A multi-scenario business collaborative management system based on digital public services intelligence

Abstract

This invention discloses a multi-scenario business collaborative management system based on digital public services intelligence, belonging to the field of public services business collaborative management technology. It addresses the problem of reduced accuracy in data fusion when managing public services data across multiple scenarios. Based on the characteristics of data in different fields, this invention employs different data mining methods and algorithms to fully leverage the value of the data and improve the accuracy and effectiveness of analysis. It utilizes multiple data fusion methods to fuse different data, leveraging the advantages of each method to improve the accuracy and reliability of data fusion. Through corresponding rules in the management platform, different corresponding attributes can be automatically matched with target public services data, reducing the tedium of manual operations. Through identity verification and authorization checks, it can flexibly control which servers can access and share specific public services management data.

Description

Technical Field

[0001] This invention relates to the field of collaborative management technology for public services, specifically a multi-scenario collaborative management system based on digital public services intelligence. Background Technology

[0002] Collaborative management of public services refers to the efficient collaboration among different business departments in the public service sector to improve production and management efficiency and support the rapid development of enterprises.

[0003] Chinese patent CN117132432A discloses an intelligent electronic information management system based on smart cities. It mainly consists of a city user layer, a city system layer, a city application layer, and a city data layer. The city user layer is open to the public, relevant leaders, and various professional units. System administrators and video surveillance personnel manage and monitor smart city operations. The city data layer integrates acquired data into the smart city framework, storing, processing, and analyzing the data based on big data analytics to meet the needs of smart city applications. The city application layer comprises three main parts: smart industry, smart management, and smart public services. It is accessible to all users of the smart city, covering areas such as communities, healthcare, education, logistics, and manufacturing. While this patent solves the problem of multi-scenario data collaboration, the following issues still exist in practical operation:

[0004] 1. After acquiring public welfare data, the original public welfare data was not effectively integrated and managed, resulting in poor data integrity.

[0005] 2. The management platform was not effectively managed for data sharing, which led to a decrease in the security of data storage.

[0006] 3. The optimal integration method was not adopted, which resulted in a decrease in the accuracy of data integration when data on people's livelihoods were crossed across different sectors. Summary of the Invention

[0007] The purpose of this invention is to provide a multi-scenario business collaborative management system based on digital public services intelligence. It employs different data mining methods and algorithms according to the characteristics of data in different fields, fully leveraging the value of data and improving the accuracy and effectiveness of analysis. It utilizes multiple data fusion methods to integrate different data, taking full advantage of the strengths of each method to improve the accuracy and reliability of data fusion. Through corresponding rules in the management platform, it can automatically match different corresponding attributes with target public services data, reducing the tedium of manual operations. Through identity verification and authorization checks, it can flexibly control which servers can access and share specific public services management data, thus solving problems in existing technologies.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A multi-scenario business collaborative management system based on digital public services intelligence includes:

[0010] The public service data integration unit is used for:

[0011] The data on people's livelihood is retrieved from the database and integrated. After the data integration is completed, the data on people's livelihood is uniquely coded and labeled according to its data attributes. The data on people's livelihood with unique coding and labeling is marked as the target data on people's livelihood.

[0012] The integrated data management unit is used for:

[0013] The target livelihood data is associated with management platforms, and the target livelihood data in each management platform is managed collaboratively. The target livelihood data that has been collaboratively managed is marked as livelihood data to be analyzed.

[0014] The management data analysis unit is used for:

[0015] The data on people's livelihood to be analyzed is mined and analyzed, and the data on people's livelihood to be analyzed after the mining and analysis is completed is integrated across different fields. The data on people's livelihood to be analyzed after the cross-field integration is marked as standard data on people's livelihood.

[0016] Analysis and data display unit, used for:

[0017] Standard public service data is transmitted wirelessly to mobile display terminals for display. Users can then log in to the mobile display terminals to interact with the standard public service data.

[0018] Preferably, the public service data integration unit includes:

[0019] The data integration and control module is used for:

[0020] The database will be used to acquire data on people's livelihood, including urban data, medical data, education data, and transportation data.

[0021] Standardize the formats of urban data, medical data, education data, and transportation data;

[0022] The standardized format will be used to transmit public service data to a shared platform for storage.

[0023] ETL tools are used to integrate public service data from the shared platform, and government data within the public service data is integrated through logical aggregation.

[0024] Access control and data encryption will be implemented for the integrated public service data in the shared platform.

[0025] Preferably, the public service data integration unit further includes:

[0026] The public service data labeling module is used for:

[0027] The data on people's livelihood in the shared platform will be uniquely encoded and labeled using database auto-incrementing IDs and timestamps;

[0028] Among these measures, the public's livelihood data in the shared platform will be stored in the platform's database, and after storage, a unique ID will be assigned using the platform's database's auto-incrementing ID.

[0029] The public service data stored in the platform database and assigned unique IDs will be timestamped.

[0030] The shared platform data on people's livelihood, which has been assigned unique IDs in the platform database, will be time-stamped based on the timestamps.

[0031] The data on people's livelihood is generated by generating a timestamp to obtain a unique code label for the completed data on people's livelihood, and then marked as the target data on people's livelihood.

[0032] Preferably, the integrated data management unit includes:

[0033] The data management module is used for:

[0034] Confirm the urban data, medical data, education data, and transportation data within the target public welfare data;

[0035] According to the corresponding rules in the management platform, city data, medical data, education data, and transportation data are managed and matched separately.

[0036] The corresponding rules in the management platform are to match different corresponding attributes with target livelihood data. The corresponding attributes include city data attributes, medical data attributes, education data attributes and transportation data attributes.

