An intelligent social governance method and system based on data interconnection

Abstract

This application relates to the field of cloud computing technology, and provides an intelligent social governance method and system based on data interoperability. The method includes: collecting and obtaining a service data set; integrating and updating the service data set to obtain integrated and updated data; privately identifying the integrated and updated data, classifying the data according to the private identification results, and obtaining a public cloud data set and a private cloud data set; forming a hybrid cloud set by combining the public cloud data set and the private cloud data set; matching access permissions through authentication information; calling data from the hybrid cloud set according to the access permissions, and constructing a temporary data model based on the call results; inputting demand information into the temporary data model, and outputting feedback results. This method can solve the technical problems of low intelligence level and inaccurate intelligent calling of government data in the data application process.

Description

Technical Field

[0001] This application relates to the field of cloud computing technology, specifically to an intelligent social governance method and system based on data interoperability. Background Technology

[0002] In recent years, with the development of the mobile internet, the penetration of information technology into all aspects of society and the economy, as well as people's daily lives, has accelerated. my country has the largest number of internet users in the world, and the amount of data it generates every day is also among the highest in the world. The larger the scale of data, the greater the difficulty in processing it, but the greater the value that can be obtained through data mining.

[0003] In the era of big data, information resources are growing explosively, and people have more and more channels to obtain information and data. In the process of using data for scientific management, the low level of intelligence in various levels of competent departments has prevented them from realizing the application value of data after acquisition. Therefore, it is necessary to optimize and upgrade the way data is used to improve public satisfaction.

[0004] In summary, existing technologies suffer from low levels of intelligence in the application of government data and the inability to accurately and intelligently access it. Summary of the Invention

[0005] Therefore, it is necessary to provide an intelligent social governance method and system based on data interoperability to address the aforementioned technical issues.

[0006] A data-interoperable intelligent social governance method is disclosed, applied to an intelligent social governance system. The method includes: collecting a service data set, wherein the service data set includes a source identifier and a time identifier; integrating and updating the service data set to obtain integrated and updated data; assigning a privacy identifier to the integrated and updated data, and classifying the data according to the privacy identifier results to obtain a public cloud data set and a private cloud data set; constructing a hybrid cloud set using the public cloud data set and the private cloud data set; reading the authentication information of the end user, and matching the access permissions based on the authentication information after successful verification; calling data from the hybrid cloud set according to the access permissions, and constructing a temporary data model based on the call results; obtaining the user's demand information, inputting the demand information into the temporary data model, and outputting a feedback result.

[0007] In one embodiment, the method further includes: setting a data update cycle; obtaining a data update identifier for the service data set; when the service data set meets the data update cycle, performing update filtering on the service data set according to the data update identifier; when the data in the update filtering result is updated data, replacing and updating the existing data based on the updated data; and obtaining the integrated updated data based on the replacement update result.

[0008] In one embodiment, the method further includes: obtaining historical operation data of the terminal user, wherein the historical operation data includes search data and browsing data; obtaining permission change nodes of the terminal user and generating node identifiers based on the permission change nodes; dividing the historical operation data into time intervals and generating feature weakening coefficients for the interval division results using the permission change node identifiers; constructing a terminal user profile based on the interval division results with feature weakening coefficients; and managing the terminal user's usage through the terminal user profile.

[0009] In one embodiment, the method further includes: constructing an interactive user set, wherein the interactive user set is a set of users who interact with the terminal user; extracting interaction features of the terminal user and the interactive user set to obtain an interactive user feature set; and constructing the terminal user profile based on the interactive user feature set.

[0010] In one embodiment, the method further includes: obtaining private data with a privacy identifier from the integrated update data; comparing the privacy level of the private data to obtain the highest privacy level identifier; encrypting the private data using the highest privacy level identifier; and obtaining the private cloud data set based on the encryption result.

[0011] In one embodiment, the method further includes: reading and obtaining real-time computing power allocation information of the system; determining whether the allocated computing power information meets a preset computing power threshold; when the allocated computing power information does not meet the preset computing power threshold, generating a keyword collection instruction; collecting keywords from the terminal user through the keyword collection instruction; matching the data of the hybrid cloud collection based on the keyword collection results, the allocated computing power information, and the calling permissions; and constructing the temporary data model based on the matching results.

