Examination department database optimization method and system

Abstract

The invention provides an optimization method and system for an examination department database, and the method comprises the steps: obtaining examination departments which all sub-databases of the examination department database belong to and department detection information, and recognizing the data generation information of the detection data type of each examination department; for each examination department, based on the data generation information of each detection data type of the examination department, identifying a data replacement logic and a data criticality identification strategy of a sub-database corresponding to the examination department, and screening each target replacement data; and based on each piece of target replacement data of the sub-database, through the data replacement logic of the sub-database, identifying a data replacement strategy of each piece of target replacement data, and based on the data replacement strategy of each piece of target replacement data, performing data storage optimization processing on the sub-database. And completing a data optimization task of the sub-database. By adopting the scheme, the storage optimization effect of the examination department database can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of database construction and data optimized storage, and particularly relates to an optimization method and system for an examination department database. Background Art

[0002] The traditional way to construct a hospital database is to integrate the data of each department and summarize the data by department to obtain a comprehensive database. However, there is a large amount of new data content in each examination department every day, which easily saturates the database memory of the examination department database. And when data is updated and iterated, the data content in the examination department database cannot be updated and replaced in time, resulting in poor storage optimization effect of the examination department database. Summary of the Invention

[0003] The main object of the present invention is to provide an optimization method and system for an examination department database, aiming to solve the problem in the prior art that due to a large amount of new data content in the examination department every day, the database memory of the examination department database is easily saturated, and when data is updated and iterated, the data content in the examination department database cannot be updated and replaced in time, resulting in poor storage optimization effect of the examination department database.

[0004] To achieve the above object, the present invention provides an optimization method for an examination department database, the method comprising:

[0005] Obtain the examination departments to which the sub-databases of the examination department database belong, and the department detection information of each of the examination departments, and based on the department detection information of each examination department, identify the detection data types of each examination department and the data generation information corresponding to each detection data type;

[0006] For each examination department, based on the various detection data types of the examination department and the data generation information corresponding to each of the detection data types, identify the data change logic of the sub-database corresponding to the examination department and the data criticality identification strategy of the sub-database, and based on the data criticality identification strategy of the sub-database, screen out each target changed data from the current data contents of the sub-database;

[0007] Based on the various target changed data of the sub-database, through the data change logic of the sub-database, identify the data change strategies of each of the target changed data, and based on the data change strategies of each of the target changed data, perform data storage optimization processing on the sub-database to complete the data optimization task of the sub-database.

[0008] Optionally, identifying the detection data types of each inspection department and the data generation information corresponding to each detection data type based on the department detection information of each inspection department includes:

[0009] For each inspection department, based on the department detection information of the inspection department, identify the detection types of the inspection department and the detection methods of the inspection department for each detection type, and for each detection type, based on the detection methods of the detection type, identify the various detection data types generated by the detection type and the data generation methods of the various detection data types;

[0010] Based on the data generation methods of the various detection data types, query the data generation information of the various detection data types in the database.

[0011] Optionally, identifying the data change logic of the sub-database corresponding to the inspection department and the data criticality identification strategy of the sub-database based on the various detection data types of the inspection department and the data generation information corresponding to each detection data type includes:

[0012] For each detection data type, based on the detection data type, query the data change requirement information of the detection data type in the detection database, and based on the data generation information corresponding to the detection data type, identify the data generation frequency of the detection data type and the basic data information of the detection data type;

[0013] Based on the data change requirement information of the detection data type, identify the data change method and the data change frequency of the detection data type, and based on the data change methods and the data change frequencies of the various detection data types, determine the data change logic of the sub-database;

[0014] Based on the basic data information of the various detection data types and the data generation frequencies of the various detection data types, identify the sub-criticality identification strategies of the various detection data types, and use the sub-criticality identification strategies of all detection data types as the data criticality identification strategy of the sub-database.

[0015] Optionally, screening each target changed data from the current data contents of the sub-database based on the data criticality identification strategy of the sub-database includes:

[0016] Identify the detection data type corresponding to each current data content and the basic data information of each current data content, and based on the basic data information of each current data content, identify the data criticality of each current data content through the sub-criticality identification strategy of the detection data type corresponding to each current data content;

[0017] Screen the current data content corresponding to the data criticality less than the data criticality threshold as the target replacement data.

