E-government big data processing optimization method and system

Abstract

The present invention relates to the technical field of big data processing optimization, and in particular to an e-government big data processing optimization method and system. The method comprises the following steps: obtaining big data of an e-government system; classifying the big data by permission labels to obtain permission label classification data; performing business matching on the permission label classification data to obtain business matching data; performing conflict alignment on the business matching data to obtain conflict alignment data; performing data set fusion on the conflict alignment data to obtain a fused data set; performing sensitive threshold division and encryption label division on the fused data set to obtain encrypted label division data; performing data authorization matching on the encrypted label division data to obtain data authorization matching data; performing distributed authorization processing on the encrypted label division data according to the data authorization matching data, thereby obtaining distributed authorization processing data, so as to optimize the security of e-government big data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing optimization, and in particular to an e-government big data processing optimization method and system. Background Art

[0002] The processing of e-government big data is one of the important technologies in the field of e-government. It is widely used in the aggregation, integration, sharing, development and utilization of government data. For example, relevant departments can use this technology to manage and analyze government data to improve decision-making capabilities and efficiency. However, existing e-government big data processing technologies have problems with data security, data quality, and data sharing. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an e-government big data processing optimization method to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, an e-government big data processing optimization method includes the following steps:

[0005] Step S1: Obtaining input end big data of the e-government system; classifying the input end big data by permission labels, thereby obtaining permission label classification data;

[0006] Step S2: performing business matching on the permission tag classification data to obtain business matching data; performing conflict alignment on the business matching data to obtain conflict alignment data;

[0007] Step S3: performing data set fusion on the conflicting aligned data to obtain a fused data set;

[0008] Step S4: performing sensitive threshold division on the fused data set to obtain a sensitive divided data set; performing encrypted label division on the sensitive divided data set to obtain encrypted label division data; performing data authorization matching on the encrypted label division data to obtain data authorization matching data;

[0009] Step S5: Perform distributed authorization processing on the encrypted label division data according to the data authorization matching data, thereby obtaining distributed authorization processing data to perform processing optimization operations for e-government big data.

[0010] The present invention firstly obtains the input end big data of the e-government system, and can comprehensively analyze and monitor various e-government businesses, thereby improving the efficiency and quality of e-government. By classifying the input end big data with permission labels, different permission management can be performed on different data, the security and privacy of data can be protected, and the leakage and abuse of data can be prevented. By performing business matching on the permission label classification data, appropriate data can be selected for processing according to different business needs, thereby improving the utilization rate and value of data. By performing conflict alignment on the business matching data, conflicts and inconsistencies between different data can be resolved, thereby improving the accuracy and credibility of data. By performing data set fusion on the conflict alignment data, data from different sources and formats can be unified and integrated, thereby improving the integrity and consistency of data, and providing richer and more comprehensive data resources for subsequent data processing. By performing sensitive threshold division on the fused data set, different protection levels can be performed on the data according to the sensitivity of the data, thereby improving the security and compliance of the data. By performing encryption label division on the sensitive division data set, different encryption methods can be performed on the data, thereby improving the confidentiality and reliability of the data. By performing data authorization matching on the encryption label division data and the permission classification data, the data can be reasonably authorized according to the permission and encryption status of the data, thereby improving the availability and legitimacy of the data. By performing distributed authorization processing on encrypted tag-divided data according to data authorization matching data, distributed storage and access of data can be achieved, improving the scalability and accessibility of data, while also reducing the risk of data centralization and the possibility of single point failures, thereby performing processing optimization operations for e-government big data and improving the processing effect and quality of e-government big data.

[0011] Preferably, the present invention further provides an e-government big data processing optimization system for executing the e-government big data processing optimization method as described above, the e-government big data processing optimization system comprising:

[0012] The permission label module is used to obtain the input end big data of the e-government system; classify the input end big data by permission labels, so as to obtain permission label classification data;

[0013] The conflict alignment module performs business matching on the permission tag classification data to obtain business matching data; performs conflict alignment on the business matching data to obtain conflict alignment data;

[0014] A data set fusion module is used to fuse the conflicting aligned data to obtain a fused data set;

[0015] The permission matching module is used to divide the fused data set into sensitive thresholds, thereby obtaining a sensitive divided data set; to divide the sensitive divided data set into encrypted labels, thereby obtaining encrypted label divided data; to perform data authorization matching on the encrypted label divided data, thereby obtaining data authorization matching data;

[0016] The distributed authorization module is used to perform distributed authorization processing on the encrypted tag division data according to the data authorization matching data, so as to obtain the distributed authorization processing data to perform the processing optimization operation of the e-government big data.

[0017] In summary, the present invention provides an e-government big data processing optimization system, which consists of a permission label module, a conflict alignment module, a data set fusion module, a permission matching module and a distributed authorization module. It can implement any e-government big data processing optimization method described in the present invention. The internal structure of the system cooperates with each other. By adopting the mapping of different user permissions of a large amount of data in the e-government system to the regional level corresponding to the department, the difficulty of permission search of a large amount of departmental corresponding regional business data is reduced for the subsequent authorization matching and distributed storage of sensitive data, and targeted encryption measures of different sensitivities are performed on the data, and adaptive processing is performed on the data before distributed data, which reduces the time spent on encryption and decryption of a large amount of sensitive data in the e-government big data, thereby simplifying the operation process of the e-government big data processing optimization system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0019] Figure 1 This is a schematic diagram of the steps of the e-government big data processing optimization method of the present invention;

[0020] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0021] Figure 3 for Figure 2 Detailed step flow chart of step S15 in FIG. DETAILED DESCRIPTION

[0022] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0024] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0025] To achieve this, please refer to Figures 1 to 3 The present invention provides an e-government big data processing optimization method, the method comprising the following steps:

[0026] Step S1: Obtaining input end big data of the e-government system; classifying the input end big data by permission labels, thereby obtaining permission label classification data;

[0027] Step S2: performing business matching on the permission tag classification data to obtain business matching data; performing conflict alignment on the business matching data to obtain conflict alignment data;

[0028] Step S3: performing data set fusion on the conflicting aligned data to obtain a fused data set;

[0029] Step S4: performing sensitive threshold division on the fused data set to obtain a sensitive divided data set; performing encrypted label division on the sensitive divided data set to obtain encrypted label division data; performing data authorization matching on the encrypted label division data to obtain data authorization matching data;

[0030] Step S5: Perform distributed authorization processing on the encrypted label division data according to the data authorization matching data, thereby obtaining distributed authorization processing data to perform processing optimization operations for e-government big data.

[0031] In the embodiment of the present invention, please refer to Figure 1 As shown, it is a schematic diagram of the steps of the e-government big data processing optimization method of the present invention. In this example, the steps of the e-government big data processing optimization method include:

[0032] Step S1: Obtaining input end big data of the e-government system; classifying the input end big data by permission labels, thereby obtaining permission label classification data;

[0033] The embodiment of the present invention obtains big data from the input end of the e-government system, for example, from different data channels such as websites, applications, interfaces, etc. The input end big data is classified by permission labels, for example, different permission labels are assigned to the data according to different dimensions such as the source, attribute, content, etc., such as different permissions corresponding to different powers, including access, modification, etc., so as to obtain permission label classification data.

[0034] Step S2: performing business matching on the permission tag classification data to obtain business matching data; performing conflict alignment on the business matching data to obtain conflict alignment data;

[0035] The embodiment of the present invention performs business matching on the permission tag classification data, for example, according to different business needs and scenarios, selects appropriate data for processing, such as matching corresponding data according to different business goals such as government services, government supervision, and government decision-making, thereby obtaining business matching data. Conflict alignment is performed on the business matching data, for example, conflicts and inconsistencies between different data are resolved, such as aligning the same or similar data according to different dimensions such as data structure, attributes, content, and time, thereby obtaining conflict alignment data.

[0036] Step S3: performing data set fusion on the conflicting aligned data to obtain a fused data set;

[0037] The embodiments of the present invention fuse the conflicting aligned data, for example, unify and integrate data from different sources and formats, such as converting, mapping, merging, extracting and other operations on the data according to different dimensions such as metadata, models, standards, protocols, etc., to obtain a fused data set.

[0038] Step S4: performing sensitive threshold division on the fused data set to obtain a sensitive divided data set; performing encrypted label division on the sensitive divided data set to obtain encrypted label division data; performing data authorization matching on the encrypted label division data to obtain data authorization matching data;

[0039] The embodiment of the present invention divides the fused data set into sensitive thresholds, for example, according to the sensitivity of the data, different protection levels are performed on the data, and appropriate sensitivity thresholds, such as high, medium, and low, are determined, thereby obtaining a sensitive divided data set. The sensitive divided data set is divided into encrypted labels, for example, different encryption methods are performed on the data, such as selecting appropriate encryption algorithms such as symmetric encryption, asymmetric encryption, and multi-level encryption according to different dimensions such as confidentiality, reliability, and recoverability of the data, thereby obtaining encrypted label divided data. Data authorization matching is performed on the encrypted label divided data, for example, according to the authority and encryption status of the data, the data is reasonably authorized, such as matching the corresponding authorization methods according to different dimensions such as the access right, operation right, and transmission right of the data, thereby obtaining data authorization matching data.

