Policy matching analysis system and precise pushing method based on big data

Abstract

The invention discloses a policy matching analysis system and an accurate pushing method based on big data, and relates to the technical field of policy matching analysis and pushing, and the policy matching analysis system comprises a pushing receiving control end, a policy matching analysis module, a data summarization module and a monitoring evaluation module. The policy matching analysis system is generated by arranging the push receiving control end, the policy matching analysis module, the data summarization module and the monitoring evaluation module, multiple push channels are comprehensively utilized during actual use, a proper push channel is selected according to user characteristics and preferences, the policy transmission accuracy and effectiveness are improved, and the policy transmission efficiency is improved. Each link is conveniently supervised and processed in real time, policy matching analysis data, an accurate pushing method and a corresponding analysis result of big data are managed, visualized and stored, policy matching analysis and accurate pushing management of the big data can be realized through internet cloud management and control, and the management efficiency is improved. And the intelligent level of big data policy matching analysis and accurate pushing management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of policy matching analysis and push, and specifically to a policy matching analysis system and precise push method based on big data. Background Art

[0002] In order to improve the level of industrial development, promote scientific and technological progress, and enhance the independent innovation capabilities of enterprises, my country will issue a series of science and technology policies every year. Understanding the policies, grasping the policies, and successfully applying for the policies, using the policies to help the development of enterprises, and obtaining the maximum benefits at a lower cost have begun to attract the attention of more and more enterprises. At present, the project application in the domestic market is mainly based on human screening, using a large amount of manpower to analyze the existing policies, and then serving enterprises based on the interpretation of the policies. The use of manpower to interpret policies is prone to problems such as untimely acquisition of information by manpower and inaccurate interpretation of policies.

[0003] However, in the prior art, it is impossible to intelligently manage policy matching analysis and real-time data push, and it is impossible to timely query and process each link. When an abnormality occurs in a certain link, it is difficult to make timely improvements. At the same time, there are security risks, which will also affect the subsequent accurate push of data. The efficiency of processing the received big data is poor. Therefore, the present invention needs to design a policy matching analysis system and a precise push method based on big data to solve the above-mentioned problems. Summary of the invention

[0004] The purpose of the present invention is to provide a policy matching analysis system and a precise push method based on big data in order to solve the above problems. It solves the problems mentioned in the background technology that it is impossible to intelligently manage policy matching analysis and real-time data push, and it is impossible to timely query and process each link. When an abnormality occurs in a certain link, it is difficult to make timely improvements. At the same time, there are security risks, which will also affect the subsequent precise push of data and the poor efficiency of processing the received big data.

[0005] In order to solve the above problems, the present invention provides a technical solution:

[0006] The policy matching analysis system based on big data includes a push receiving control terminal, a policy matching analysis module, a data aggregation module and a monitoring and evaluation module. The push receiving control terminal respectively shares and communicates data with the policy matching analysis module, the data aggregation module and the monitoring and evaluation module in real time through the Internet. The staff logs into the push receiving control terminal to enter the policy matching analysis system and view the actual operation data of the policy matching analysis module, the data aggregation module and the monitoring and evaluation module one by one.

[0007] The push receiving control terminal is used to build a user portrait based on the data analysis results, describe and model the characteristics, preferences, and interests of the target audience, achieve accurate push, and assist the receiving personnel to manually control the receiving device and perform normal data query. The push receiving control terminal is also used to design a visualization interface to intuitively display the data analysis results and policy matching recommendations, providing support and reference for decision makers;

[0008] The policy matching analysis module is used to analyze and mine various data sources using big data technology to identify the characteristics, behavior patterns and demand trends of the audience, adopt a multivariate maximum and minimum decision-making method, design and apply a personalized recommendation algorithm, and recommend policy information, services or resources to users;

[0009] The data aggregation module is used to collect big data from various sources, integrate the source, source reality, and source transmission path respectively, and perform multi-level processing on the collected data to ensure data quality and availability;

[0010] The monitoring and evaluation module is used to regularly evaluate and analyze the push effects, and adjust and optimize the push strategy based on the evaluation results. By establishing a real-time data monitoring method, it can track user behavior and feedback data and adjust the push strategy and content in a timely manner.

