Multi-scene business collaborative management system based on digital livelihood intelligence

Abstract

The invention discloses a multi-scene business collaborative management system based on digital livelihood intelligence, relates to the technical field of livelihood business collaborative management, and aims to solve the problem that the precision of data collaborative fusion is reduced when livelihood data is subjected to multi-scene management. According to data characteristics of different fields, different data mining methods and algorithms are adopted, the value of data can be fully played, the accuracy and effectiveness of analysis are improved, different data are fused by utilizing multiple data fusion methods, and the diversity can fully utilize the advantages of various methods, so that the data fusion efficiency is improved. According to the method, the data fusion accuracy and reliability are improved, different corresponding attributes can be automatically matched with the target livelihood data through the corresponding rules in the management platform, the complexity of manual operation is reduced, and which servers can access and share specific livelihood management corresponding data can be flexibly controlled through identity verification and authorization check.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative management of people's livelihood services, and specifically to a multi-scenario business collaborative management system based on digital people's livelihood intelligence. Background Art

[0002] Collaborative management of people's livelihood business refers to the efficient coordination of business processes through collaborative cooperation between different business departments in the people's livelihood field, so as to improve production efficiency and management efficiency and support the rapid development of the enterprise.

[0003] The Chinese patent with publication number CN117132432A discloses an intelligent electronic information management system based on smart city, which is mainly composed of city user layer, city system layer, city application layer and city data layer. The city user layer is for the public, relevant leaders and professional units, system administrators and video monitors to manage and monitor smart city management. The city data layer integrates the acquired data into the field of smart city. The city data is stored based on big data analysis technology. Processing and analysis to meet the needs of smart city application business. The city application layer consists of three parts: smart industry, smart management and smart people's livelihood. The city application layer is for all users of smart city, which covers communities, medical care, education, logistics and manufacturing. Although the above patent solves the problem of multi-scenario data collaboration, there are still the following problems in actual operation:

[0004] 1. After obtaining the people's livelihood data, the original people's livelihood data is not effectively integrated and managed, resulting in poor data integrity.

[0005] 2. The management platform fails to effectively manage data sharing, resulting in reduced security of data storage.

[0006] 3. The optimal fusion method is not adopted, resulting in reduced accuracy in the cross-border fusion of people’s livelihood data. Summary of the invention

[0007] The purpose of the present invention is to provide a multi-scenario business collaborative management system based on digital people's livelihood intelligence. According to the data characteristics of different fields, different data mining methods and algorithms are adopted to give full play to the value of data, improve the accuracy and effectiveness of analysis, and use a variety of data fusion methods to fuse different data. This diversity can make full use of the advantages of various methods, improve the accuracy and reliability of data fusion, and automatically match different corresponding attributes with target people's livelihood data through corresponding rules in the management platform, reducing the tediousness of manual operations. Through identity authentication and authorization checks, it is possible to flexibly control which servers can access and share specific people's livelihood management corresponding data, which can solve the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A multi-scenario business collaborative management system based on digital livelihood intelligence, including:

[0010] The people's livelihood data integration unit is used to:

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

[0012] Integrated data management unit for:

[0013] The target livelihood data are managed and matched with the management platforms respectively, and the target livelihood data in each management platform are collaboratively managed, and the target livelihood data that have been collaboratively managed are marked as the livelihood data to be analyzed;

[0014] Management Data Analysis Unit for:

[0015] Mining and analyzing the livelihood data to be analyzed, and integrating the livelihood data to be analyzed after the mining and analysis, and marking the livelihood data to be analyzed after the cross-border integration as standard livelihood data;

[0016] Analytical data display unit, used to:

[0017] The standard livelihood data is transmitted wirelessly to the mobile display terminal for display, and the user logs in to the mobile display terminal to interact with the standard livelihood data.

[0018] Preferably, the livelihood data integration unit includes:

[0019] Data integration control module, used for:

[0020] Acquire the people's livelihood data in the database, where the people's livelihood data includes urban data, medical data, education data and transportation data;

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

[0022] The livelihood data is transmitted to the shared platform in a unified format for storage;

[0023] Use ETL tools to integrate people's livelihood data in the shared platform, and integrate government data in people's livelihood data through logical aggregation;

[0024] The people’s livelihood data integrated in the sharing platform will be subject to access permission control and data encryption.

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

[0026] The livelihood data labeling module is used to:

[0027] The livelihood data in the sharing platform is uniquely coded and labeled through the database self-increment ID and timestamp;

[0028] Among them, the livelihood data in the shared platform is stored in the platform database, and after the storage is completed, a unique ID is allocated through the self-incrementing ID of the platform database;

[0029] Timestamp the livelihood data in the shared platform that has been stored in the platform database and has been assigned a unique ID;

[0030] Generate a time label for the livelihood data in the shared platform that has been assigned a unique ID in the platform database according to the timestamp;

[0031] The people's livelihood data is uniquely coded and labeled by generating a time label based on the timestamp, and is marked as the target people's livelihood data.

