Management method, system and storage medium for government data asset security

Abstract

The present invention belongs to the field of data management technology, and specifically relates to a control method, system and storage medium for the security of government data assets. The method includes: S1, the control module divides the complete government data into a first part of government data and a second part of government data, stores the first part of government data after transformation in a central module, and stores the second part of government data after transformation in a backup module; S2, the control module obtains the data group of the central module corresponding to the past time period, and the first data group and the second data group of different customer modules; S3, the control module inputs the feature graph data and the second data group of different customer modules corresponding to the past time period into an analysis model, the analysis model outputs the code of the suspicious customer module, and the control module also generates analysis data according to the code of the suspicious customer module. The present invention can ensure that government data is not leaked, tampered with or abused.

Description

Technical Field

[0001] The present invention belongs to the field of data management technology, and specifically relates to a management and control method, system and storage medium for the security of government data assets. Background Art

[0002] With the in-depth development of government informationization, the security management and control of government data assets faces more and more challenges. How to build a comprehensive government data asset security management and control method to achieve comprehensive protection of government data assets and prevent government data from being leaked, tampered with and abused has become an urgent problem that needs to be solved.

[0003] The Chinese patent application with publication number CN116415220A discloses a data security management system, including a user terminal, a security monitoring host, a cloud database and a management server. The security monitoring host is connected to multiple encryption transmission modules via a network; the cloud database is connected to the management server network; the cloud database network is connected to a data processing module including a data identification unit, which is used to obtain data from the cloud database and perform hierarchical identification through the data identification unit. The data in the cloud database can only be sent after being processed by the data service module. The data associated with the hierarchical identification information is obtained from the cloud database and transmitted to the user terminal through the encryption transmission module. However, this patent cannot identify user terminals that may have suspicious behavior. In addition, the Chinese patent application with publication number CN118378303A discloses a database security management method and system, which includes: obtaining the user's registered account and password, and using the preset encryption rules to mask the password, the number of masks is at least one, generating ciphertext, determining the replacement rule, and replacing the mask in the ciphertext; determining the hash function, and hashing the replaced ciphertext, defining the obtained hash value as the target value, configuring the corresponding relationship between the account and the target value, and constructing a storage table. However, the patent directly stores the complete password data, and the security of the password data is poor. Summary of the invention

[0004] The present invention divides the complete government data into a first part of government data and a second part of government data, and stores the transformed first part of government data and the transformed second part of government data separately, and constructs an analysis model to identify suspicious customer modules that may have performed illegal behaviors from multiple customer modules, and generates analysis data about the suspicious customer modules. The present invention aims to provide a comprehensive government data asset security management and control method to ensure the security of government data.

[0005] In order to achieve the above-mentioned purpose of the invention, the present invention provides the following management and control method for government data asset security, which mainly includes the following steps:

[0006] S1. The control module divides the complete government data into a first part of government data and a second part of government data. The control module transforms the first part of government data and the second part of government data, and stores the transformed first part of government data in the central module and stores the transformed second part of government data in the backup module.

[0007] S2, the control module obtains a data group of the central module corresponding to a preset past time period, and a first data group and a second data group of different client modules, the data group of the central module is composed of a plurality of first data, the first data includes the time when the data application is sent to the central module, the code of the client module that sends the data application to the central module, and the relevant information of the data processing performed by the central module, the first data group of the client module is composed of a plurality of second data, the second data includes the communication start time, the communication duration, the code of the client module, the network where the client module is located, and the code of the terminal module for communication, the second data group of the client module is composed of a plurality of third data, the third data includes the time when the client module first initiates the data application, the code of the client module, the number of data applications initiated by the client module, and the scale of data obtained by the client module from the central module;

[0008] S3. The management and control module generates feature graph data according to the data group of the central module corresponding to the past time period and the first data group of different customer modules, and the management and control module inputs the feature graph data and the second data group of different customer modules corresponding to the past time period into a trained analysis model, and the analysis model outputs the code of the suspicious customer module, and the management and control module also generates analysis data according to the code of the suspicious customer module.

