Data management method, device and equipment and storage medium thereof

Abstract

The embodiment of the invention belongs to the technical field of medical data management, is applied to a medical data management scene, and relates to a data governance method, device and equipment and a storage medium thereof.All data files in a target database are pre-classified through a preset classification dimension, and the pre-classified data files are obtained; and according to the screening and marking rules, screening and distinguishing marking are carried out on the data files, and then treatment operation is carried out on the corresponding data files according to different treatment strategies. The data management method is applied to a medical service data intelligent management scene, data storage and space utilization conditions can be effectively and reasonably optimized, a medical service party can be helped to quickly screen matched and professional medical health services for a target user from huge medical service data subsequently, and the user experience is improved. High-quality medical and service resources are quickly linked; and the personal exclusive medical service archive can be efficiently generated in combination with the historical inquiry condition of the user.

Description

Technical Field

[0001] The present application relates to the field of medical data management technology, and is applied to medical data management scenarios, and in particular to a data governance method, apparatus, device, and storage medium thereof. Background Art

[0002] With the rapid development of healthcare business, the amount of data in the business is increasing, and the corresponding data management costs are also growing explosively. Among the many management costs of healthcare business, data management costs are often the most easily overlooked. In fact, data management costs not only involve workload issues, but also involve the problem of unreasonable use of computer resources.

[0003] For example, in the APP applications currently developed for medical services, a large number of temporary patient tables occupy a lot of storage space. Most of the temporary table data is form data that no one accesses later, resulting in a large waste of computing resources and development resources. In addition, the large number of medical and patient data files generated for different patient users leads to huge memory consumption of HDFS, which also has a great impact on the stability and call of the database. Improper management of data resources and lack of scientific management methods ultimately lead to high management and data storage consumption, which is not conducive to the management of medical and health service providers and also affects the user experience of patient users. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a data governance method, device, equipment and storage medium thereof to improve the governance of medical and health business data, which is beneficial to the management of medical and health service providers and also improves the user experience of the patients being served.

[0005] In order to solve the above technical problems, the present application embodiment provides a data governance method, which adopts the following technical solutions:

[0006] A data governance method comprises the following steps:

[0007] Based on the preset classification dimension, pre-classify all data files in the target database to obtain pre-classified data files;

[0008] Marking a first type of screening data file in the target database according to a first screening condition and a first distinguishing mark, wherein the first screening condition includes a data file size;

[0009] Marking the second type of screening data files in the target database according to the second screening condition and the second distinguishing mark, wherein the second screening condition includes data called information;

[0010] Identify the screening data file with only one distinguishing mark, and continue to monitor the data file using a preset monitoring tool;

[0011] Identifying a screening data file marked by both a first distinguishing mark and a second distinguishing mark, and performing a first governance operation on the data file according to a first governance policy;

[0012] All data files without distinguishing marks are identified, and a second governance operation is performed on the data files according to a second governance policy.

[0013] Further, the target database includes a medical data management database, the preset classification dimensions include the data file's own attribute dimensions and non-self attribute dimensions, the data file's non-self attribute dimensions include the medical service dimension and the disease classification dimension, the data file's own attribute dimensions include the storage resource occupation dimension and the data content being called total frequency dimension, and the step of performing pre-classification processing on all data files in the target database based on the preset classification dimensions to obtain the pre-classified data files specifically includes:

[0014] Pre-classifying all data files in the target database according to different medical services to obtain a first classification result;

[0015] According to different disease classifications, pre-classify all data files in the target database to obtain a second classification result;

[0016] Pre-classifying all data files in the target database according to the difference in occupied storage resources to obtain a third classification result;

[0017] According to the total frequency of data content being called, all data files in the target database are pre-classified to obtain a fourth classification result.

[0018] Furthermore, after executing the step of performing pre-classification processing on all data files in the target database based on the preset classification dimension to obtain the pre-classified data files, the method further includes:

[0019] According to the first classification result, identifying the medical service dimensions corresponding to all the data files respectively;

[0020] According to the second classification result, identifying the disease classification dimensions corresponding to all the data files respectively;

[0021] If the medical service dimension corresponding to the current data file is the general medical service dimension, and the disease classification dimension corresponding to the current data file is the fourth-level disease classification dimension, the monitoring tool is used to continue monitoring the current data file;

[0022] If the medical service dimension corresponding to the current data file is a common medical service dimension, and the disease classification dimension corresponding to the current data file is a non-fourth-level disease classification dimension, then the medical service dimension upgrade mark and processing are performed on the current data file;

[0023] If the medical service dimension corresponding to the current data file is a non-ordinary medical service dimension, and the disease classification dimension corresponding to the current data file is a fourth-level disease classification dimension, then the medical service dimension downgrade mark and processing are performed on the current data file;

[0024] If the medical service dimension corresponding to the current data file is not a common medical service dimension, and the disease classification dimension corresponding to the current data file is not a fourth-level disease classification dimension, the current data file is marked as a key file and is managed as a separate backup file.