[0037] After the management is completed, we obtain the corresponding management data for cities, healthcare, education, and transportation, and uniformly label them as the corresponding data for people's livelihood management.

[0038] Preferably, the integrated data management unit further includes:

[0039] The data sharing and collaboration module is used for:

[0040] Retrieve API interface data from the management platform from the database;

[0041] When data related to public welfare management is shared on the management platform, the digital signature of the management platform is used to confirm whether the server sharing the data is a secure server.

[0042] If it is a secure server, data exchange will occur between the corresponding data for public services management; if it is an insecure server, data exchange will cease between the corresponding data for public services management.

[0043] When data exchange is performed on the data related to public welfare management, the server requesting the data exchange will be subject to identity verification and authorization checks. First, identity verification will be performed. After successful identity verification, the management platform will access and share the data related to public welfare management on the server through the access control list.

[0044] The data related to public welfare management in the servers that can be accessed and shared will be marked as public welfare data to be analyzed.

[0045] Preferably, when data related to public welfare management is shared within the management platform, the digital signature of the management platform is used to verify whether the server sharing the data is a secure server, including:

[0046] When data related to public welfare management is shared on the management platform, the digital signature of the management platform is extracted to confirm the historical operating parameters of the server sharing the data; wherein, the historical operating parameters include CPU utilization, memory utilization, network bandwidth parameters and concurrent processing operating parameters;

[0047] The initial operational evaluation parameters of the server are obtained using the CPU utilization and memory utilization, wherein the initial operational evaluation parameters of the server are obtained using the following formula:

[0048]

[0049] Among them, S c This represents the initial operational evaluation parameters; n represents the number of time units experienced by the server during operation, and the time unit is 1 second; P ni P represents the server's memory utilization rate for the i-th unit of time; ci P represents the CPU utilization of the server corresponding to the i-th unit of time; cu This represents the upper limit of the preset CPU utilization range for the server; P cd This indicates the lower limit of the preset CPU utilization range for the server.

[0050] The initial operation evaluation parameters of the server are compared with the preset initial operation evaluation parameter thresholds to obtain the comparison results;

[0051] When the comparison result shows that the primary operation evaluation parameters of the server exceed the preset primary operation evaluation parameter threshold, the network bandwidth parameter combined with the first security evaluation model is used to determine whether the server is a secure server.

[0052] When the comparison result shows that the primary operation evaluation parameters of the server do not exceed the preset primary operation evaluation parameter threshold, the concurrent processing operation parameters are combined with the second security evaluation model to determine whether the server is a secure server.

[0053] Preferably, when the comparison result indicates that the server's primary operational evaluation parameters exceed a preset primary operational evaluation parameter threshold, the determination of whether the server is a secure server is made using network bandwidth parameters combined with a first security evaluation model, including:

[0054] When the comparison result shows that the primary operation evaluation parameters of the server exceed the preset primary operation evaluation parameter threshold, the network bandwidth parameters corresponding to the server are extracted, wherein the network bandwidth parameters include the amount of input and output data of the network interface, network latency, and packet loss rate.

[0055] An evaluation coefficient is obtained by utilizing the input and output data volume and packet loss rate of the network interface; wherein, the evaluation coefficient is obtained by the following formula:

[0056]

[0057] Among them, S p The evaluation coefficient is represented by ; n represents the number of time units during which the server runs, and the time unit is 1 second; C ri C represents the amount of input data to the server corresponding to the i-th unit of time; si P represents the amount of output data from the server corresponding to the i-th unit of time; xi This represents the packet loss rate during the server's data processing in the i-th unit of time.

[0058] The first security evaluation parameter is obtained by using the network latency combined with the evaluation coefficient and the first security evaluation model, wherein the first security evaluation parameter is obtained by the following formula:

[0059]

[0060] Among them, S 01 The parameter represents the first security evaluation parameter; n represents the number of time units during server operation, and the time unit is 1 second; P ti This represents the network latency rate of the server corresponding to the i-th unit of time;

[0061] The first security evaluation parameter is compared with a preset first security evaluation threshold. If the first security evaluation parameter exceeds the preset first security evaluation threshold, the server is determined to be a secure server.

[0062] Preferably, when the comparison result indicates that the server's primary operational evaluation parameters do not exceed a preset primary operational evaluation parameter threshold, the server is then judged as a secure server using concurrent processing operational parameters combined with a second security evaluation model, including:

[0063] When the comparison result shows that the primary operation evaluation parameter of the server does not exceed the preset primary operation evaluation parameter threshold, the concurrent processing operation parameter corresponding to the server is extracted, wherein the concurrent processing operation parameter refers to the number of data threads that the server performs data processing operation simultaneously per unit time.

[0064] The second security evaluation parameter is obtained by combining the concurrent processing operation parameters with the second security evaluation model, wherein the second security evaluation parameter is obtained by the following formula:

[0065]

[0066] Among them, S 02 S represents the second safety evaluation parameter. c Indicates the initial operational evaluation parameters; S y This represents the preset threshold for primary operational evaluation parameters; n represents the number of time units experienced by the server during operation, and the time unit is 1 second; M i This represents the number of data threads running simultaneously on the server for the i-th unit of time.

[0067] The second security evaluation parameter is compared with a preset second security evaluation threshold. If the second security evaluation parameter exceeds the preset second security evaluation threshold, the server is determined to be a secure server.

[0068] Preferably, the management data analysis unit includes:

[0069] The data mining and analysis module is used for:

[0070] The city-related management data, medical-related management data, education-related management data, and transportation-related management data in the data on people's livelihood to be analyzed will be subjected to data mining analysis in sequence.