[0012] In one embodiment, the method further includes: collecting user error feedback to obtain an error feedback set; performing cumulative statistics on the error feedback set to generate cumulative statistical identification information; and managing data services through the cumulative statistical identification information.

[0013] An intelligent social governance system based on data interoperability, the system comprising:

[0014] A data acquisition module is used to acquire a service data set, wherein the service data set includes a source identifier and a time identifier;

[0015] A data integration and update module is used to integrate and update the service data set to obtain integrated and updated data.

[0016] The data classification module is used to perform private identification on the integrated and updated data, classify the data according to the private identification results, and obtain a public cloud data set and a private cloud data set.

[0017] A hybrid cloud component module, wherein the hybrid cloud component module is used to form a hybrid cloud set by the public cloud data set and the private cloud data set;

[0018] The permission matching module is invoked. The permission matching module is used to read the authentication information of the terminal user. When the verification is successful, the invocation permission is matched according to the authentication information.

[0019] A data model building module is used to call data from the hybrid cloud collection according to the calling permissions, and to build a temporary data model based on the calling results.

[0020] The result acquisition module is used to obtain the user demand information of the terminal user, input the demand information into the temporary data model, and output feedback results.

[0021] The aforementioned intelligent social governance method and system based on data interoperability can solve the technical problems of low intelligence level and inaccurate intelligent retrieval of government data during data application. By integrating, updating, and privately identifying service data sets, and constructing a hybrid cloud data set based on public and private cloud data sets, and by building a temporary data model, government data can be intelligently mobilized for data interoperability. Under the premise of ensuring data security, data mobilization can be intelligently completed based on user and device information, thereby improving user satisfaction.

[0022] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0023] Figure 1 This application provides a flowchart illustrating an intelligent social governance method based on data interoperability.

[0024] Figure 2 This application provides a flowchart illustrating the process of obtaining integrated and updated data in an intelligent social governance method based on data interoperability.

[0025] Figure 3 This application provides a schematic diagram of the structure of an intelligent social governance system based on data interoperability.

[0026] Figure labeling: 1. Data acquisition module; 2. Data integration and update module; 3. Data classification module; 4. Hybrid cloud composition module; 5. Access permission matching module; 6. Data model construction module; 7. Result acquisition module. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0028] like Figure 1 As shown, this application provides an intelligent social governance method based on data interoperability. The method is applied to an intelligent social governance system and includes:

[0029] Step S100: Collect and obtain a service data set, wherein the service data set includes a source identifier and a time identifier;

[0030] Step S200: Perform data integration and update on the service data set to obtain integrated and updated data;

[0031] like Figure 2 As shown, in one embodiment, step S200 of this application further includes:

[0032] Step S210: Set the data update cycle;

[0033] Step S220: Obtain the data update identifier of the service data set. When the service data set meets the data update cycle, update and filter the service data set according to the data update identifier.

[0034] Step S230: When the data in the updated filtering results is updated, the existing data is replaced and updated based on the updated data;

[0035] Step S240: Obtain the integrated update data based on the replacement update results.

[0036] Specifically, service data is collected using big data technology to obtain a service data set. This service data set includes a source identifier and a time identifier. The source of the service data set mainly includes two types of data: internal data from the competent authority and third-party data from enterprises, individuals, etc. The time identifier refers to the specific time the service data was collected. First, a data update cycle is set, which can be customized according to the data volume. Next, data update identifiers are obtained for the service data set. These identifiers include fixed data identifiers and updatable data identifiers. Fixed data identifiers refer to data that does not need updating, such as government regulations, laws and regulations, and geographic planning data. Updatable data refers to data that needs to be updated according to the update cycle, such as financial statements and public service data. When the data in the service data set is greater than or equal to the preset data update cycle, the data in the service data set is updated and filtered according to the data update identifiers to obtain data update filtering results. When updatable data is found in the data update filtering results, the existing data is replaced with the latest updatable data to obtain integrated updated data. The integrated updated data includes both updated updatable data and fixed data. By periodically integrating and updating the service data set, the latest service data can be obtained in a timely manner, avoiding data duplication and invalidity, and improving the quality of data sharing.