[0009] As a preferred technical solution of the present invention, S1 comprises the following steps:

[0010] S111, the control module performs encoding processing on the complete government data, and divides the encoded complete government data into a plurality of data groups, arbitrarily assigns the plurality of data groups to the central module and the backup module, and records the assignment information of the plurality of data groups;

[0011] S112, the control module uses the data groups assigned to the central module to form a first part of government data, and uses the data groups assigned to the backup module to form a second part of government data, and the control module performs a first transformation process on the second part of government data, and stores the second part of government data after the first transformation process in the backup module;

[0012] S113. The management and control module divides the first part of the government data into several data segments, and the data size of each data segment is the same as the data size of the second part of the government data after the first transformation processing. The management and control module uses the second part of the government data after the first transformation processing to perform a second transformation processing on each data segment, and stores the several data segments after the second transformation processing in the central module. The central module also stores the communication address of the backup module.

[0013] As a preferred technical solution of the present invention, the data scale of the second part of government data is less than or equal to the data scale of the first part of government data.

[0014] As a preferred technical solution of the present invention, the data scale of the second part of government data is less than or equal to a specific data scale, and the specific data scale refers to the product value of a preset time length and a network rate.

[0015] As a preferred technical solution of the present invention, after storing the first part of government data that has been transformed and the second part of government data that has been transformed, S1 further includes the following steps:

[0016] S121, the central module obtains the second part of government data processed by the first transformation from the backup module according to the communication address of the backup module, and the central module restores the original data segment for each data segment processed by the second transformation stored in the central module based on the second part of government data processed by the first transformation, and the central module also combines the restored original data segments into the original first part of government data;

[0017] S122, the central module performs a first inverse transformation on the second part of the government data that has undergone the first transformation to obtain the original second part of the government data, and based on the assignment information, the central module restores the original complete government data that has undergone the encoding processing from the original first part of the government data and the original second part of the government data;

[0018] S123. The central module decodes the restored original complete government data after encoding to obtain the original complete government data.

[0019] As a preferred technical solution of the present invention, the analysis model includes an input layer model, several intermediate layer models, and an output layer model. The second data groups of different customer modules corresponding to the past time period are directly input into the input layer model. The feature graph data corresponding to the past time period needs to be input into the extraction model to extract the corresponding representative data before being input into the input layer model. The representative data of the feature graph data corresponding to the past time period is directly input into the input layer model.

[0020] As a preferred technical solution of the present invention, the feature map data includes a number of feature points and a number of connecting lines between two different feature points, each feature point corresponds to a client module or a central module, and each feature point corresponds to feature point information, each connecting line corresponds to connecting line information, and the feature point information corresponding to each feature point and the connecting line information corresponding to each connecting line change dynamically over time.

[0021] As a preferred technical solution of the present invention, the process of the control module generating analysis data according to the coding of the suspicious customer module includes: the control module determines the corresponding feature points in the feature map data based on the coding of the suspicious customer module, and the control module determines the abnormal connection line information from the connection line information of the connection lines corresponding to the corresponding feature points at different time points, and uses the abnormal connection line information and the feature point information of the corresponding feature points simultaneously with the abnormal connection line information to generate analysis data.

[0022] The present invention also provides a management and control system for government data asset security, including the following modules:

[0023] A control module is used to divide the complete government data into a first part of government data and a second part of government data, and perform transformation processing on the first part of government data and the second part of government data;

[0024] It is also used to obtain a data group of the central module corresponding to a preset past time period, as well as a first data group and a second data group of different client modules, the data group of the central module is composed of a plurality of first data, the first data includes the time when a data application is sent to the central module, the code of the client module that sends the data application to the central module, and the relevant information of the data processing performed by the central module, the first data group of the client module is composed of a plurality of second data, the second data includes the communication start time, the communication duration, the code of the client module, the network where the client module is located, and the code of the terminal module for communication, the second data group of the client module is composed of a plurality of third data, the third data includes the time when the client module first initiates the data application, the code of the client module, the number of data applications initiated by the client module, and the scale of data obtained by the client module from the central module;