[0018] Optionally, for each target replacement data based on the sub-database, identify the data replacement strategy of each target replacement data through the data replacement logic of the sub-database, including:

[0019] In the sub-database, query the associated data content corresponding to each target replacement data, and for each target replacement data, identify the data replacement frequency and the data replacement range of the target replacement data based on the basic data information of the associated data content of the target replacement data and the data generation frequency of the target replacement data;

[0020] Generate the data replacement strategy of the target replacement data based on the data replacement frequency and the data replacement range of the target replacement data.

[0021] Optionally, perform data storage optimization processing on the sub-database based on the data replacement strategies of each target replacement data to complete the data optimization task of the sub-database, including:

[0022] Based on the data replacement strategy of each target replacement data, identify the data replacement time point of each target replacement data and the data replacement process of each target replacement data;

[0023] When the current time point is the data replacement time point of each target replacement data, perform data replacement processing on each target replacement data based on the data replacement range of each target replacement data and the associated data content of each target replacement data through the data replacement process of each target replacement data to obtain the new data content corresponding to each target replacement data;

[0024] Replace the target replacement data and the associated data content corresponding to each target replacement data in the sub-database with the new data content to complete the data optimization task of the sub-database.

[0025] In addition, to achieve the above object, the present invention also provides an optimization system for an examination department database, and the optimization system for the examination department database includes:

[0026] An acquisition module for acquiring the examination departments to which the sub-databases of the examination department database belong and the department detection information of each examination department, and identifying the detection data types of each examination department and the data generation information corresponding to each detection data type based on the department detection information of each examination department;

[0027] A screening module, for each examination department, based on each detection data type of the examination department and the data generation information corresponding to each detection data type, identify the data change logic of the sub-database corresponding to the examination department and the data criticality identification strategy of the sub-database, and based on the data criticality identification strategy of the sub-database, screen each target changed data from the current data contents of the sub-database;

[0028] An optimization module, for each target changed data of the sub-database, through the data change logic of the sub-database, identify the data change strategy of each target changed data, and based on the data change strategy of each target changed data, perform data storage optimization processing on the sub-database to complete the data optimization task of the sub-database.

[0029] Optionally, the obtaining module is specifically configured to:

[0030] For each examination department, based on the department detection information of the examination department, identify the detection types of the examination department and the detection methods of the examination department for each detection type, and for each detection type, based on the detection method of the detection type, identify each detection data type generated by the detection type and the data generation method of each detection data type;

[0031] Based on the data generation methods of each detection data type, query the data generation information of each detection data type in the database.

[0032] Optionally, the screening module is specifically configured to:

[0033] For each detection data type, based on the detection data type, query the data change requirement information of the detection data type in the detection database, and based on the data generation information corresponding to the detection data type, identify the data generation frequency of the detection data type and the basic data information of the detection data type;

[0034] Based on the data change requirement information of the detection data type, identify the data change method and the data change frequency of the detection data type, and based on the data change methods and the data change frequencies of each detection data type, determine the data change logic of the sub-database;

[0035] Based on the basic data information of each detection data type and the data generation frequency of each detection data type, identify the sub-criticality identification strategy of each detection data type, and use the sub-criticality identification strategies of all detection data types as the data criticality identification strategy of the sub-database.

[0036] Optionally, the screening module is specifically configured to:

[0037] Identify the detection data type corresponding to each current data content and the basic data information of each current data content, and based on the basic data information of each current data content, identify the data criticality of each current data content through the sub-criticality identification strategy of the detection data type corresponding to each current data content;

[0038] Screen the current data content corresponding to the data criticality less than the data criticality threshold as the target replacement data.

[0039] Optionally, the optimization module is specifically configured to:

[0040] In the sub-database, query the associated data content corresponding to each target replacement data, and for each target replacement data, identify the data replacement frequency and the data replacement range of the target replacement data based on the basic data information of the associated data content of the target replacement data and the data generation frequency of the target replacement data;

[0041] Generate a data replacement strategy for the target replacement data based on the data replacement frequency of the target replacement data and the data replacement range of the target replacement data.