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

[0033] Data management corresponding module is used for:

[0034] Confirm the urban data, medical data, education data and transportation data in the target people's livelihood data;

[0035] According to the corresponding rules in the management platform, urban data, medical data, education data and traffic data are managed and matched respectively;

[0036] Among them, the corresponding rules in the management platform are to correspond different corresponding attributes with the target livelihood data, and the corresponding attributes include urban data attributes, medical data attributes, education data attributes and traffic data attributes;

[0037] After the management correspondence is completed, the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data are obtained, and are uniformly marked as people's livelihood management corresponding data.

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

[0039] Data sharing collaboration module, used for:

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

[0041] When the data corresponding to people's livelihood management is shared in the management platform, the digital signature of the management platform is used to confirm whether the server for data sharing is a secure server;

[0042] If it is a secure server, the data corresponding to the livelihood management will be exchanged; if it is a non-secure server, the data corresponding to the livelihood management will stop exchanging data;

[0043] When data corresponding to people's livelihood management is exchanged, the server requesting the data exchange will be subject to identity authentication and authorization checks, wherein identity authentication is performed first, and after the identity authentication is passed, the management platform accesses and shares the data corresponding to people's livelihood management on the server through the access control table;

[0044] The corresponding data of people's livelihood management in the server that can be accessed and shared are marked as people's livelihood data to be analyzed.

[0045] Preferably, when the data corresponding to people's livelihood management is shared in the management platform, whether the server for data sharing is a secure server is confirmed based on the digital signature of the management platform, including:

[0046] When the data corresponding to people's livelihood management is shared in the management platform, the digital signature of the management platform is extracted to confirm the historical operating parameters of the server for data sharing; wherein the historical operating parameters include CPU utilization, memory utilization, network bandwidth parameters and concurrent processing operating parameters;

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

[0048]

[0049] Among them, S c represents the primary operation evaluation parameter; n represents the number of unit time experienced by the server operation, and the unit time is 1s; P ni represents the memory utilization of the server corresponding to the i-th unit time; P ci represents the CPU utilization of the server corresponding to the i-th unit time; P cu Indicates the upper limit value corresponding to the preset range of CPU utilization of the server; P cd Indicates the lower limit of the preset server CPU utilization range;

[0050] Comparing the primary operation evaluation parameter of the server with a preset primary operation evaluation parameter threshold to obtain a comparison result;

[0051] When the comparison result indicates that the primary operation evaluation parameter of the server exceeds a preset primary operation evaluation parameter threshold, the network bandwidth parameter is combined with the first security evaluation model to determine whether the server is a secure server;

[0052] 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 is combined with the second security evaluation model to determine whether the server is a safe server.

[0053] Preferably, when the comparison result shows that the primary operation evaluation parameter of the server exceeds a preset primary operation evaluation parameter threshold, the network bandwidth parameter is combined with the first security evaluation model to determine whether the server is a secure server, including:

[0054] When the comparison result shows that the primary operation evaluation parameter of the server exceeds the preset primary operation evaluation parameter threshold, extracting the network bandwidth parameter corresponding to the server, wherein the network bandwidth parameter includes the input and output data volume of the network interface, network delay and packet loss rate;

[0055] The evaluation coefficient is obtained by using the amount of data input and output and the packet loss rate of the network interface; wherein the evaluation coefficient is obtained by the following formula:

[0056]

[0057] Among them, S p represents the evaluation coefficient; n represents the number of unit times experienced by the server operation, and the unit time is 1s; C ri represents the amount of input data of the server corresponding to the i-th unit time; C si represents the output data volume of the server corresponding to the i-th unit time; P xi represents the packet loss rate of the server during data processing in the i-th unit time;

[0058] The first safety evaluation model is used to obtain a first safety evaluation parameter by combining the network delay with the evaluation coefficient, wherein the first safety evaluation parameter is obtained by the following formula:

[0059]

[0060] Among them, S 01 represents the first security evaluation parameter; n represents the number of unit times experienced by the server operation, and the unit time is 1s; P ti Indicates the network delay rate of the server corresponding to the i-th unit time;

[0061] The first security evaluation parameter is compared with a preset first security evaluation threshold, and when 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 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 is combined with the second security evaluation model to determine whether the server is a secure server, 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, extracting the concurrent processing operation parameter corresponding to the server, wherein the concurrent processing operation parameter refers to the number of data threads that the server performs data processing operation at the same time per unit time;

[0064] The concurrent processing operation parameter is combined with the second safety evaluation model to obtain a second safety evaluation parameter, wherein the second safety evaluation parameter is obtained by the following formula:

[0065]

[0066] Among them, S 02 Represents the second safety evaluation parameter; S c Represents the primary operation evaluation parameter; S y represents the preset primary operation evaluation parameter threshold; n represents the number of unit times experienced by the server operation, and the unit time is 1s; M i Indicates the number of data threads that are processing data at the same time on the server corresponding to the i-th unit time;

[0067] The second security evaluation parameter is compared with a preset second security evaluation threshold, and when 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 comprises:

[0069] Data mining analysis module for:

[0070] Conduct data mining and analysis on the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data in the livelihood data to be analyzed;

[0071] Among them, the corresponding management data of the city is used to divide the regional differentiation and population characteristics of the city by cluster analysis;

[0072] Use classification algorithms to classify patient information, medical record information, and drug use information in medical management data;