[0025] and used to generate feature graph data according to the data group of the central module corresponding to the past time period and the first data group of different customer modules, input the feature graph data and the second data group of different customer modules corresponding to the past time period into the trained analysis model to obtain the code of the suspicious customer module, and generate analysis data according to the code of the suspicious customer module;

[0026] A central module, used for storing the first part of government data after transformation;

[0027] A backup module, used for storing the second part of government affairs data after transformation;

[0028] The client module is used to communicate data with the central module or other client modules.

[0029] The present invention also provides a storage medium, wherein the storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute any one of the above methods.

[0030] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0031] In the present invention, first, the control module divides the complete government data into a first part of government data and a second part of government data, performs transformation processing on the first part of government data and the second part of government data, stores the transformed first part of government data in the central module, and stores the transformed second part of government data in the backup module; secondly, the control module obtains the data group of the central module corresponding to the past time period, as well as the first data group and the second data group of different customer modules; finally, the control module generates feature graph data according to the data group of the central module corresponding to the past time period and the first data group of different customer modules, inputs the feature graph data and the second data group of different customer modules corresponding to the past time period into the trained analysis model, the analysis model outputs the code of the suspicious customer module, and the control module also generates analysis data according to the code of the suspicious customer module, and performs control processing in a targeted manner based on the analysis data. Through the present invention, not only can the complete government data be separated for secret storage to ensure the data security of the government data, but also the suspicious customer module that may have performed suspicious behavior can be identified from multiple customer modules, so as to perform data control processing in a targeted manner, and further improve the data security of the government data. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of the steps of the method for managing and controlling the security of government data assets of the present invention;

[0033] Figure 2 This is a structural diagram of the composition of the management and control system for government data asset security of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0036] References Figure 1 As shown, the present invention provides a method for managing and controlling the security of government data assets, which is implemented by executing the following main steps:

[0037] S1. The control module divides the complete government data into a first part of government data and a second part of government data. At the same time, the control module performs transformation processing on the first part of government data and the second part of government data, and the control module stores the transformed first part of government data in the central module and stores the transformed second part of government data in the backup module.

[0038] S2, the control module obtains a data group of the central module corresponding to a preset past time period, and a first data group and a second data group of different client modules, the data group of the central module is composed of a plurality of first data, the first data includes the time when the data application is sent to the central module, the code of the client module that sends the data application to the central module, and the relevant information of the data processing performed by the central module, the first data group of the client module is composed of a plurality of second data, the second data includes the communication start time, the communication duration, the code of the client module, the network where the client module is located, and the code of the terminal module for communication, the second data group of the client module is composed of a plurality of third data, the third data includes the time when the client module first initiates the data application, the code of the client module, the number of data applications initiated by the client module, and the scale of data obtained by the client module from the central module;

[0039] S3. The management and control module generates feature graph data based on the data group of the central module corresponding to the past time period and the first data group of different customer modules, and the management and control module inputs the feature graph data and the second data group of different customer modules corresponding to the past time period into the trained analysis model, the analysis model outputs the code of the suspicious customer module, and the management and control module also generates analysis data based on the code of the suspicious customer module.

[0040] Specifically, in order to provide a comprehensive method for secure management and control of government data assets, the present invention stores complete government data separately in a central module and a backup module. The central module can restore the complete government data when necessary. In addition, suspicious customer modules are identified from multiple customer modules that can directly or indirectly access the central module, and management and control are performed based on the suspicious customer modules.