[0040] Step S5: Perform distributed authorization processing on the encrypted label division data according to the data authorization matching data, thereby obtaining distributed authorization processing data to perform processing optimization operations for e-government big data.

[0041] The embodiment of the present invention performs distributed authorization processing on the encrypted label partition data according to the data authorization matching data, for example, to realize the distributed storage and access of data, such as optimizing the distributed resources of data according to different dimensions such as the computing resource coefficient, heat coefficient, decryption coefficient of the data, such as using data sharding, data replication, data migration and other technologies to distribute the data to different nodes, thereby obtaining a distributed data set. Distributed authorization processing is performed on the distributed data set, for example, to realize the distributed authorization and management of data, such as performing distributed authorization matching on the data according to different dimensions such as the encryption label, permission label, business label of the data, such as using data signature, data authentication, data authorization and other technologies to perform distributed authorization verification on the data, thereby obtaining distributed authorization processing data, so as to perform the processing optimization operation of the e-government big data. If a certain data in the distributed data set needs to be queried out for processing, or the data needs to be processed and analyzed and the result output is output, the corresponding authority-related personnel in the corresponding authority chain visualization data can be found according to the authority label, and the authority of the data, including ownership and control rights, can be associated to facilitate the next step of processing.

[0042] The present invention firstly obtains the input end big data of the e-government system, and can comprehensively analyze and monitor various e-government businesses, thereby improving the efficiency and quality of e-government. By classifying the input end big data with permission labels, different permission management can be performed on different data, the security and privacy of data can be protected, and the leakage and abuse of data can be prevented. By performing business matching on the permission label classification data, appropriate data can be selected for processing according to different business needs, thereby improving the utilization rate and value of data. By performing conflict alignment on the business matching data, conflicts and inconsistencies between different data can be resolved, thereby improving the accuracy and credibility of data. By performing data set fusion on the conflict alignment data, data from different sources and formats can be unified and integrated, thereby improving the integrity and consistency of data, and providing richer and more comprehensive data resources for subsequent data processing. By performing sensitive threshold division on the fused data set, different protection levels can be performed on the data according to the sensitivity of the data, thereby improving the security and compliance of the data. By performing encryption label division on the sensitive division data set, different encryption methods can be performed on the data, thereby improving the confidentiality and reliability of the data. By performing data authorization matching on the encryption label division data and the permission classification data, the data can be reasonably authorized according to the permission and encryption status of the data, thereby improving the availability and legitimacy of the data. By performing distributed authorization processing on encrypted tag-divided data according to data authorization matching data, distributed storage and access of data can be achieved, improving the scalability and accessibility of data, while also reducing the risk of data centralization and the possibility of single point failures, thereby performing processing optimization operations for e-government big data and improving the processing effect and quality of e-government big data.

[0043] Preferably, step S1 comprises the following steps:

[0044] Step S11: Obtaining input end big data of the e-government system;

[0045] Step S12: performing metadata analysis on the input end big data, thereby obtaining metadata analysis data;

[0046] Step S13: performing fine-grained feature extraction on the metadata analysis data, thereby obtaining fine-grained feature data;

[0047] Step S14: performing government affairs clustering on the input end big data according to the fine-grained feature data, thereby obtaining a government affairs classification data set;

[0048] Step S15: Perform authority chain visualization analysis on the government division data set, thereby obtaining authority chain visualization data;

[0049] Step S16: classify the authority labels of the government division data set according to the authority chain visualization data, so as to obtain weight label classification data.

[0050] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0051] Step S11: Obtaining input end big data of the e-government system;

[0052] The embodiment of the present invention obtains big data from the input end of the e-government system, for example, obtains data from a system interface.

[0053] Step S12: performing metadata analysis on the input end big data, thereby obtaining metadata analysis data;

[0054] The embodiment of the present invention performs metadata analysis on the input big data, for example, deeply understands and analyzes the structure and attributes of the data, such as obtaining metadata of different dimensions such as data type, format, size, source, time, etc., thereby obtaining metadata analysis data.

[0055] Step S13: performing fine-grained feature extraction on the metadata analysis data, thereby obtaining fine-grained feature data;

[0056] The embodiments of the present invention perform fine-grained feature extraction on metadata analysis data, for example, extracting more detailed and useful features from the data, such as extracting different fine-grained features according to different dimensions of the data, such as government content, government purpose, government value, government field, and government business, thereby obtaining fine-grained feature data.

[0057] Step S14: performing government affairs clustering on the input end big data according to the fine-grained feature data, thereby obtaining a government affairs classification data set;

[0058] The embodiment of the present invention performs government affairs clustering on the input big data according to fine-grained feature data. For example, the data is grouped according to different government affairs themes and contents, such as government affairs services, government affairs supervision, government affairs decision-making and other different government affairs objectives, thereby obtaining a government affairs classification data set.

[0059] Step S15: Perform authority chain visualization analysis on the government division data set, thereby obtaining authority chain visualization data;

[0060] The embodiment of the present invention performs a visual analysis of the authority chain on the government division data set, for example, the authority relationship and process of the data are displayed in a graphical manner, such as according to different dimensions of the data such as authority level, authority scope, authority responsibility, etc., to obtain visualization data of the authority chain.

[0061] Step S16: classify the authority labels of the government division data set according to the authority chain visualization data, so as to obtain weight label classification data.

[0062] The embodiment of the present invention classifies the government data set into permission labels according to the authority chain visualization data, and performs different permission management on different data, that is, assigns different authority-related permissions to different government-related data, thereby obtaining permission label classification data.

[0063] The present invention firstly obtains the input end big data of the e-government system, and can comprehensively analyze and monitor the various businesses of e-government, thereby improving the efficiency and quality of e-government. By performing metadata analysis on the input end big data, the structure and attributes of the data can be deeply understood, and the comprehensibility and operability of the data can be improved. By performing fine-grained feature extraction on the metadata analysis data, more detailed and useful features can be extracted from the data, and the distinguishability and classifiability of the data can be improved. According to the fine-grained feature data, the input end big data is clustered and divided into government affairs, and the data can be grouped according to different government affairs themes and contents, thereby improving the organization and queryability of the data. By performing authority chain visualization analysis on the government affairs division data set, the authority relationship and process of the data can be displayed in a graphical manner, thereby improving the visualization and interactivity of the data. According to the authority chain visualization data, the authority label classification of the government affairs division data set can be performed on different data. Different authority management can be performed on different data, the security and privacy of the data can be protected, and the leakage and abuse of the data can be prevented.

[0064] Preferably, step S15 comprises the following steps:

[0065] Step S151: Performing ownership visualization analysis on the government division data set, thereby obtaining ownership chain visualization data;

[0066] Step S152: Perform control right visualization analysis on the government affairs division data set, thereby obtaining control right chain visualization data;

[0067] Step S153: performing attribute association analysis on the ownership chain visualization data and the control right chain visualization data, thereby obtaining attribute association analysis data;

[0068] Step S154: performing feature dimension reduction on the attribute association analysis data, thereby obtaining a feature dimension reduction data set;

[0069] Step S155: performing authority matching on the feature dimension reduction data set, thereby obtaining an authority matching data set;

[0070] Step S156: Perform authority chain visualization analysis on the authority matching data set to obtain authority chain visualization data.

[0071] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S15 in FIG. 1 , in this embodiment, step S15 includes the following steps:

[0072] Step S151: Performing ownership visualization analysis on the government division data set, thereby obtaining ownership chain visualization data;

[0073] The embodiments of the present invention utilize data graph technology to perform ownership visualization analysis on government-divided data sets. For example, the ownership relationship and distribution of the data are displayed in a graphical manner, such as by performing data visualization analysis based on different dimensions such as the owner of the data, all scopes, all responsibilities, etc., thereby obtaining ownership chain visualization data.

[0074] Step S152: Perform control right visualization analysis on the government affairs division data set, thereby obtaining control right chain visualization data;

[0075] The embodiments of the present invention utilize data graph technology to perform a control rights visualization analysis on a government-divided data set. For example, the control rights relationship and process of the data are displayed in a graphical manner, such as by performing a visualization analysis of the data based on different dimensions such as the controller of the data, the scope of control, and the control responsibility, thereby obtaining visualization data of the control rights chain.