[0025] Furthermore, the step of marking the first type of screening data files in the target database according to the first screening condition and the first distinguishing mark specifically includes:

[0026] Filter out all data files whose storage resource occupancy reaches a preset occupancy rate from the third classification result according to the size of the data files;

[0027] Using the first distinguishing mark to mark all data files whose storage resource occupancy reaches a preset occupancy rate;

[0028] All data files marked by the first distinguishing mark are classified as the first type of screening data files.

[0029] Furthermore, the step of marking the second type of screening data files in the target database according to the second screening condition and the second distinguishing mark specifically includes:

[0030] According to the data called information in the data files, all data files whose total frequency of data content called reaches a preset first frequency value are screened out from the fourth classification results;

[0031] Using the second distinguishing mark, all data files whose total frequency of data content being called reaches a preset first frequency value are marked respectively;

[0032] All data files marked by the second distinguishing mark are classified as the second type of screening data files.

[0033] Furthermore, the step of identifying the screening data files marked by both the first distinguishing mark and the second distinguishing mark, and performing a first governance operation on the data files according to the first governance strategy, specifically includes:

[0034] identifying a screening data file marked with both a first distinguishing mark and a second distinguishing mark;

[0035] According to the third classification result, identifying the storage resources respectively occupied by all the screening data files;

[0036] Based on the preset storage resource occupation limiting condition and the storage resources respectively occupied by all the screening data files, splitting all the screening data files;

[0037] According to the fourth classification result, identifying the total frequency of calling of the data contents corresponding to all the screened data files respectively;

[0038] According to the data calling information corresponding to all the screening data files, respectively, identifying the calling frequencies corresponding to different data contents in each screening data file;

[0039] The data content whose calling frequency exceeds the preset second frequency value is stored in a separate file.

[0040] Furthermore, the step of identifying all data files without distinguishing marks and performing a second governance operation on the data files according to a second governance strategy specifically includes:

[0041] Identify all data files that do not carry a distinguishing mark;

[0042] According to the third classification result, identifying the storage resources respectively occupied by all the data files;

[0043] Based on a preset storage resource occupation limiting condition and the storage resources respectively occupied by all the data files, merging all the data files;

[0044] According to the fourth classification result, identifying the total frequency of calling of the data contents corresponding to all the data files respectively;

[0045] According to the data calling information corresponding to all the data files, respectively, identifying the calling frequencies corresponding to different data contents in each data file;

[0046] The data files are merged based on the total frequency of calling the data contents respectively corresponding to all the data files, the preset first frequency value and the frequency of calling corresponding to different data contents in each data file.

[0047] In order to solve the above technical problems, the embodiment of the present application also provides a data management device, which adopts the following technical solutions:

[0048] A data management device, comprising:

[0049] A pre-classification processing module is used to perform pre-classification processing on all data files in the target database based on a preset classification dimension to obtain pre-classified data files;

[0050] A first screening processing module, used for marking a first type of screening data file in the target database according to a first screening condition and a first distinguishing mark, wherein the first screening condition includes a data file size;

[0051] A second screening processing module, used for marking the second type of screening data files in the target database according to a second screening condition and a second distinguishing mark, wherein the second screening condition includes data call information;

[0052] A first identification processing module, used to identify the screening data file with only one distinguishing mark, and continue to monitor the data file using a preset monitoring tool;

[0053] A second identification processing module is used to identify the screening data file marked by both the first distinguishing mark and the second distinguishing mark, and perform a first governance operation on the data file according to the first governance strategy;

[0054] The third identification processing module is used to identify all data files without distinguishing marks and perform a second governance operation on the data files according to a second governance strategy.

[0055] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0056] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the data governance method described above when executing the computer-readable instructions.

[0057] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0058] A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of the data governance method as described above.

