M to the target medical sub-data x, the processing module M is determined to be the target processing module, that is, the processing module M performs the splitting processing on the medical sub-data x to obtain the tributary data including the medical sub-data x.
S204, the server analyzes each piece of tributary data through the stream computing platform to obtain a plurality of pieces of analysis data.
The specific embodiment of step S204 can be referred to the specific embodiment of step S103, and will not be described herein.
S205, the server stores each piece of analysis data in association with each other in the object-relational database, and all analysis data stored in association with each other constitute target medical data corresponding to the initial medical data.
After the server stores each piece of analysis data in association in an object-relational database (such as the PG database described above), it can be detected whether there are two analysis data in the target medical data, and the at least two analysis data point to at least two medical sub-data of the same user from different HIS systems in the same time period; if yes, data cleaning is carried out on the at least two analysis data so as to improve the accuracy and the effectiveness of the target medical data stored in the PG database. When the analysis data stored in association is subjected to data cleansing, the duplicate data in the analysis data stored in association may be deleted, or the analysis data stored in association with the duplicate data may be deleted directly. For example, two medical sub-data uploaded by the same user to different HIS systems for a simultaneous period of time may be data purged.
In one embodiment, the data cleansing process may include two processes of data deduplication and data complementation, where data deduplication refers to removing repeatedly recorded medical data and redundant data, and the amount of data to be stored may be reduced by data deduplication, so as to increase the speed of storing data, and data complementation refers to when analytic data is found to be missing, determining missing data in the analytic data by searching historical medical sub-data obtained from the HI system by the server, and complementing the missing data.
In one embodiment, when performing data deduplication on the parsed data included in the target medical data, specific execution is performed: acquiring each piece of analysis data constituting the target medical data; calculating the similarity between every two pieces of analysis data; and when the similarity is greater than a preset threshold value, performing de-duplication processing on the two analysis data.
In one embodiment, when the server performs data filling on the analysis data included in the target medical data, the steps are specifically performed: acquiring each piece of analysis data constituting the target medical data; detecting whether filling information corresponding to preset attributes exists in each piece of analysis data; if not, acquiring the historical medical data, and performing attribute filling on the analysis data without filling information based on the historical medical data. The preset attribute may be, for example: name, sex, age, medicine name, number, price, etc., by detecting whether the above preset attribute information exists in the analysis data, it is determined whether to perform data patch processing on the analysis information.
After the server performs data cleaning on the stored analysis data, the data amount of performing data cleaning on the analysis data included in the target medical information obtained by analyzing the initial medical information corresponding to each HIS system, that is, the repeated data and the data amount of missing data in the analysis data obtained by each HIS system, can be determined, so that feedback data can be sent to the HIS system according to the data amount, and the feedback data is used for indicating a sending mechanism of the HIS system modifier to send the initial medical data, so that the error rate of the initial medical data in the sending process is reduced, and the processing efficiency of the initial medical data is improved.
S206, when receiving the inquiry request sent by the client, the server searches the target medical data in the object-relation type database by adopting a streaming algorithm and sends the target medical data to the client.
In the embodiment of the invention, the server can acquire initial medical data from at least one HIS system, and adopts kafka to broadcast each piece of medical sub-data included in the initial medical data to at least two processing modules, after receiving response information of the target processing module to the target medical sub-data, the server determines to carry out shunt processing on the target medical sub-information through the target processing module to obtain branch data containing the target medical sub-information, and further can analyze each piece of branch data through a Jstorm platform to obtain a plurality of pieces of analysis data, so that each piece of analysis data can be stored in a PG database in an associated manner, the analysis data stored in the associated manner is the target medical data corresponding to the initial medical data, when the acquisition requirement of acquiring the target medical data is met, a streaming algorithm (such as the above-mentioned pipeline streaming algorithm) can be adopted to search the target medical data in the PG database, and the target medical data is sent to the client, thereby realizing real-time processing on a large amount of initial medical data, and simultaneously ensuring the processing accuracy of the initial medical data.
The embodiment of the invention also provides a data processing device, which is used for executing the unit of the method. In particular, referring to fig. 3, a schematic block diagram of a data processing apparatus according to an embodiment of the present invention is provided. The data processing apparatus of the present embodiment includes: acquisition unit 301, splitting unit 302, parsing unit 303, storage unit 304 and search unit 305. In the embodiment of the invention, the data processing device can be arranged in some data processing servers or some special data processing equipment.