[0037] Step S300: The integrated and updated data is privately identified, and the data is classified according to the private identification results to obtain a public cloud data set and a private cloud data set;

[0038] In one embodiment, step S300 of this application further includes:

[0039] Step S310: Obtain the private data with a privacy identifier from the integrated update data;

[0040] Step S320: Compare the privacy levels of the private data to obtain the highest privacy level identifier;

[0041] Step S330: Encrypt the private data using the highest privacy level identifier, and obtain the private cloud data set based on the encryption result.

[0042] Specifically, firstly, data with a privacy identifier in the integrated and updated data is filtered to obtain private data. This privacy identifier includes a privacy level and the data source. The privacy level is used to classify the private data and can be customized. For example, Level 1 private data is publicly available for 30 days and can be viewed by all users; after the public period, it becomes unviewable. The highest privacy level data is only viewable by the user who published the data; other users cannot view it. Then, the privacy levels of the private data are compared to obtain the highest privacy level identifier. Next, the private data with the highest level identifier is encrypted to obtain the encrypted data set, which is the private cloud data set. The unencrypted data set is the public cloud data set, where public cloud data refers to completely public data. By obtaining both the public and private cloud data sets, the investment costs of the cloud platform are reduced while effectively ensuring data security and maintaining the efficiency of cloud sharing.

[0043] Step S400: A hybrid cloud collection is formed by combining the public cloud data set and the private cloud data set;

[0044] Step S500: Read the authentication information of the terminal user. After the verification is successful, match the access permissions based on the authentication information.

[0045] Specifically, a hybrid cloud data set is constructed, comprising both public and private cloud data sets. When a user logs into the cloud platform, the platform reads the end-user's authentication information. These end-users include government departments, enterprise users, and individual users. A platform authentication authority is used to distinguish and authenticate end-user identities, with each end-user's identity corresponding to a specific data access permission. Once the end-user's authentication information is verified, different end-users will access different user interfaces and acquire the corresponding data access permissions. By storing data on the cloud platform, data management and platform maintenance costs are reduced, while data sharing efficiency is improved. End-user authentication enhances data storage security.

[0046] Step S600: Perform data retrieval on the hybrid cloud collection according to the retrieval permissions, and construct a temporary data model based on the retrieval results;

[0047] In one embodiment, step S600 of this application further includes:

[0048] Step S610: Read and obtain the real-time computing power allocation information of the system;

[0049] Step S620: Determine whether the allocated computing power information meets the preset computing power threshold;

[0050] Step S630: When the allocated computing power information cannot meet the preset computing power threshold, a keyword collection instruction is generated;

[0051] Step S640: Collect keywords from the terminal user using the keyword collection instruction, match the data of the hybrid cloud collection based on the keyword collection results, the allocated computing power information, and the calling permissions, and construct the temporary data model based on the matching results.

[0052] Step S700: Obtain the user demand information of the terminal user, input the demand information into the temporary data model, and output the feedback result.

[0053] Specifically, when an end user invokes data from the hybrid cloud collection using their own access permissions, the system first needs to read the real-time allocated computing power information. This information refers to the system's remaining capacity to process data inquiries, with a preset computing power threshold. This threshold represents the minimum capacity the system can normally process data inquiries. If the allocated computing power information is less than or equal to the preset threshold, it indicates that the system cannot currently execute data inquiries. Next, the system compares the allocated computing power information with the preset threshold. If the allocated computing power information is less than or equal to the preset threshold, a keyword collection instruction is generated. This instruction extracts keywords from the data inquiries made by the end user. The user's keyword collection results are then obtained through this instruction. Based on the keyword collection results, the allocated computing power information, and the access permissions, the data in the hybrid cloud collection is matched to obtain data matching results. Finally, a temporary data model is constructed based on these matching results. This temporary data model is used for critical data inquiries, satisfying the end user's needs while saving computing power and improving the efficiency of system data inquiries. The system obtains user demand information from the end users, inputs this information into the temporary data model for data matching, and outputs the data matching result, which is the feedback result. This can solve the technical problems of low intelligence level and inaccurate intelligent retrieval of government data during data application.