[0076] Step S153: performing attribute association analysis on the ownership chain visualization data and the control right chain visualization data, thereby obtaining attribute association analysis data;

[0077] The embodiments of the present invention utilize data mining technology to perform attribute association analysis on ownership chain visualization data and control right chain visualization data, such as mining the rules and patterns between the attributes of the data, such as performing data mining analysis based on different dimensions such as attribute type, attribute format, attribute quality, etc., to obtain associated attributes or associated data of relevant permission data with separation of ownership and control rights, and also obtain associated data with separation of ownership and control rights, that is, the permission data itself or the attribute unique ID of the data, thereby obtaining attribute association analysis data.

[0078] Step S154: performing feature dimension reduction on the attribute association analysis data, thereby obtaining a feature dimension reduction data set;

[0079] The embodiment of the present invention obtains directly associated data of the attribute association analysis data, that is, unique attributes or most associated data, by performing feature dimensionality reduction on the attribute association analysis data, such as reducing the dimension and complexity of the data, thereby obtaining a feature dimensionality reduction data set.

[0080] Step S155: performing authority matching on the feature dimension reduction data set, thereby obtaining an authority matching data set;

[0081] The embodiments of the present invention perform permission matching on the feature dimensionality reduction data set. For example, reasonable permissions are assigned to the data according to the characteristics and attributes of the data. For example, according to different dimensions such as data access rights, operation rights, and transmission rights, corresponding permission modes such as public, private, authorized, and encrypted are matched, thereby obtaining a permission matching data set.

[0082] Step S156: Perform authority chain visualization analysis on the authority matching data set to obtain authority chain visualization data.

[0083] The embodiment of the present invention utilizes data graph technology to perform a visual analysis of the authority chain on the authority matching data set. For example, the authority relationship and process of the data are displayed in a graphical manner, such as according to different dimensions of the data such as authority level, authority scope, authority responsibility, etc., to perform a visual analysis of the data, thereby obtaining visual data of the authority chain.

[0084] The present invention firstly performs ownership visualization analysis on the government affairs division data set, and can display the ownership relationship and distribution of the data in a graphical way, thereby improving the data visualization and interactivity. Performing control rights visualization analysis on the government affairs division data set, the control rights relationship and process of the data can be displayed in a graphical way, thereby improving the data visualization and interactivity. Performing attribute association analysis on the ownership chain visualization data and the control rights chain visualization data can discover the association and correlation between the attributes of the data, thereby improving the data analyzability and predictability. Performing feature dimension reduction on the attribute association analysis data can reduce the dimension and complexity of the data, and improve the data processability and compressibility. Performing authority matching on the feature dimension reduction data set can reasonably allocate authority to the data according to the characteristics and attributes of the data, thereby improving the data availability and legitimacy. Performing authority chain visualization analysis on the authority matching data set can display the authority relationship and process of the data in a graphical way, thereby improving the data visualization and interactivity.

[0085] Step S2 includes the following steps:

[0086] Step S21: performing business matching on the permission tag classification data, thereby obtaining business matching data;

[0087] Step S22: performing data entity association matching on the permission tag classification data according to the business matching data, thereby obtaining entity association matching data and non-entity association data;

[0088] Step S23: aligning the entity association matching data by attributes, thereby obtaining attribute alignment matching data;

[0089] Step S24: performing attribute redundancy on the attribute alignment matching data, thereby obtaining redundant matching entity data;

[0090] Step S25: filtering conflicting data on non-entity related data, thereby obtaining filtered non-entity data;

[0091] Step S26: performing label selection on the authority label classification data, thereby obtaining authority label data;

[0092] Step S27: Perform department-region alignment mapping on the filtered non-entity data and the redundant matching entity data based on the authority tag data, thereby obtaining conflict alignment data.

[0093] The embodiment of the present invention performs business matching on the permission tag classification data, matches the data classified by the permission tag according to different business needs and scenarios, and matches the corresponding data according to different business goals such as government service, government supervision, and government decision-making, thereby obtaining business matching data. According to the business matching data, the permission tag classification data is matched with data entity association, and the association relationship between entities in the data is analyzed to perform data association matching, thereby obtaining entity association matching data and non-entity association data. Attribute alignment is performed on the entity association matching data, for example, the same or similar attributes of different entities are unified and standardized, such as according to different dimensions such as attribute value, attribute type, and attribute transmission format of the data, and the attribute alignment matching data is obtained. Attribute redundancy is performed on the attribute alignment matching data, that is, after the data alignment, if the attributes of the entity data are not aligned, data redundancy is directly performed to ensure the integrity of the data, thereby obtaining redundant matching entity data. Conflict data filtering is performed on non-entity association data, for example, conflict and inconsistent data in the data are removed, such as conflict filtering of data is performed according to different dimensions such as conflict value, conflict frequency, and conflict relevance of the data, thereby obtaining filtered non-entity data. The permission label classification data is selected by label selection, that is, the label is selected according to the dimensions of data access rights, operation rights, transmission rights, ownership, control rights, use rights, etc., so as to obtain permission label data. Based on the permission label data, the filtered non-entity data and redundant matching entity data are mapped between departments and regions, and the data is grouped and mapped according to different departments and regions, that is, the filtered non-entity data and redundant matching entity data are associated with permissions between departments and regions according to the permission label data, so as to obtain the department and region-level permission association mapping data of all filtered data including entities and non-entities, that is, for the permission association data at the department and region levels, the data conflicts between data permission associations are aligned, so as to obtain conflict alignment data.

[0094] The present invention firstly performs business matching on the permission tag classification data, and can select corresponding data for matching according to different businesses, thereby ensuring the business relevance during subsequent data processing. According to the business matching data, the permission tag classification data is matched with data entity association, and the association relationship between entities in the data can be found, thereby improving the relevance and analyzability of the data. At the same time, non-entity-related data can also be distinguished to improve the distinguishability and filterability of the data. Attribute alignment is performed on the entity association matching data, and the same or similar attributes of different entities can be unified and standardized, thereby improving the alignability and consistency of the data. Attribute redundancy is performed on the attribute alignment matching data, and the attribute quantity and information volume of the data can be increased, thereby improving the redundancy and enrichment of the data. Conflict data filtering is performed on non-entity-related data, and conflicting and inconsistent data in the data can be removed. Label selection is performed on the permission tag classification data, and appropriate permission labels can be selected according to the characteristics and attributes of the data, thereby improving the labelability and manageability of the data. Based on the permission tag data, department-region alignment mapping is performed on the filtering of non-entity data and redundant matching entity data, and the data can be grouped and mapped according to different departments and regions, thereby improving the queryability of the data. At the same time, it can also resolve data conflicts and inconsistencies and improve data consistency.

[0095] Step S27 includes the following steps:

[0096] Step S271: performing data integration on the filtered non-entity data and the redundant matching entity data, thereby obtaining filtered integrated data;

[0097] Step S272: extracting department-level tags from the authority tag data, thereby obtaining department authority tag data;

[0098] Step S273: performing department-level data conflict alignment on the filtered integrated data according to the department authority label data, thereby obtaining department conflict alignment data;

[0099] Step S274: obtaining regional level interface output data according to department conflict alignment data;

[0100] Step S275: extracting regional level labels from the authority label data, thereby obtaining regional authority label data;

[0101] Step S276: performing regional conflict alignment on the filtered integrated data and the regional interface output data according to the regional authority label data, thereby obtaining regional conflict alignment data;

[0102] Step S277: Perform department-region alignment mapping on the regional conflict alignment data, thereby obtaining conflict alignment data.

[0103] The embodiment of the present invention integrates the filtered non-entity data and the redundant matching entity data, that is, performs data conversion, mapping, and merging operations according to different dimensions such as data structure, attribute, content, and time, thereby obtaining filtered integrated data. Department-level label extraction is performed on the authority label data, including extracting department-level authority labels according to the department attributes of the data, such as extracting department-level label data according to different dimensions such as department name, department number, and department responsibility of the data, thereby obtaining department authority label data. Department-level data conflict alignment is performed on the filtered integrated data according to the department authority label data, such as resolving data conflicts and inconsistencies between different departments, such as performing department-level data conflict alignment according to the dimensions of department attributes and department relationships of the data, thereby obtaining department conflict alignment data. Regional-level interface output data is obtained according to the department conflict alignment data, such as obtaining the final output of the regional business-level interface according to the department conflict alignment data, that is, obtaining the interface data that is finally output to different regional docking services in the department conflict alignment data, such as performing regional-level interface output of the data according to the dimensions of regional attributes and regional numbers of the data, thereby obtaining regional-level interface output data. Perform regional-level label extraction on the authority label data. For example, extract regional-level authority labels based on the regional attributes of the data, such as extracting regional-level labels based on different dimensions of the data, such as regional name, regional number, regional responsibilities, etc., to obtain regional authority label data. Perform regional-level conflict alignment on the filtered and integrated data and regional-level interface output data based on the regional authority label data, such as resolving data conflicts and inconsistencies between different regions, such as performing regional-level data conflict alignment based on the regional attributes of the data and the dimensions of regional relationships, to obtain regional conflict alignment data. Perform department-region alignment mapping on the regional conflict alignment data, group and map the data according to different departments and regions, that is, perform conflict alignment between department and regional authority relationships on the regional conflict alignment data, to obtain conflict alignment data.