An acquisition unit 301 for acquiring initial medical data from at least one hospital information system, the initial medical data comprising medical sub-data of at least two dimensions;
the splitting unit 302 is configured to perform splitting processing on the initial medical data by using a distributed publish-subscribe message system based on different dimensions corresponding to the medical sub-data, so as to obtain a plurality of pieces of tributary data, where any of the tributary data corresponds to medical sub-data including one dimension;
an analyzing unit 303, configured to analyze each piece of tributary data through a streaming computing platform, so as to obtain a plurality of pieces of analysis data;
A storage unit 304, configured to store each piece of analysis data in an object-relational database in an associated manner, where all pieces of analysis data stored in the associated manner form target medical data corresponding to the initial medical data;
and the searching unit 305 is configured to, when receiving a query request sent by the client, search the object-relational database for the target medical data by using a streaming algorithm, and send the target medical data to the client.
In one embodiment, the apparatus further comprises: a processing unit 306 and an insertion unit 307.
A processing unit 306, configured to load balance the initial medical data;
a loading unit 307, configured to load-balance the medical sub-data into a message queue;
the shunt unit 302 is specifically configured to:
and carrying out shunting processing on the message queue by adopting the distributed publish-subscribe message system to obtain the plurality of pieces of tributary data.
In one embodiment, the splitting unit 302 is specifically configured to:
broadcasting each medical sub-data to at least two processing modules by adopting a distributed publishing and subscribing message system;
and carrying out splitting processing on the target medical sub-data through a target processing module to obtain tributary data containing the target medical sub-data, wherein the target processing module is any one of the at least two processing modules, and the target medical sub-data is any one of the medical sub-data with at least two dimensions.
In one embodiment, the apparatus further comprises: a detection unit 308.
A detection unit 308, configured to detect whether at least two pieces of analysis data exist in the target medical data, where the at least two pieces of analysis data point to at least two pieces of medical sub-data of the same user from different hospital information systems in the same time period;
the processing unit 306 is further configured to perform data cleansing on at least two resolved data if the detecting unit 308 detects that the at least two resolved data exist in the target medical data, but the at least two resolved data point to at least two medical sub-data of the same user from different hospital information systems in the same time period.
In one embodiment, the apparatus further comprises: a calculation unit 309.
The acquiring unit 301 is further configured to acquire each piece of resolution data that constitutes the target medical data;
a calculation unit 309 configured to calculate a similarity between each two pieces of the analysis data;
the processing unit 306 is further configured to perform deduplication processing on the two parsed data when the similarity is greater than a preset threshold.
In one embodiment, the apparatus further comprises:
The acquiring unit 301 is further configured to acquire each piece of resolution data that constitutes the target medical data;
the detecting unit 308 is further configured to detect whether filling information corresponding to a preset attribute exists in each of the parsed data;
the processing unit 306 is further configured to obtain historical medical data if the detection unit 308 detects that filling information corresponding to a preset attribute does not exist in each piece of analysis data, and perform attribute filling on the analysis data without the filling information based on the historical medical data.
In one embodiment, the obtaining unit 301 is specifically configured to:
transmitting an initial medical data acquisition request to the at least one hospital information system, wherein the initial medical data acquisition request comprises authority information;
and receiving initial medical data sent by the at least one hospital information system, wherein the initial medical data is determined by the at least one hospital information system according to the type and/or the number of the medical data, and the type and/or the number of the medical data are determined according to the authority information.
In the embodiment of the present invention, the acquiring unit 301 acquires initial medical data from at least one hospital information system, and processes the initial medical data through a distributed publish-subscribe message system, so that a plurality of pieces of tributary data can be obtained, so that a plurality of pieces of analysis data obtained by analyzing and processing each piece of tributary data including medical sub-data with different dimensions through a streaming computing platform can be stored in an object-relational database in an associated manner, wherein all analysis data stored in the association form target medical data corresponding to the initial medical data, so that a streaming algorithm can be adopted to search the target medical data in the object-relational database, and send the target medical data to a client, thereby realizing real-time processing of a large amount of initial medical data, and simultaneously ensuring processing accuracy of the initial medical data.
Referring to fig. 4, a schematic block diagram of a server according to an embodiment of the present invention is provided. The server in the present embodiment as shown in the drawings may include: power supplies, housings, various required interfaces, etc., such as network interfaces, user interfaces, etc. The server further includes: one or more processors 401 and a storage device 402. The processor 401 is connected to the storage device 402, and in one embodiment, the processor 401 and the storage device 402 may be connected through a bus 403.
The server may include a user interface, which may include an interface module formed of physical keys or touch keys, etc., capable of receiving user operations, and a structure such as a display screen capable of prompting the user for information such as the operation state of the server.
The storage 402 may include volatile memory (RAM) such as random-access memory (RAM); the storage device 402 may also include a non-volatile memory (non-volatile memory), such as a flash memory (flash memory), a Solid State Drive (SSD), etc.; storage 402 may also include a combination of the types of storage described above.