[0011] Preferably, the output end of the data aggregation module is communicatively connected to the input end of the policy matching analysis module, the output end of the policy matching analysis module is communicatively connected to the input end of the push receiving control end, and the push receiving control end, the policy matching analysis module, and the data aggregation module are respectively communicatively connected to the monitoring and evaluation module in two-way communication.

[0012] Preferably, the push receiving control end includes a receiving front end, a push terminal, a data protection unit and a visualization and decision support unit. The receiving front end and the push terminal are bidirectionally communicated, the output end of the visualization and decision support unit is communicated with the input end of the receiving front end, and the data protection unit maintains a bidirectional data communication connection with the receiving front end and the push terminal respectively.

[0013] Preferably, the receiving front end is used to construct a user portrait based on the data analysis results, describe and model the characteristics, preferences and interests of the target audience, and achieve accurate push; the receiving front end is also used to assist the receiving personnel to manually control the receiving device and perform normal data query;

[0014] The push terminal is used to comprehensively utilize multiple push channels and select appropriate push channels according to user characteristics and preferences to achieve diversified and all-round coverage of information;

[0015] The data protection unit is used to fully consider data privacy and security issues when designing and implementing policy matching analysis operations, ensuring the protection of user data and the security of the overall system;

[0016] The visualization and decision support unit is used to design a visualization interface to intuitively display data analysis results and policy matching recommendations, providing support and reference for decision makers.

[0017] Preferably, the policy matching analysis module includes a policy analysis and mining unit, a matching algorithm and recommendation unit and a simulation analysis unit, the output end of the policy analysis and mining unit is communicatively connected to the input end of the simulation analysis unit, the output end of the policy analysis and mining unit is communicatively connected to the input end of the matching algorithm and recommendation unit, and the output end of the matching algorithm and recommendation unit is communicatively connected to the input end of the push receiving control end;

[0018] The policy analysis and mining unit is used to analyze and mine various data sources using big data technology to identify audience characteristics, behavior patterns and demand trends. The mined data includes social media data, user behavior data, and transaction data to obtain in-depth insights into audience groups.

[0019] The matching algorithm and recommendation unit are used to design and apply personalized recommendation algorithms based on user portraits and historical behavior data, using multivariate maximum and minimum decision-making methods to recommend policy information, services or resources to users. Multivariate maximum and minimum decision-making is used to solve multi-objective decision-making problems;

[0020] The simulation analysis unit demand identification and analysis is used to analyze and mine existing policy documents and regulations to understand the measures and policy directions that the government has taken.

[0021] Preferably, the data aggregation module includes a data collection and integration unit, a data cleaning and processing unit and a data aggregation and screening unit, the output end of the data collection and integration unit is communicatively connected to the input end of the data cleaning and processing unit, the output end of the data cleaning and processing unit is communicatively connected to the input end of the data aggregation and screening unit, and the output end of the data aggregation and screening unit is communicatively connected to the input end of the policy matching analysis module;

[0022] The data collection and integration unit is used to collect big data from various sources, and integrate the source, source reality, and source transmission path respectively;

[0023] The data cleaning and processing unit is used to perform multi-level processing on the collected data to ensure data quality and availability;

[0024] The data aggregation and screening unit is used to aggregate the cleaned data sent by the data cleaning and processing unit, arrange them one by one by generating tables, and screen the tables containing the required keywords.