[0059] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0060] The data governance method described in the embodiment of the present application pre-classifies all data files in the target database through preset classification dimensions to obtain pre-classified data files; screens and distinguishes data files according to screening and marking rules, and subsequently performs governance operations on corresponding data files according to different governance strategies. Applying the data governance method described in the present application to the intelligent management scenario of medical service data can effectively optimize data storage and space utilization, and subsequently help medical service providers to quickly select matching and professional medical and health services for target users from huge medical service data, and quickly link to high-quality medical and service resources; and it can also combine the user's historical consultation situation to efficiently generate personal exclusive medical service files. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0063] Figure 2 is a flow chart of an embodiment of a data governance method according to the present application;

[0064] Figure 3 yes Figure 2 A flowchart of a specific embodiment of step 201 is shown;

[0065] Figure 4 It is a flowchart of a specific embodiment of data management in combination with non-self-attribute dimensions of data text in the data governance method described in this application;

[0066] Figure 5 yes Figure 2 A flowchart of a specific embodiment of step 202 is shown;

[0067] Figure 6 yes Figure 2 A flowchart of a specific embodiment of step 203 is shown;

[0068] Figure 7 yes Figure 2 A flowchart of a specific embodiment of step 205 is shown;

[0069] Figure 8 yes Figure 2 A flowchart of a specific embodiment of step 206 is shown;

[0070] Fig. 9 is a schematic diagram of the structure of an embodiment of a data management device according to the present application;

[0071] Fig.10 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0072] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0073] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0074] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0075] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0076] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0077] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0078] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0079] It should be noted that the data management method provided in the embodiment of the present application is generally executed by a server, and accordingly, the data management device is generally set in the server.

[0080] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0081] Continue to refer Figure 2 , shows a flow chart of an embodiment of a data governance method according to the present application. The data governance method comprises the following steps:

[0082] Step 201 : pre-classify all data files in the target database based on a preset classification dimension to obtain pre-classified data files.

[0083] In this embodiment, based on the preset classification dimensions, all data files in the target database are pre-classified to obtain the pre-classified data files. By first performing a preliminary classification on the data files using different classification dimensions, subsequent screening and marking processes can be performed directly according to the corresponding classification results, thereby reducing the complexity of screening and marking.

[0084] Step 202: Mark the first type of screening data files in the target database according to the first screening condition and the first distinguishing mark, wherein the first screening condition includes the size of the data file.

[0085] Specifically, a corresponding file size threshold or file size classification threshold is set according to the size of the data file. Then, according to the file size threshold or file size classification threshold, a first screening and a first distinguishing marking operation are performed on the data files in the target database. The first screening and the first distinguishing marking operation are performed based on the data file size to facilitate the subsequent management and operation of data files of different sizes.

[0086] Step 203: Mark the second type of screening data files in the target database according to the second screening condition and the second distinguishing mark, wherein the second screening condition includes data calling information.

[0087] Specifically, a corresponding calling frequency threshold or calling frequency grading threshold is set according to the data called information. Then, according to the calling frequency threshold or calling frequency grading threshold, a second screening and a second distinguishing marking operation are performed on the data files in the target database. The second screening and the second distinguishing marking operation are performed through the data called information to facilitate the subsequent management and operation of data files with different calling frequencies.

[0088] In this embodiment, the acquisition scope of the data called information can also pre-set the corresponding time range, for example: data called information within the past three months, data called information within the past week, or data called information within the past 2 hours, which is specifically set by the data administrator based on actual business conditions.

[0089] Step 204, identifying the screening data file with only one distinguishing mark, and continuing to monitor the data file using a preset monitoring tool.

[0090] In this embodiment, in combination with the above-mentioned step 202 and step 203, the screened data file with only one distinguishing mark can be understood as a file with a relatively large data file size but a relatively low calling frequency, or a file with a relatively small data file size but a relatively high calling frequency. For these two types of data files, the stability in the database is generally strong, and they are usually more suitable in terms of storage and calling. Therefore, the preset monitoring tool is continued to be used to continue monitoring the data files, and no substantial governance or optimization operations are taken to ensure the stability of the database.

[0091] Step 205: Identify the screening data files marked by both the first distinguishing mark and the second distinguishing mark, and perform a first governance operation on the data files according to the first governance policy.

[0092] In this embodiment, in combination with the above-mentioned step 202 and step 203, the screened data file marked by both the first distinguishing mark and the second distinguishing mark can be understood as a file with a relatively large data file size and a relatively large calling frequency. For this type of data file, it has a greater impact on the stability of the database. The relatively large data file size and the relatively large calling frequency indicate that the number of calls is large and the amount of data queried and screened during data acquisition is also large. Therefore, according to the first governance strategy, the first governance operation is performed on the data file to optimize the data file.

[0093] In this embodiment, the first governance strategy can generally be understood as a table partitioning strategy or a splitting strategy.

[0094] Through the first governance strategy, the first governance operation is performed on data files with large size and high calling frequency to ensure that files with large data volume in the database are processed, avoid calling resource consumption, and reduce database pressure.

[0095] Step 206: Identify all data files without distinguishing marks, and perform a second governance operation on the data files according to a second governance policy.