[0054] In one embodiment, step S800 of this application further includes:

[0055] Step S810: Obtain the historical operation data of the terminal user, wherein the historical operation data includes search data and browsing data;

[0056] Step S820: Obtain the permission change node of the terminal user, and generate a node identifier based on the permission change node;

[0057] Step S830: Divide the historical operation data into time intervals, and generate feature weakening coefficients for the interval division results using the permission change node identifier;

[0058] Step S840: Construct a terminal user profile based on the interval division results with feature weakening coefficients;

[0059] In one embodiment, step S840 of this application further includes:

[0060] Step S841: Construct an interactive user set, wherein the interactive user set is a set of users who interact with the terminal user;

[0061] Step S842: Extract the interaction features of the terminal user and the set of interactive users to obtain the set of interactive user features;

[0062] Step S843: Construct the terminal user profile based on the set of interactive user features.

[0063] Step S850: Manage the use of the terminal user through the terminal user profile.

[0064] Specifically, firstly, historical operation data of the end user is obtained through big data analysis. This historical operation data includes the end user's search and browsing data. Then, permission change nodes are obtained, which are the time points when the user accesses different data. Node identifiers are generated based on these permission change nodes. Next, the historical operation data is divided according to different operation time periods. For example, based on time, the historical operation data can be divided into historical operations from three years ago, historical operations from one year ago, etc. The closer the historical operation data is to the previous time period, the higher its reference value. Then, feature weakening coefficients are generated for the interval division results based on the permission change node identifiers. The higher the feature weakening coefficient, the lower the reference value of the historical operation data. The historical operation data is arranged in ascending order of the feature weakening coefficients. Finally, an end user profile is constructed based on the interval division results with feature weakening coefficients. An interactive user set is constructed, consisting of the end user and users interacting with the end user. Then, interaction features between the end user and the users interacting with the end user are extracted. Feature extraction refers to extracting the keywords used in the interaction between the two, obtaining an interactive user feature set. Finally, user terminal profiles are constructed based on the identities and interaction characteristics of the interactive users in the interactive user feature set. These profiles include the user's needs, the objects of interaction, and the content of the interaction. Data retrieval management is then performed on these user profiles. By constructing user profiles, a better understanding of the characteristics of users who require departmental government data can be achieved, which is conducive to promoting positive interaction between government departments and the public.

[0065] In one embodiment, step S900 of this application further includes:

[0066] Step S910: Collect user error feedback and obtain an error feedback set;

[0067] Step S920: Perform cumulative statistics on the error feedback set and generate cumulative statistics identification information;

[0068] Step S930: Perform data service management using the cumulative statistical identification information.

[0069] Specifically, error feedback from end users is collected through questionnaires. This error feedback refers to the discrepancy between the end user's data request and the system's response. An error feedback set is obtained, and then the frequency of error feedback in the set is counted to generate cumulative statistical identification information. Finally, the data matching results of keywords that appear frequently in the cumulative statistical identification information are analyzed to improve the feedback results and enhance the quality of data application.

[0070] In one embodiment, such as Figure 3 The diagram illustrates an intelligent social governance system based on data interoperability, comprising: a data acquisition module 1, a data integration and update module 2, a data classification module 3, a hybrid cloud component module 4, an access permission matching module 5, a data model construction module 6, and a result acquisition module 7. Wherein:

[0071] Data acquisition module 1, the data acquisition module 1 is used to acquire a service data set, wherein the service data set includes a source identifier and a time identifier;

[0072] Data integration and update module 2, which is used to integrate and update the service data set to obtain integrated and updated data;

[0073] Data classification module 3 is used to perform private identification on the integrated and updated data, classify the data according to the private identification results, and obtain public cloud data set and private cloud data set;

[0074] Hybrid cloud component module 4, which is used to form a hybrid cloud set by the public cloud data set and the private cloud data set;

[0075] The permission matching module 5 is invoked. The permission matching module 5 is used to read the authentication information of the terminal user. When the verification is successful, the invocation permission is matched according to the authentication information.