[0073] Use learning robots to digitize educational management information, including textbooks, lesson plans, test questions, student information, and teacher information, and use big data technology to classify the use and distribution characteristics of educational resources in digital processing;

[0074] Use clustering methods to classify traffic flow and congestion conditions using traffic management data;

[0075] The urban management data, medical management data, education management data and transportation management data are mined and analyzed respectively to obtain the livelihood data to be integrated;

[0076] Data cross-border fusion module, used for:

[0077] The city corresponding management data, medical corresponding management data, education corresponding management data and traffic corresponding management data in the livelihood data to be integrated are respectively associated with each other through association rules, wherein the association rules are retrieved from the database;

[0078] The association rules are used to conduct cross-domain analysis on the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data;

[0079] The people's livelihood data to be integrated after cross-border field analysis is integrated by using image fusion method, spatial data fusion method, temporal data fusion method and statistical fusion method;

[0080] After data fusion is completed, standard people's livelihood data is obtained.

[0081] Preferably, the analysis data display unit is further used for:

[0082] Users log in to the smart livelihood platform on a mobile display terminal to view standard livelihood data;

[0083] Users are authenticated when logging in, and can view standard livelihood data after passing the authentication;

[0084] At the same time, when users view content on mobile display terminals, they can query, process and provide feedback on the scene in the user interface according to their needs.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

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

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

[0088] 3. The multi-scenario business collaborative management system based on digital livelihood intelligence provided by the present 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 a variety of data fusion methods to fuse different data. This diversity can make full use of the advantages of various methods and improve the accuracy and reliability of data fusion. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0090] Figure 2 It is a flow chart of the intelligent livelihood multi-scenario business collaborative management method of the present invention. DETAILED DESCRIPTION

[0091] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0092] In order to solve the problem in the prior art that after obtaining the livelihood data, the original livelihood data is not effectively integrated and managed, resulting in poor data integrity, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0093] A multi-scenario business collaborative management system based on digital livelihood intelligence, including:

[0094] The people's livelihood data integration unit is used to:

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

[0096] Integrated data management unit for:

[0097] The target livelihood data are managed and matched with the management platforms respectively, and the target livelihood data in each management platform are collaboratively managed, and the target livelihood data that have been collaboratively managed are marked as the livelihood data to be analyzed;

[0098] Management Data Analysis Unit for:

[0099] Mining and analyzing the livelihood data to be analyzed, and integrating the livelihood data to be analyzed after the mining and analysis, and marking the livelihood data to be analyzed after the cross-border integration as standard livelihood data;

[0100] Analytical data display unit for:

[0101] The standard livelihood data is transmitted wirelessly to the mobile display terminal for display, and the user logs in to the mobile display terminal to interact with the standard livelihood data.

[0102] Specifically, the people's livelihood data integration unit can automatically match different corresponding attributes with the target people's livelihood data. This regular management method greatly improves the efficiency of data processing and reduces the tediousness of manual operations. By integrating the data management unit and adopting different data mining methods and algorithms, the value of data can be fully utilized and the accuracy and effectiveness of analysis can be improved. By managing the data analysis unit, the appropriate data fusion method can be selected according to different data types and application requirements, which can improve the efficiency and effect of data processing. Through the analysis of the data display unit, users can log in to the people's livelihood smart platform anytime and anywhere on the mobile display terminal to view standard people's livelihood data. This portability provides great convenience for users.

[0103] People's livelihood data integration unit, including:

[0104] Data integration control module, used for:

[0105] Acquire the people's livelihood data in the database, where the people's livelihood data includes urban data, medical data, education data and transportation data;

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

[0107] The livelihood data is transmitted to the shared platform in a unified format for storage;

[0108] Use ETL tools to integrate people's livelihood data in the shared platform, and integrate government data in people's livelihood data through logical aggregation;

[0109] The people’s livelihood data integrated in the sharing platform will be subject to access permission control and data encryption.

[0110] Specifically, the unified format ensures that urban data, medical data, education data, and traffic data from different sources can be integrated and analyzed under a common framework, thereby improving the readability and comparability of the data. The use of ETL tools can effectively integrate the people's livelihood data in the shared platform, including not only the physical integration of data, but also the logical integration of data. In particular, government data is integrated through logical aggregation to ensure the integrity and accuracy of the data. Access control and data encryption measures greatly improve the security of the data. This can not only prevent unauthorized access and data leakage, but also ensure the integrity and credibility of the data, thereby protecting personal privacy and institutional interests.

[0111] The livelihood data labeling module is used to:

[0112] The livelihood data in the sharing platform is uniquely coded and labeled through the database self-increment ID and timestamp;

[0113] Among them, the livelihood data in the shared platform is stored in the platform database, and after the storage is completed, a unique ID is allocated through the self-incrementing ID of the platform database;

[0114] Timestamp the livelihood data in the shared platform that has been stored in the platform database and has been assigned a unique ID;

[0115] Generate a time label for the livelihood data in the shared platform that has been assigned a unique ID in the platform database according to the timestamp;

[0116] The people's livelihood data is uniquely coded and labeled by generating a time label based on the timestamp, and is marked as the target people's livelihood data.