[0071] Among them, cluster analysis is used to differentiate the city's internal regions and classify them according to population characteristics based on the city's corresponding management data;

[0072] The medical management data is classified using a classification algorithm to categorize patient information, medical record information, and medication usage information.

[0073] The educational management information is digitized using learning robots, including textbooks, lesson plans, test questions, student information, and teacher information. Big data technology is used to classify the usage and distribution characteristics of educational resources in the digitized process.

[0074] Traffic management data is classified into traffic flow and congestion levels using clustering methods.

[0075] After mining and analyzing the corresponding management data for cities, healthcare, education, and transportation, we obtained the public welfare data to be integrated.

[0076] The cross-domain data fusion module is used for:

[0077] The city-related management data, medical-related management data, education-related management data, and transportation-related management data in the data to be integrated for public services will be linked together using association rules, which will be retrieved from the database.

[0078] The association rule involves cross-domain analysis of management data related to cities, healthcare, education, and transportation.

[0079] The cross-domain analysis of livelihood data to be integrated will be carried out by using image fusion, spatial data fusion, temporal data fusion and statistical fusion methods to fuse different data.

[0080] After data fusion, standard public service data is obtained.

[0081] Preferably, the analysis and data display unit is further configured to:

[0082] Users can log in to the smart public service platform on mobile display terminals to view standard public service data.

[0083] Users are verified upon login, and after successful verification, they can view standard public service data.

[0084] Meanwhile, when users view information on mobile display terminals, they can query, process, and provide feedback on specific scenarios through the user interface as needed.

[0085] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0086] 1. The multi-scenario business collaborative management system based on digital public services intelligence provided by this invention can effectively integrate public service data in the shared platform by using ETL tools. This includes not only physical integration of data but also logical integration of data, ensuring the integrity and accuracy of data. The introduction of timestamps not only provides time dimension information for the data but also makes the data generation, storage and processing processes traceable.

[0087] 2. The multi-scenario business collaborative management system based on digital public services provided by this invention can automatically match different corresponding attributes with target public services data through corresponding rules in the management platform, reducing the tedium of manual operation. Through identity verification and authorization checks, it can flexibly control which servers can access and share specific public services management data.

[0088] 3. The multi-scenario business collaborative management system based on digital public services provided by this invention adopts different data mining methods and algorithms according to the data characteristics of different fields, which can give full play to the value of data, improve the accuracy and effectiveness of analysis, and use multiple data fusion methods to fuse different data. This diversity can give full play to the advantages of various methods and improve the accuracy and reliability of data fusion. Attached Figure Description

[0089] Figure 1 This is a schematic diagram of the intelligent public service multi-scenario business collaborative management module of the present invention;

[0090] Figure 2 This is a schematic diagram of the intelligent public service multi-scenario business collaborative management method of the present invention. Detailed Implementation

[0091] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0092] To address the issue of incomplete data due to the lack of effective integration and management of raw public service data after acquisition in existing technologies, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0093] A multi-scenario business collaborative management system based on digital public services intelligence includes:

[0094] The public service data integration unit is used for:

[0095] The data on people's livelihood is retrieved from the database and integrated. After the data integration is completed, the data on people's livelihood is uniquely coded and labeled according to its data attributes. The data on people's livelihood with unique coding and labeling is marked as the target data on people's livelihood.

[0096] The integrated data management unit is used for:

[0097] The target livelihood data is associated with management platforms, and the target livelihood data in each management platform is managed collaboratively. The target livelihood data that has been collaboratively managed is marked as livelihood data to be analyzed.

[0098] The management data analysis unit is used for:

[0099] The data on people's livelihood to be analyzed is mined and analyzed, and the data on people's livelihood to be analyzed after the mining and analysis is completed is integrated across different fields. The data on people's livelihood to be analyzed after the cross-field integration is marked as standard data on people's livelihood.

[0100] Analysis and data display unit, used for:

[0101] Standard public service data is transmitted wirelessly to mobile display terminals for display. Users can then log in to the mobile display terminals to interact with the standard public service data.

[0102] Specifically, the public service data integration unit can automatically match different corresponding attributes with target public service data. This rule-based management approach greatly improves data processing efficiency and reduces the tedium of manual operations. By integrating different data mining methods and algorithms, the data management unit can fully leverage the value of the data and improve the accuracy and effectiveness of the analysis. By managing the data analysis unit, appropriate data fusion methods can be selected according to different data types and application needs, which can improve the efficiency and effectiveness of data processing. Through the data display unit, users can log in to the smart public service platform anytime, anywhere on mobile display terminals to view standard public service data. This portability provides users with great convenience.

[0103] The public service data integration unit includes:

[0104] The data integration and control module is used for:

[0105] The database will be used to acquire data on people's livelihood, including urban data, medical data, education data, and transportation data.

[0106] Standardize the formats of urban data, medical data, education data, and transportation data;

[0107] The standardized format will be used to transmit public service data to a shared platform for storage.

[0108] ETL tools are used to integrate public service data from the shared platform, and government data within the public service data is integrated through logical aggregation.

[0109] Access control and data encryption will be implemented for the integrated public service data in the shared platform.

[0110] Specifically, standardized formats ensure that urban, medical, educational, and transportation data from different sources can be integrated and analyzed within a common framework, thereby improving data readability and comparability. ETL tools effectively integrate public service data from the shared platform, encompassing both physical and logical integration. Government data, in particular, is integrated through logical convergence, ensuring data integrity and accuracy. Access control and data encryption measures significantly enhance data security. This not only prevents unauthorized access and data leaks but also ensures data integrity and credibility, thus protecting individual privacy and institutional interests.