The processor 401 may be a central processing unit (central processing unit, CPU). The processor 401 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (programmable logic device, PLD), or the like. The PLD may be a field-programmable gate array (field-programmable gate array, FPGA), general-purpose array logic (generic array logic, GAL), or the like. The processor 401 may be a combination of the above structures.
In an embodiment of the present invention, the storage device 402 is configured to store a computer program, where the computer program includes program instructions, and the processor 401 is configured to execute the program instructions stored in the storage device 402, so as to implement the corresponding method steps in the foregoing embodiment.
In one embodiment, the processor 401 is configured to call the program instructions for executing:
acquiring initial medical data from at least one hospital information system, the initial medical data comprising medical sub-data of at least two dimensions;
based on different dimensionalities corresponding to the medical sub-data, carrying out shunting processing on the initial medical data by adopting a distributed release subscription message system to obtain a plurality of pieces of tributary data, wherein any one piece of tributary data corresponds to the medical sub-data comprising one dimensionality;
Analyzing each piece of tributary data through a stream computing platform to obtain a plurality of pieces of analysis data;
storing each piece of analysis data in an object-relation database in an associated mode, wherein all analysis data stored in an associated mode form target medical data corresponding to the initial medical data;
and when a query request sent by the client is received, searching the target medical data in the object-relation database by adopting a streaming algorithm, and sending the target medical data to the client.
In one embodiment, the processor 401 is further configured to call the program instructions for executing:
load balancing the initial medical data;
the medical sub-data after load balancing is put into a message queue;
the adoption of the distributed publish-subscribe message system to carry out the distribution processing on the initial medical data to obtain a plurality of pieces of tributary data comprises the following steps:
and carrying out shunting processing on the message queue by adopting the distributed publish-subscribe message system to obtain the plurality of pieces of tributary data.
In one embodiment, the processor 401 is further configured to call the program instructions for executing:
broadcasting each medical sub-data to at least two processing modules by adopting a distributed publishing and subscribing message system;
And carrying out splitting processing on the target medical sub-data through a target processing module to obtain tributary data containing the target medical sub-data, wherein the target processing module is any one of the at least two processing modules, and the target medical sub-data is any one of the medical sub-data with at least two dimensions.
In one embodiment, the processor 401 is further configured to call the program instructions for executing:
detecting whether at least two pieces of analysis data exist in the target medical data, wherein the at least two pieces of analysis data point to at least two pieces of medical sub-data of the same user from different hospital information systems in the same time period;
and if so, performing data cleaning on the at least two analysis data.
In one embodiment, the processor 401 is further configured to call the program instructions for executing:
acquiring each piece of analysis data constituting the target medical data;
calculating the similarity between every two pieces of analysis data;
and when the similarity is greater than a preset threshold, performing deduplication processing on the two analysis data.
In one embodiment, the processor 401 is further configured to call the program instructions for executing:
Acquiring each piece of analysis data constituting the target medical data;
detecting whether filling information corresponding to preset attributes exists in each piece of analysis data;
if not, acquiring the historical medical data, and performing attribute filling on the analysis data without filling information based on the historical medical data.
In one embodiment, the processor 401 is further configured to call the program instructions for executing:
transmitting an initial medical data acquisition request to the at least one hospital information system, wherein the initial medical data acquisition request comprises authority information;
and receiving initial medical data sent by the at least one hospital information system, wherein the initial medical data is determined by the at least one hospital information system according to the type and/or the number of the medical data, and the type and/or the number of the medical data are determined according to the authority information.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
The above disclosure is illustrative only of some embodiments of the invention and is not intended to limit the scope of the invention, which is defined by the claims and their equivalents.
Claims (9)
1. A method of data processing, comprising:
sending an initial medical data acquisition request to at least one hospital information system, wherein the initial medical data acquisition request comprises authority information, and the hospital information system establishes types and numbers of acquired medical data corresponding to different authority information;
receiving initial medical data sent by the at least one hospital information system, wherein the initial medical data is determined by the at least one hospital information system according to the type and the quantity of the medical data, and the type or the quantity of the medical data is determined according to the authority information; the initial medical data comprises at least two-dimensional medical sub-data, wherein the at least two-dimensional medical sub-data comprises identity sub-data for determining the identity dimension of a patient and medicine purchasing sub-data for determining the medicine purchasing behavior of the patient;
based on different dimensionalities corresponding to the medical sub-data, carrying out shunting processing on the initial medical data by adopting a distributed release subscription message system to obtain a plurality of pieces of tributary data, wherein any one piece of tributary data corresponds to the medical sub-data comprising one dimensionality;
Analyzing each piece of tributary data through a stream computing platform to obtain a plurality of pieces of analysis data;
storing each piece of analysis data in an object-relation database in an associated mode, wherein all analysis data stored in an associated mode form target medical data corresponding to the initial medical data;
and when a query request sent by the client is received, searching the target medical data in the object-relation database by adopting a streaming algorithm, and sending the target medical data to the client.