[0117] Specifically, through the combination of the database's self-incrementing ID and timestamp, it can ensure that each piece of people's livelihood data obtains a unique coding number. The allocation of the database's self-incrementing ID is an automated process that can be completed quickly, greatly improving the speed and efficiency of data processing. At the same time, the generation of timestamps is also instant, and each piece of data can be quickly time-stamped. The introduction of timestamps not only provides information on the time dimension of the data, but also makes the generation, storage and processing of data traceable. Through the unique coding number, people's livelihood data becomes more orderly and easy to manage in the platform database. Whether it is querying, updating or deleting data, it can be quickly located and operated through the unique coding number, which greatly simplifies the data management process.

[0118] In order to solve the problem in the prior art that after the livelihood data is transmitted to the management platform, the management platform does not perform effective data sharing management, which leads to reduced security of data storage, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0119] Integrated data management unit, including:

[0120] Data management corresponding module is used for:

[0121] Confirm the urban data, medical data, education data and transportation data in the target people's livelihood data;

[0122] According to the corresponding rules in the management platform, urban data, medical data, education data and traffic data are managed and matched respectively;

[0123] Among them, the corresponding rules in the management platform are to correspond different corresponding attributes with the target livelihood data, and the corresponding attributes include urban data attributes, medical data attributes, education data attributes and traffic data attributes;

[0124] After the management correspondence is completed, the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data are obtained, and are uniformly marked as people's livelihood management corresponding data.

[0125] Specifically, the corresponding rules in the management platform can automatically match different corresponding attributes with the target livelihood data. This rule-based management method greatly improves the efficiency of data processing, reduces the tediousness of manual operations, and reduces the possibility of errors. The livelihood data is subdivided into urban data, medical data, education data, and traffic data, and managed according to different corresponding attributes. This classification and refinement method makes the application of data more flexible, and customized analysis and processing can be performed according to the needs of different fields.

[0126] Data sharing collaboration module, used for:

[0127] Retrieve the API interface data of the management platform from the database;

[0128] When the data corresponding to people's livelihood management is shared in the management platform, the digital signature of the management platform is used to confirm whether the server for data sharing is a secure server;

[0129] If it is a secure server, the data corresponding to the livelihood management will be exchanged; if it is a non-secure server, the data corresponding to the livelihood management will stop exchanging data;

[0130] When data corresponding to people's livelihood management is exchanged, the server requesting the data exchange will be subject to identity authentication and authorization checks, wherein identity authentication is performed first, and after the identity authentication is passed, the management platform accesses and shares the data corresponding to people's livelihood management on the server through the access control table;

[0131] The corresponding data of people's livelihood management in the server that can be accessed and shared are marked as people's livelihood data to be analyzed.

[0132] Specifically, before data sharing, the target server will first be digitally signed and verified to ensure that data exchange occurs on a secure server. This mechanism effectively prevents data leakage and illegal access, greatly improving data security. When exchanging data, the server requesting data exchange is authenticated and authorized, which ensures that only authorized and verified servers can access and share data corresponding to people's livelihood management, further enhancing data security and controllability. Through identity authentication and authorization checks, it is possible to flexibly control which servers can access and share specific data corresponding to people's livelihood management. This flexibility makes data sharing more in line with actual needs and can meet data sharing needs in different scenarios.

[0133] Specifically, when the data corresponding to people's livelihood management is shared in the management platform, the digital signature of the management platform is used to confirm whether the server for data sharing is a secure server, including:

[0134] When the data corresponding to people's livelihood management is shared in the management platform, the digital signature of the management platform is extracted to confirm the historical operating parameters of the server for data sharing; wherein the historical operating parameters include CPU utilization, memory utilization, network bandwidth parameters and concurrent processing operating parameters;

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

[0136]

[0137] Among them, Sc represents the primary operation evaluation parameter; n represents the number of unit time experienced by the server operation, and the unit time is 1s; P ni represents the memory utilization of the server corresponding to the i-th unit time; P ci represents the CPU utilization of the server corresponding to the i-th unit time; P cu Indicates the upper limit value corresponding to the preset range of CPU utilization of the server; P cd Indicates the lower limit of the preset server CPU utilization range;

[0138] Comparing the primary operation evaluation parameter of the server with a preset primary operation evaluation parameter threshold to obtain a comparison result;

[0139] When the comparison result indicates that the primary operation evaluation parameter of the server exceeds a preset primary operation evaluation parameter threshold, the network bandwidth parameter is combined with the first security evaluation model to determine whether the server is a secure server;

[0140] 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 is combined with the second security evaluation model to determine whether the server is a safe server.

[0141] The technical effect of the above technical solution is: by introducing the digital signature of the management platform to verify whether the data sharing server 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 unsafe servers, thereby protecting sensitive people's livelihood management data from being leaked or tampered with.

[0142] The technical solution not only considers the two common performance indicators of CPU utilization and memory utilization, but also combines network bandwidth parameters and concurrent processing operation parameters to conduct a comprehensive assessment of the server's operating status. This comprehensive evaluation method can more accurately reflect the actual operating status of the server, thereby more accurately judging whether it is suitable for data sharing.