[0111] The public service data labeling module is used for:

[0112] The data on people's livelihood in the shared platform will be uniquely encoded and labeled using database auto-incrementing IDs and timestamps;

[0113] Among these measures, the public's livelihood data in the shared platform will be stored in the platform's database, and after storage, a unique ID will be assigned using the platform's database's auto-incrementing ID.

[0114] The public service data stored in the platform database and assigned unique IDs will be timestamped.

[0115] The shared platform data on people's livelihood, which has been assigned unique IDs in the platform database, will be time-stamped based on the timestamps.

[0116] The data on people's livelihood is generated by generating a timestamp to obtain a unique code label for the completed data on people's livelihood, and then marked as the target data on people's livelihood.

[0117] Specifically, by combining database auto-incrementing IDs with timestamps, each piece of public service data is ensured to receive a unique coded identifier. The allocation of database auto-incrementing IDs is an automated process that can be completed quickly, significantly improving data processing speed and efficiency. Simultaneously, timestamp generation is instantaneous, enabling rapid time-stamping of each data entry. The introduction of timestamps not only provides the data with a time dimension but also makes the generation, storage, and processing of data traceable. Through unique coded identifiers, public service data becomes more organized and easier to manage within the platform's database. Whether querying, updating, or deleting data, the unique coded identifier allows for quick location and operation, greatly simplifying the data management process.

[0118] To address the issue of reduced data storage security in existing technologies where data from public services is transmitted to a management platform but not effectively shared and managed, leading to compromised data security, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0119] The integrated data management unit includes:

[0120] The data management module is used for:

[0121] Confirm the urban data, medical data, education data, and transportation data within the target public welfare data;

[0122] According to the corresponding rules in the management platform, city data, medical data, education data, and transportation data are managed and matched separately.

[0123] The corresponding rules in the management platform are to match different corresponding attributes with target livelihood data. The corresponding attributes include city data attributes, medical data attributes, education data attributes and transportation data attributes.

[0124] After the management is completed, we obtain the corresponding management data for cities, healthcare, education, and transportation, and uniformly label them as the corresponding data for people's livelihood management.

[0125] Specifically, the management platform automatically matches different attributes with target public service data based on corresponding rules. This rule-based management approach significantly improves data processing efficiency, reduces the tedium of manual operations, and lowers the possibility of errors. Public service data is subdivided into urban data, medical data, education data, and transportation data, and managed according to different corresponding attributes. This refined classification makes data application more flexible, allowing for customized analysis and processing tailored to the needs of different fields.

[0126] The data sharing and collaboration module is used for:

[0127] Retrieve API interface data from the database;

[0128] When data related to public welfare management is shared on the management platform, the digital signature of the management platform is used to confirm whether the server sharing the data is a secure server.

[0129] If it is a secure server, data exchange will occur between the corresponding data for public services management; if it is an insecure server, data exchange will cease between the corresponding data for public services management.

[0130] When data exchange is performed on the data related to public welfare management, the server requesting the data exchange will be subject to identity verification and authorization checks. First, identity verification will be performed. After successful identity verification, the management platform will access and share the data related to public welfare management on the server through the access control list.

[0131] The data related to public welfare management in the servers that can be accessed and shared will be marked as public welfare data to be analyzed.

[0132] Specifically, before data sharing, the target server undergoes digital signature verification to ensure data exchange occurs on a secure server. This mechanism effectively prevents data leakage and unauthorized access, significantly improving data security. During data exchange, the server requesting the exchange is authenticated and authorized, ensuring that only authorized and verified servers can access and share the corresponding public services management data. This further enhances data security and controllability. Authentication and authorization checks allow for flexible control over which servers can access and share specific public services management data. This flexibility makes data sharing more aligned with practical needs and can meet the data sharing requirements of different scenarios.

[0133] Specifically, when data related to public services management is shared within the management platform, the platform verifies whether the server sharing the data is a secure server based on its digital signature, including:

[0134] When data related to public welfare management is shared on the management platform, the digital signature of the management platform is extracted to confirm the historical operating parameters of the server sharing the data; wherein, the historical operating parameters include CPU utilization, memory utilization, network bandwidth parameters and concurrent processing operating parameters;

[0135] The initial operational evaluation parameters of the server are obtained using the CPU utilization and memory utilization, wherein the initial operational evaluation parameters of the server are obtained using the following formula:

[0136]

[0137] Among them, Sc This represents the initial operational evaluation parameters; n represents the number of time units experienced by the server during operation, and the time unit is 1 second; P ni P represents the server's memory utilization rate for the i-th unit of time; ci P represents the CPU utilization of the server corresponding to the i-th unit of time; cu This represents the upper limit of the preset CPU utilization range for the server; P cd This indicates the lower limit of the preset CPU utilization range for the server.

[0138] The initial operation evaluation parameters of the server are compared with the preset initial operation evaluation parameter thresholds to obtain the comparison results;

[0139] When the comparison result shows that the primary operation evaluation parameters of the server exceed the preset primary operation evaluation parameter threshold, the network bandwidth parameter combined with the first security evaluation model is used to determine whether the server is a secure server.

[0140] When the comparison result shows that the primary operation evaluation parameters of the server do not exceed the preset primary operation evaluation parameter threshold, the concurrent processing operation parameters are combined with the second security evaluation model to determine whether the server is a secure server.

[0141] The technical effect of the above solution is as follows: By introducing digital signatures from the management platform to verify whether the server sharing the data is a secure server, this step adds a layer of security to the data sharing process, which can effectively prevent data from being accessed by unauthorized or insecure servers, thereby protecting sensitive public welfare management data from being leaked or tampered with.