[0104] The present invention firstly integrates the filtered non-entity data and the redundant matching entity data, so as to unify and integrate the data, improve the integrity and consistency of the data, and provide richer and more comprehensive data resources for subsequent data processing. The authority label data is extracted at the department level, and the authority label at the department level can be extracted according to the department attribute of the data, thereby improving the manageability of the data. The filtered and integrated data is aligned at the department level according to the department authority label data, so as to resolve the data conflicts and inconsistencies between different departments and improve the consistency of the data. The regional interface output data is obtained according to the department conflict alignment data, and the authority label data is extracted at the regional level, so as to extract the authority label at the regional level according to the regional attribute of the data, thereby improving the manageability of the data. The filtered and integrated data and the regional interface output data are aligned at the regional level according to the regional authority label data, so as to resolve the data conflicts and inconsistencies between different regions and improve the consistency of the data. The regional conflict alignment data is mapped between departments and regions, so as to group and map the data according to different departments and regions, thereby improving the queryability of the data. At the same time, the data conflicts and inconsistencies can also be resolved, thereby improving the consistency of the data.

[0105] Step S3 includes the following steps:

[0106] Step S31: performing parameter data flow analysis on the business matching data, thereby obtaining parameter data flow analysis data;

[0107] Step S32: performing department-region parameter mapping on the parameter data flow analysis data, thereby obtaining parameter mapping data;

[0108] Step S33: performing parameter compatibility analysis on the parameter mapping data, thereby obtaining parameter compatibility data;

[0109] Step S34: performing format compatibility conversion on the conflict alignment data according to the parameter compatibility data, thereby obtaining compatible conversion data;

[0110] Step S35: performing business relevance analysis on the parameter mapping data, thereby obtaining business relevance data;

[0111] Step S36: performing feature mining on the business correlation data to obtain business correlation feature data;

[0112] Step S37: performing data set fusion on the compatible conversion data according to the business-related feature data, thereby obtaining a fused data set.

[0113] The embodiment of the present invention performs parameter data flow analysis on the business matching data, that is, analyzes the flow and transfer between parameters in the data, such as performing parameter data flow analysis on the data according to different dimensions such as parameter value, parameter type, parameter dependency, etc., thereby obtaining parameter data flow analysis data. Department-region parameter mapping is performed on the parameter data flow analysis data, that is, the parameters in the data are grouped and mapped according to different department and regional businesses, such as performing department-region parameter mapping on the data according to the dimensions of parameter attributes, department attributes, regional attributes, and department-region relationship of the data, thereby obtaining parameter mapping data. Parameter compatibility analysis is performed on the parameter mapping data, such as analyzing the compatibility and adaptability between parameters in the data, such as performing parameter compatibility analysis on the data according to different dimensions such as parameter value, parameter type, parameter format, parameter standard, etc., thereby obtaining parameter compatibility data. Format compatibility conversion is performed on the conflict alignment data according to the parameter compatibility data, such as unifying and standardizing incompatible or unsuitable formats in the data, such as performing format compatibility conversion on the data according to different dimensions such as format value, format type, format rule, format standard, etc., thereby obtaining compatible conversion data. Perform business relevance analysis on parameter mapping data, such as analyzing the business relevance and correlation between parameters in the data, such as performing business relevance analysis on data based on different dimensions such as parameter value, parameter type, parameter purpose, and parameter value, so as to obtain business relevance data. Perform feature mining on business relevance data, that is, perform feature mining of correlation based on the correlation features of the business relevance of the data, so as to obtain business relevance feature data. Perform data set fusion on compatible conversion data based on business relevance feature data, that is, unify and integrate data from different business sources and compatible formats, so as to obtain a fused data set.

[0114] The present invention firstly performs parameter data flow analysis on business matching data, and can perform in-depth analysis and understanding on the parameters and data flow of the data, thereby improving the comprehensibility and operability of the data. By performing department-region parameter mapping on the parameter data flow analysis data, the parameters of the data can be grouped and mapped according to different departments and regions, thereby improving the mappability and queryability of the data. By performing parameter compatibility analysis on the parameter mapping data, the compatibility and adaptability between the parameters of the data can be found, thereby improving the compatibility and adaptability of the data. By performing format compatibility conversion on the conflict alignment data according to the parameter compatibility data, the format of the data can be unified and converted, thereby improving the convertibility and consistency of the data. By performing business relevance analysis on the parameter mapping data, the relevance and correlation between the businesses of the data can be found, thereby improving the relevance of the data. By performing feature mining on the business relevance data, more valuable and meaningful features can be mined from the data, thereby improving the analyzability of the data. By performing data set fusion on the compatible conversion data according to the business relevance feature data, data from different sources and formats can be unified and integrated, thereby improving the integrity and consistency of the data, thereby providing richer and more comprehensive data resources for subsequent data processing.

[0115] Step S4 includes the following steps:

[0116] Step S41: performing permission classification on the permission label classification data, thereby obtaining permission classification data;

[0117] Step S42: performing sensitivity threshold evaluation on the authority classification data and the fused data set, thereby obtaining a sensitivity threshold evaluation coefficient;

[0118] Step S43: performing encryption sensitivity division on the fused data set according to the encryption sensitivity calculation formula and the sensitivity threshold evaluation coefficient, thereby obtaining a sensitive division data set;

[0119] Step S44: filtering the authority classification data for government affairs data, thereby obtaining government affairs screening data;

[0120] Step S45: Performing value assessment on the screened data in the government affairs field, thereby obtaining government affairs value assessment data, wherein the government affairs value assessment data includes social security assessment data, natural resource assessment data, direct economic assessment data, educational resource assessment data, public security assessment data, and basic livelihood assessment data;

[0121] Step S46: performing high-sensitivity correlation analysis on the social security assessment data and the public security assessment data, thereby obtaining first sensitive correlation data; performing medium-sensitivity correlation analysis on the education resource assessment data and the natural resource assessment data, thereby obtaining second sensitive correlation data; performing low-sensitivity correlation analysis on the direct economic assessment data and the basic livelihood assessment data, thereby obtaining third sensitive correlation data;

[0122] Step S47: performing high-sensitivity feature downgrading on the first sensitive associated data and the sensitive divided data set, thereby obtaining first sensitive downgraded data; performing medium-sensitivity feature downgrading on the second sensitive associated data and the sensitive divided data set, thereby obtaining second sensitive downgraded data; performing low-sensitivity feature downgrading on the third sensitive associated data and the sensitive divided data set, thereby obtaining third sensitive downgraded data, wherein the third sensitive downgraded data includes a high-sensitivity data set, a medium-sensitivity data set, and a low-sensitivity data set;

[0123] Step S48: performing multi-level encryption on the high-sensitivity data set to obtain multi-level encrypted data; performing asymmetric encryption on the medium-sensitivity data set to obtain asymmetric encrypted data; performing hash encryption on the low-sensitivity data set to obtain hash encrypted data;

[0124] Step S49: performing encryption label division on the multi-level encrypted data, the asymmetric encrypted data and the hash encrypted data, thereby obtaining encryption label division data;

[0125] Step S410: Perform data authorization matching on the encrypted label division data and the authority classification data, thereby obtaining data authorization matching data.