[0042] Optionally, the optimization module is specifically configured to:

[0043] Identify the data replacement time point and the data replacement process of each target replacement data based on the data replacement strategy of each target replacement data;

[0044] When the current time point is the data replacement time point of each target replacement data, perform data replacement processing on each target replacement data based on the data replacement range of each target replacement data and the associated data content of each target replacement data through the data replacement process of each target replacement data to obtain the new data content corresponding to each target replacement data;

[0045] Replace each of the new data contents with each of the target replacement data and the associated data content corresponding to each of the target replacement data in the sub-database to complete the data optimization task of the sub-database.

[0046] In a third aspect, the present application provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the first aspects are implemented.

[0047] Fourthly, the present application provides a computer-readable storage medium. A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0048] Fifthly, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0049] The present invention provides an optimization method and system for an examination department database. The method includes: obtaining the examination departments to which the sub-databases of the examination department database belong, and the department detection information of each of the examination departments, and based on the department detection information of each examination department, identifying the detection data types of each examination department and the data generation information corresponding to each detection data type; for each examination department, based on the various detection data types of the examination department and the data generation information corresponding to each of the detection data types, identifying the data change logic of the sub-database corresponding to the examination department and the data criticality identification strategy of the sub-database, and based on the data criticality identification strategy of the sub-database, screening out each target changed data from the current data contents of the sub-database; based on the various target changed data of the sub-database, through the data change logic of the sub-database, identifying the data change strategy of each of the target changed data, and based on the data change strategy of each of the target changed data, performing data storage optimization processing on the sub-database to complete the data optimization task of the sub-database. In this solution, by performing criticality analysis on each data content in the sub-database corresponding to each examination department, starting from the data generation information corresponding to each detection data type, analyzing the data change analysis and data criticality identification of each detection data type, so as to screen out the target changed data corresponding to each detection data type. And performing data change optimization processing on each target changed data, avoiding the problem that the data volume in the database continues to be saturated and a large amount of non-important iterative data occupies the database space, enabling the data content in the examination department database to be continuously updated and iterated, while ensuring that the data content with high criticality in the examination department database is retained after the update and iteration, improving the data space optimization and adjustment effect of the examination department database, and thus comprehensively improving the storage optimization effect of the examination department database. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a flowchart of an optimization method for the examination department database provided by an embodiment of the present invention;

[0052] Figure 2 It is a schematic structural diagram of an optimization system for the examination department database provided by an embodiment of the present invention;

[0053] Figure 3 It is an internal structure diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0054] The optimization method for the examination department database provided by an embodiment of the present invention is applied to an optimization system for the examination department database. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used in the description of the present application in this specification are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the description and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the description and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0055] Referring to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0056] In order to enable those skilled in the technical field to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the drawings.

[0057] The optimization method for the examination department database provided by the embodiments of the present application can be applied to the application environment of optimizing the storage of the examination department database. Among them, this method can be applied to the terminal, or to the server, or to a system including the terminal and the server, and is realized through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, etc. Among them, the terminal analyzes the key degree of each data content in the sub-database corresponding to each examination department, starts from the data generation information generated by the data corresponding to each detection data type, analyzes the data change analysis and data key degree recognition of each detection data type, so as to screen the target change data corresponding to each detection data type. And perform data change optimization processing on each target change data, avoiding the problem that the data volume in the database continues to be saturated and a large amount of unimportant iterative data occupies the database space, enabling the data content in the examination department database to be continuously updated and iterated, while ensuring that the data content with high key degree in the examination department database is retained after the update and iteration, improving the data space optimization and adjustment effect of the examination department database, and thus comprehensively improving the storage optimization effect of the examination department database.

[0058] In one embodiment, as Figure 1 shown, a method for optimizing an examination department database is provided. Taking the application of this method to a terminal as an example, the method includes the following steps:

[0059] Step S101, obtain the examination departments to which the sub-databases of the examination department database belong, and the department detection information of each examination department, and based on the department detection information of each examination department, identify the detection data types of each examination department and the data generation information corresponding to each detection data type.