[0041] Here, the main steps of the above method are introduced. In S1, the control module first divides the complete government data into a first part of government data and a second part of government data, and then transforms the first part of government data and the second part of government data, stores the transformed first part of government data in the central module, and stores the transformed second part of government data in the backup module. The details of S1 will be described below. In S2, the control module obtains the data group of the central module corresponding to the preset past time period, as well as the first data group and the second data group of different client modules. The past time period can be, for example, the past two hours. The different data groups are obtained in order to identify suspicious client modules that may have performed suspicious behaviors in the past time period in subsequent steps. Suspicious behaviors include behaviors of illegally stealing data from the central module, behaviors of illegally attacking the central module, etc., wherein the data group of the central module is composed of a plurality of first data, the first data includes the time of sending a data application to the central module, the code of the client module sending a data application to the central module, and the relevant information of the data processing performed by the central module, and the relevant information includes the type of data processing performed by the central module, such as modification and deletion of data, etc., the first data of the client module The first data group is composed of a number of second data, the second data includes the communication start time, the communication duration, the client module code, the network where the client module is located, and the code of the terminal module for communication, the terminal module refers to other client modules or the central module. Of course, the second data can also include other information, such as network traffic, the number of communication failures of the client module, etc. The second data group of the client module is composed of a number of third data, the third data includes the time when the client module first initiates a data application, the client module code, the number of data applications initiated by the client module, and the size of the data obtained by the client module from the central module. It should be noted that the first data, the second data, and the third data can also include other information in addition to all the information mentioned above, as long as it is information that can be used to identify suspicious client modules. In S3, the control module generates feature graph data based on the data group of the central module corresponding to the past time period and the first data group of different customer modules. The feature graph data will be described in detail below. Then the control module inputs the feature graph data and the second data group of different customer modules corresponding to the past time period into the trained analysis model. The analysis model will also be described in detail below. The analysis model outputs the code of the suspicious customer module. The control module also generates analysis data based on the code of the suspicious customer module. The analysis data refers to the suspicious reason of the suspicious customer module, so that the control module can perform targeted control processing based on the code of the suspicious customer module and the corresponding analysis data. For example, before determining whether the suspicious customer module is safe, the data application of the suspicious customer module is rejected.

[0042] Furthermore, S1 comprises the following steps:

[0043] S111, the control module performs encoding processing on the complete government data, and divides the encoded complete government data into a plurality of data groups, arbitrarily assigns the plurality of data groups to the central module and the backup module, and records the assignment information of the plurality of data groups;

[0044] S112, the control module uses the data groups assigned to the central module to form a first part of government data, and uses the data groups assigned to the backup module to form a second part of government data, and the control module performs a first transformation process on the second part of government data, and stores the second part of government data that has undergone the first transformation process in the backup module;

[0045] S113. The management and control module divides the first part of the government data into several data segments. The data size of each data segment is the same as the data size of the second part of the government data after the first transformation. The management and control module uses the second part of the government data after the first transformation to perform a second transformation on each data segment, and stores the several data segments after the second transformation in the central module. The central module also stores the communication address of the backup module.

[0046] Specifically, the execution process of S1 is introduced in detail here. In S111, the control module performs encoding processing on the complete government data to destroy the original data regularity of the complete government data and improve the difficulty of cracking. The encoding processing may use the Run-Length Encoding algorithm in the prior art, for example, and the control module divides the complete government data after encoding into several data groups, the data sizes of the several data groups may be the same or different, and arbitrarily assigns several data groups to the central module and the backup module, and records the assignment information of the several data groups, the assignment information including the unique identifier, data size and position of each data group assigned to the central module in the complete government data after encoding, and the unique identifier, data size and position of each data group assigned to the backup module in the complete government data after encoding. In S112, the management and control module uses several data groups assigned to the central module to form the first part of government data, and uses several data groups assigned to the backup module to form the second part of government data. Then the management and control module performs a first transformation on the second part of government data, and stores the second part of government data after the first transformation in the backup module. It should be noted that the data scale of the second part of government data after the first transformation remains unchanged. The first transformation can use the EDS algorithm in the prior art, for example. In S113, the control module divides the first part of the government data into several data segments in sequence. The data size of each data segment is the same as the data size of the second part of the government data after the first transformation. If the data size of the first part of the government data is not an integer multiple of the data size of the second part of the government data, then the first part of the government data can be padded. Then the control module uses the second part of the government data after the first transformation to perform a second transformation on each data segment. To facilitate understanding of the second transformation, the second transformation of one bit of data is used as an example for explanation. The result value of the second transformation of 0 and 1 is 1, and the result value of the second transformation of 0 and 0, as well as 1 and 1 is 0. The original data segment cannot be restored based on the data segment that has been processed by the second transformation. The control module stores the several data segments that have been processed by the second transformation in the central module. The several data segments are stored sequentially. The central module also stores the communication address of the backup module to facilitate obtaining the second part of the government data that has been processed by the first transformation from the backup module when needed. Through the above method, the complete government data is stored separately and secretly. Even if the data stored in the central module or the data stored in the backup module is illegally obtained, the complete government data cannot be restored.