[0126] The embodiment of the present invention removes invalid or repeated permission labels by deduplicating and cleaning the permission label classification data, and then sorts and groups the permission label classification data according to the type and level of the permission label, such as sorting and grouping according to department, position, role, etc. Finally, according to the results of sorting and grouping, the permission label classification data is permission graded, such as high, medium, low, etc., so as to obtain permission graded data. According to the permission graded data, the sensitive threshold evaluation requirements of data users and data providers with different permission levels are determined, such as data users and data providers with high permission levels require higher sensitive threshold evaluation, while data users and data providers with low permission levels require lower sensitive threshold evaluation. Then, according to the fused data set, the sensitive threshold evaluation standards of different data attributes and data contents are determined, such as data attributes and data contents involving personal privacy or public security require higher sensitive threshold evaluation, while public or insignificant data attributes and data contents require lower sensitive threshold evaluation. Finally, according to the sensitive threshold evaluation requirements and sensitive threshold evaluation standards, the permission graded data and the fused data set are subjected to sensitive threshold evaluation, such as using a scoring or grading method, so as to obtain a sensitive threshold evaluation coefficient. According to the sensitivity threshold evaluation coefficient, the data division range of different sensitivity threshold evaluation levels is determined, such as the data division range of the high sensitivity threshold evaluation level is 0.8-1.0, and the data division range of the low sensitivity threshold evaluation level is 0.0-0.2. Then, according to the data division range, the encryption sensitivity calculation is performed on the fused data set, such as using the encryption sensitivity calculation formula. Finally, according to the result of the encryption sensitivity calculation, the encryption sensitivity division is performed on the fused data set, such as high, medium, low, etc., so as to obtain the sensitive division data set. According to the authority classification data, the government data screening needs of data users and data providers with different authority levels are determined, such as data users and data providers with high authority levels need more government data screening, while data users and data providers with low authority levels need less government data screening. Then, according to the government data screening needs, the authority classification data is screened in the government data field, such as using keywords or tags. Finally, according to the results of the government data screening, the authority classification data is classified in the government data field, such as social security, natural resources, direct economy, educational resources, public security and other people's livelihood, so as to obtain the government data screening data.According to the data screened in the government affairs field, the data value assessment needs of different government affairs fields are determined. For example, the data value assessment demand in the social security field is social security assessment data, while the data value assessment demand in the education resources field is education resource assessment data. Then, according to the data value assessment needs, data value assessment is performed on the screened data in the government affairs field, such as using a data value assessment model or data value assessment indicators. Finally, according to the results of the data value assessment, the data value of the screened data in the government affairs field is ranked, such as high, medium, and low, so as to obtain government affairs value assessment data, where the government affairs value assessment data includes social security assessment data, natural resources assessment data, direct economic assessment data, education resources assessment data, public safety assessment data, and basic people's livelihood assessment data. Based on the government affairs value assessment data, determine the data association analysis needs of different government affairs value assessment levels. For example, the data association analysis needs of high government affairs value assessment levels are social security assessment data and public security assessment data, while the data association analysis needs of low government affairs value assessment levels are direct economic assessment data and basic people's livelihood assessment data. Then, based on the data association analysis needs, perform data association analysis on the government affairs value assessment data, such as using data association analysis methods or data association analysis tools. Finally, based on the results of the data association analysis, perform data association classification on the government affairs value assessment data. One category involves highly sensitive and high-value data on social security, social assistance, and social welfare, including information on registration, payment calculation, benefit payment, supervision and management of pension insurance, medical insurance, unemployment insurance, work-related injury insurance, maternity insurance, subsistence allowances, special hardship assistance, preferential treatment and resettlement, etc. This type of data needs to protect personal rights and interests, prevent fraud, loopholes, and waste, and also needs to optimize services to achieve data interconnection, mutual recognition, and intercommunication, and provide convenient, efficient, and fair services for social security, social assistance, and social welfare; a type of highly sensitive and high-value data involving public safety, including prevention and control, emergency response, and consequence assessment information for public safety incidents, emergencies, production safety, food and drugs, fire rescue, etc. This type of data needs to be kept confidential to prevent leakage, abuse, and misuse, and also needs to be strengthened to achieve rapid acquisition, accurate analysis, and effective use of data to provide support for public safety and social stability; public safety data reflects the risks, events, and responses of public safety, and is closely related to risk prevention, relief, and safety assurance of social security, such as production safety, food and drugs, and fire rescue. This can be done by using a support vector machine model or a genetic forest algorithm to perform association analysis on these two types of data, thereby performing data association classification and obtaining association data of different sensitivity levels, including first sensitive association data, second sensitive association data, and third sensitive association data.According to the associated data of different sensitivity levels, the requirements for downgrading data sensitive features of different sensitivity levels are determined, such as the downgrading requirements for data sensitive features of high sensitivity levels are first sensitive associated data, while the downgrading requirements for data sensitive features of low sensitivity levels are third sensitive associated data. Then, according to the requirements for downgrading data sensitive features, the associated data of different sensitivity levels and sensitive partitioned data sets are downgraded, such as using a data sensitive feature downgrading method or a data sensitive feature downgrading tool. Finally, according to the results of the data sensitive feature downgrading, the associated data of different sensitivity levels and sensitive partitioned data sets are classified according to data sensitive features, such as classifying data sensitive features according to high, medium, low, etc., so as to obtain sensitive downgraded data of different sensitivity levels, including first sensitive downgraded data, second sensitive downgraded data and third sensitive downgraded data, wherein the third sensitive downgraded data includes a high sensitive data set, a medium sensitive data set and a low sensitive data set. According to the sensitive downgraded data of different sensitivity levels, the data encryption methods and strength requirements of different sensitivity levels are determined, such as the data encryption method and strength requirement of the high sensitivity level is a high sensitivity data set, while the data encryption method and strength requirement of the low sensitivity level is a low sensitivity data set. Then, according to the data encryption method and strength requirement, different encryption processing is performed on the sensitive downgraded data of different sensitivity levels, such as using different encryption algorithms or encryption keys. Finally, according to the results of the encryption processing, the sensitive downgraded data of different sensitivity levels are encrypted data classification, such as encrypted data classification according to multi-level encryption, asymmetric encryption, hash encryption, etc., so as to obtain data with different encryption methods and strengths, including multi-level encrypted data, asymmetric encrypted data and hash encrypted data. According to the data of different encryption methods and strengths, the requirements for data encryption label division of different encryption methods and strengths are determined, such as the data encryption label division requirements of multi-level encryption methods and strengths are multi-level encrypted data, while the data encryption label division requirements of hash encryption methods and strengths are hash encrypted data. Then, according to the data encryption label division requirements, encryption label division is performed on the data of different encryption methods and strengths, such as using an encryption label division method or an encryption label division tool. Finally, according to the results of encryption label division, encryption label classification is performed on the data of different encryption methods and strengths, such as encryption label classification is performed according to encryption method, encryption strength, encryption key, etc., so as to obtain data divided by different encryption labels, including multi-level encryption label division data, asymmetric encryption label division data and hash encryption label division data.According to the data divided by different encryption labels, the authorization matching requirements of the data divided by different encryption labels are determined. For example, the authorization matching requirements of the data divided by multi-level encryption labels are for the data divided by multi-level encryption labels, while the authorization matching requirements of the data divided by hash encryption labels are for the data divided by hash encryption labels. Then, according to the data authorization matching requirements, data authorization matching is performed on the data divided by different encryption labels and the permission-graded data, that is, the permissions and labels of the encrypted data are matched according to different permission grades and encrypted labels. For example, for highly sensitive encrypted data of the social security type, it is necessary to find the permissions of the corresponding data, such as ownership and control, according to the assigned permission labels, and match the permissions with the encryption labels of the data, such as the encryption method. When the data needs to be decrypted, the corresponding permissions and the users with the permissions can be found through the authorization matching labels. Subsequently, the security of the data can be ensured by judging whether the user has authorized the decryption of the data, thereby obtaining the data authorization matching data.