[0060] In this embodiment, the terminal identifies the examination department to which each sub-database belongs by querying the database information of each sub-database. Among them, the examination departments include, but are not limited to, electrocardiogram rooms, 24-hour ambulatory electrocardiogram rooms, exercise stress test rooms, 24-hour ambulatory blood pressure rooms, bone density rooms, etc. Among them, each examination department corresponds to a sub-database. And the department detection information of each examination department is the function information corresponding to the detection functions configured by each examination department. Among them, the function information includes the data types that the examination department can detect, and the generation frequency, generation efficiency, and generation volume of the detection data generated by the examination department for each data type. Therefore, based on the department detection information of the examination department, the terminal can directly identify the detection data types of each examination department and the data generation information corresponding to each detection data type. Specifically, the identification process of the above information will be described in detail later.

[0061] Step S102: For each examination department, based on the various types of detection data of the examination department and the data generation information corresponding to each type of detection data, identify the data change logic of the sub-database corresponding to the examination department and the data criticality identification strategy of the sub-database, and based on the data criticality identification strategy of the sub-database, screen each target changed data from the current data contents of the sub-database.

[0062] In this embodiment, the terminal, for each examination department, based on the various types of detection data of the examination department and the data generation information corresponding to each type of detection data, identifies the data change logic of the sub-database corresponding to the examination department and the data criticality identification strategy of the sub-database, and based on the data criticality identification strategy of the sub-database, screens each target changed data from the current data contents of the sub-database. Among them, the content represented by the data change logic is the data change method of each type of detection data in the sub-database of this examination department and the data change logic corresponding to the data change frequency of each type of detection data. Among them, the data criticality identification strategy is the criticality identification strategy for each data content detected by each type of detection data. The specific identification process will be described in detail later.

[0063] Step S103: Based on the target changed data of the sub-database, through the data change logic of the sub-database, identify the data change strategy of each target changed data, and based on the data change strategy of each target changed data, perform data storage optimization processing on the sub-database to complete the data optimization task of the sub-database.

[0064] In this embodiment, the terminal, based on the target changed data of the sub-database, through the data change logic of the sub-database, identifies the data change strategy of each target changed data, and based on the data change strategy of each target changed data, performs data storage optimization processing on the sub-database to complete the data optimization task of the sub-database. Among them, the data change strategy is an optimization method for optimizing each target changed data to reduce the redundant data with low criticality in the sub-database. The specific optimization process will be described in detail later.

[0065] Based on the above solution, by performing criticality analysis on each data content in the sub-database corresponding to each examination department, starting from the data generation information of each detection data type, analyzing the data change analysis and data criticality identification of each detection data type, the target change data corresponding to each detection data type is screened. And perform data change optimization processing on each target change data, avoiding the problem that the data volume in the database continues to be saturated and a large amount of non-important iterative data occupies the database space, enabling the data content in the examination department database to be continuously updated and iterated. While ensuring that the data content with high criticality in the examination department database is retained after the update and iteration, the data space optimization and adjustment effect of the examination department database is improved, thereby comprehensively improving the storage optimization effect of the examination department database.

[0066] Optionally, based on the department detection information of each examination department, identify the detection data types of each examination department and the data generation information corresponding to each detection data type, including: for each examination department, based on the department detection information of the examination department, identify the detection types of the examination department and the detection methods of the examination department for each detection type, and for each detection type, based on the detection methods of the detection type, identify the various detection data types generated by the detection type and the data generation methods of the various detection data types; based on the data generation methods of the various detection data types, query the data generation information of the various detection data types in the database.

[0067] In this embodiment, for each examination department, the terminal, based on the department detection information of the examination department, identifies the detection types of the examination department and the detection methods of the examination department for each detection type, and for each detection type, based on the detection methods of the detection type, identifies the various detection data types generated by the detection type and the data generation methods of the various detection data types. Among them, the detection types of each examination department include but are not limited to electrocardiogram detection type, ambulatory blood pressure detection type, bone density detection type, etc. Among them, the detection methods include but are not limited to data generation detection, manual analysis detection, data analysis detection, etc. The data generation detection is a detection method that directly outputs the detected data based on the detection instrument; the manual analysis detection is the data detection result obtained by manually analyzing the detected data; and the data analysis detection is the detection result obtained by the detection instrument through data statistics, summary, and analysis of the detected data. The data generation methods of the various detection data types are the output methods of the detection results of the various detection data types. The output methods include but are not limited to direct output, stage output, loop output, etc.