[0143] The technical solution adopts a hierarchical judgment method. First, the operation status of the server is preliminarily screened through the primary operation evaluation parameters. For servers whose primary evaluation parameters exceed the threshold, the more stringent first security evaluation model is used for judgment; and for servers whose primary evaluation parameters do not exceed the threshold, the second security evaluation model is used for judgment. This hierarchical judgment mechanism not only improves the accuracy of the judgment, but also avoids unnecessary waste of computing resources.

[0144] The two safety evaluation models mentioned in the technical solution (the first safety evaluation model and the second safety evaluation model) 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; in scenarios with high concurrent processing capabilities, more attention can be paid to the evaluation of concurrent processing operation parameters.

[0145] By real-time monitoring and evaluation of the server's operating status, the technical solution can promptly detect and resolve potential problems, thereby avoiding data sharing interruptions or data loss caused by server overload or failure. This helps improve the stability and reliability of the entire system and ensure the continuity and integrity of people's livelihood management data.

[0146] Specifically, when the comparison result shows that the primary operation evaluation parameter of the server exceeds a preset primary operation evaluation parameter threshold, the network bandwidth parameter is combined with the first security evaluation model to determine whether the server is a secure server, including:

[0147] When the comparison result shows that the primary operation evaluation parameter of the server exceeds the preset primary operation evaluation parameter threshold, extracting the network bandwidth parameter corresponding to the server, wherein the network bandwidth parameter includes the input and output data volume of the network interface, network delay and packet loss rate;

[0148] The evaluation coefficient is obtained by using the amount of data input and output and the packet loss rate of the network interface; wherein the evaluation coefficient is obtained by the following formula:

[0149]

[0150] Among them, S p represents the evaluation coefficient; n represents the number of unit times experienced by the server operation, and the unit time is 1s; C ri represents the amount of input data of the server corresponding to the i-th unit time; C si represents the output data volume of the server corresponding to the i-th unit time; P xi represents the packet loss rate of the server during data processing in the i-th unit time;

[0151] The first safety evaluation model is used to obtain a first safety evaluation parameter by combining the network delay with the evaluation coefficient, wherein the first safety evaluation parameter is obtained by the following formula:

[0152]

[0153] Among them, S 01represents the first security evaluation parameter; n represents the number of unit times experienced by the server operation, and the unit time is 1s; P ti Indicates the network delay rate of the server corresponding to the i-th unit time;

[0154] The first security evaluation parameter is compared with a preset first security evaluation threshold, and when 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 technical solution is: when the primary operation evaluation parameters exceed the threshold, the network bandwidth parameters (including the input and output data volume of the network interface, network delay and packet loss rate) are further introduced for security evaluation. This refines the dimensions of security evaluation, not only considering the computing resource utilization of the server, but also the performance at the network level, so as to more comprehensively evaluate the security and stability of the server.

[0156] By introducing the evaluation coefficient, combined with the input and output data volume and packet loss rate of the network interface, the network performance of the server can be accurately evaluated. This evaluation method can reflect the efficiency and stability of the server during network data transmission, and provides an important basis for judging whether the server is suitable for data sharing.

[0157] On the basis of obtaining the evaluation coefficient, the first safety evaluation parameter is calculated using the first safety evaluation model in combination with the network delay rate. This comprehensive judgment method combined with the network delay can more comprehensively reflect the performance of the server at the network level, including the real-time and reliability of data transmission. This is especially important for data sharing scenarios that require high real-time and low latency.

[0158] Through multi-dimensional evaluation and refined calculation, this technical solution can more accurately determine whether a server is a secure server. This accuracy is not only reflected in the accurate grasp of the current operating status of the server, but also in the prediction of potential risks in the future, which helps to discover and solve potential security issues in advance.

[0159] Through a strict security assessment process, we can ensure that only servers that are running stably and performing well are allowed to share data. This helps reduce problems such as data sharing interruptions or data loss caused by server failures or insufficient performance, thereby enhancing the stability and reliability of the entire system.

[0160] The network bandwidth parameters and network delay rate in this technical solution are dynamically obtained according to the actual network environment, so it has strong adaptability and flexibility. No matter how the network environment changes, the accuracy and effectiveness of the evaluation results can be ensured by adjusting the relevant parameters.

[0161] Specifically, 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 is combined with the second security evaluation model to determine whether the server is a secure server, 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, extracting the concurrent processing operation parameter corresponding to the server, wherein the concurrent processing operation parameter refers to the number of data threads that the server performs data processing operation at the same time per unit time;

[0163] The concurrent processing operation parameter is combined with the second safety evaluation model to obtain a second safety evaluation parameter, wherein the second safety evaluation parameter is obtained by the following formula:

[0164]

[0165] Among them, S 02 Represents the second safety evaluation parameter; S c Represents the primary operation evaluation parameter; S y represents the preset primary operation evaluation parameter threshold; n represents the number of unit times experienced by the server operation, and the unit time is 1s; M i Indicates the number of data threads that are processing data at the same time on the server corresponding to the i-th unit time;

[0166] The second security evaluation parameter is compared with a preset second security evaluation threshold, and when 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 technical solution is: when the primary operation evaluation parameter of the server does not exceed the preset threshold, special attention is paid to its concurrent processing operation parameter, that is, 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 and high-concurrency scenarios, and is of great significance for evaluating the stability and reliability of the server.