[0127] The present invention firstly performs permission classification on permission label classification data, and can reasonably divide and manage data according to the permission requirements of different data users and data providers, thereby improving the security and compliance of data. For example, higher-level departments can access more data, while lower-level departments can only access part of the data, which can ensure the minimum exposure and maximum utilization of data. Permission classification data can help data users and data providers to establish trust and cooperation, and can also promote data sharing and communication. By performing sensitive threshold evaluation on permission classification data and fused data sets, appropriate sensitive threshold evaluation coefficients can be determined according to the sensitivity and importance of the data, thereby providing a basis for subsequent encryption sensitivity division. For example, for data involving personal privacy or public safety, a higher sensitive threshold evaluation coefficient can be set, while for public or insignificant data, a lower sensitive threshold evaluation coefficient can be set, thereby ensuring the reasonable classification and moderate protection of data. The sensitive threshold evaluation coefficient can reflect the sensitivity and value of the data, and can also provide a reference for the encryption method and strength of the data. By performing encryption sensitivity calculation formula and performing encryption sensitivity division on the fused data set according to the sensitive threshold evaluation coefficient, the data can be divided into different sensitivity levels, thereby providing a basis for subsequent encryption processing. For example, for highly sensitive data, it can be divided into highly sensitive data sets, while for low-sensitivity data, it can be divided into low-sensitivity data sets, so as to ensure differentiated data processing and customized services. Encryption sensitivity division can optimize and streamline data according to the sensitivity and importance of data, and can also facilitate the use and analysis of data. By filtering government data in the authority classification data, data can be filtered and classified according to the data needs and characteristics of different government fields, thereby improving the relevance and effectiveness of data. For example, for data in the social security field, data related to social security can be filtered out, while for data in the educational resources field, data related to educational resources can be filtered out, so as to ensure the pertinence and professionalism of data. Data filtering in the government field can classify and organize data according to the purpose and characteristics of data, and can also provide support for the display and application of data. By conducting value assessment on the data screened in the government field, data can be evaluated and sorted according to the social, economic, educational, security and other aspects of the data, thereby improving the value and priority of data. For example, for social security assessment data, the data can be evaluated and sorted according to the social security value of the data, such as medical care, pension, employment, etc., and for education resource assessment data, the data can be evaluated and sorted according to the education resource value of the data, such as schools, teachers, courses, etc., so as to ensure the importance and urgency of the data. Government value assessment data can reflect the social and economic benefits of the data, and can also provide a basis for the allocation and use of data.By performing association analysis on government value assessment data of different sensitivity levels, the potential relationships and rules between data can be discovered, thereby improving the knowledge and insight of data. For example, for social security assessment data and public security assessment data, high sensitivity association analysis can be performed to obtain the first sensitive association data, so that the high correlation and influence between data can be found, such as the relationship between social security and public security, while for education resource assessment data and natural resource assessment data, medium sensitivity association analysis can be performed to obtain the second sensitive association data, so that the medium correlation and influence between data can be found, such as the relationship between education resources and natural resources, and for direct economic assessment data and basic livelihood assessment data, low sensitivity association analysis can be performed to obtain the third sensitive association data, so that the low correlation and influence between data can be found, such as the relationship between direct economy and other livelihood. Association analysis of different sensitivity levels can conduct in-depth and extensive analysis of data based on the sensitivity and value of data, and can also provide guidance for the understanding and application of data. By downgrading the sensitive features of association data of different sensitivity levels and sensitive classification data sets, the features of data can be downgraded and abstracted based on the sensitivity and importance of data, thereby improving the security and availability of data. For example, for the first sensitive associated data, the high-sensitivity feature can be downgraded to obtain the first sensitive downgraded data, so that the high-sensitivity features of the data, such as personal identity information or public confidential information, can be removed or hidden, while for the second sensitive associated data, the medium-sensitivity feature can be downgraded to obtain the second sensitive downgraded data, so that the medium-sensitivity features of the data, such as personal preference information or overall strategic information, can be removed or hidden, and for the third sensitive associated data, the low-sensitivity feature can be downgraded to obtain the third sensitive downgraded data, so that the low-sensitivity features of the data, such as personal consumption information or public statistical information, can be removed or hidden. The third sensitive downgraded data includes a high-sensitivity data set, a medium-sensitivity data set, and a low-sensitivity data set. Sensitive feature downgrade can appropriately process the features of the data according to the sensitivity and importance of the data, and can also provide guarantees for the protection and use of the data. By using different encryption methods and strengths for data sets of different sensitivity levels, the data can be appropriately protected and transmitted according to the sensitivity and importance of the data, thereby improving the security and reliability of the data.For example, for highly sensitive data sets, multi-level encryption can be performed to obtain multi-level encrypted data, so that the data can be encrypted and decrypted multiple times, such as symmetric encryption, asymmetric encryption, homomorphic encryption, etc. For medium-sensitive data sets, asymmetric encryption can be performed to obtain asymmetric encrypted data, so that the data can be encrypted and decrypted with public and private keys, such as RSA, ECC, etc. For low-sensitivity data sets, hash encryption can be performed to obtain hash encrypted data, so that the data can be encrypted and verified one-way, such as MD5, SHA, etc. Different encryption methods and strengths can be used to properly process data according to the sensitivity and importance of the data, and can also provide protection for data transmission and storage. By dividing data with different encryption methods and strengths into encryption labels, data can be identified and distinguished according to the encryption method and strength of the data, thereby improving the recognition and traceability of the data. For example, for multi-level encrypted data, multi-level encryption label division can be performed to obtain multi-level encryption label division data, so that data can be labeled and distinguished at multiple levels, such as encryption method, encryption strength, encryption key, etc., and for asymmetric encrypted data, asymmetric encryption label division can be performed to obtain asymmetric encryption label division data, so that data can be asymmetric labeled and distinguished, such as encryption method, encryption key, encryption certificate, etc., and for hash encrypted data, hash encryption label division can be performed to obtain hash encryption label division data, so that data can be hashed and distinguished, such as encryption method, encryption summary, encryption signature, etc. Encryption label division can identify and distinguish data according to the encryption method and strength of the data, and can also provide convenience for the management and use of the data. By performing data authorization matching on encryption label division data and permission classification data, data can be authorized and matched according to the encryption label and permission classification of the data, thereby improving the legitimacy and rationality of the data. For example, for data divided by multi-level encryption labels, multi-level encryption data authorization matching can be performed to obtain multi-level encryption data authorization matching data, so that data can be authorized and matched at multiple levels, such as according to encryption method, encryption strength, encryption key, etc., and for data divided by asymmetric encryption labels, asymmetric encryption data authorization matching can be performed to obtain asymmetric encryption data authorization matching data, so that data can be asymmetric authorized and matched, such as according to encryption method, encryption key, encryption certificate, etc., and for data divided by hash encryption labels, hash encryption data authorization matching can be performed to obtain hash encryption data authorization matching data, so that data can be hashed and authorized and matched, such as according to encryption method, encryption summary, encryption signature, etc. Data authorization matching can authorize and match data according to the encryption label and permission classification of the data, and can also provide guarantees for the access and use of the data.

[0128] Preferably, the encryption sensitivity calculation formula in step S43 is as follows:

[0129] ;

[0130] In the formula, is the sensitivity threshold, is the data encryption adaptation weight value, It is the first The frequency of sensitive words in a data block, It is the first The sensitivity threshold evaluation coefficient of the data block, is the number of data blocks of the fused dataset, It is the first The number of words in a data block, It is the first In the data block The frequency of the word, It is the first The business association characteristic sequence value of a data block and adjacent related data blocks, It is the first The business sensitivity of each data block, It is the first The average data update time of each data block, It is the first The number of desensitized data blocks is on the order of magnitude.

[0131] The present invention constructs an encryption sensitivity calculation formula, which is used to calculate the encryption sensitivity, thereby performing encryption sensitivity division on the fused data set to obtain a sensitive division data set, wherein the sensitive division data set includes a high sensitivity data set, a medium sensitivity data set, and a low sensitivity data set. Specifically, It is the sensitivity threshold, which is a value between 0 and 1, indicating the sensitivity of the data. The closer it is to 1, the more sensitive the data is, requiring a higher level of encryption; The closer it is to 0, the less sensitive the data is and a lower level of encryption can be used. is the data encryption adaptation weight value, which is a value between 0 and 1, indicating the The importance of a data block in data encryption. The closer it is to 1, the The greater the impact of each data block on data encryption, the more consideration is needed; The closer to 0, the The smaller the impact of a data block on data encryption, the smaller it is and can be ignored. It is the first The sensitive word frequency of the data block, which is a non-negative integer, represents the The number of sensitive words contained in a data block. Sensitive words refer to words that may leak data privacy or cause data security issues, such as name, ID number, bank account number, etc. The higher the frequency of sensitive words, the more sensitive the data is and the higher the level of encryption is needed. It is the first The sensitivity threshold evaluation coefficient of the data block is a value between 0 and 1, which indicates The sensitivity level of each data block. The sensitivity threshold evaluation coefficient is obtained by evaluating the sensitivity threshold of the fused data set, which reflects the sensitivity distribution of the data. The closer the sensitivity threshold evaluation coefficient is to 1, the higher the sensitivity of the data block. The more sensitive a data block is, the higher the level of encryption required; the closer the sensitivity threshold evaluation coefficient is to 0, the higher the level of encryption required. The less sensitive a block of data is, the lower level of encryption can be used. It is the number of data blocks in the fused data set. It is a positive integer, indicating the number of data blocks contained in the fused data set. A data block refers to each smallest independent business block data in the fused data set, such as a business entity data in the e-government big data, a piece of independent text data, etc. The more data blocks there are, the larger the corresponding calculation data volume is, but the more accurate the corresponding results are. It is the first The number of words in a data block, which is a positive integer, represents the The number of words contained in a data block. A word refers to the basic component of data, including a term or a word. The more words there are, the richer the data is, and the more accurate the corresponding results are. It is the first In the data block The frequency of the word, which is a non-negative integer, represents the In the data block The number of times a sensitive word appears in the data block. The higher the word frequency, the more important the data block is and the stronger the encryption is needed. It is the first The business association feature sequence value of a data block and adjacent related data blocks is a value between 0 and 1, indicating The business association degree between a data block and its adjacent data blocks. Business association refers to the logical connection between data, such as time, place, event, etc. The business association feature sequence value is obtained by performing business association analysis on the data, which reflects the business association distribution of the data. It is the first The business sensitivity of a data block is a value between 0 and 1, indicating the The business sensitivity of a data block. Business sensitivity refers to the importance of data to the business, such as the value, impact, and risk of the data. It is the first The average data update time of a data block, which is a positive number, indicating the The average interval between data updates of a data block. Data update refers to changes in data, such as addition, modification, deletion, etc. The closer the service association feature sequence value is to 1, the The stronger the business correlation between a data block and its adjacent data blocks, the closer the business sensitivity is to 1, indicating that the The more business-sensitive a data block is, The impact of data block relevance and business sensitivity on data encryption can be balanced. For example, for related businesses, using the same encryption method can make data decryption more efficient. Business sensitivity data is an assessment of the sensitivity of the business itself. The higher the business sensitivity, the higher the sensitivity of related businesses, and the higher the relevance. The shorter the average data update time, the faster the data decryption efficiency. The more frequently the data in a data block is updated, the more Generally speaking, in order to reduce the efficiency problems caused by multi-level decryption, it is better to use a lower level of encryption for frequently updated data. It is the first The magnitude of the desensitized data in the data block, which is a positive integer. The magnitude of the data that has been desensitized in the data block. Desensitized data refers to data that has had sensitive information removed or has been obfuscated, such as replacing names with *, replacing ID numbers with random numbers, etc. The larger the magnitude of desensitized data, the more The data in each data block has been subjected to a lot of desensitization processing. Relatively speaking, the privacy level should be higher, so a higher level of encryption is required. In summary, this formula takes into account the balance between the association and business sensitivity in the data and the analysis method of word sensitivity, while taking into account the problem of data decryption efficiency and the sensitivity threshold evaluation coefficient. Therefore, this formula is comprehensive and computationally effective in solving the division of data encryption sensitivity. It should be noted that in step S42 of the present invention, this formula divides the encryption sensitivity of the fused data set according to the sensitivity threshold evaluation coefficient, thereby obtaining a sensitive division data set, wherein the sensitive division data set includes a high sensitivity data set, a medium sensitivity data set and a low sensitivity data set, but the present invention is not limited to the use of this encryption sensitivity calculation formula, and can also directly use the sensitivity threshold evaluation coefficient for sensitivity division, but this formula is more comprehensive and effective in solving the division of data encryption sensitivity.