[0142] The technical solution not only considers common performance metrics such as CPU utilization and memory utilization, but also incorporates network bandwidth and concurrent processing parameters to comprehensively evaluate the server's operating status. This comprehensive evaluation method can more accurately reflect the server's actual operating condition, thereby more accurately determining whether it is suitable for data sharing.

[0143] The technical solution employs a tiered judgment approach. First, initial operational evaluation parameters are used to screen the server's operational status. For servers whose initial evaluation parameters exceed a certain threshold, a more stringent first security evaluation model is applied for judgment; while for servers whose initial evaluation parameters do not exceed the threshold, a second security evaluation model is used. This tiered judgment mechanism improves the accuracy of the judgment while avoiding unnecessary waste of computational resources.

[0144] The two security evaluation models (the first security evaluation model and the second security evaluation model) mentioned in the technical solution can be customized and optimized according to specific application scenarios and requirements, making the entire technical solution more flexible and applicable. For example, in scenarios with high real-time requirements, network bandwidth parameters can be given priority; while in scenarios with high concurrent processing requirements, more attention can be paid to evaluating concurrent processing operation parameters.

[0145] By monitoring and evaluating the server's operational status in real time, the technical solution can promptly identify and resolve potential problems, thereby preventing issues such as data sharing interruptions or data loss due to server overload or failure. This helps improve the stability and reliability of the entire system, ensuring the continuity and integrity of public service management data.

[0146] Specifically, when the comparison result indicates that the server's primary operational evaluation parameters exceed a preset primary operational evaluation parameter threshold, the determination of whether the server is a secure server is made using network bandwidth parameters combined with a first security evaluation model, including:

[0147] When the comparison result shows that the primary operation evaluation parameters of the server exceed the preset primary operation evaluation parameter threshold, the network bandwidth parameters corresponding to the server are extracted, wherein the network bandwidth parameters include the amount of input and output data of the network interface, network latency, and packet loss rate.

[0148] An evaluation coefficient is obtained by utilizing the input and output data volume and packet loss rate of the network interface; wherein, the evaluation coefficient is obtained by the following formula:

[0149]

[0150] Among them, S p The evaluation coefficient is represented by ; n represents the number of time units during which the server runs, and the time unit is 1 second; C ri C represents the amount of input data to the server corresponding to the i-th unit of time; si P represents the amount of output data from the server corresponding to the i-th unit of time; xi This represents the packet loss rate during the server's data processing in the i-th unit of time.

[0151] The first security evaluation parameter is obtained by using the network latency combined with the evaluation coefficient and the first security evaluation model, wherein the first security evaluation parameter is obtained by the following formula:

[0152]

[0153] Among them, S 01The parameter represents the first security evaluation parameter; n represents the number of time units during server operation, and the time unit is 1 second; P ti This represents the network latency rate of the server corresponding to the i-th unit of time;

[0154] The first security evaluation parameter is compared with a preset first security evaluation threshold. If the first security evaluation parameter exceeds the preset first security evaluation threshold, the server is determined to be a secure server.

[0155] The technical effect of the above solution is as follows: when the initial operational evaluation parameters exceed the threshold, network bandwidth parameters (including the input and output data volume of the network interface, network latency, and packet loss rate) are further introduced for security assessment. This refines the dimensions of security assessment, considering not only the utilization of server computing resources but also network-level performance, thereby enabling a more comprehensive evaluation of server security and stability.

[0156] By introducing evaluation coefficients and combining them with the input and output data volume and packet loss rate of the network interface, the network performance of a server can be accurately assessed. This evaluation method reflects the efficiency and stability of the server during network data transmission, providing an important basis for determining whether a server is suitable for data sharing.

[0157] Based on the obtained evaluation coefficients, the first security evaluation parameters are calculated using the first security evaluation model in conjunction with network latency. This comprehensive judgment method, which incorporates network latency, can more fully reflect the server's performance at the network layer, including the real-time performance and reliability of data transmission. This is particularly important for data sharing scenarios that require high real-time performance and low latency.

[0158] Through multi-dimensional evaluation and refined calculations, this technical solution can more accurately determine whether a server is secure. This accuracy is reflected not only in the precise grasp of the server's current operating status but also in the prediction of potential future risks, helping to identify and resolve potential security issues in advance.

[0159] A rigorous security assessment process ensures that only stable and high-performance servers are permitted to share data. This helps reduce data sharing interruptions or data loss due to server failures or insufficient performance, thereby enhancing the stability and reliability of the entire system.

[0160] The network bandwidth and latency parameters in this technical solution are dynamically acquired based on the actual network environment, thus exhibiting strong adaptability and flexibility. Regardless of changes in the network environment, the accuracy and validity of the evaluation results can be ensured by adjusting the relevant parameters.

[0161] Specifically, when the comparison result indicates that the server's primary operational evaluation parameters do not exceed the preset primary operational evaluation parameter threshold, the server is then judged as a secure server using concurrent processing operational parameters combined with a second security evaluation model, including:

[0162] When the comparison result shows that the primary operation evaluation parameter of the server does not exceed the preset primary operation evaluation parameter threshold, the concurrent processing operation parameter corresponding to the server is extracted, wherein the concurrent processing operation parameter refers to the number of data threads that the server performs data processing operation simultaneously per unit time.

[0163] The second security evaluation parameter is obtained by combining the concurrent processing operation parameters with the second security evaluation model, wherein the second security evaluation parameter is obtained by the following formula:

[0164]

[0165] Among them, S 02 S represents the second safety evaluation parameter. c Indicates the initial operational evaluation parameters; S y This represents the preset threshold for primary operational evaluation parameters; n represents the number of time units experienced by the server during operation, and the time unit is 1 second; M i This represents the number of data threads running simultaneously on the server for the i-th unit of time.