[0076] Data model building module 6, which is used to call data from the hybrid cloud collection according to the calling permissions, and build a temporary data model based on the calling results;

[0077] Result acquisition module 7 is used to obtain the user demand information of the terminal user, input the demand information into the temporary data model, and output feedback results.

[0078] In one embodiment, the system further includes:

[0079] Set the data update cycle;

[0080] A data update filtering module is used to obtain the data update identifier of the service data set. When the service data set meets the data update cycle, the service data set is updated and filtered according to the data update identifier.

[0081] The existing data replacement and update module is used to replace and update the existing data based on the updated data when the data in the update filtering results is updated.

[0082] An integrated update data acquisition module is used to obtain the integrated update data based on the replacement update results.

[0083] In one embodiment, the system further includes:

[0084] A historical data acquisition module is used to acquire the historical operation data of the terminal user, wherein the historical operation data includes search data and browsing data;

[0085] A node identifier generation module is used to obtain the permission change node of the terminal user and generate a node identifier based on the permission change node.

[0086] The interval partitioning module is used to divide the historical operation data into time intervals and generate the feature weakening coefficient of the interval partitioning result through the permission change node identifier.

[0087] The user profile building module is used to build a terminal user profile based on the interval division result with feature weakening coefficients.

[0088] The user management module is used to manage the use of terminal users based on the terminal user profile.

[0089] In one embodiment, the system further includes:

[0090] An interactive user set construction module is used to construct an interactive user set, wherein the interactive user set is a set of users who interact with the terminal user.

[0091] An interaction feature extraction module is used to extract interaction features from the terminal user and the set of interaction users to obtain an interaction user feature set.

[0092] The terminal user profile building module is used to build the terminal user profile based on the set of interactive user features.

[0093] In one embodiment, the system further includes:

[0094] A private data acquisition module, which is used to acquire private data with a private identifier in the integrated and updated data;

[0095] A privacy level comparison module is used to compare the privacy level of the private data to obtain the highest privacy level identifier.

[0096] An encryption processing module is used to encrypt the private data using the highest privacy level identifier, and to obtain the private cloud data set based on the encryption processing result.

[0097] In one embodiment, the system further includes:

[0098] A computing power information acquisition module, which is used to read and obtain real-time computing power allocation information of the system;

[0099] A computing power information judgment module is used to determine whether the allocated computing power information meets a preset computing power threshold.

[0100] A keyword acquisition instruction generation module is used to generate a keyword acquisition instruction when the allocated computing power information cannot meet the preset computing power threshold.

[0101] A data model building module is used to collect keywords from the terminal user through the keyword collection instruction, match the data of the hybrid cloud collection based on the keyword collection results, the allocated computing power information, and the calling permissions, and build the temporary data model according to the matching results.

[0102] In one embodiment, the system further includes:

[0103] An error feedback set acquisition module is used to collect user error feedback and obtain an error feedback set.

[0104] The cumulative statistics module is used to perform cumulative statistics on the error feedback set and generate cumulative statistics identification information.

[0105] The data service management module is used to manage data services through the cumulative statistical identification information.

[0106] In summary, this application provides an intelligent social governance method and system based on data interoperability, which has the following technical effects:

[0107] 1. This solution addresses the technical challenges of low intelligence levels and inaccurate intelligent retrieval of government data during data application. By integrating, updating, and privately identifying service data sets, and constructing a hybrid cloud data set based on public and private cloud data sets, a temporary data model can be built to intelligently mobilize government data for data interoperability. Under the premise of ensuring data security, data retrieval can be completed intelligently based on user and device information, thereby improving user satisfaction.

[0108] 2. By periodically integrating and updating the service dataset, the latest service data can be obtained in a timely manner, avoiding data duplication and invalidity, and improving the quality of data sharing. By obtaining public cloud datasets and private cloud datasets, the investment costs of the cloud platform can be reduced while effectively ensuring data security and maintaining the efficiency of cloud sharing.