[0096] In this embodiment, in combination with the above-mentioned step 202 and step 203, the data file without a distinguishing mark can be understood as a file with a relatively small data file size and a relatively low calling frequency. For such data files, too many directory resources are occupied in the database. Therefore, according to the second governance strategy, the second governance operation is performed on the data file to merge and optimize the data file.

[0097] In this embodiment, the second governance strategy can generally be understood as a merging strategy.

[0098] Through the second management strategy, the second management operation is performed on data files with small size and low calling frequency to avoid such data files occupying too many directory resources in the database, thereby ensuring the rationality of data file storage in the database.

[0099] Applying the data governance method described in this application to the intelligent management scenario of medical service data can effectively optimize data storage and space utilization, and subsequently help medical service providers to quickly screen out matching and professional medical and health services for target users from the huge amount of medical service data, and quickly link them to high-quality medical and service resources; it can also combine the user's historical consultation history to efficiently generate personal exclusive medical service files.

[0100] In this embodiment, all data files in the target database are pre-classified through preset classification dimensions to obtain pre-classified data files; data files are screened and distinguished according to screening and marking rules, and then corresponding data files are governed according to different governance strategies. Applying the data governance method described in this application to the intelligent management scenario of medical service data can effectively optimize data storage and space utilization, and subsequently help medical service providers to quickly select matching and professional medical and health services for target users from huge medical service data, and quickly link to high-quality medical and service resources; and it can also combine the user's historical consultation situation to efficiently generate personal exclusive medical service files.

[0101] In this embodiment, the target database includes a medical data management database, and the preset classification dimensions include the data file's own attribute dimensions and non-own attribute dimensions. The data file's non-own attribute dimensions include the medical service dimension and the disease classification dimension. The data file's own attribute dimensions include the storage resource occupation dimension and the total frequency dimension of data content being called.

[0102] Specifically, the medical service dimension includes general medical service dimension and special medical service dimension, the difference between which lies in the service objects, medical resource allocation, diagnosis and treatment time arrangement, registration fees and medical environment. Under normal circumstances, general medical services are aimed at non-special groups with common symptoms, while special medical services are aimed at the elderly, young people, pregnant women and other people who are in emergency and prone to subsequent serious diseases; the disease classification dimension is divided into four levels in medical classification, namely level I, level II, level III and level IV, corresponding to level I, level II, level III and level IV. The fourth level is routine medical treatment and consultation, which is not a serious life-threatening situation; while the first, second and third levels are delayed conditions that pose a life-threatening situation.

[0103] Continue to refer Figure 3 , Figure 3 yes Figure 2 The flowchart of a specific embodiment of step 201 shown includes the following steps:

[0104] Step 301, pre-classifying all data files in the target database according to different medical services to obtain a first classification result;

[0105] Step 302, pre-classifying all data files in the target database according to different disease classifications to obtain a second classification result;

[0106] Step 303, pre-classifying all data files in the target database according to the difference in occupied storage resources to obtain a third classification result;

[0107] Step 304: pre-classify all data files in the target database according to the total frequencies of data content being called, to obtain a fourth classification result.

[0108] By pre-classifying all data files in the target database, i.e., the medical data management database, from a preset classification dimension, the medical data management database can be subsequently governed in combination with the classification results, thereby effectively optimizing data storage and space utilization.

[0109] Continue to refer Figure 4 In some optional implementations, after step 201, a step of combining the data text with non-self-attribute dimensions for data management is also included. Figure 4 It is a flowchart of a specific embodiment of data management in combination with the non-self-attribute dimension of data text in the data governance method described in this application, including the following steps:

[0110] Step 401, identifying the medical service dimensions corresponding to all the data files respectively according to the first classification result;

[0111] Step 402, identifying the disease classification dimensions corresponding to all the data files respectively according to the second classification result;

[0112] Step 403: If the medical service dimension corresponding to the current data file is the general medical service dimension, and the disease classification dimension corresponding to the current data file is the fourth-level disease classification dimension, the monitoring tool is used to continue monitoring the current data file;

[0113] Step 404: if the medical service dimension corresponding to the current data file is a common medical service dimension, and the disease classification dimension corresponding to the current data file is a non-fourth-level disease classification dimension, then the medical service dimension upgrade mark and processing are performed on the current data file;

[0114] Specifically, for medical service dimensions that are general medical service dimensions and disease classification dimensions that are not the fourth-level disease classification dimensions, corresponding medical service dimension upgrade markings and processing are provided to facilitate medical and health service providers to provide better medical services to patients with serious or higher levels of danger.