[0068] Finally, the terminal queries the data generation information of each detection data type in the database based on the data generation methods of each detection data type. Among them, each data generation method and each detection data type correspond to a data generation information. The data generation information includes specific generation parameters for data generation, such as generation frequency, generation rate, generation time period, and generation quantity, etc.

[0069] Based on the above solution, by identifying the data generation methods of each detection data type, the data generation information of each detection data type is queried, improving the comprehensiveness and accuracy of identifying the data generation information of each detection data type.

[0070] Optionally, based on the detection data types of each examination department and the data generation information corresponding to each detection data type, identify the data change logic of the sub-database corresponding to the examination department and the data criticality identification strategy of the sub-database, including: for each detection data type, based on the detection data type, query the data change requirement information of the detection data type in the detection database, and based on the data generation information corresponding to the detection data type, identify the data generation frequency of the detection data type and the basic data information of the detection data type; based on the data change requirement information of the detection data type, identify the data change method and the data change frequency of the detection data type, and based on the data change methods and the data change frequencies of each detection data type, determine the data change logic of the sub-database; based on the basic data information and the data generation frequencies of each detection data type, identify the sub-criticality identification strategies of each detection data type, and use the sub-criticality identification strategies of all detection data types as the data criticality identification strategy of the sub-database.

[0071] In this embodiment, for each detection data type, the terminal queries the data change requirement information of the detection data type in the detection database based on the detection data type, and based on the data generation information corresponding to the detection data type, identifies the data generation frequency of the detection data type and the basic data information of the detection data type. Among them, the data change requirement information in the detection database is the change frequency and change method of each data content in the detection database. Among them, the change method includes but is not limited to range change, data value change, continuous change, and overall change. Among them, different change methods represent information such as the data range and the amount of data to be changed for the data content to be changed.

[0072] Then, based on the data change requirement information of the detected data types, the terminal identifies the data change methods and data change frequencies of the detected data types, and determines the data change logic of the sub-database based on the data change methods and data change frequencies of each detected data type. Among them, the data change logic of the sub-database includes the sub-data change logic of each detected data type, and each sub-data change logic represents the change information of each data content of the detected data type. That is, the change parameters related to time, scope, data volume, frequency, etc. when each data content undergoes data change.

[0073] After that, based on the basic data information of each detected data type and the data generation frequency of each detected data type, the terminal identifies the sub-key degree identification strategies of each detected data type. Among them, the sub-key degree identification strategy is a strategy for identifying the key degree of the data content of each data detection type. Among them, the basic data information of each detected data type includes the data information of each data content generated by this data type, and this data information includes but is not limited to information such as data volume, the user to which the data content belongs, and the generation frequency of the data content. By identifying the data information of each data content, it is convenient to accurately identify the data key degree of each data content.

[0074] Finally, the terminal takes the sub-key degree identification strategies of all detected data types as the data key degree identification strategy of the sub-database.

[0075] Based on the above solution, by identifying the sub-key degree identification strategies and data change logic of each detected data type, the data key degree identification strategy of the sub-database and the data identification of the sub-database are identified, improving the comprehensiveness and accuracy of the analysis of each sub-database.

[0076] Optionally, based on the data key degree identification strategy of the sub-database, in each current data content of the sub-database, each target changed data is screened, including: identifying the detected data type corresponding to each current data content and the basic data information of each current data content, and based on the basic data information of each current data content, through the sub-key degree identification strategy of the detected data type corresponding to each current data content, identifying the data key degree of each current data content; screening the current data content corresponding to the data key degree less than the data key degree threshold as the target changed data.

[0077] In this embodiment, the terminal identifies the detection data type corresponding to each current data content and the basic data information of each current data content, and based on the basic data information of each current data content, through the sub-key degree identification strategy of the detection data type corresponding to each current data content, identifies the data key degree of each current data content. Among them, in the sub-key degree identification strategy, there is a corresponding relationship between different basic data information and data key degrees. The terminal identifies the data key degree of each current data content based on this corresponding relationship.

[0078] Finally, the terminal filters out the current data content corresponding to the data key degree less than the data key degree threshold as the target replacement data. Among them, the data key degree threshold is a value preset by the staff in the terminal.