[0047] Furthermore, the data size of the second part of government data is less than or equal to the data size of the first part of government data.

[0048] Furthermore, the data size of the second part of government data is less than or equal to a specific data size, and the specific data size refers to the product value of a preset time length and a network rate.

[0049] Specifically, the data scale of the second part of the government data is explained here. First, in order to smoothly use the second part of the government data after the first transformation to perform the second transformation on the first part of the government data, the data scale of the second part of the government data must be less than or equal to the data scale of the first part of the government data. Secondly, in order to improve the unpredictability of the second part of the government data, and also improve the unpredictability of the second part of the government data after the first transformation, the specific data scale is set according to the product value of the network rate of the communication network between the central module and the backup module and the preset time length. The time length can be set based on the longest time that the central module can maintain a connection with the backup module. It should be noted that the network rate is always dynamically changing, so that the data scale of the second part of the government data is less than or equal to the data scale of the first part of the government data, and must also be less than or equal to the specific data scale. Through the above method, since it is difficult to determine the data scale of the second part of the government data, that is, the data scale of the second part of the government data after the first transformation, the difficulty of cracking can be further improved.

[0050] Furthermore, after storing the transformed first part of government data and the transformed second part of government data, S1 further includes the following steps:

[0051] S121, the central module obtains the second part of government data processed by the first transformation from the backup module according to the communication address of the backup module, and the central module restores the original data segment for each data segment processed by the second transformation stored in the central module based on the second part of government data processed by the first transformation, and the central module also combines the restored original data segments into the original first part of government data;

[0052] S122, the central module performs a first inverse transformation on the second part of the government data that has undergone the first transformation to obtain the original second part of the government data, and the central module restores the original encoded complete government data from the original first part of the government data and the original second part of the government data based on the assignment information;

[0053] S123. The central module decodes the restored original complete government data after encoding to obtain the original complete government data.

[0054] Specifically, the detailed process of the central module restoring the original complete government data when needed is introduced here. For example, when the data application of the client module is legal, the central module needs to restore the original complete government data. In S121, according to the communication address of the backup module, the central module obtains the second part of the government data processed by the first transformation from the backup module, and the central module sequentially takes out several data segments that have been processed by the second transformation and are stored in sequence, and uses the second part of the government data processed by the first transformation to repeat the second transformation for each data segment, and restores the original data segment from each data segment, and then according to the order of taking out, the several original data segments are combined into the original first part of the government data. It should be noted that if the filling process is performed at the beginning, the removal process is also required at this time. In S122, the central module performs the first inverse transformation process on the second part of the government data processed by the first transformation, the purpose is to obtain the original second part of the government data, and the central module restores the original complete government data after the encoding process based on the above-mentioned assignment information, the original first part of the government data and the original second part of the government data. In S123, the central module decodes the restored original complete government data after encoding to obtain the original complete government data, and deletes the restored original complete government data when not needed. Through the above method, the central module can restore the original complete government data when needed, so as not to affect the normal use of the complete government data.