[0115] Step 405: if the medical service dimension corresponding to the current data file is a non-ordinary medical service dimension, and the disease classification dimension corresponding to the current data file is a fourth-level disease classification dimension, then the medical service dimension downgrade mark and process the current data file;

[0116] Specifically, for the medical service dimension that is a non-ordinary medical service dimension and the disease classification dimension that is a fourth-level disease classification dimension, corresponding medical service dimension downgrade marking and processing are provided to facilitate medical and health service providers to reasonably arrange limited medical service resources.

[0117] Step 406: If the medical service dimension corresponding to the current data file is not a common medical service dimension, and the disease classification dimension corresponding to the current data file is not a fourth-level disease classification dimension, the current data file is marked as a key file and is managed as a separate backup file.

[0118] Specifically, data management is performed in combination with non-attribute dimensions of the data text, such as the medical service dimension and the disease classification dimension, so that data files corresponding to patients with serious or high-risk conditions can be upgraded, marked or separately backed up, so that medical and health service providers can give priority to medical services.

[0119] Continue to refer Figure 5 , Figure 5 yes Figure 2 The flowchart of a specific embodiment of step 202 shown includes the following steps:

[0120] Step 501, filtering out all data files whose storage resource occupancy reaches a preset occupancy rate from the third classification result according to the size of the data files;

[0121] Step 502, using a first distinguishing mark to mark all data files whose storage resource occupancy reaches a preset occupancy rate;

[0122] Step 503: classify all data files marked by the first distinguishing mark as the first type of screening data files.

[0123] Specifically, all data files whose storage resource occupancy reaches a preset occupancy rate are selected from the third classification results, or all data files whose storage resource occupancy reaches a preset occupancy value are selected from the third classification results. The first screening and first distinguishing marking operations are performed based on the size of the data files, so as to facilitate the subsequent management and operation of data files of different sizes.

[0124] Continue to refer Figure 6 , Figure 6 yes Figure 2 The flowchart of a specific embodiment of step 203 shown includes the following steps:

[0125] Step 601, filtering out from the fourth classification results all data files whose total frequency of data content being called reaches a preset first frequency value according to the data called information in the data files;

[0126] Step 602, using a second distinguishing mark to mark all data files whose total frequency of data content being called reaches a preset first frequency value;

[0127] Step 603: classify all data files marked by the second distinguishing mark as the second type of screening data files.

[0128] The second screening and second distinguishing marking operations are performed through the data called information to facilitate the subsequent management and operation of data files with different calling frequencies.

[0129] Continue to refer Figure 7 , Figure 7 yes Figure 2 The flowchart of a specific embodiment of step 205 shown includes the following steps:

[0130] Step 701, identifying a screening data file marked by both a first distinguishing mark and a second distinguishing mark;

[0131] Step 702, identifying the storage resources occupied by all the screening data files respectively according to the third classification result;

[0132] Step 703, splitting all the screening data files based on the preset storage resource occupation limiting condition and the storage resources occupied by all the screening data files respectively;

[0133] Specifically, the preset storage resource occupation limit condition can be understood as the maximum storage resource that each data file can occupy. All the screened data files are split so that the storage resources corresponding to the sub-data files after the splitting process are all less than the preset storage resource occupation limit condition, thereby avoiding excessive data resources stored in a single data file, which is not conducive to database management and data call.

[0134] Step 704, identifying the total frequency of calling the data contents corresponding to all the screened data files respectively according to the fourth classification result;

[0135] Step 705, identifying the calling frequencies corresponding to different data contents in each of the screening data files according to the data calling information corresponding to all the screening data files;

[0136] Step 706: Data contents whose calling frequency exceeds the preset second frequency value are stored in a separate file.

[0137] In this embodiment, the preset second frequency value is a call frequency threshold set for the data content in the data file; and the preset first frequency value is a call frequency threshold set for the total call frequency of the entire data file, and the preset second frequency value is much smaller than the preset first frequency value. Steps 704 to 706 are to conduct a detailed check of the call frequency for the specific data content in the data file for the data file whose total call frequency reaches the preset first frequency value, so as to identify the high-frequency called data content in the data file for separate file storage.

[0138] Specifically, the storage resources occupied and the calling frequencies of all the screened data files are identified directly based on the third classification results and the fourth classification results, thereby improving the recognition efficiency. Moreover, the first governance operation is performed on the data files with large size and large calling frequency to ensure that the files with large data volume in the database are processed, avoiding the consumption of calling resources and reducing the pressure on the database.