[0168] When calculating the second safety evaluation parameter, not only the concurrent processing operation parameters are taken into account, but also the primary operation evaluation parameters and the preset primary operation evaluation parameter thresholds are combined. This comprehensive consideration makes the evaluation results more comprehensive and accurate, and can better reflect the performance of the server in multiple dimensions.

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

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

[0171] By evaluating the concurrent processing capabilities of the servers, the resource allocation strategy can be further optimized. For servers with strong concurrent processing capabilities, more data processing tasks or higher loads can be allocated preferentially to improve the overall system's operating efficiency and resource utilization. For servers with weak concurrent processing capabilities, corresponding optimization measures can be taken or their loads can be limited to avoid data sharing interruptions or performance degradation caused by overload.

[0172] Through strict concurrent processing capability assessment and security determination process, we can ensure that only servers with good concurrent processing capability and stability are allowed to share data. This helps to 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] In order to solve the problem that the best fusion method is not adopted when integrating people's livelihood data in multiple scenarios in the existing technology, resulting in reduced fusion accuracy when people's livelihood data is integrated across data boundaries, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0174] Manage the Data Analysis Unit, including:

[0175] Data mining analysis module for:

[0176] Conduct data mining and analysis on the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data in the livelihood data to be analyzed;

[0177] Among them, the corresponding management data of the city is used to divide the regional differentiation and population characteristics of the city by cluster analysis;

[0178] Use classification algorithms to classify patient information, medical record information, and drug use information in medical management data;

[0179] Use learning robots to digitize educational management information, including textbooks, lesson plans, test questions, student information, and teacher information, and use big data technology to classify the use and distribution characteristics of educational resources in digital processing;

[0180] Use clustering methods to classify traffic flow and congestion conditions using traffic management data;

[0181] The urban management data, medical management data, education management data and transportation management data are mined and analyzed respectively to obtain the people's livelihood data to be integrated.

[0182] Specifically, by classifying the livelihood data in different fields such as cities, medical care, education and transportation and conducting data mining and analysis respectively, more refined data insights can be achieved. This refined analysis helps to more accurately grasp the characteristics and problems of each field and provide more scientific and accurate support for policy formulation and decision-making. According to the characteristics of data in different fields, different data mining methods and algorithms are used to give full play to the value of data and improve the accuracy and effectiveness of analysis. After mining and analysis, the livelihood data in the fields of cities, medical care, education and transportation can be further integrated. This data fusion can break down field barriers, realize information sharing and complementarity, and thus discover more valuable information and insights.

[0183] Data cross-border fusion module, used for:

[0184] The city corresponding management data, medical corresponding management data, education corresponding management data and traffic corresponding management data in the livelihood data to be integrated are respectively associated with each other through association rules, wherein the association rules are retrieved from the database;

[0185] The association rules are used to conduct cross-domain analysis on the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data;

[0186] The people's livelihood data to be integrated after cross-border field analysis is integrated by using image fusion method, spatial data fusion method, temporal data fusion method and statistical fusion method;

[0187] After data fusion is completed, standard people's livelihood data is obtained.

[0188] Specifically, cross-domain analysis of people's livelihood data in different fields through association rules can reveal the inherent connections and mutual influences between various fields, and the fusion of different data using a variety of data fusion methods. This diversity can make full use of the advantages of various methods and improve the accuracy and reliability of data fusion. At the same time, selecting appropriate data fusion methods according to different data types and application requirements can improve the efficiency and effectiveness of data processing. Among them, the cross-border fusion of traffic corresponding management data and city corresponding management data can provide scientific traffic management solutions for traffic management departments by analyzing urban planning and traffic flow data, reduce congestion and traffic accidents, and the cross-border fusion of medical corresponding management data combined with medical emergency data and traffic data can optimize the dispatch and route planning of emergency vehicles and improve emergency efficiency; the cross-border fusion of education corresponding management data and medical corresponding management data can provide teachers with targeted teaching suggestions by analyzing students' health data and learning data, helping students better overcome learning obstacles; the cross-border fusion of city corresponding management data combined with the city's economic, cultural and other data can provide data support for education policy formulation and promote the balanced distribution of education resources; the cross-border fusion of medical corresponding management data and city corresponding management data can provide targeted suggestions for public health management by analyzing urban environment, living habits and other data, and prevent and control the spread of diseases; Combining students' health data and academic data can provide schools with personalized health education programs and promote students' all-round development; cross-border integration of urban corresponding management data and traffic corresponding management data can optimize traffic layout, reduce congestion, and improve urban operation efficiency by integrating urban planning and traffic flow data; at the same time, image fusion If the data contains images or visual information (such as medical images, traffic monitoring videos, etc.), image fusion technology can be used to create clearer and more information-rich images. Spatial data fusion Data from different observation platforms (such as satellites, aircraft, ground observation points, etc.) can be integrated through spatial data fusion methods to form a more comprehensive spatial data three-dimensional 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 use 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 analytical data display unit is also used to:

[0190] Users log in to the smart livelihood platform on a mobile display terminal to view standard livelihood data;

[0191] Users are authenticated when logging in, and can view standard livelihood data after passing the authentication;

[0192] At the same time, when users view content on mobile display terminals, they can query, process and provide feedback on the scene in the user interface according to their needs.