[0166] The second security evaluation parameter is compared with a preset second security evaluation threshold. If the second security evaluation parameter exceeds the preset second security evaluation threshold, the server is determined to be a secure server.

[0167] The technical effect of the above solution is as follows: when the server's initial operational evaluation parameters do not exceed the preset threshold, special attention is paid to its concurrent processing parameters, namely the number of data threads that the server can process simultaneously per unit time. This indicator directly reflects the server's ability to handle multi-task, high-concurrency scenarios and is of great significance for evaluating the server's stability and reliability.

[0168] When calculating the second security evaluation parameter, not only concurrent processing parameters were considered, but also the primary operational evaluation parameters and preset primary operational evaluation parameter thresholds. This comprehensive approach makes the evaluation results more complete and accurate, and better reflects the server's performance across multiple dimensions.

[0169] By introducing concurrent processing parameters, this technical solution can better adapt to server evaluation needs under different load scenarios. In high-concurrency, high-load scenarios, the server's concurrent processing capability is particularly important, and this technical solution can accurately evaluate and select servers with good concurrent processing capabilities, ensuring the stability and efficiency of data sharing.

[0170] By applying the second security evaluation model and combining it with specific concurrent processing parameters, it is possible to more accurately determine whether a server is a secure server. This evaluation method, based on actual operational data, avoids the subjectivity and uncertainty of judging based on a single indicator or experience, thus improving the accuracy and reliability of the determination.

[0171] By evaluating the server's concurrent processing capabilities, resource allocation strategies can be further optimized. For servers with strong concurrent processing capabilities, more data processing tasks or higher loads can be prioritized to improve the overall system's operating efficiency and resource utilization. Conversely, for servers with weak concurrent processing capabilities, corresponding optimization measures or load limits can be implemented to avoid data sharing interruptions or performance degradation due to overload.

[0172] Through rigorous concurrency assessment and security procedures, only servers with good concurrency capabilities and stability are allowed to share data. This helps reduce system crashes and data loss caused by insufficient server performance or excessive load, thereby enhancing the stability and reliability of the entire system.

[0173] To address the issue of reduced accuracy in cross-sectoral data fusion of public service data due to the lack of optimal fusion methods in existing technologies, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0174] The data analysis management unit includes:

[0175] The data mining and analysis module is used for:

[0176] The city-related management data, medical-related management data, education-related management data, and transportation-related management data in the data on people's livelihood to be analyzed will be subjected to data mining analysis in sequence.

[0177] Among them, cluster analysis is used to differentiate the city's internal regions and classify them according to population characteristics based on the city's corresponding management data;

[0178] The medical management data is classified using a classification algorithm to categorize patient information, medical record information, and medication usage information.

[0179] The educational management information is digitized using learning robots, including textbooks, lesson plans, test questions, student information, and teacher information. Big data technology is used to classify the usage and distribution characteristics of educational resources in the digitized process.

[0180] Traffic management data is classified into traffic flow and congestion levels using clustering methods.

[0181] After mining and analyzing the corresponding management data for cities, healthcare, education, and transportation, we obtained the livelihood data to be integrated.

[0182] Specifically, by classifying and conducting data mining analysis on public welfare data from different sectors such as urban development, healthcare, education, and transportation, more refined data insights can be achieved. This refined analysis helps to more accurately grasp the characteristics and problems of each sector, providing more scientific and precise support for policy-making and decision-making. Different data mining methods and algorithms are employed based on the characteristics of data in different sectors, fully leveraging the value of the data and improving the accuracy and effectiveness of the analysis. The public welfare data from urban development, healthcare, education, and transportation, after mining and analysis, can be further integrated. This data integration can break down sectoral barriers, achieve information sharing and complementarity, and thus uncover more valuable information and insights.

[0183] The cross-domain data fusion module is used for:

[0184] The city-related management data, medical-related management data, education-related management data, and transportation-related management data in the data to be integrated for public services will be linked together using association rules, which will be retrieved from the database.

[0185] The association rule involves cross-domain analysis of management data related to cities, healthcare, education, and transportation.

[0186] The cross-domain analysis of livelihood data to be integrated will be carried out by using image fusion, spatial data fusion, temporal data fusion and statistical fusion methods to fuse different data.

[0187] After data fusion, standard public service data is obtained.

[0188] Specifically, cross-domain analysis of livelihood data from different fields through association rules can reveal the inherent connections and mutual influences between these fields. By using multiple data fusion methods to fuse different data, this diversity can fully utilize the advantages of various methods and improve the accuracy and reliability of data fusion. Meanwhile, selecting appropriate data fusion methods based on different data types and application needs can improve the efficiency and effectiveness of data processing. Specifically, cross-border fusion of traffic management data and urban management data, through analysis of urban planning and traffic flow data, can provide traffic management departments with scientific traffic management solutions, reducing congestion and traffic accidents. Cross-border fusion with medical management data, combining medical emergency data and traffic data, can optimize emergency vehicle dispatching and route planning, improving emergency response efficiency. Cross-border fusion of education management data and medical management data, through analysis of students' health and learning data, can provide teachers with targeted teaching suggestions to help students better overcome learning obstacles. Cross-border fusion with urban management data, combining urban economic and cultural data, can provide data support for education policy-making and promote the equitable distribution of educational resources. Cross-border fusion of medical management data and urban management data, through analysis of urban environment and lifestyle data, can provide targeted suggestions for public health management, preventing and controlling disease transmission. Cross-border fusion with education management data... Combining students' health and academic data can provide schools with personalized health education programs, promoting students' all-round development. Cross-border fusion of urban and traffic management data, by integrating urban planning and traffic flow data, can optimize traffic layout, reduce congestion, and improve urban operational efficiency. Simultaneously, image fusion, if the data contains images or visual information (such as medical images, traffic monitoring videos, etc.), can use image fusion technology to create clearer and more information-rich images. Spatial data fusion can integrate data from different observation platforms (such as satellites, aircraft, ground observation points, etc.) to form a more comprehensive three-dimensional spatial data structure, which is particularly useful in urban planning, traffic flow analysis, and environmental monitoring. Temporal data fusion is used to integrate data from different time periods to improve the spatiotemporal resolution and accuracy of the data. For example, in traffic analysis, traffic flow data from different time periods can be fused to better predict traffic congestion. Statistical fusion methods utilize statistical analysis and comparison of data from different sources to obtain information and quantitatively estimate measurement errors. This is very useful when dealing with complex data relationships and uncertainties.