[0139] Continue to refer Figure 8 , Figure 8 yes Figure 2 The flowchart of a specific embodiment of step 206 shown includes the following steps:

[0140] Step 801, identifying all data files without distinguishing marks;

[0141] Step 802, identifying storage resources respectively occupied by all the data files according to the third classification result;

[0142] Step 803, based on the preset storage resource occupation limiting condition and the storage resources respectively occupied by all the data files, merging all the data files;

[0143] Specifically, when all the data files are merged based on the preset storage resource occupancy limit condition and the storage resources respectively occupied by all the data files, it is ensured that the storage resources occupied by the final merged data files do not exceed the preset storage resource occupancy limit condition.

[0144] Step 804, identifying the total frequency of calling the data contents corresponding to all the data files respectively according to the fourth classification result;

[0145] Step 805, identifying the calling frequencies corresponding to different data contents in each data file according to the data calling information corresponding to all the data files;

[0146] Step 806: Based on the total call frequency of the data contents corresponding to all the data files, the preset first frequency value, and the call frequency corresponding to different data contents in each data file, the data files are merged.

[0147] Specifically, when the data files are merged based on the total frequency of calling the data contents corresponding to all the data files, the preset first frequency value and the calling frequency corresponding to different data contents in each data file, it is ensured that the total calling frequency of the final merged data file is not higher than the preset first frequency value.

[0148] It should be understood that in this embodiment, Figure 7 and Figure 8 For the same data file, only one of the two data processing methods can be selected. Figure 7 and Figure 4 ,as well as Figure 8 and Figure 4 The data processing method provided allows both to process the same data file simultaneously.

[0149] In addition, the data governance method described in this application, its governance steps can also be displayed on the target interface in combination with corresponding display tools for data management personnel to conduct real-time governance viewing, for example: connecting the Grafana display component to display the data governance process. Grafana is an open source data visualization tool that is widely used in data analysis and system monitoring.

[0150] In this embodiment, the data governance method is applied to the intelligent management scenario of medical service data, which not only avoids blindly merging the data in the medical service database according to the storage capacity, but also can reasonably adjust the risk level of the disease data stored in the data file, and can effectively optimize the data storage and space utilization. Subsequently, it will help medical service providers to quickly screen out matching and professional medical and health services for target users from the huge medical service data, and quickly link to high-quality medical and service resources; and it can also combine the user's historical consultation situation to efficiently generate personal exclusive medical service files.

[0151] This application pre-classifies all data files in the target database through preset classification dimensions to obtain pre-classified data files; according to screening and marking rules, the data files are screened and distinguished and marked, and then the corresponding data files are governed according to different governance strategies. Applying the data governance method described in this application to the intelligent management scenario of medical service data can effectively optimize data storage and space utilization, and subsequently help medical service providers to quickly select matching and professional medical and health services for target users from the huge amount of medical service data, and quickly link to high-quality medical and service resources; it can also combine the user's historical consultation situation to efficiently generate personal exclusive medical service files.

[0152] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0153] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0154] In the embodiment of the present application, all data files in the target database are pre-classified through preset classification dimensions to obtain pre-classified data files; data files are screened and distinguished according to screening and marking rules, and then corresponding data files are governed according to different governance strategies. Applying the data governance method described in this application to the intelligent management scenario of medical service data can effectively optimize data storage and space utilization, and subsequently help medical service providers to quickly select matching and professional medical and health services for target users from the huge medical service data, and quickly link to high-quality medical and service resources; and it can also combine the user's historical consultation situation to efficiently generate personal exclusive medical service files.

[0155] Further references Fig. 9 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a data management device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0156] like Fig. 9 As shown, the data management device 900 described in this embodiment includes: a pre-classification processing module 901, a first screening processing module 902, a second screening processing module 903, a first identification processing module 904, a second identification processing module 905 and a third identification processing module 906. Among them:

[0157] The pre-classification processing module 901 is used to perform pre-classification processing on all data files in the target database based on a preset classification dimension to obtain pre-classified data files;

[0158] A first screening processing module 902, used to mark the first type of screening data files in the target database according to a first screening condition and a first distinguishing mark, wherein the first screening condition includes the size of the data file;

[0159] A second screening processing module 903, used for marking the second type of screening data files in the target database according to a second screening condition and a second distinguishing mark, wherein the second screening condition includes data call information;

[0160] A first identification processing module 904 is used to identify the screening data file with only one distinguishing mark, and continue to monitor the data file using a preset monitoring tool;

[0161] The second identification processing module 905 is used to identify the screening data files marked by both the first distinguishing mark and the second distinguishing mark, and perform a first governance operation on the data files according to the first governance strategy;

[0162] The third identification processing module 906 is used to identify all data files without distinguishing marks and perform a second governance operation on the data files according to a second governance strategy.