[0025] Preferably, the monitoring and evaluation module includes a solution evaluation unit, a mechanism improvement unit and a system real-time monitoring unit, the output end of the mechanism improvement unit is communicatively connected to the input end of the solution evaluation unit, the output end of the solution evaluation unit is communicatively connected to the input end of the push receiving control end, and the solution evaluation unit is communicatively connected to the input end of the system real-time monitoring unit;

[0026] The solution evaluation unit is used to regularly evaluate and analyze the push effect, and adjust and optimize the push strategy according to the evaluation results;

[0027] The mechanism improvement unit is used to establish a monitoring and evaluation mechanism for the push effect, and to simultaneously improve the loopholes and new regulations therein;

[0028] The system real-time monitoring unit is used to track user behavior and feedback data by establishing a real-time data monitoring method, timely adjust the push strategy and content, and continuously optimize the push effect by analyzing the user's real-time feedback and behavior, thereby improving the accuracy and effectiveness of policy communication.

[0029] The precise push method for policy matching analysis based on big data is used to implement the policy matching analysis system based on big data, including the following specific steps:

[0030] S1. Generate a policy matching analysis system, build a push receiving control terminal, and comprehensively utilize multiple push channels to cover a wider audience. At the same time, select appropriate push channels based on user characteristics and preferences to achieve diversified and comprehensive information coverage. By designing a visual interface, the data analysis results and policy matching recommendations are intuitively displayed to provide support and reference for decision makers.

[0031] S2. Collect big data from various sources, integrate the source, actual source, and source delivery path respectively, perform multi-level processing on the collected data to ensure data quality and availability, and summarize the cleaned data in a unified manner, arrange them one by one by generating tables, and screen the tables with the required keywords; use big data technology to analyze and mine various data sources to identify the characteristics, behavior patterns, and demand trends of the audience, and use multivariate maximum and minimum decision-making methods based on user portraits and historical behavior data to design and apply personalized recommendation algorithms;

[0032] S3. Establish a monitoring and evaluation mechanism for push effects, make synchronous improvements to loopholes and new regulations, regularly evaluate and analyze push effects, and adjust and optimize push strategies based on evaluation results. Establish real-time data monitoring to track user behavior and feedback data, adjust push strategies and content in a timely manner, and continuously optimize push effects by analyzing users' real-time feedback and behavior, thereby improving the accuracy and effectiveness of policy communication.

[0033] Preferably, the push channels in step S1 include mobile applications, social media, emails, and text messages.

[0034] Preferably, the big data in step S2 includes citizens' opinions, citizens' behavioral preferences, enterprises' needs, and enterprises' operating conditions, and the multi-level processing method in step S2 includes data cleaning, deduplication, standardization, and structured step processing.

[0035] The beneficial effects of the present invention are as follows: the present invention generates a policy matching analysis system by setting a push receiving control terminal, a policy matching analysis module, a data aggregation module and a monitoring and evaluation module; in actual use, it comprehensively utilizes a variety of push channels to cover a wider audience group, selects appropriate push channels according to user characteristics and preferences, realizes information diversification and all-round coverage, and designs a visual interface to intuitively display data analysis results and policy matching recommendations to provide support and reference for decision makers; by collecting big data from various sources, the source, the actual source, and the source transmission path are respectively integrated, and the collected data are processed at multiple levels to ensure data quality and availability; the cleaned data are uniformly summarized and arranged one by one by generating tables, and the tables with required keywords are screened; various data sources are analyzed and mined using big data technology to identify audience characteristics, Behavioral patterns and demand trends, based on user portraits and historical behavioral data, adopt a multivariate maximum and minimum decision-making method, design and apply personalized recommendation algorithms, establish a monitoring and evaluation mechanism for push effects, synchronously improve loopholes and new regulations, regularly evaluate and analyze push effects, and adjust and optimize push strategies based on evaluation results. By establishing real-time data monitoring methods, track user behavior and feedback data, adjust push strategies and content in a timely manner, and continuously optimize push effects by analyzing users' real-time feedback and behavior, improve the accuracy and effectiveness of policy communication, and facilitate real-time supervision of each link. Manage, visualize and store big data policy matching analysis data, precise push methods and corresponding analysis results, which will help to realize big data policy matching analysis and precise push management through Internet cloud management and control, and improve the intelligence level of big data policy matching analysis and precise push management. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] For ease of explanation, the present invention is described in detail with reference to the following specific implementations and the accompanying drawings.