[0132] Step S5 includes the following steps:

[0133] Step S51: dividing the encrypted label division data into business blocks, thereby obtaining business block data;

[0134] Step S52: performing decryption coefficient evaluation on the business block data, thereby obtaining decryption coefficient data;

[0135] Step S53: evaluating the heat coefficient of the business block data, thereby obtaining heat coefficient data;

[0136] Step S54: evaluating the computational complexity coefficient of the business block data, thereby obtaining computational complexity coefficient data;

[0137] Step S55: performing weighted averaging on the decryption coefficient data, the heat coefficient data and the computational complexity coefficient data, thereby obtaining computational resource coefficient data;

[0138] Step S56: Utilizing node distribution technology and performing distributed resource optimization on the service block data according to the calculation resource coefficient data, thereby obtaining a distributed data set;

[0139] Step S57: Perform distributed authorization processing on the distributed data set according to the data authorization matching data, thereby obtaining distributed authorization processing data to perform processing optimization operations for e-government big data.

[0140] The embodiment of the present invention divides the encrypted label data into business blocks, groups the data according to different business needs and scenarios, and divides the data into business blocks, thereby obtaining business block data. The decryption coefficient of the business block data is evaluated, for example, according to the decryption difficulty of the data, that is, according to the time complexity of the decryption algorithm and the average time required for decryption, the data is evaluated at different decryption levels, thereby obtaining decryption coefficient data. The heat coefficient of the business block data is evaluated, for example, according to the access frequency of the data, the data is evaluated at different heat levels, such as according to the number of accesses, access time, and dimensions of the accessing users, thereby obtaining heat coefficient data. The computational complexity coefficient of the business block data is evaluated, and according to the overall computational difficulty of the business data, the data is evaluated at different computational levels, such as according to the computational scale of the data, computational time, and dimensions of computational resources, to determine the appropriate computational complexity coefficient, thereby obtaining computational complexity coefficient data. The decryption coefficient data, heat coefficient data and computational complexity coefficient data are weighted averaged. Different weights are assigned to the data according to the different importance of the data. For example, different weight coefficients are assigned according to the dimensions of data sensitivity, value and risk, such as 0.5, 0.3, 0.2, etc. The weights are assigned according to the actual business. At the same time, different goals need to be considered for weight assignment. For example, if the security or privacy of the data is high, the encryption complexity of the data will be high, and the corresponding decryption time complexity and required computing resources will be high. The data volume of different businesses will also lead to different computing complexities of the business. Finally, the data is weighted averaged to obtain the computing resource coefficient data. According to the computing resource coefficient data, the distributed resources of the business block data are optimized. For example, the data is distributed to different nodes according to different computing resource requirements. For example, according to different dimensions such as the computing resource coefficient of the data, node resources, and node performance, the distributed resources of the data are optimized, such as using data sharding, data replication, data migration and other technologies to obtain distributed data sets. Distributed authorization processing is performed on distributed data sets according to data authorization matching data, that is, distributed adaptive processing is performed on the data according to the authorization matching situation of the data. Specifically, the distributed data sets are indexed and associated according to the permission relationship of the data and the corresponding association characteristics of the encrypted data. This facilitates the subsequent processing of e-government big data. When the data is associated and analyzed, the corresponding permissions and users with corresponding permissions can be found more quickly, and the computing advantages of distributed nodes can be used to perform a safer decryption process to execute the processing optimization operations of e-government big data.

[0141] The present invention first divides the encrypted label data into business blocks, and can divide the data into blocks according to different business needs and contents, thereby improving the data's organization and queryability. The decryption coefficient of the business block data is evaluated, and the appropriate decryption coefficient can be determined according to the data's decryption difficulty and decryption time, thereby improving the data's security. The heat coefficient of the business block data is evaluated, and the appropriate heat coefficient can be determined according to the data's access frequency and access volume. The calculation complexity coefficient of the business block data is evaluated, and the appropriate calculation complexity coefficient can be determined according to the data's calculation difficulty and calculation time. The decryption coefficient data, the heat coefficient data, and the calculation complexity coefficient data are weighted averaged, and the decryption complexity, heat degree, and calculation complexity of the data can be comprehensively considered to obtain the appropriate calculation resource coefficient, thereby improving the coefficient's balance. By utilizing node distribution technology and optimizing the distributed resources of the business block data according to the calculation resource coefficient data, the distributed storage and access of the data can be realized, and the scalability and accessibility of the data can be improved, while also reducing the risk of data centralization and the possibility of single point failure, thereby improving the data's security and recoverability. Distributed authorization processing of distributed data sets based on data authorization matching data can realize distributed authorization and management of data and improve the manageability of data. At the same time, it also ensures the consistency of data permissions and encryption and improves the adaptability of data, thereby executing the processing optimization operations of e-government big data and improving the processing effect and quality of e-government big data.

[0142] Preferably, the present invention further provides an e-government big data processing optimization system for executing the e-government big data processing optimization method as described above, the e-government big data processing optimization system comprising:

[0143] The permission label module is used to obtain the input end big data of the e-government system; classify the input end big data by permission labels, so as to obtain permission label classification data;

[0144] The conflict alignment module performs business matching on the permission tag classification data to obtain business matching data; performs conflict alignment on the business matching data to obtain conflict alignment data;

[0145] A data set fusion module is used to fuse the conflicting aligned data to obtain a fused data set;

[0146] The permission matching module is used to divide the fused data set into sensitive thresholds, thereby obtaining a sensitive divided data set; to divide the sensitive divided data set into encrypted labels, thereby obtaining encrypted label divided data; to perform data authorization matching on the encrypted label divided data, thereby obtaining data authorization matching data;

[0147] The distributed authorization module is used to perform distributed authorization processing on the encrypted tag division data according to the data authorization matching data, so as to obtain the distributed authorization processing data to perform the processing optimization operation of the e-government big data.

[0148] In summary, the present invention provides an e-government big data processing optimization system, which consists of a permission label module, a conflict alignment module, a data set fusion module, a permission matching module and a distributed authorization module. It can implement any e-government big data processing optimization method described in the present invention. The internal structure of the system cooperates with each other. By adopting the mapping of different user permissions of a large amount of data in the e-government system to the regional level corresponding to the department, the difficulty of permission search of a large amount of departmental corresponding regional business data is reduced for the subsequent authorization matching and distributed storage of sensitive data, and targeted encryption measures of different sensitivities are performed on the data, and adaptive processing is performed on the data before distributed data, which reduces the time spent on encryption and decryption of a large amount of sensitive data in the e-government big data, thereby simplifying the operation process of the e-government big data processing optimization system.