[0079] Based on the above solution, through the sub-key degree identification strategies of each detection data type, the target replacement data in the current data content of each detection data type is respectively identified, improving the identification accuracy of the target replacement data.

[0080] Optionally, based on each target replacement data in the sub-database, through the data replacement logic of the sub-database, identify the data replacement strategy of each target replacement data, including: in the sub-database, query the associated data content corresponding to each target replacement data, and for each target replacement data, based on the basic data information of the associated data content of the target replacement data and the data generation frequency of the target replacement data, identify the data replacement frequency and the data replacement range of the target replacement data; based on the data replacement frequency and the data replacement range of the target replacement data, generate the data replacement strategy of the target replacement data.

[0081] In this embodiment, the terminal queries the associated data content corresponding to each target replacement data in the sub-database, and for each target replacement data, based on the basic data information of the associated data content of the target replacement data and the data generation frequency of the target replacement data, identifies the data replacement frequency and the data replacement range of the target replacement data. Among them, the associated data content of each target replacement data is the replacement content corresponding to each target replacement data, that is, the data that needs to be replaced in the target replacement data. Among them, except that the data volume of the associated data content is different from that of the target replacement data, other basic data information is the same as the basic data information of the target replacement data.

[0082] Finally, the terminal generates a data change strategy for the target change data based on the data change frequency of the target change data and the data change range of the target change data. Among them, the data change strategy is a positioning replacement strategy for the data change amount and the data change range of the target change data. Through this positioning replacement strategy, the change of the target change data can be realized, and the redundant data or discarded data after the change of the target change data can be screened for data optimization.

[0083] Based on the above solution, by identifying the associated data content corresponding to each target change data, the data change frequency of each target change data and the data change range of the target change data are analyzed, improving the recognition accuracy of the data change frequency and change range of each target change data.

[0084] Optionally, based on the data change strategies of each target change data, data storage optimization processing is performed on the sub-database to complete the data optimization task of the sub-database, including: based on the data change strategies of each target change data, identifying the data change time point of each target change data and the data change process of each target change data; when the current time point is the data change time point of each target change data, based on the data change range of each target change data and the associated data content of each target change data, through the data change process of each target change data, data change processing is performed on each target change data to obtain the new data content corresponding to each target change data; replacing each target change data and the associated data content corresponding to each target change data in the sub-database with each new data content to complete the data optimization task of the sub-database.

[0085] In this embodiment, the terminal identifies the data change time point of each target change data and the data change process of each target change data based on the data change strategy of each target change data. Among them, the data change time point is the time point when each target change data undergoes data change, and each data change process includes the process of data change for the target change data and data optimization after the change.

[0086] When the current time point is the data change time point of each target change data, based on the data change range of each target change data and the associated data content of each target change data, through the data change process of each target change data, data change processing is performed on each target change data to obtain the new data content corresponding to each target change data. Finally, the terminal replaces each target change data and the associated data content corresponding to each target change data in the sub-database with each new data content to complete the data optimization task of the sub-database.

[0087] Based on the above solution, by screening the target iteration data corresponding to each detected data type and performing data iteration optimization processing on each target iteration data, the problem that the data volume in the database continues to saturate and a large amount of unimportant iterative data occupies the database space is avoided, enabling the data content in the inspection department database to be continuously updated and iterated. While ensuring the retention of high-key data content in the inspection department database after the update iteration, the optimization effect of the data space in the inspection department database is improved, thereby comprehensively enhancing the storage optimization effect of the inspection department database.

[0088] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0089] Based on the same inventive concept, an embodiment of the present application further provides an optimization system for an inspection department database for implementing the optimization method for the inspection department database described above. The solution provided by this system to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the optimization system for the inspection department database provided below can refer to the limitations on the optimization method for the inspection department database in the above text, and will not be repeated here.

