Rural development level evaluation digital display management and control platform

Abstract

The invention belongs to the technical field of rural big data, and particularly provides a rural development level evaluation digital display management and control platform comprising a management and control module which receives a data acquisition trigger instruction input by a user and generates a data acquisition instruction; the data acquisition module synchronizes original data from an original data source to an original library of the platform based on the data acquisition instruction; development level evaluation index data are collected based on data in the original library, and the collected index data are sent to the data processing module; the data processing module performs format conversion on the index data according to a set data standard and then stores the index data into a standard library; generating cascaded three-level indexes according to the attributes of the indexes, and storing the three-level indexes in a platform theme library; calculating a comprehensive index value of each village based on the display processing instruction; and the display module is used for performing rural GIS point location display and three-level evaluation index display based on the comprehensive index value of each village. And the flexibility and interactivity of data display are improved.

Description

Technical Field

[0001] This invention relates to the field of rural big data technology, specifically to a digital display and management platform for evaluating the level of rural development. Background Technology

[0002] With the continuous innovation of information technology, the methods for evaluating rural development levels are gradually transforming, moving from the traditional model that relies on paper reports and simple statistical tables to a new era of digitalization and intelligence. Although the current digital platforms for evaluating rural development levels have significantly improved efficiency and accuracy in data processing, they still reveal significant shortcomings in data presentation.

[0003] Specifically, while existing platforms possess certain capabilities in data extraction, processing, and basic display, and can respond to users' basic needs for data acquisition and analysis, they exhibit significant shortcomings in the flexibility and interactivity of data presentation. Most platforms lack mechanisms for customized calculations and processing based on user needs when displaying data, leading to unnecessary waste of computing resources. Furthermore, when presenting rural development level evaluation data, these platforms generally rely on traditional tables or charts. While these formats are visually acceptable, they lack interactivity and fail to effectively guide users to quickly identify key information from massive amounts of data. The deficiencies in data presentation by existing platforms also affect the timeliness of rural development level evaluations. Summary of the Invention

[0004] To address the problems existing in the data display process of existing platforms, this invention provides a digital display and management platform for evaluating rural development levels.

[0005] The present invention provides a digital display and management platform for evaluating rural development levels, comprising a management module and a data acquisition module, a data processing module, and a display module connected to the management module; the data acquisition module is connected to the data processing module; the display module is connected to the data processing module. The control module receives a data acquisition trigger command input by the user, generates a data acquisition command, and sends it to the data acquisition module. The control module has a built-in display processing component. In response to the display trigger command input by the user, the control module pops up the display processing component, which includes a display area. At the same time, it generates a display processing command and sends it to the data processing module. The data acquisition module synchronizes the raw data from the original data source to the platform's raw database based on the received data acquisition instructions; it then collects development level evaluation index data based on the data in the raw database and sends the collected index data to the data processing module. The data processing module converts the acquired indicator data into a standard library according to the set data standards; it also generates cascaded third-level indicators based on the attributes of the indicators and stores them in the platform's theme library; the data processing module calculates the comprehensive index value of each village based on the received display processing instructions. The display module showcases rural GIS locations and three-level evaluation indicators based on the comprehensive index values ​​of each village.

[0006] As a preferred embodiment of the technical solution of the present invention, the control module has a built-in data source extraction component, which includes a data source extraction interface; in response to the data collection trigger command input by the user, the data source extraction interface is displayed, and the user's selection operation on the data source extraction interface is converted into a data collection command and sent to the data collection module.

[0007] As a preferred embodiment of the technical solution of the present invention, the data acquisition module has a built-in large language model. The data acquisition module uses the large language model to collect development level evaluation index data based on the data in the original database and sends the collected index data to the data processing module.

[0008] As a preferred embodiment of the technical solution of the present invention, the data processing module has a built-in comprehensive index calculation model. The data processing module calls the comprehensive index calculation model based on the received display processing instructions and calculates the comprehensive index value of each village based on the data in the standard library. The control module receives the comprehensive index calculation results of each village through the display area. The display module displays rural GIS points and three-level evaluation indicators based on information from the control component's display area.

[0009] By embedding the comprehensive index calculation model into the data processing module and combining it with the functions of the control and display modules for result presentation, the platform not only enhances the intelligence level of data processing and analysis and improves the accuracy and reliability of comprehensive index calculation, but also optimizes the data processing workflow, increases work efficiency, and enables data visualization and intuitive display. This supports decision-making and optimization, and promotes the standardization and normalization of rural development level evaluation. These beneficial effects have jointly driven the widespread application and sustainable development of the platform, providing strong digital support for rural development.