[0037] Figure 1 It is the overall system topology diagram of the policy matching analysis system and precise push method based on big data of the present invention;

[0038] Figure 2 It is an overall flow chart of the policy matching analysis system and precise push method based on big data of the present invention. DETAILED DESCRIPTION

[0039] like Figure 1 and Figure 2 As shown, this specific implementation adopts the following technical solutions:

[0040] Example 1

[0041] The policy matching analysis system based on big data includes a push receiving control terminal, a policy matching analysis module, a data aggregation module and a monitoring and evaluation module. The push receiving control terminal respectively maintains real-time data sharing and intercommunication with the policy matching analysis module, the data aggregation module and the monitoring and evaluation module through the Internet. The staff enters the policy matching analysis system by logging into the push receiving control terminal and checks the actual operation data of the policy matching analysis module, the data aggregation module and the monitoring and evaluation module one by one; the output end of the data aggregation module is connected to the input end of the policy matching analysis module in communication, the output end of the policy matching analysis module is connected to the input end of the push receiving control terminal in communication, and the push receiving control terminal, the policy matching analysis module and the data aggregation module are respectively connected to the monitoring and evaluation module in two-way communication;

[0042] The push receiving control terminal is used to build a user portrait based on the data analysis results, describe and model the characteristics, preferences, and interests of the target audience, achieve accurate push, and assist the receiving personnel to manually control the receiving device and perform normal data query. The push receiving control terminal is also used to design a visualization interface to intuitively display the data analysis results and policy matching recommendations, providing support and reference for decision makers;

[0043] The policy matching analysis module is used to analyze and mine various data sources using big data technology to identify the characteristics, behavior patterns and demand trends of the audience, adopt a multivariate maximum and minimum decision-making method, design and apply a personalized recommendation algorithm, and recommend policy information, services or resources to users;

[0044] The data aggregation module is used to collect big data from various sources, integrate the source, source reality, and source transmission path respectively, and perform multi-level processing on the collected data to ensure data quality and availability;

[0045] The monitoring and evaluation module is used to regularly evaluate and analyze the push effects, and adjust and optimize the push strategy based on the evaluation results. By establishing a real-time data monitoring method, it can track user behavior and feedback data and adjust the push strategy and content in a timely manner.

[0046] Furthermore, the push receiving control end includes a receiving front end, a push terminal, a data protection unit and a visualization and decision support unit. The receiving front end and the push terminal are bidirectionally connected, the output end of the visualization and decision support unit is communicatively connected to the input end of the receiving front end, and the data protection unit maintains bidirectional data communication connection with the receiving front end and the push terminal respectively. The receiving front end is used to build a user portrait based on the data analysis results, describe and model the characteristics, preferences and interests of the target audience, and achieve accurate push; the receiving front end is also used to assist the receiving personnel to manually control the receiving device and perform normal data query; the push terminal is used to comprehensively utilize multiple push channels, select appropriate push channels according to user characteristics and preferences, and achieve diversified and all-round coverage of information; the data protection unit is used to fully consider data privacy and security issues when designing and implementing policy matching analysis operations, to ensure the protection of user data and the security of the overall system; the visualization and decision support unit is used to design a visualization interface to intuitively display data analysis results and policy matching recommendations, and provide support and reference for decision makers.

[0047] Furthermore, the policy matching analysis module includes a policy analysis and mining unit, a matching algorithm and recommendation unit, and a simulation analysis unit. The output end of the policy analysis and mining unit is communicatively connected to the input end of the simulation analysis unit, the output end of the policy analysis and mining unit is communicatively connected to the input end of the matching algorithm and recommendation unit, and the output end of the matching algorithm and recommendation unit is communicatively connected to the input end of the push receiving control end; the policy analysis and mining unit is used to use big data technology to analyze and mine various data sources to identify the characteristics, behavior patterns and demand trends of the audience. The mined data includes social media data, user behavior data, and transaction data to obtain in-depth insights about the audience group; the matching algorithm and recommendation unit is used based on user portraits and historical behavior data; the simulation analysis unit demand identification and analysis is used to analyze and mine existing policy documents and regulations to understand the measures and policy directions that the government has taken.