2. The method of claim 1, wherein before the splitting the initial medical data using the distributed publish-subscribe messaging system to obtain a plurality of pieces of tributary data, the method further comprises:
load balancing the initial medical data;
the medical sub-data after load balancing is put into a message queue;
the adoption of the distributed publish-subscribe message system to carry out the distribution processing on the initial medical data to obtain a plurality of pieces of tributary data comprises the following steps:
and carrying out shunting processing on the message queue by adopting the distributed publish-subscribe message system to obtain the plurality of pieces of tributary data.
3. The method of claim 1, wherein the splitting the initial medical data using a distributed publish-subscribe messaging system to obtain a plurality of pieces of tributary data comprises:
broadcasting each medical sub-data to at least two processing modules by adopting the distributed publish-subscribe message system;
and carrying out splitting processing on the target medical sub-data through a target processing module to obtain tributary data containing the target medical sub-data, wherein the target processing module is any one of the at least two processing modules, and the target medical sub-data is any one of the medical sub-data with at least two dimensions.
4. The method according to claim 1, wherein the method further comprises:
detecting whether at least two pieces of analysis data exist in the target medical data, wherein the at least two pieces of analysis data point to at least two pieces of medical sub-data of the same user from different hospital information systems in the same time period;
and if so, performing data cleaning on the at least two analysis data.
5. The method according to claim 1, wherein the method further comprises:
Acquiring each piece of analysis data constituting the target medical data;
calculating the similarity between every two pieces of analysis data;
and when the similarity is greater than a preset threshold, performing deduplication processing on the two analysis data.
6. The method according to claim 1, wherein the method further comprises:
acquiring each piece of analysis data constituting the target medical data;
detecting whether filling information corresponding to preset attributes exists in each piece of analysis data;
if not, acquiring the historical medical data, and performing attribute filling on the analysis data without filling information based on the historical medical data.
7. A data processing apparatus, comprising:
the acquisition unit is used for sending an initial medical data acquisition request to at least one hospital information system, wherein the initial medical data acquisition request comprises authority information, and the hospital information system establishes types and numbers of acquired medical data corresponding to different authority information; receiving initial medical data sent by the at least one hospital information system, wherein the initial medical data is determined by the at least one hospital information system according to the type and the quantity of the medical data, and the type or the quantity of the medical data is determined according to the authority information; the initial medical data comprises at least two-dimensional medical sub-data, wherein the at least two-dimensional medical sub-data comprises identity sub-data for determining the identity dimension of a patient and medicine purchasing sub-data for determining the medicine purchasing behavior of the patient;
The distribution unit is used for distributing the initial medical data by adopting a distributed publishing and subscribing message system based on different dimensionalities corresponding to the medical sub-data to obtain a plurality of pieces of branch data, wherein any branch data corresponds to the medical sub-data comprising one dimensionality;
the analysis unit is used for respectively analyzing each piece of tributary data through the stream computing platform to obtain a plurality of pieces of analysis data;
the storage unit is used for storing each piece of analysis data in an object-relation type database in an associated mode, and all analysis data stored in the associated mode form target medical data corresponding to the initial medical data;
and the searching unit is used for searching the target medical data in the object-relation type database by adopting a streaming algorithm when receiving the query request sent by the client, and sending the target medical data to the client.
8. A server comprising a processor and a storage device, the processor and the storage device being interconnected, wherein the storage device is configured to store computer program instructions, the processor being configured to execute the program instructions to implement the method of any of claims 1-6.
9. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of any of claims 1-6.
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| CN110851444A (en) * | 2019-11-06 | 2020-02-28 | 北京许继电气有限公司 | Data acquisition and analysis method for industrial workshop |
| CN111276231A (en) * | 2020-02-27 | 2020-06-12 | 平安医疗健康管理股份有限公司 | Medical data monitoring method and device, computer equipment and storage medium |
| CN111930861A (en) * | 2020-08-19 | 2020-11-13 | 上海繁易信息科技股份有限公司 | Kafka-based real-time data analysis subscription processing method and device |
| CN112287216B (en) * | 2020-10-23 | 2023-02-17 | 微医云(杭州)控股有限公司 | Information pushing method and device, server and storage medium |
| CN114067976B (en) * | 2021-11-23 | 2022-06-17 | 南京理工大学 | Information cascade processing method and system suitable for medical treatment extrusion |
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