[0090] Further reference Figure 2 , as an implementation of the method shown above Figure 1 , an embodiment of an optimization system 200 for an inspection department database is provided in the present application. The optimization system for the inspection department database includes an acquisition module 210, a screening module 220, and an optimization module 230, where:

[0091] The acquisition module 210 is configured to acquire the inspection departments to which the sub-databases of the inspection department database belong, as well as the department detection information of each of the inspection departments, and based on the department detection information of each inspection department, identify the detected data types of each inspection department and the data generation information corresponding to each detected data type;

[0092] A screening module 220, which is used to, for each examination department, based on various detection data types of the examination department and data generation information corresponding to each of the detection data types, identify the data change logic of the sub-database corresponding to the examination department and the data key degree identification strategy of the sub-database, and based on the data key degree identification strategy of the sub-database, screen out each target changed data from each current data content of the sub-database;

[0093] An optimization module 230, which is used to, based on each target changed data of the sub-database, through the data change logic of the sub-database, identify the data change strategy of each of the target changed data, and based on the data change strategy of each of the target changed data, perform data storage optimization processing on the sub-database to complete the data optimization task of the sub-database.

[0094] Optionally, the obtaining module 210 is specifically used for:

[0095] For each examination department, based on the department detection information of the examination department, identify the detection types of the examination department and the detection methods of the examination department for each detection type, and for each detection type, based on the detection method of the detection type, identify each detection data type generated by the detection type and the data generation method of each of the detection data types;

[0096] Based on the data generation methods of each of the detection data types, query the data generation information of each of the detection data types in the database.

[0097] Optionally, the screening module 220 is specifically used for:

[0098] For each detection data type, based on the detection data type, query the data change requirement information of the detection data type in the detection database, and based on the data generation information corresponding to the detection data type, identify the data generation frequency of the detection data type and the data basic information of the detection data type;

[0099] Based on the data change requirement information of the detection data type, identify the data change method and the data change frequency of the detection data type, and based on the data change methods and the data change frequencies of each of the detection data types, determine the data change logic of the sub-database;

[0100] Based on the data basic information of each of the detection data types and the data generation frequencies of each of the detection data types, identify the sub-key degree identification strategies of each of the detection data types, and use the sub-key degree identification strategies of all detection data types as the data key degree identification strategy of the sub-database.

[0101] Optionally, the screening module 220 is specifically configured to:

[0102] Identify the detection data type corresponding to each current data content and the basic data information of each current data content, and based on the basic data information of each current data content, identify the data criticality of each current data content through the sub-criticality identification strategy of the detection data type corresponding to each current data content;

[0103] Screen the current data content corresponding to the data criticality less than the data criticality threshold as the target replacement data.

[0104] Optionally, the optimization module 230 is specifically configured to:

[0105] In the sub-database, query the associated data content corresponding to each target replacement data, and for each target replacement data, identify the data replacement frequency and the data replacement range of the target replacement data based on the basic data information of the associated data content of the target replacement data and the data generation frequency of the target replacement data;

[0106] Generate a data replacement strategy for the target replacement data based on the data replacement frequency and the data replacement range of the target replacement data.

[0107] Optionally, the optimization module 230 is specifically configured to:

[0108] Identify the data replacement time point and the data replacement process of each target replacement data based on the data replacement strategy of each target replacement data;

[0109] When the current time point is the data replacement time point of each target replacement data, perform data replacement processing on each target replacement data through the data replacement process of each target replacement data based on the data replacement range of each target replacement data and the associated data content of each target replacement data, to obtain the new data content corresponding to each target replacement data;

[0110] Replace each of the new data contents with each of the target replacement data and the associated data content corresponding to each of the target replacement data in the sub-database to complete the data optimization task of the sub-database.

[0111] Each module in the above-mentioned optimization system of the examination department database can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0112] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input system connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes an optimization method for the examination department database. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input system of the computer device can be a touch layer covered on the display screen, or buttons, a trackball, or a touchpad set on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0113] Those skilled in the art can understand that Figure 3 the structure shown in

[0114] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0115] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it realizes the steps of the method described in any item of the first aspect.

[0116] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it realizes the steps of the method described in any item of the first aspect.