[0048] Furthermore, the data aggregation module includes a data collection and integration unit, a data cleaning and processing unit, and a data aggregation and screening unit. The output end of the data collection and integration unit is communicatively connected to the input end of the data cleaning and processing unit, the output end of the data cleaning and processing unit is communicatively connected to the input end of the data aggregation and screening unit, and the output end of the data aggregation and screening unit is communicatively connected to the input end of the policy matching analysis module; the data collection and integration unit is used to collect big data from various sources, and integrate the source, the actual source, and the source transmission path respectively; the data cleaning and processing unit is used to perform multi-level processing on the collected data to ensure data quality and availability; the data aggregation and screening unit is used to uniformly summarize the data sent by the data cleaning and processing unit to complete the cleaning, arrange them one by one by generating tables, and screen the tables with required keywords.

[0049] Furthermore, the monitoring and evaluation module includes a solution evaluation unit, a mechanism improvement unit and a system real-time monitoring unit. The output end of the mechanism improvement unit is communicatively connected to the input end of the solution evaluation unit, the output end of the solution evaluation unit is communicatively connected to the input end of the push receiving control end, and the solution evaluation unit is communicatively connected to the input end of the system real-time monitoring unit; the solution evaluation unit is used to regularly evaluate and analyze the push effect, and adjust and optimize the push strategy according to the evaluation result; the mechanism improvement unit is used to establish a monitoring and evaluation mechanism for the push effect, and synchronously improve the loopholes and new regulations therein; the system real-time monitoring unit is used to track user behavior and feedback data by establishing real-time data monitoring, timely adjust the push strategy and content, and continuously optimize the push effect by analyzing the real-time feedback and behavior of users, thereby improving the accuracy and effectiveness of policy communication.

[0050] Example 2

[0051] When the policy matching analysis system is connected to the precision push management:

[0052] The precise push method for policy matching analysis based on big data is used to implement the policy matching analysis system based on big data, including the following specific steps:

[0053] S1. Generate a policy matching analysis system, build a push receiving control terminal, and comprehensively utilize multiple push channels to cover a wider audience. At the same time, select appropriate push channels based on user characteristics and preferences to achieve diversified and comprehensive information coverage. By designing a visual interface, the data analysis results and policy matching recommendations are intuitively displayed to provide support and reference for decision makers.

[0054] S2. Collect big data from various sources, integrate the source, actual source, and source delivery path respectively, perform multi-level processing on the collected data to ensure data quality and availability, and summarize the cleaned data in a unified manner, arrange them one by one by generating tables, and screen the tables with the required keywords; use big data technology to analyze and mine various data sources to identify the characteristics, behavior patterns, and demand trends of the audience, and use multivariate maximum and minimum decision-making methods based on user portraits and historical behavior data to design and apply personalized recommendation algorithms;

[0055] Adopting the multivariate maximum and minimum decision-making method, design and apply personalized recommendation algorithms to recommend policy information, services or resources to users. Multivariate maximum and minimum decision-making is used to solve multi-objective decision-making problems. First, it is necessary to determine the weight or importance of each goal, and to determine the maximum or minimum value of each goal, find a set of decision-making schemes, and then compare the performance of each scheme under different goals to find a scheme that can maximize or minimize all goals, so that each goal can reach its maximum or minimum value without compromising the achievement of other goals;

[0056] Assume there are (m) decision objectives (f1(y), f2(y), ..., fm(y)), where (y) is a decision variable vector, representing a decision solution. For each objective function (fi(y)), there is an optimization problem, which can be a maximization problem or a minimization problem:

[0057] The case of maximizing the objective function: [max{f1(y),f2(y),...,fm(y)}];

[0058] The case of minimizing the objective function: [min{f1(y),f2(y),...,fm(y)}];

[0059] The core of multivariate max-min decision making is to find a solution (y^*) such that for any decision variable (y), the following conditions are met:

[0060] For all (i=1,2,...,m), (fi(y^*)) is the maximum value (for maximization problem) or the minimum value (for minimization problem) among all feasible solutions (y);

[0061] When the above situation is reached, (fj(y^*)) can no longer be optimized, where (j) is any objective function index except (i). The best matching policy is obtained through the above method, so as to carry out subsequent push processing;

[0062] Push channels include mobile apps, social media, email, and SMS;

[0063] Big data includes citizens' opinions, citizens' behavioral preferences, enterprises' needs, and enterprises' operating conditions. The multi-level processing method in step S2 includes data cleaning, deduplication, standardization, and structured processing;

[0064] S3. Establish a monitoring and evaluation mechanism for push effects, make synchronous improvements to loopholes and new regulations, regularly evaluate and analyze push effects, and adjust and optimize push strategies based on evaluation results. Establish real-time data monitoring to track user behavior and feedback data, adjust push strategies and content in a timely manner, and continuously optimize push effects by analyzing users' real-time feedback and behavior, thereby improving the accuracy and effectiveness of policy communication.

[0065] Specifically: In actual applications, there are multiple push receiving control terminals, which are respectively used in conjunction with the policy matching analysis module, the data aggregation module and the monitoring and evaluation module. The multiple push receiving control terminals are located in different geographical locations. The present invention generates a policy matching analysis system by setting a push receiving control terminal, a policy matching analysis module, a data aggregation module and a monitoring and evaluation module. When actually used, a variety of push channels are comprehensively utilized to cover a wider audience group. Appropriate push channels are selected according to user characteristics and preferences to achieve diversified and all-round information coverage. A visual interface is designed to intuitively display data analysis results and policy matching recommendations to provide support and reference for decision makers. Big data from various sources are collected, the source, the actual source, and the source transmission path are respectively integrated, and the collected data are processed at multiple levels to ensure data quality and availability. The cleaned data are uniformly summarized, arranged one by one by generating a table, and the data with the required keywords are screened. table; use big data technology to analyze and mine various data sources to identify the characteristics, behavior patterns and demand trends of the audience; based on user portraits and historical behavior data, adopt a multivariate maximum and minimum decision-making method to design and apply personalized recommendation algorithms; establish a monitoring and evaluation mechanism for push effects, synchronously improve loopholes and new regulations, regularly evaluate and analyze push effects, and adjust and optimize push strategies based on evaluation results; establish real-time data monitoring to track user behavior and feedback data, and adjust push strategies and content in a timely manner; continuously optimize push effects by analyzing users' real-time feedback and behavior, improve the accuracy and effectiveness of policy communication, and facilitate real-time supervision of each link; manage, visualize and store big data policy matching analysis data, precise push methods and corresponding analysis results, which will help to realize big data policy matching analysis and precise push management through Internet cloud management and control, and improve the intelligence level of big data policy matching analysis and precise push management.

[0066] Those of ordinary skill in the art will appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, equipment and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0068] In the several embodiments provided in the present application, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or equipment, which can be electrical, mechanical or other forms.

[0069] The modules for push receiving control, policy matching analysis, data aggregation and monitoring evaluation may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0071] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program instructions, such as a USB flash drive, a mobile hard disk, a read-only storage server, a random access storage server, a magnetic disk or an optical disk.

[0072] In addition, it should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0073] It should be noted that the above examples are only specific embodiments of the present invention, and the present invention is obviously not limited to the above examples, and there are many similar variations. All variations directly derived or associated from the contents disclosed by the technicians in this field should fall within the protection scope of the present invention.