[0109] 3. By constructing end-user profiles, we can better understand the characteristics of users who need government data, which is conducive to promoting positive interaction between government departments and the public.

[0110] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. An intelligent social governance method based on data interoperability, characterized in that, The method is applied to an intelligent social governance system, and the method includes: A service data set is collected, wherein the service data set includes a source identifier and a time identifier; The service data set is integrated and updated to obtain integrated and updated data; The integrated and updated data is privately identified, and the data is classified according to the private identification results to obtain a public cloud data set and a private cloud data set; A hybrid cloud collection is formed by combining the public cloud data set and the private cloud data set; Read the authentication information of the end user, and when the verification is successful, match the access permissions based on the authentication information; Data is retrieved from the hybrid cloud collection according to the access permissions, and a temporary data model is constructed based on the retrieval results; Obtain the user demand information of the terminal user, input the demand information into the temporary data model, and output the feedback result.

2. The method as described in claim 1, characterized in that, The method includes: Set the data update cycle; Obtain the data update identifier of the service data set; when the service data set meets the data update cycle, update and filter the service data set according to the data update identifier. If the data in the updated filter results is updated, then the existing data is replaced and updated based on the updated data; The integrated update data is obtained based on the replacement update results.

3. The method as described in claim 1, characterized in that, The method includes: Obtain the historical operation data of the terminal user, wherein the historical operation data includes search data and browsing data; Obtain the permission change node of the terminal user, and generate a node identifier based on the permission change node, wherein the permission change node refers to the time node when the user accesses different data; The historical operation data is divided into time intervals, and the feature weakening coefficient of the interval division result is generated by the permission change node identifier; Construct an end-user profile based on the interval division results with feature weakening coefficients; The terminal user profile is used to manage the use of the terminal user.

4. The method as described in claim 3, characterized in that, The method includes: Construct an interactive user set, wherein the interactive user set is a set of users who interact with the terminal user; Interaction features of the terminal users and the set of interactive users are extracted to obtain the set of interactive user features; The terminal user profile is constructed based on the set of interactive user features.

5. The method as described in claim 1, characterized in that, The method includes: Obtain the private data with a privacy identifier from the integrated and updated data; The privacy level of the private data is compared to obtain the highest privacy level identifier; The private data is encrypted using the highest privacy level identifier, and the private cloud data set is obtained based on the encryption result.

6. The method as described in claim 1, characterized in that, The method includes: Read and obtain real-time computing power allocation information from the system; Determine whether the allocated computing power information meets the preset computing power threshold; When the allocated computing power information cannot meet the preset computing power threshold, a keyword collection instruction is generated; Keywords are collected from the terminal user through the keyword collection command. Based on the keyword collection results, the allocated computing power information, and the calling permissions, the data of the hybrid cloud collection is matched, and the temporary data model is constructed according to the matching results.

7. The method as described in claim 1, characterized in that, The method includes: Collect user error feedback to obtain a set of error feedback reports; The error feedback set is cumulatively statistically analyzed to generate cumulative statistical identification information; Data service management is performed using the cumulative statistical identification information.

8. An intelligent social governance system based on data interoperability, characterized in that, The system includes: A data acquisition module is used to acquire a service data set, wherein the service data set includes a source identifier and a time identifier; A data integration and update module is used to integrate and update the service data set to obtain integrated and updated data. The data classification module is used to perform private identification on the integrated and updated data, classify the data according to the private identification results, and obtain a public cloud data set and a private cloud data set. A hybrid cloud component module, wherein the hybrid cloud component module is used to form a hybrid cloud set by the public cloud data set and the private cloud data set; The permission matching module is invoked. The permission matching module is used to read the authentication information of the terminal user. When the verification is successful, the invocation permission is matched according to the authentication information. A data model building module is used to call data from the hybrid cloud collection according to the calling permissions, and to build a temporary data model based on the calling results. The result acquisition module is used to obtain the user demand information of the terminal user, input the demand information into the temporary data model, and output feedback results.

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