[0163] This application pre-classifies all data files in the target database through preset classification dimensions to obtain pre-classified data files; according to screening and marking rules, the data files are screened and distinguished and marked, and then the corresponding data files are governed according to different governance strategies. Applying the data governance method described in this application to the intelligent management scenario of medical service data can effectively optimize data storage and space utilization, and subsequently help medical service providers to quickly select matching and professional medical and health services for target users from the huge amount of medical service data, and quickly link to high-quality medical and service resources; it can also combine the user's historical consultation situation to efficiently generate personal exclusive medical service files.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0165] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0166] To solve the above technical problems, the present application also provides a computer device. Fig.10 , Fig.10 This is a basic structural block diagram of the computer device in this embodiment.

[0167] The computer device 10 includes a memory 10a, a processor 10b, and a network interface 10c which are interconnected and communicated via a system bus. It should be noted that: Fig.10 Only a computer device 10 having a component memory 10a, a processor 10b, and a network interface 10c is shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0168] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0169] The memory 10a includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 10a can be an internal storage unit of the computer device 10, such as a hard disk or memory of the computer device 10. In other embodiments, the memory 10a can also be an external storage device of the computer device 10, such as a plug-in hard disk equipped on the computer device 10, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 10a can also include both the internal storage unit of the computer device 10 and its external storage device. In this embodiment, the memory 10a is generally used to store the operating system and various application software installed on the computer device 10, such as a computer-readable instruction of a data management method, etc. In addition, the memory 10a can also be used to temporarily store various types of data that have been output or are to be output.

[0170] The processor 10b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 10b is generally used to control the overall operation of the computer device 10. In this embodiment, the processor 10b is used to run the computer-readable instructions stored in the memory 10a or process data, such as running the computer-readable instructions of the data governance method.

[0171] The network interface 10c may include a wireless network interface or a wired network interface. The network interface 10c is generally used to establish a communication connection between the computer device 10 and other electronic devices.

[0172] The computer device proposed in this embodiment belongs to the field of medical data management technology and is applied to medical data management scenarios. This application pre-classifies all data files in the target database through preset classification dimensions to obtain pre-classified data files; according to screening and marking rules, the data files are screened and distinguished, and then the corresponding data files are governed according to different governance strategies. Applying the data governance method described in this application to the intelligent management scenario of medical service data can effectively optimize the data storage and space utilization, and subsequently help medical service providers to quickly select matching and professional medical and health services for target users from the huge medical service data, and quickly link to high-quality medical and service resources; and it can also combine the user's historical consultation situation to efficiently generate personal exclusive medical service files.

[0173] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by a processor to enable the processor to perform the steps of the data governance method as described above.

[0174] The computer-readable storage medium proposed in this embodiment belongs to the field of medical data management technology and is applied to medical data management scenarios. This application pre-classifies all data files in the target database through preset classification dimensions to obtain pre-classified data files; according to screening and marking rules, the data files are screened and distinguished, and then the corresponding data files are governed according to different governance strategies. Applying the data governance method described in this application to the intelligent management scenario of medical service data can effectively optimize the data storage and space utilization, and subsequently help medical service providers to quickly select matching and professional medical and health services for target users from the huge medical service data, and quickly link to high-quality medical and service resources; and it can also combine the user's historical consultation situation to efficiently generate personal exclusive medical service files.

[0175] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0176] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A data governance method, characterized in that: The steps include: Based on the preset classification dimension, pre-classify all data files in the target database to obtain pre-classified data files; Marking a first type of screening data file in the target database according to a first screening condition and a first distinguishing mark, wherein the first screening condition includes a data file size; Marking the second type of screening data files in the target database according to the second screening condition and the second distinguishing mark, wherein the second screening condition includes data called information; Identify the screening data file with only one distinguishing mark, and continue to monitor the data file using a preset monitoring tool; Identifying a screening data file marked by both a first distinguishing mark and a second distinguishing mark, and performing a first governance operation on the data file according to a first governance policy; All data files without distinguishing marks are identified, and a second governance operation is performed on the data files according to a second governance policy.

2. The data governance method according to claim 1, characterized in that: The target database includes a medical data management database, the preset classification dimensions include the data file's own attribute dimension and the non-self attribute dimension, the data file's non-self attribute dimension includes the medical service dimension and the disease classification dimension, the data file's own attribute dimension includes the storage resource occupation dimension and the data content is called the total frequency dimension, the step of pre-classifying all data files in the target database based on the preset classification dimensions to obtain the pre-classified data files specifically includes: Pre-classifying all data files in the target database according to different medical services to obtain a first classification result; According to different disease classifications, pre-classify all data files in the target database to obtain a second classification result; Pre-classifying all data files in the target database according to the difference in occupied storage resources to obtain a third classification result; According to the total frequency of data content being called, all data files in the target database are pre-classified to obtain a fourth classification result.