[0189] The data analysis and display unit is also used for:

[0190] Users can log in to the smart public service platform on mobile display terminals to view standard public service data.

[0191] Users are verified upon login, and after successful verification, they can view standard public service data.

[0192] Meanwhile, when users view information on mobile display terminals, they can query, process, and provide feedback on specific scenarios through the user interface as needed.

[0193] Specifically, users can log in to the smart public services platform anytime, anywhere on their mobile devices to view standard public services data. This portability provides users with great convenience, as it is not limited by geographical location or device. User verification during login ensures that only legitimately verified users can view the data, effectively preventing unauthorized access and data leaks, and improving data security. Users can handle related matters directly on their mobile devices without having to go to physical service locations, thus improving efficiency. At the same time, through the online feedback mechanism, users' questions and opinions can be quickly conveyed to relevant departments, facilitating timely resolution of issues.

[0194] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0195] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-scenario business collaborative management system based on digital public services intelligence, characterized in that, include: The public service data integration unit is used for: The data on people's livelihood is retrieved from the database and integrated. After the data integration is completed, the data on people's livelihood is uniquely coded and labeled according to its data attributes. The data on people's livelihood with unique coding and labeling is marked as the target data on people's livelihood. The integrated data management unit is used for: The target livelihood data is associated with management platforms, and the target livelihood data in each management platform is managed collaboratively. The target livelihood data that has been collaboratively managed is marked as livelihood data to be analyzed. The management data analysis unit is used for: The data on people's livelihood to be analyzed is mined and analyzed, and the data on people's livelihood to be analyzed after the mining and analysis is completed is integrated across different fields. The data on people's livelihood to be analyzed after the cross-field integration is marked as standard data on people's livelihood. Analysis and data display unit, used for: Standard public service data is wirelessly transmitted to mobile display terminals for display. Users can then log in to the mobile display terminals to interact with the standard public service data. The integrated data management unit also includes: The data sharing and collaboration module is used for: Retrieve API interface data from the database; When data related to public welfare management is shared on the management platform, the digital signature of the management platform is used to confirm whether the server sharing the data is a secure server. If it is a secure server, data exchange will occur between the corresponding data for public services management; if it is an insecure server, data exchange will cease between the corresponding data for public services management. When data exchange is performed on the data related to public welfare management, the server requesting the data exchange will be subject to identity verification and authorization checks. First, identity verification will be performed. After successful identity verification, the management platform will access and share the data related to public welfare management on the server through the access control list. The data related to public welfare management in the accessible and shareable servers will be marked as public welfare data to be analyzed. When data related to public services management is shared within the management platform, the platform verifies whether the server sharing the data is a secure server based on the platform's digital signature, including: When data related to public welfare management is shared on the management platform, the digital signature of the management platform is extracted to confirm the historical operating parameters of the server sharing the data; wherein, the historical operating parameters include CPU utilization, memory utilization, network bandwidth parameters and concurrent processing operating parameters; The initial operational evaluation parameters of the server are obtained using the CPU utilization and memory utilization, wherein the initial operational evaluation parameters of the server are obtained using the following formula: ； Among them, S c This represents the initial operational evaluation parameters; n represents the number of time units experienced by the server during operation, and the time unit is 1 second; P ni P represents the server's memory utilization rate for the i-th unit of time; ci P represents the CPU utilization of the server corresponding to the i-th unit of time; cu This represents the upper limit of the preset CPU utilization range for the server; P cd This indicates the lower limit of the preset CPU utilization range for the server. The initial operation evaluation parameters of the server are compared with the preset initial operation evaluation parameter thresholds to obtain the comparison results; When the comparison result shows that the server's primary operation evaluation parameters exceed the preset primary operation evaluation parameter threshold, the network bandwidth parameter combined with the first security evaluation model is used to determine whether the server is a secure server. When the comparison result shows that the primary operation evaluation parameters of the server do not exceed the preset primary operation evaluation parameter threshold, the concurrent processing operation parameters are combined with the second security evaluation model to determine whether the server is a secure server.

2. The multi-scenario business collaborative management system based on digital public services intelligence as described in claim 1, characterized in that: The aforementioned public service data integration unit includes: The data integration and control module is used for: The database will be used to acquire data on people's livelihood, including urban data, medical data, education data, and transportation data. Standardize the formats of urban data, medical data, education data, and transportation data; The standardized format will be used to transmit public service data to a shared platform for storage. ETL tools are used to integrate public service data from the shared platform, and government data within the public service data is integrated through logical aggregation. Access control and data encryption will be implemented for the integrated public service data in the shared platform.