[0117] It should be noted that the patient information involved in this application (including but not limited to patient device information, patient personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) are all information and data authorized by the patient or fully authorized by all parties.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0119] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0120] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An optimization method for the inspection department database, characterized in that The method includes: Obtain the inspection departments to which each sub-database in the inspection department database belongs, and the department detection information of each inspection department, and based on the department detection information of each inspection department, identify the detection data types of each inspection department and the data generation information corresponding to each detection data type; For each inspection department, based on the various detection data types of the inspection department and the data generation information corresponding to each detection data type, identify the data change logic of the sub-database corresponding to the inspection department and the data criticality identification strategy of the sub-database, and based on the data criticality identification strategy of the sub-database, screen each target changed data from the current data contents of the sub-database; Based on the various target changed data of the sub-database, through the data change logic of the sub-database, identify the data change strategies of each target changed data, and based on the data change strategies of each target changed data, perform data storage optimization processing on the sub-database to complete the data optimization task of the sub-database.

2. The method according to claim 1, characterized in that, The identifying the detection data types of each inspection department and the data generation information corresponding to each detection data type based on the department detection information of each inspection department includes: For each inspection department, based on the department detection information of the inspection department, identify the detection types of the inspection department and the detection methods of the inspection department for each detection type, and for each detection type, based on the detection method of the detection type, identify the various detection data types generated by the detection type and the data generation methods of each detection data type; Based on the data generation methods of the various detection data types, query the data generation information of the various detection data types in the database.

3. The method according to claim 1, characterized in that The identifying the data change logic of the sub-database corresponding to the inspection department and the data criticality identification strategy of the sub-database based on the various detection data types of the inspection department and the data generation information corresponding to each detection data type includes: For each detection data type, based on the detection data type, query the data change requirement information of the detection data type in the detection database, and based on the data generation information corresponding to the detection data type, identify the data generation frequency of the detection data type and the basic data information of the detection data type; Based on the data change requirement information of the detection data type, identify the data change method and the data change frequency of the detection data type, and based on the data change methods and the data change frequencies of the various detection data types, determine the data change logic of the sub-database; Based on the basic data information of the various detection data types and the data generation frequencies of the various detection data types, identify the sub-criticality identification strategies of the various detection data types, and use the sub-criticality identification strategies of all detection data types as the data criticality identification strategy of the sub-database.

4. The method according to claim 3, characterized in that The data criticality identification strategy based on the sub-database screens each target changing data from the current data contents of the sub-database, including: Identifying the detection data type corresponding to each current data content and the data basic information of each current data content, and based on the data basic information of each current data content, identifying the data criticality of each current data content through the sub-criticality identification strategy of the detection data type corresponding to each current data content; Screening the current data content corresponding to the data criticality less than the data criticality threshold as the target changing data.

5. The method according to claim 3, characterized in that, The data change strategy of each target changing data is identified through the data change logic of the sub-database based on the target changing data of the sub-database, including: In the sub-database, querying the associated data content corresponding to each target changing data, and for each target changing data, identifying the data change frequency and the data change range of the target changing data based on the data basic information of the associated data content of the target changing data and the data generation frequency of the target changing data; Generating the data change strategy of the target changing data based on the data change frequency and the data change range of the target changing data.

6. The method according to claim 5, wherein The data storage optimization process of the sub-database is completed by the data change strategy based on each target changing data, including: Identifying the data change time point and the data change process of each target changing data based on the data change strategy of each target changing data; When the current time point is the data change time point of each target changing data, performing data change processing on each target changing data through the data change process of each target changing data based on the data change range of each target changing data and the associated data content of each target changing data, to obtain the new data content corresponding to each target changing data; Replacing each target changing data and the associated data content corresponding to each target changing data in the sub-database with the new data contents to complete the data optimization task of the sub-database.

7. An optimization system for the inspection department database, characterized in that, The system includes: An acquisition module, configured to acquire the inspection departments to which the sub-databases of the inspection department database belong, and the department inspection information of each inspection department, and based on the department inspection information of each inspection department, identify the detection data type of each inspection department and the data generation information corresponding to each detection data type; A screening module, configured to, for each inspection department, identify the data change logic of the sub-database corresponding to the inspection department and the data criticality identification strategy of the sub-database based on the detection data types of the inspection department and the data generation information corresponding to each detection data type, and based on the data criticality identification strategy of the sub-database, screen each target changing data from the current data contents of the sub-database; An optimization module, configured to identify data change strategies for each of the target change data based on the data change logic of the sub-database from the respective target change data of the sub-database, and perform data storage optimization processing on the sub-database based on the data change strategies for each of the target change data, thereby completing the data optimization task of the sub-database.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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