[0010] As a preferred embodiment of the technical solution of the present invention, the control platform is equipped with a data acquisition trigger button and a display trigger button connected to the control module; The control module receives data collection trigger commands from users via the data collection trigger button; The control module receives the display trigger command input by the user through the display trigger button.

[0011] As a preferred embodiment of the technical solution of the present invention, the platform further includes a parameter setting trigger button and a parameter update module connected to the control module; The control module has a built-in parameter setting component, which includes a parameter setting interface; The control module receives the parameter setting trigger command input by the user through the setting trigger button, and in response to the parameter setting trigger command, displays the parameter setting interface, and converts the user's operation on the parameter setting interface into a parameter update command and sends it to the parameter update module; The parameter update module updates the data resource catalog, data standards, and database structure; specifically, it includes: dividing the original data resources into multiple data resource catalogs according to their business attributes; defining the data standards for each original data; and storing all data resources under multiple data resource catalogs in a table and naming them according to the set format.

[0012] As a preferred embodiment of the technical solution of the present invention, the data processing module converts the data according to the established data standard and stores it in the standard library; specifically, it includes: determining whether the acquired raw data meets the input data type requirements of the comprehensive index calculation model of each village, and converting the data of data that does not meet the requirements; determining whether the acquired raw data meets the input data floating-point number requirements of the comprehensive index calculation model of each village, and performing data calculation on the data that does not meet the requirements.

[0013] As a preferred embodiment of the technical solution of the present invention, the data processing module is further provided with a model generation module; The model generation module standardizes each indicator; calculates the... i The village accounted for the first j The weight of each indicator; based on the first i The village accounted for the first j Calculation of the weight of each indicator j The entropy value of the index; calculate the first... j The coefficient of difference for each indicator; the calculation of the first indicator based on the coefficient of difference. j The weight of each indicator is assigned to all indicators; the deviation maximization method is used to measure the weight by the proportion of the deviation of a single indicator to the total deviation of the indicators, and the deviation proportion is calculated; the deviation proportion is normalized by applying a Lagrange function and taking the partial derivative; the comprehensive index of each village is calculated by combining the various indicators, and the comprehensive index calculation model of each village is output.

[0014] As a preferred embodiment of the technical solution of the present invention, the standardization process of the model generation module for each indicator includes: non-negating the positive and negative indicators, as follows: Negative indicators:

[0015] Positive indicators:

[0016] In the formula, For the processed first i The first village j The value of the indicator, among which, i =1,2,3,…,n; j=1,2,3,…,m.

[0017] As a preferred embodiment of the technical solution of the present invention, the model generation module adopts the deviation maximization method to measure the weight by the proportion of the deviation of a single indicator to the total deviation of the indicators, and calculates the deviation proportion. The formula is as follows:

[0018]

[0019] In the formula, For the first j The weight of each indicator among all indicators. For the first i The first village j The value of the indicator; The model generation module normalizes the bias proportions by applying the Lagrange function and calculating the partial derivatives, as shown in the following formula:

[0020] The comprehensive index calculation model for each village: .

[0021] As can be seen from the above technical solutions, this invention has the following advantages: Through the integrated data acquisition module, the platform can quickly respond to the data acquisition instructions sent by the control module, synchronize raw data from the original data source to the original database, and automatically collect development level evaluation indicator data. This process not only improves the efficiency and accuracy of data acquisition but also ensures the timeliness and integrity of the data, laying a solid foundation for subsequent data processing and analysis. The data processing module converts the collected indicator data according to the set data standards and stores it in the standard database, effectively avoiding processing difficulties caused by inconsistent data formats. Simultaneously, by generating cascaded three-level indicators and storing them in the platform's thematic database, the platform can provide users with more detailed and comprehensive rural development level evaluation data, helping users to gain a deeper understanding of all aspects of rural development.

[0022] The platform has a built-in display processing component that can respond to user-inputted display trigger commands, pop up display areas, and perform personalized display processing according to user instructions. This feature allows users to selectively view and analyze data according to their needs, greatly improving the flexibility and interactivity of data display. Simultaneously, by displaying rural GIS points and three-level evaluation indicators based on the comprehensive index values ​​of each village, the platform provides users with intuitive evaluation results, helping them quickly grasp the overall status and key indicators of rural development.