[0074] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Policy matching analysis system based on big data, characterized by: It includes a push receiving control terminal, a policy matching analysis module, a data aggregation module and a monitoring and evaluation module. The push receiving control terminal respectively shares and communicates data with the policy matching analysis module, the data aggregation module and the monitoring and evaluation module in real time through the Internet. The staff logs into the push receiving control terminal to enter the policy matching analysis system and view the actual operation data of the policy matching analysis module, the data aggregation module and the monitoring and evaluation module one by one. The push receiving control terminal is used to build a user portrait based on the data analysis results, describe and model the characteristics, preferences, and interests of the target audience, achieve accurate push, and assist the receiving personnel to manually control the receiving device and perform normal data query. The push receiving control terminal is also used to design a visualization interface to intuitively display the data analysis results and policy matching recommendations, providing support and reference for decision makers; The policy matching analysis module is used to analyze and mine various data sources using big data technology to identify the characteristics, behavior patterns and demand trends of the audience, adopt a multivariate maximum and minimum decision-making method, design and apply a personalized recommendation algorithm, and recommend policy information, services or resources to users; The data aggregation module is used to collect big data from various sources, integrate the source, source reality, and source transmission path respectively, and perform multi-level processing on the collected data to ensure data quality and availability; The monitoring and evaluation module is used to regularly evaluate and analyze the push effects, and adjust and optimize the push strategy based on the evaluation results. By establishing a real-time data monitoring method, it can track user behavior and feedback data and adjust the push strategy and content in a timely manner.

2. The policy matching analysis system based on big data according to claim 1 is characterized by: The output end of the data aggregation module is communicatively connected to the input end of the policy matching analysis module, the output end of the policy matching analysis module is communicatively connected to the input end of the push receiving control end, and the push receiving control end, the policy matching analysis module, and the data aggregation module are respectively bidirectionally communicatively connected to the monitoring and evaluation module.

3. The policy matching analysis system based on big data according to claim 1 is characterized by: The push receiving control end includes a receiving front end, a push terminal, a data protection unit and a visualization and decision support unit. The receiving front end and the push terminal are bidirectionally communicated, the output end of the visualization and decision support unit is communicated with the input end of the receiving front end, and the data protection unit maintains a bidirectional data communication connection with the receiving front end and the push terminal respectively.

4. The policy matching analysis system based on big data according to claim 3 is characterized by: The receiving front end is used to build a user portrait based on the data analysis results, describe and model the characteristics, preferences, and interests of the target audience, and achieve accurate push; the receiving front end is also used to assist the receiving personnel to manually control the receiving device and perform normal data query; The push terminal is used to comprehensively utilize multiple push channels and select appropriate push channels according to user characteristics and preferences to achieve diversified and all-round coverage of information; The data protection unit is used to fully consider data privacy and security issues when designing and implementing policy matching analysis operations, ensuring the protection of user data and the security of the overall system; The visualization and decision support unit is used to design a visualization interface to intuitively display data analysis results and policy matching recommendations, providing support and reference for decision makers.

5. The policy matching analysis system based on big data according to claim 1 is characterized by: The policy matching analysis module includes a policy analysis and mining unit, a matching algorithm and recommendation unit and a simulation analysis unit, wherein the output end of the policy analysis and mining unit is communicatively connected to the input end of the simulation analysis unit, the output end of the policy analysis and mining unit is communicatively connected to the input end of the matching algorithm and recommendation unit, and the output end of the matching algorithm and recommendation unit is communicatively connected to the input end of the push receiving control end; The policy analysis and mining unit is used to analyze and mine various data sources using big data technology to identify audience characteristics, behavior patterns and demand trends. The mined data includes social media data, user behavior data, and transaction data to obtain in-depth insights into audience groups. The matching algorithm and recommendation unit are used to design and apply personalized recommendation algorithms based on user portraits and historical behavior data, using multivariate maximum and minimum decision-making methods to recommend policy information, services or resources to users. Multivariate maximum and minimum decision-making is used to solve multi-objective decision-making problems; The simulation analysis unit demand identification and analysis is used to analyze and mine existing policy documents and regulations to understand the measures and policy directions that the government has taken.