3. The multi-scenario business collaborative management system based on digital public services intelligence as described in claim 2, characterized in that: The public service data integration unit also includes: The public service data labeling module is used for: The data on people's livelihood in the shared platform will be uniquely encoded and labeled using database auto-incrementing IDs and timestamps; Among these measures, the public's livelihood data in the shared platform will be stored in the platform's database, and after storage, a unique ID will be assigned using the platform's database's auto-incrementing ID. The public service data stored in the platform database and assigned unique IDs will be timestamped. The shared platform data on people's livelihood, which has been assigned unique IDs in the platform database, will be time-stamped based on the timestamps. The data on people's livelihood is generated by generating a timestamp to obtain a unique code label for the completed data on people's livelihood, and then marked as the target data on people's livelihood.

4. The multi-scenario business collaborative management system based on digital public services intelligence as described in claim 3, characterized in that: The integrated data management unit includes: The data management module is used for: Confirm the urban data, medical data, education data, and transportation data within the target public welfare data; According to the corresponding rules in the management platform, city data, medical data, education data, and transportation data are managed and matched separately. The corresponding rules in the management platform are to match different corresponding attributes with target livelihood data. The corresponding attributes include city data attributes, medical data attributes, education data attributes, and transportation data attributes. After the management is completed, we obtain the corresponding management data for cities, healthcare, education, and transportation, and uniformly label them as the corresponding data for people's livelihood management.

5. A multi-scenario business collaborative management system based on digital public services intelligence as described in claim 4, characterized in that: When the comparison result indicates that the server's primary operational evaluation parameters exceed a preset primary operational evaluation parameter threshold, the determination of whether the server is a secure server is made using network bandwidth parameters combined with a first security evaluation model, including: When the comparison result shows that the primary operation evaluation parameters of the server exceed the preset primary operation evaluation parameter threshold, the network bandwidth parameters corresponding to the server are extracted, wherein the network bandwidth parameters include the input and output data volume of the network interface, network latency, and packet loss rate. An evaluation coefficient is obtained by utilizing the input and output data volume and packet loss rate of the network interface; wherein, the evaluation coefficient is obtained by the following formula: ； Among them, S p The evaluation coefficient is represented by ; n represents the number of time units during which the server runs, and the time unit is 1 second; C ri C represents the amount of input data to the server corresponding to the i-th unit of time; si P represents the amount of output data from the server corresponding to the i-th unit of time; xi This represents the packet loss rate during the server's data processing in the i-th unit of time. The first security evaluation parameter is obtained by using the network latency combined with the evaluation coefficient and the first security evaluation model, wherein the first security evaluation parameter is obtained by the following formula: ； Among them, S 01 The parameter represents the first security evaluation parameter; n represents the number of time units during server operation, and the time unit is 1 second; P ti This represents the network latency rate of the server corresponding to the i-th unit of time; The first security evaluation parameter is compared with a preset first security evaluation threshold. If the first security evaluation parameter exceeds the preset first security evaluation threshold, the server is determined to be a secure server.

6. A multi-scenario business collaborative management system based on digital public services intelligence as described in claim 5, characterized in that: When the comparison result indicates that the server's primary operational evaluation parameters do not exceed the preset primary operational evaluation parameter threshold, the server is then judged as a secure server using concurrent processing operational parameters combined with a second security evaluation model, including: When the comparison result shows that the primary operation evaluation parameter of the server does not exceed the preset primary operation evaluation parameter threshold, the concurrent processing operation parameter corresponding to the server is extracted, wherein the concurrent processing operation parameter refers to the number of data threads that the server performs data processing operation simultaneously per unit time. The second security evaluation parameter is obtained by combining the concurrent processing operation parameters with the second security evaluation model, wherein the second security evaluation parameter is obtained by the following formula: ； Among them, S 02 S represents the second safety evaluation parameter. c Indicates the initial operational evaluation parameters; S y This represents the preset threshold for primary operational evaluation parameters; n represents the number of time units experienced by the server during operation, and the time unit is 1 second; M i This represents the number of data threads running simultaneously on the server for the i-th unit of time. The second security evaluation parameter is compared with a preset second security evaluation threshold. If the second security evaluation parameter exceeds the preset second security evaluation threshold, the server is determined to be a secure server.

7. A multi-scenario business collaborative management system based on digital public services intelligence as described in claim 6, characterized in that: The management data analysis unit includes: The data mining and analysis module is used for: The city-related management data, medical-related management data, education-related management data, and transportation-related management data in the data on people's livelihood to be analyzed will be subjected to data mining analysis in sequence. Among them, cluster analysis is used to differentiate the city's internal regions and classify them according to population characteristics based on the city's corresponding management data; The medical management data is classified using a classification algorithm to categorize patient information, medical record information, and medication usage information. The educational management information is digitized using learning robots, including textbooks, lesson plans, test questions, student information, and teacher information. Big data technology is used to classify the usage and distribution characteristics of educational resources in the digitized process. Traffic management data is classified into traffic flow and congestion levels using clustering methods. After mining and analyzing the corresponding management data for cities, healthcare, education, and transportation, we obtained the public welfare data to be integrated. The cross-domain data fusion module is used for: The city-related management data, medical-related management data, education-related management data, and transportation-related management data in the data to be integrated for public services will be linked together using association rules, which will be retrieved from the database. The association rule involves cross-domain analysis of management data related to cities, healthcare, education, and transportation. The cross-domain analysis of livelihood data to be integrated will be carried out by using image fusion, spatial data fusion, temporal data fusion and statistical fusion methods to fuse different data. After data fusion, standard public service data is obtained.

8. A multi-scenario business collaborative management system based on digital public services intelligence as described in claim 7, characterized in that: The analysis and data display unit is also used for: Users can log in to the smart public service platform on mobile display terminals to view standard public service data. Users are verified upon login, and after successful verification, they can view standard public service data. Meanwhile, when users view information on mobile display terminals, they can query, process, and provide feedback on specific scenarios through the user interface as needed.

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