[0055] Furthermore, the analysis model includes an input layer model, several intermediate layer models, and an output layer model. The second data groups of different customer modules corresponding to the past time period are directly input into the input layer model. The feature graph data corresponding to the past time period needs to be input into the extraction model to extract the corresponding representative data before being input into the input layer model. The representative data of the feature graph data corresponding to the past time period is directly input into the input layer model.

[0056] Specifically, an analysis model is introduced here. The analysis model can be a neural network model in the prior art, which can ensure the accuracy of identifying suspicious customer modules. The analysis model includes an input layer model, several intermediate layer models, and an output layer model. When the analysis model is used to identify suspicious customer modules, the second data group of different customer modules corresponding to the past time period is directly input into the input layer model. However, the feature graph data corresponding to the past time period needs to be input into the extraction model to extract the corresponding representative data before being input into the input layer model. The extraction model can be, for example, Graph Temporal Networks in the prior art, and then the representative data of the feature graph data corresponding to the past time period is directly input into the input layer model.

[0057] Furthermore, the feature map data includes a number of feature points and a number of connecting lines between two different feature points. Each feature point corresponds to a client module or a central module. At the same time, each feature point corresponds to feature point information, and each connecting line corresponds to connecting line information. The feature point information corresponding to each feature point and the connecting line information corresponding to each connecting line change dynamically over time.

[0058] Specifically, the structure of the feature map data is introduced here. The feature map data includes a number of feature points and a number of connecting lines connecting two different feature points, wherein each feature point corresponds to a client module or a central module, and each feature point also corresponds to feature point information, and each connecting line also corresponds to connecting line information. It should be noted that the feature point information corresponding to each feature point and the connecting line information corresponding to each connecting line are not fixed, and the feature point information corresponding to each feature point and the connecting line information corresponding to each connecting line will change dynamically with time. To facilitate understanding of feature map data, for example, feature map data includes feature point 1, feature point 2, and feature point 3. Feature point 1 corresponds to the central module, feature point 2 and feature point 3 correspond to a client module respectively, feature point 2 is directly connected to feature point 1, feature point 3 can be directly connected to feature point 1 or indirectly connected to feature point 1 through feature point 2, feature point information corresponding to feature point 1 includes the code of the central module, feature point information corresponding to feature point 2 and feature point 3 both include the code of the corresponding client module, and the number of communication failures of the corresponding client module, etc., connection line information corresponding to the connection line from feature point 2 to feature point 1 includes the type of data processing performed by the central module, the communication start time, the communication duration, the network where the client module is located, and the network traffic, etc., the connection line information corresponding to the connection line from feature point 3 to feature point 1 is the same as the connection line information corresponding to the connection line from feature point 2 to feature point 1, and the connection line information corresponding to the connection line from feature point 2 to feature point 3 does not include the type of data processing performed by the central module compared to the connection line information corresponding to the connection line from feature point 2 to feature point 1.

[0059] Furthermore, the process of the management and control module generating analysis data according to the code of the suspicious customer module includes: the management and control module determines the corresponding feature points in the feature map data based on the code of the suspicious customer module, and the management and control module determines the abnormal connection line information from the connection line information of the connection lines corresponding to the corresponding feature points at different time points, and uses the abnormal connection line information and the feature point information of the corresponding feature points simultaneously with the abnormal connection line information to generate analysis data.

[0060] Specifically, the process of generating analysis data according to the code of the suspicious customer module by the control module is introduced here. First, the control module determines the corresponding feature point in the feature map data according to the code of the suspicious customer module output by the analysis model, that is, the customer module corresponding to the code of the suspicious customer module. Secondly, the control module determines the abnormal connection line information from the connection line information of the connection line corresponding to the corresponding feature point at different time points, and uses the abnormal connection line information and the feature point information of the corresponding feature point at the same time as the abnormal connection line information to generate analysis data. For ease of understanding, following the above example of feature map data, if the code of the suspicious customer module output by the analysis model is 2, then the customer module corresponding to feature point 2 is the suspicious customer module, and the types of data contained in the connection line information corresponding to the connection line from feature point 2 to feature point 3 have been described above, and the content of the different types of data contained in the connection line information corresponding to the connection line from feature point 2 to feature point 3 will change over time. Based on the connection line from feature point 2 to feature point 3, the content of the different types of data contained in the connection line information at a certain time point that is significantly different from the content of the different types of data contained in the connection line information at other time points, and the feature point information of feature point 2 at this time point are used as analysis data. Through the above method, the management and control module can further determine whether the suspicious customer module has performed suspicious behavior based on the analysis data.