6. The policy matching analysis system based on big data according to claim 5 is characterized by: The data aggregation module includes a data collection and integration unit, a data cleaning and processing unit, and a data aggregation and screening unit. The output end of the data collection and integration unit is communicatively connected to the input end of the data cleaning and processing unit, the output end of the data cleaning and processing unit is communicatively connected to the input end of the data aggregation and screening unit, and the output end of the data aggregation and screening unit is communicatively connected to the input end of the policy matching analysis module. The data collection and integration unit is used to collect big data from various sources, and integrate the source, source reality, and source transmission path respectively; The data cleaning and processing unit is used to perform multi-level processing on the collected data to ensure data quality and availability; The data aggregation and screening unit is used to aggregate the cleaned data sent by the data cleaning and processing unit, arrange them one by one by generating tables, and screen the tables containing the required keywords.

7. The policy matching analysis system based on big data according to claim 1 is characterized by: The monitoring and evaluation module includes a solution evaluation unit, a mechanism improvement unit and a system real-time monitoring unit, wherein the output end of the mechanism improvement unit is communicatively connected to the input end of the solution evaluation unit, the output end of the solution evaluation unit is communicatively connected to the input end of the push receiving control end, and the solution evaluation unit is communicatively connected to the input end of the system real-time monitoring unit; The solution evaluation unit is used to regularly evaluate and analyze the push effect, and adjust and optimize the push strategy according to the evaluation results; The mechanism improvement unit is used to establish a monitoring and evaluation mechanism for the push effect, and to simultaneously improve the loopholes and new regulations therein; The system real-time monitoring unit is used to track user behavior and feedback data by establishing a real-time data monitoring method, timely adjust the push strategy and content, and continuously optimize the push effect by analyzing the user's real-time feedback and behavior, thereby improving the accuracy and effectiveness of policy communication.

8. A precise push method for policy matching analysis based on big data, used to implement the policy matching analysis system based on big data as claimed in any one of claims 1 to 7, characterized in that: The specific steps include: S1. Generate a policy matching analysis system, build a push receiving control terminal, and comprehensively utilize multiple push channels to cover a wider audience. At the same time, select appropriate push channels based on user characteristics and preferences to achieve diversified and comprehensive information coverage. By designing a visual interface, the data analysis results and policy matching recommendations are intuitively displayed to provide support and reference for decision makers. S2. Collect big data from various sources, integrate the source, actual source, and source delivery path respectively, perform multi-level processing on the collected data to ensure data quality and availability, and summarize the cleaned data in a unified manner, arrange them one by one by generating tables, and screen the tables with the required keywords; use big data technology to analyze and mine various data sources to identify the characteristics, behavior patterns, and demand trends of the audience, and use multivariate maximum and minimum decision-making methods based on user portraits and historical behavior data to design and apply personalized recommendation algorithms; S3. Establish a monitoring and evaluation mechanism for push effects, make synchronous improvements to loopholes and new regulations, regularly evaluate and analyze push effects, and adjust and optimize push strategies based on evaluation results. Establish real-time data monitoring to track user behavior and feedback data, adjust push strategies and content in a timely manner, and continuously optimize push effects by analyzing users' real-time feedback and behavior, thereby improving the accuracy and effectiveness of policy communication.

9. The accurate push method for policy matching analysis based on big data according to claim 8 is characterized by: The push channels in step S1 include mobile applications, social media, emails, and text messages.

10. The accurate push method for policy matching analysis based on big data according to claim 8 is characterized by: The big data in step S2 includes citizens' opinions, citizens' behavioral preferences, enterprises' needs, and enterprises' operating conditions. The multi-level processing method in step S2 includes data cleaning, deduplication, standardization, and structured processing.

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