[0193] Specifically, users can log in to the People's Livelihood Smart Platform anytime and anywhere on a mobile display terminal to view standard livelihood data. This portability provides great convenience for users, without being restricted by geographic location or device. User verification during login ensures that only legally verified users can view the data, effectively preventing unauthorized access and data leakage, and improving data security. Users can handle related matters directly on the mobile display terminal without going to the physical service location, which improves service 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 problems.

[0194] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

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

Claims

1. A multi-scenario business collaborative management system based on digital livelihood intelligence, characterized in that: include: The people's livelihood data integration unit is used to: Retrieving people's livelihood data from the database, and integrating the retrieved people's livelihood data. After the data integration is completed, the people's livelihood data are uniquely coded and labeled according to the data attributes, and the people's livelihood data with the unique coding and labeling are marked as target people's livelihood data; Integrated data management unit for: The target livelihood data are managed and matched with the management platforms respectively, and the target livelihood data in each management platform are collaboratively managed, and the target livelihood data that have been collaboratively managed are marked as the livelihood data to be analyzed; Management Data Analysis Unit for: Mining and analyzing the livelihood data to be analyzed, and integrating the livelihood data to be analyzed after the mining and analysis, and marking the livelihood data to be analyzed after the cross-border integration as standard livelihood data; Analytical data display unit for: The standard livelihood data is transmitted wirelessly to the mobile display terminal for display, and the user logs in to the mobile display terminal to interact with the standard livelihood data.

2. According to claim 1, a multi-scenario business collaborative management system based on digital livelihood intelligence is characterized by: The livelihood data integration unit includes: Data integration control module, used for: Acquire the people's livelihood data in the database, where the people's livelihood data includes urban data, medical data, education data and transportation data; Unify the formats of urban data, medical data, education data, and transportation data; The livelihood data is transmitted to the shared platform in a unified format for storage; Use ETL tools to integrate people's livelihood data in the shared platform, and integrate government data in people's livelihood data through logical aggregation; The people’s livelihood data integrated in the sharing platform will be subject to access permission control and data encryption.

3. According to claim 2, a multi-scenario business collaborative management system based on digital livelihood intelligence is characterized by: The people's livelihood data integration unit also includes: The livelihood data labeling module is used to: The livelihood data in the sharing platform is uniquely coded and labeled through the database self-increment ID and timestamp; Among them, the livelihood data in the shared platform is stored in the platform database, and after the storage is completed, a unique ID is allocated through the self-incrementing ID of the platform database; Timestamp the livelihood data in the shared platform that has been stored in the platform database and has been assigned a unique ID; Generate a time label for the livelihood data in the shared platform that has been assigned a unique ID in the platform database according to the timestamp; The people's livelihood data is uniquely coded and labeled by generating a time label based on the timestamp, and is marked as the target people's livelihood data.

4. According to claim 3, a multi-scenario business collaborative management system based on digital livelihood intelligence is characterized by: The integrated data management unit comprises: Data management corresponding module is used for: Confirm the urban data, medical data, education data and transportation data in the target people's livelihood data; According to the corresponding rules in the management platform, urban data, medical data, education data and traffic data are managed and matched respectively; Among them, the corresponding rules in the management platform are to correspond different corresponding attributes with the target livelihood data, and the corresponding attributes include urban data attributes, medical data attributes, education data attributes and traffic data attributes; After the management correspondence is completed, the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data are obtained, and are uniformly marked as people's livelihood management corresponding data.

5. According to claim 4, a multi-scenario business collaborative management system based on digital livelihood intelligence is characterized by: The integrated data management unit further includes: Data sharing collaboration module, used for: Retrieve the API interface data of the management platform from the database; When the data corresponding to people's livelihood management is shared in the management platform, the digital signature of the management platform is used to confirm whether the server for data sharing is a secure server; If it is a secure server, the data corresponding to the livelihood management will be exchanged; if it is a non-secure server, the data corresponding to the livelihood management will stop exchanging data; When data corresponding to people's livelihood management is exchanged, the server requesting the data exchange will be subject to identity authentication and authorization checks, wherein identity authentication is performed first, and after the identity authentication is passed, the management platform accesses and shares the data corresponding to people's livelihood management on the server through the access control table; The corresponding data of people's livelihood management in the server that can be accessed and shared are marked as people's livelihood data to be analyzed.