[0061] According to another aspect of the embodiment of the present invention, Figure 2 As shown, a management and control system for the security of government data assets is also provided, including a management and control module, a central module, and a backup module, which are used to implement the management and control method for the security of government data assets as described above.

[0062] The specific functions of each module are as follows:

[0063] A control module is used to divide the complete government data into a first part of government data and a second part of government data, and perform transformation processing on the first part of government data and the second part of government data;

[0064] It is also used to obtain a data group of the central module corresponding to a preset past time period, as well as a first data group and a second data group of different client modules, the data group of the central module is composed of a plurality of first data, the first data includes the time when a data application is sent to the central module, the code of the client module that sends the data application to the central module, and relevant information of data processing performed by the central module, the first data group of the client module is composed of a plurality of second data, the second data includes the communication start time, the communication duration, the code of the client module, the network where the client module is located, and the code of the terminal module for communication, the second data group of the client module is composed of a plurality of third data, the third data includes the time when the client module first initiates the data application, the code of the client module, the number of data applications initiated by the client module, and the scale of data obtained by the client module from the central module;

[0065] and used to generate feature graph data according to the data group of the central module corresponding to the past time period and the first data group of different customer modules, input the feature graph data and the second data group of different customer modules corresponding to the past time period into the trained analysis model to obtain the code of the suspicious customer module, and generate analysis data according to the code of the suspicious customer module;

[0066] A central module, used for storing the first part of government data after transformation;

[0067] A backup module, used for storing the second part of government affairs data after transformation;

[0068] The client module is used to communicate data with the central module or other client modules.

[0069] According to another aspect of an embodiment of the present invention, a storage medium is provided, wherein the storage medium stores program instructions, wherein when the program instructions are executed, a device where the storage medium is located is controlled to execute any one of the above methods.

[0070] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0071] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0072] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] The above embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0074] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for managing and controlling the security of government data assets, characterized in that: The method comprises the following steps: S1. The control module divides the complete government data into a first part of government data and a second part of government data. At the same time, the control module performs transformation processing on the first part of government data and the second part of government data, and the control module stores the transformed first part of government data in the central module and stores the transformed second part of government data in the backup module. S2, the control module obtains a data group of the central module corresponding to a preset past time period, and a first data group and a second data group of different client modules, the data group of the central module is composed of a plurality of first data, the first data includes the time when the data application is sent to the central module, the code of the client module that sends the data application to the central module, and the relevant information of the data processing performed by the central module, the first data group of the client module is composed of a plurality of second data, the second data includes the communication start time, the communication duration, the code of the client module, the network where the client module is located, and the code of the terminal module for communication, the second data group of the client module is composed of a plurality of third data, the third data includes the time when the client module first initiates the data application, the code of the client module, the number of data applications initiated by the client module, and the scale of data obtained by the client module from the central module; S3, the control module generates feature graph data according to the data group of the central module corresponding to the past time period and the first data group of different customer modules, and the control module inputs the feature graph data and the second data group of different customer modules corresponding to the past time period into the trained analysis model, the analysis model outputs the code of the suspicious customer module, and the control module further generates analysis data according to the code of the suspicious customer module; S1 includes the following steps: S111, dividing the complete government affairs data into several data groups; S112, using different data groups assigned to the central module to form a first part of government data, using different data groups assigned to the backup module to form a second part of government data, and storing the second part of government data after the first transformation processing in the backup module; S113. Divide the first part of the government data into several data segments, the data size of each data segment is the same as the data size of the second part of the government data after the first transformation processing, use the second part of the government data after the first transformation processing to perform a second transformation processing on each data segment, store the several data segments after the second transformation processing in the central module, and the central module also stores the communication address of the backup module.