3. The data governance method according to claim 2, characterized in that: After executing the step of performing pre-classification processing on all data files in the target database based on the preset classification dimension to obtain the pre-classified data files, the method further includes: According to the first classification result, identifying the medical service dimensions corresponding to all the data files respectively; According to the second classification result, identifying the disease classification dimensions corresponding to all the data files respectively; If the medical service dimension corresponding to the current data file is the general medical service dimension, and the disease classification dimension corresponding to the current data file is the fourth-level disease classification dimension, the monitoring tool is used to continue monitoring the current data file; If the medical service dimension corresponding to the current data file is a common medical service dimension, and the disease classification dimension corresponding to the current data file is a non-fourth-level disease classification dimension, then the medical service dimension upgrade mark and processing are performed on the current data file; If the medical service dimension corresponding to the current data file is a non-ordinary medical service dimension, and the disease classification dimension corresponding to the current data file is a fourth-level disease classification dimension, then the medical service dimension downgrade mark and processing are performed on the current data file; If the medical service dimension corresponding to the current data file is not a common medical service dimension, and the disease classification dimension corresponding to the current data file is not a fourth-level disease classification dimension, the current data file is marked as a key file and is managed as a separate backup file.

4. The data governance method according to claim 2, characterized in that: The step of marking the first type of screening data files in the target database according to the first screening condition and the first distinguishing mark specifically includes: Filter out all data files whose storage resource occupancy reaches a preset occupancy rate from the third classification result according to the size of the data files; Using the first distinguishing mark to mark all data files whose storage resource occupancy reaches a preset occupancy rate; All data files marked by the first distinguishing mark are classified as the first type of screening data files.

5. The data governance method according to claim 2, characterized in that: The step of marking the second type of screening data files in the target database according to the second screening condition and the second distinguishing mark specifically includes: According to the data called information in the data files, all data files whose total frequency of data content called reaches a preset first frequency value are screened out from the fourth classification results; Using the second distinguishing mark, all data files whose total frequency of data content being called reaches a preset first frequency value are marked respectively; All data files marked by the second distinguishing mark are classified as the second type of screening data files.

6. The data governance method according to claim 2, characterized in that: The step of identifying the screening data file marked by both the first distinguishing mark and the second distinguishing mark, and performing a first governance operation on the data file according to the first governance strategy specifically includes: identifying a screening data file marked with both a first distinguishing mark and a second distinguishing mark; According to the third classification result, identifying the storage resources respectively occupied by all the screening data files; Based on the preset storage resource occupation limiting condition and the storage resources respectively occupied by all the screening data files, splitting all the screening data files; According to the fourth classification result, identifying the total frequency of calling of the data contents corresponding to all the screened data files respectively; According to the data calling information corresponding to all the screening data files, respectively, identifying the calling frequencies corresponding to different data contents in each screening data file; The data content whose calling frequency exceeds the preset second frequency value is stored in a separate file.

7. The data governance method according to claim 2, characterized in that: The step of identifying all data files without distinguishing marks and performing a second governance operation on the data files according to the second governance strategy specifically includes: Identify all data files that do not carry a distinguishing mark; According to the third classification result, identifying the storage resources respectively occupied by all the data files; Based on a preset storage resource occupation limiting condition and the storage resources respectively occupied by all the data files, merging all the data files; According to the fourth classification result, identifying the total frequency of calling of the data contents corresponding to all the data files respectively; According to the data calling information corresponding to all the data files, respectively, identifying the calling frequencies corresponding to different data contents in each data file; The data files are merged based on the total frequency of calling the data contents respectively corresponding to all the data files, the preset first frequency value and the frequency of calling corresponding to different data contents in each data file.

8. A data management device, characterized in that: include: A pre-classification processing module is used to perform pre-classification processing on all data files in the target database based on a preset classification dimension to obtain pre-classified data files; A first screening processing module, used for marking a first type of screening data file in the target database according to a first screening condition and a first distinguishing mark, wherein the first screening condition includes a data file size; A second screening processing module, used for marking the second type of screening data files in the target database according to a second screening condition and a second distinguishing mark, wherein the second screening condition includes data call information; A first identification processing module, used to identify the screening data file with only one distinguishing mark, and continue to monitor the data file using a preset monitoring tool; A second identification processing module is used to identify the screening data file marked by both the first distinguishing mark and the second distinguishing mark, and perform a first governance operation on the data file according to the first governance strategy; The third identification processing module is used to identify all data files without distinguishing marks and perform a second governance operation on the data files according to a second governance strategy.

9. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the data governance method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data governance method as described in any one of claims 1 to 7.

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