6. According to claim 5, a multi-scenario business collaborative management system based on digital livelihood intelligence is characterized by: When the data corresponding to people's livelihood management is shared in the management platform, the digital signature of the management platform is used to confirm whether the server for data sharing is a secure server, including: When the data corresponding to people's livelihood management is shared in the management platform, the digital signature of the management platform is extracted to confirm the historical operating parameters of the server for data sharing; wherein the historical operating parameters include CPU utilization, memory utilization, network bandwidth parameters and concurrent processing operating parameters; The primary operation evaluation parameters of the server are obtained by using the CPU utilization and the memory utilization, wherein the primary operation evaluation parameters of the server are obtained by the following formula: Among them, S c represents the primary operation evaluation parameter; n represents the number of unit times experienced by the server operation, and the unit time is 1s; P ni represents the memory utilization of the server corresponding to the i-th unit time; P ci represents the CPU utilization of the server corresponding to the i-th unit time; P cu Indicates the upper limit value corresponding to the preset range of CPU utilization of the server; P cd Indicates the lower limit of the preset server CPU utilization range; Comparing the primary operation evaluation parameter of the server with a preset primary operation evaluation parameter threshold to obtain a comparison result; When the comparison result indicates that the primary operation evaluation parameter of the server exceeds a preset primary operation evaluation parameter threshold, the network bandwidth parameter is combined with the first security evaluation model to determine whether the server is a secure server; 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 is combined with the second security evaluation model to determine whether the server is a safe server.

7. According to claim 6, a multi-scenario business collaborative management system based on digital livelihood intelligence is characterized by: When the comparison result shows that the primary operation evaluation parameter of the server exceeds a preset primary operation evaluation parameter threshold, the network bandwidth parameter is combined with the first security evaluation model to determine whether the server is a secure server, including: When the comparison result shows that the primary operation evaluation parameter of the server exceeds the preset primary operation evaluation parameter threshold, extracting the network bandwidth parameter corresponding to the server, wherein the network bandwidth parameter includes the input and output data volume of the network interface, network delay and packet loss rate; The evaluation coefficient is obtained by using the amount of data input and output and the packet loss rate of the network interface; wherein the evaluation coefficient is obtained by the following formula: Among them, S p represents the evaluation coefficient; n represents the number of unit times experienced by the server operation, and the unit time is 1s; C ri represents the amount of input data of the server corresponding to the i-th unit time; C si represents the output data volume of the server corresponding to the i-th unit time; P xi represents the packet loss rate of the server during data processing in the i-th unit time; The first safety evaluation model is used to obtain a first safety evaluation parameter by combining the network delay with the evaluation coefficient, wherein the first safety evaluation parameter is obtained by the following formula: Among them, S 01 represents the first security evaluation parameter; n represents the number of unit times experienced by the server operation, and the unit time is 1s; P ti Indicates the network delay rate of the server corresponding to the i-th unit time; The first security evaluation parameter is compared with a preset first security evaluation threshold, and when the first security evaluation parameter exceeds the preset first security evaluation threshold, the server is determined to be a secure server.

8. According to claim 6, a multi-scenario business collaborative management system based on digital livelihood intelligence is characterized by: 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 is combined with the second security evaluation model to determine whether the server is a secure server, 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, extracting the concurrent processing operation parameter corresponding to the server, wherein the concurrent processing operation parameter refers to the number of data threads that the server performs data processing operation at the same time per unit time; The concurrent processing operation parameter is combined with the second safety evaluation model to obtain a second safety evaluation parameter, wherein the second safety evaluation parameter is obtained by the following formula: Among them, S 02 Represents the second safety evaluation parameter; S c Represents the primary operation evaluation parameter; S y represents the preset primary operation evaluation parameter threshold; n represents the number of unit times experienced by the server operation, and the unit time is 1s; M i Indicates the number of data threads that are processing data at the same time on the server corresponding to the i-th unit time; The second security evaluation parameter is compared with a preset second security evaluation threshold, and when the second security evaluation parameter exceeds the preset second security evaluation threshold, the server is determined to be a secure server.

9. According to claim 5, a multi-scenario business collaborative management system based on digital livelihood intelligence is characterized by: The management data analysis unit comprises: Data mining analysis module for: Conduct data mining and analysis on the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data in the livelihood data to be analyzed; Among them, the corresponding management data of the city is used to divide the regional differentiation and population characteristics of the city by cluster analysis; Use classification algorithms to classify patient information, medical record information, and drug use information in medical management data; Use learning robots to digitize educational management information, including textbooks, lesson plans, test questions, student information, and teacher information, and use big data technology to classify the use and distribution characteristics of educational resources in digital processing; Use clustering methods to classify traffic flow and congestion using traffic management data; The urban management data, medical management data, education management data and transportation management data are mined and analyzed respectively to obtain the livelihood data to be integrated; Data cross-border fusion module, used for: The city corresponding management data, medical corresponding management data, education corresponding management data and traffic corresponding management data in the livelihood data to be integrated are respectively associated with each other through association rules, wherein the association rules are retrieved from the database; The association rules are used to conduct cross-domain analysis on the city corresponding management data, medical corresponding management data, education corresponding management data and transportation corresponding management data; The people's livelihood data to be integrated after cross-border field analysis is integrated by using image fusion method, spatial data fusion method, temporal data fusion method and statistical fusion method; After data fusion is completed, standard people's livelihood data is obtained.

10. A multi-scenario business collaborative management system based on digital livelihood intelligence according to claim 9, characterized in that: The analysis data display unit is further used for: Users log in to the smart livelihood platform on a mobile display terminal to view standard livelihood data; Users are authenticated when logging in, and can view standard livelihood data after passing the authentication; At the same time, when users view content on mobile display terminals, they can query, process and provide feedback on the scene in the user interface according to their needs.

Images