[0149] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0150] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An e-government big data processing optimization method, characterized in that: The following steps are involved: Step S1: Obtaining input big data of the e-government system; The input end big data is classified by permission labels, thereby obtaining permission label classification data; wherein step S1 includes: Step S11: Obtaining input end big data of the e-government system; Step S12: performing metadata analysis on the input end big data, thereby obtaining metadata analysis data; Step S13: performing fine-grained feature extraction on the metadata analysis data, thereby obtaining fine-grained feature data; Step S14: performing government affairs clustering on the input end big data according to the fine-grained feature data, thereby obtaining a government affairs classification data set; Step S15: Performing authority chain visualization analysis on the government division data set, thereby obtaining authority chain visualization data; wherein step S15 includes: Step S151: Performing ownership visualization analysis on the government division data set, thereby obtaining ownership chain visualization data; Step S152: Perform control right visualization analysis on the government affairs division data set, thereby obtaining control right chain visualization data; Step S153: performing attribute association analysis on the ownership chain visualization data and the control right chain visualization data, thereby obtaining attribute association analysis data; Step S154: performing feature dimension reduction on the attribute association analysis data, thereby obtaining a feature dimension reduction data set; Step S155: performing authority matching on the feature dimension reduction data set, thereby obtaining an authority matching data set; Step S156: Performing authority chain visualization analysis on the authority matching data set, thereby obtaining authority chain visualization data; Step S16: classifying the authority labels of the government division data set according to the authority chain visualization data, thereby obtaining weight label classification data; Step S2: performing business matching on the permission tag classification data to obtain business matching data; performing conflict alignment on the business matching data to obtain conflict alignment data; wherein step S2 includes: Step S21: performing business matching on the permission tag classification data, thereby obtaining business matching data; Step S22: performing data entity association matching on the permission tag classification data according to the business matching data, thereby obtaining entity association matching data and non-entity association data; Step S23: aligning the entity association matching data by attributes, thereby obtaining attribute alignment matching data; Step S24: performing attribute redundancy on the attribute alignment matching data, thereby obtaining redundant matching entity data; Step S25: filtering conflicting data on non-entity related data, thereby obtaining filtered non-entity data; Step S26: performing label selection on the authority label classification data, thereby obtaining authority label data; Step S27: performing department-region alignment mapping on the filtered non-entity data and the redundant matching entity data based on the authority tag data, thereby obtaining conflict alignment data; wherein step S27 includes: Step S271: performing data integration on the filtered non-entity data and the redundant matching entity data, thereby obtaining filtered integrated data; Step S272: extracting department-level tags from the authority tag data, thereby obtaining department authority tag data; Step S273: performing department-level data conflict alignment on the filtered integrated data according to the department authority label data, thereby obtaining department conflict alignment data; Step S274: obtaining regional level interface output data according to department conflict alignment data; Step S275: extracting regional level labels from the authority label data, thereby obtaining regional authority label data; Step S276: performing regional conflict alignment on the filtered integration data and the regional interface output data according to the regional authority label data, thereby obtaining regional conflict alignment data; Step S277: performing department-region alignment mapping on the regional conflict alignment data, thereby obtaining conflict alignment data; Step S3: performing data set fusion on the conflicting aligned data to obtain a fused data set; wherein step S3 includes: Step S31: performing parameter data flow analysis on the business matching data, thereby obtaining parameter data flow analysis data; Step S32: performing department-region parameter mapping on the parameter data flow analysis data, thereby obtaining parameter mapping data; Step S33: performing parameter compatibility analysis on the parameter mapping data, thereby obtaining parameter compatibility data; Step S34: performing format compatibility conversion on the conflict alignment data according to the parameter compatibility data, thereby obtaining compatible conversion data; Step S35: performing business relevance analysis on the parameter mapping data, thereby obtaining business relevance data; Step S36: performing feature mining on the business correlation data to obtain business correlation feature data; Step S37: performing data set fusion on the compatible conversion data according to the business-related feature data, thereby obtaining a fused data set; Step S4: performing sensitive threshold division on the fused data set to obtain a sensitive divided data set; performing encrypted label division on the sensitive divided data set to obtain encrypted label division data; performing data authorization matching on the encrypted label division data to obtain data authorization matching data; wherein step S4 includes: Step S41: performing permission classification on the permission label classification data, thereby obtaining permission classification data; Step S42: performing sensitivity threshold evaluation on the authority classification data and the fused data set, thereby obtaining a sensitivity threshold evaluation coefficient; Step S43: performing encryption sensitivity division on the fused data set according to the encryption sensitivity calculation formula and the sensitivity threshold evaluation coefficient, thereby obtaining a sensitive division data set; The encryption sensitivity calculation formula is as follows: ； In the formula, is the sensitivity threshold, is the data encryption adaptation weight value, It is the first The frequency of sensitive words in a data block, It is the first The sensitivity threshold evaluation coefficient of the data block, is the number of data blocks of the fused dataset, It is the first The number of words in a data block, It is the first In the data block The frequency of the word, It is the first The business association characteristic sequence value of a data block and adjacent related data blocks, It is the first The business sensitivity of each data block, It is the first The average data update time of each data block, It is the first The number of desensitized data blocks; Step S44: filtering the authority classification data for government affairs data, thereby obtaining government affairs screening data; Step S45: Performing value assessment on the screened data in the government affairs field, thereby obtaining government affairs value assessment data, wherein the government affairs value assessment data includes social security assessment data, natural resource assessment data, direct economic assessment data, educational resource assessment data, public security assessment data, and basic livelihood assessment data; Step S46: performing high-sensitivity correlation analysis on the social security assessment data and the public security assessment data, thereby obtaining first sensitive correlation data; performing medium-sensitivity correlation analysis on the education resource assessment data and the natural resource assessment data, thereby obtaining second sensitive correlation data; performing low-sensitivity correlation analysis on the direct economic assessment data and the basic livelihood assessment data, thereby obtaining third sensitive correlation data; Step S47: performing high-sensitivity feature downgrading on the first sensitive associated data and the sensitive divided data set, thereby obtaining first sensitive downgraded data; performing medium-sensitivity feature downgrading on the second sensitive associated data and the sensitive divided data set, thereby obtaining second sensitive downgraded data; performing low-sensitivity feature downgrading on the third sensitive associated data and the sensitive divided data set, thereby obtaining third sensitive downgraded data, wherein the third sensitive downgraded data includes a high-sensitivity data set, a medium-sensitivity data set, and a low-sensitivity data set; Step S48: performing multi-level encryption on the high-sensitivity data set to obtain multi-level encrypted data; performing asymmetric encryption on the medium-sensitivity data set to obtain asymmetric encrypted data; performing hash encryption on the low-sensitivity data set to obtain hash encrypted data; Step S49: performing encryption label division on the multi-level encrypted data, the asymmetric encrypted data and the hash encrypted data, thereby obtaining encryption label division data; Step S410: performing data authorization matching on the encrypted label division data and the authority classification data, thereby obtaining data authorization matching data; Step S5: Perform distributed authorization processing on the encrypted label division data according to the data authorization matching data, thereby obtaining distributed authorization processing data to perform processing optimization operations for e-government big data.

2. The e-government big data processing optimization method according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: dividing the encrypted label division data into business blocks, thereby obtaining business block data; Step S52: performing decryption coefficient evaluation on the business block data, thereby obtaining decryption coefficient data; Step S53: evaluating the heat coefficient of the business block data, thereby obtaining heat coefficient data; Step S54: evaluating the computational complexity coefficient of the business block data, thereby obtaining computational complexity coefficient data; Step S55: performing weighted averaging on the decryption coefficient data, the heat coefficient data and the computational complexity coefficient data, thereby obtaining computational resource coefficient data; Step S56: Utilizing node distribution technology and performing distributed resource optimization on the service block data according to the calculation resource coefficient data, thereby obtaining a distributed data set; Step S57: Perform distributed authorization processing on the distributed data set according to the data authorization matching data, thereby obtaining distributed authorization processing data to perform processing optimization operations for e-government big data.

3. An e-government big data processing optimization system, characterized in that: Used to execute the e-government big data processing optimization method as claimed in claim 1, the e-government big data processing optimization system comprises: The permission label module is used to obtain the input end big data of the e-government system; classify the input end big data by permission labels, so as to obtain permission label classification data; The conflict alignment module performs business matching on the permission tag classification data to obtain business matching data; performs conflict alignment on the business matching data to obtain conflict alignment data; A data set fusion module is used to fuse the conflicting aligned data to obtain a fused data set; The permission matching module is used to divide the fused data set into sensitive thresholds, thereby obtaining a sensitive divided data set; to divide the sensitive divided data set into encrypted labels, thereby obtaining encrypted label divided data; to perform data authorization matching on the encrypted label divided data, thereby obtaining data authorization matching data; The distributed authorization module is used to perform distributed authorization processing on the encrypted tag division data according to the data authorization matching data, so as to obtain the distributed authorization processing data to perform the processing optimization operation of the e-government big data.

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