2. The method according to claim 1, characterized in that The data size of the second part of government data is less than or equal to the data size of the first part of government data.

3. The method according to claim 2, characterized in that The data size of the second part of government data is less than or equal to a specific data size, and the specific data size refers to the product of a preset time length and a network rate.

4. The method according to claim 1, characterized in that After storing the transformed first part of government data and the transformed second part of government data, S1 further includes the following steps: S121, the central module obtains the second part of government data processed by the first transformation from the backup module according to the communication address of the backup module, and the central module restores the original data segment for each data segment processed by the second transformation stored in the central module based on the second part of government data processed by the first transformation, and the central module also combines the restored original data segments into the original first part of government data; S122, the central module performs a first inverse transformation on the second part of the government data that has undergone the first transformation to obtain the original second part of the government data, and the central module restores the original encoded complete government data from the original first part of the government data and the original second part of the government data based on the assignment information; S123. The central module decodes the restored original complete government data after encoding to obtain the original complete government data.

5. The method according to claim 1, characterized in that The analysis model includes an input layer model, several intermediate layer models, and an output layer model. The second data groups of different customer modules corresponding to the past time period are directly input into the input layer model. The feature graph data corresponding to the past time period needs to be input into the extraction model to extract the corresponding representative data before being input into the input layer model. The representative data of the feature graph data corresponding to the past time period is directly input into the input layer model.

6. The method according to claim 1, characterized in that The feature map data includes a number of feature points and a number of connecting lines between two different feature points. Each feature point corresponds to a client module or a central module. At the same time, each feature point corresponds to feature point information, and each connecting line corresponds to connecting line information. The feature point information corresponding to each feature point and the connecting line information corresponding to each connecting line change dynamically over time.

7. The method according to claim 6, characterized in that The process of the control module generating analysis data according to the code of the suspicious customer module includes: the control module determines the corresponding feature points in the feature map data based on the code of the suspicious customer module, and the control module determines the abnormal connection line information from the connection line information of the connection lines corresponding to the corresponding feature points at different time points, and also determines the feature point information of the corresponding feature points whose corresponding time points are the same as the time points corresponding to the abnormal connection line information, and uses the abnormal connection line information and the determined feature point information of the corresponding feature points to generate analysis data.

8. A management and control system for government data asset security, used to implement the method according to any one of claims 1 to 7, characterized in that: Includes the following modules: A control module is used to divide the complete government data into a first part of government data and a second part of government data, and perform transformation processing on the first part of government data and the second part of government data; It is also used to obtain a data group of the central module corresponding to a preset past time period, as well as a first data group and a second data group of different client modules, the data group of the central module is composed of a plurality of first data, the first data includes the time when a data application is sent to the central module, the code of the client module that sends the data application to the central module, and the relevant information of the data processing performed by the central module, the first data group of the client module is composed of a plurality of second data, the second data includes the communication start time, the communication duration, the code of the client module, the network where the client module is located, and the code of the terminal module for communication, the second data group of the client module is composed of a plurality of third data, the third data includes the time when the client module first initiates the data application, the code of the client module, the number of data applications initiated by the client module, and the scale of data obtained by the client module from the central module; and used to generate feature graph data according to the data group of the central module corresponding to the past time period and the first data group of different customer modules, input the feature graph data and the second data group of different customer modules corresponding to the past time period into the trained analysis model to obtain the code of the suspicious customer module, and generate analysis data according to the code of the suspicious customer module; A central module, used for storing the first part of government data after transformation; A backup module, used for storing the second part of government affairs data after transformation; The client module is used to communicate data with the central module or other client modules.

9. A storage medium, characterized in that: The storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute any one of the methods in claims 1 to 7.

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