[0023] This platform allows users to more easily access and analyze data on rural development levels, enabling them to more accurately grasp the current state and future trends of rural development. This not only helps improve the efficiency and accuracy of decision-making but also provides strong data support for the formulation and implementation of rural development policies, promoting the sustainable and healthy development of the rural economy. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic block diagram of a platform according to an embodiment of the present invention. Detailed Implementation

[0026] This invention establishes a digital display and management platform for evaluating rural development levels, realizing automated data collection, governance, storage, indicator calculation, and visualization. To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0027] like Figure 1 As shown in the figure, this embodiment provides a digital display and management platform for evaluating the level of rural development, including a management module and a data acquisition module, a data processing module, and a display module connected to the management module; the data acquisition module is connected to the data processing module; the display module is connected to the data processing module. The control module receives a data acquisition trigger command input by the user, generates a data acquisition command, and sends it to the data acquisition module. The control module has a built-in display processing component. In response to the display trigger command input by the user, the control module pops up the display processing component, which includes a display area. At the same time, it generates a display processing command and sends it to the data processing module. The data acquisition module synchronizes the raw data from the original data source to the platform's raw database based on the received data acquisition instructions; it then collects development level evaluation index data based on the data in the raw database and sends the collected index data to the data processing module. The data processing module converts the acquired indicator data into a standard library according to the set data standards; it also generates cascaded third-level indicators based on the attributes of the indicators and stores them in the platform's theme library; the data processing module calculates the comprehensive index value of each village based on the received display processing instructions. The display module showcases rural GIS locations and three-level evaluation indicators based on the comprehensive index values ​​of each village.

[0028] The data collection module receives user-input data collection trigger commands through the control module, enabling rapid response and synchronization of raw data from the original data source to the platform's raw database, significantly improving data collection efficiency and accuracy. The data processing module converts the acquired indicator data according to set data standards and stores it in the standard database. It also generates cascaded third-level indicators based on indicator attributes and stores them in the platform's theme database. The indicator system framework is defined, dividing rural development indicators into five primary indicators: industrial development, talent development, cultural development, ecological development, and organizational development. Using LLM (Local Level Management), secondary indicators are obtained for each primary indicator based on its attributes, and tertiary indicators are obtained for each secondary indicator. By constructing a rural development level evaluation indicator system including three levels of evaluation indicators, it covers multiple aspects of rural development, including economic, social, and environmental aspects, comprehensively and accurately reflecting the actual situation of rural development. This process ensures data standardization and consistency, providing a reliable foundation for subsequent comprehensive index calculations. The display module displays rural GIS points and third-level evaluation indicators based on the comprehensive index values ​​of each village, making the evaluation results more intuitive and easier to understand. This helps relevant agencies better grasp the overall situation and changing trends of rural development, providing strong support for formulating scientific and reasonable rural development plans.

[0029] Through this platform, relevant institutions can more conveniently obtain various data indicators and comprehensive evaluation results on rural development, thereby making decisions more quickly. This not only improves the scientific nature and accuracy of decision-making but also shortens the decision-making cycle, contributing to the modernization of rural governance systems and capabilities.

[0030] In some embodiments, the control module has a built-in data source extraction component, which includes a data source extraction interface; in response to a data collection trigger command input by the user, the data source extraction interface is displayed, and the user's selection operation on the data source extraction interface is converted into a data collection command and sent to the data collection module.

[0031] In the data source extraction interface, the data source, update frequency, and collection method of each evaluation indicator are determined to obtain the raw data. In this embodiment, all indicator data comes from the agricultural and rural big data platform base of the Big Data Development Center. All indicator data is updated once a year. The collection method of each evaluation indicator is determined, including but not limited to system entry, data purchase, database integration, file acquisition, and interface docking. In this embodiment, the database integration method is used for collection.

[0032] The data source extraction component significantly enhances the user experience by providing an intuitive and user-friendly interface. Users can easily select and configure data sources without requiring complex technical backgrounds or specialized knowledge. The interface is highly responsive, instantly displaying user selections and converting them into data acquisition commands sent to the data acquisition module, simplifying the data acquisition process and improving operational convenience. The component supports a variety of data sources, including but not limited to databases, files, and API interfaces, meeting the data acquisition needs of different scenarios. Users can freely combine and configure data sources according to their actual needs, increasing the flexibility and diversity of data acquisition and providing a rich data foundation for subsequent data processing and analysis. The data source extraction interface clearly displays detailed information about the data source, such as data type, format, and update frequency, helping users make the correct selections. User selections are instantly converted into data acquisition commands, avoiding errors and delays that may occur with manual input, thus improving the accuracy and efficiency of data acquisition.

[0033] The data source extraction component provides high-quality data input to the data processing module, ensuring the accuracy and reliability of data processing and analysis. Combined with other platform functions, such as format conversion, standard library storage, and comprehensive index calculation, the data source extraction component provides strong support for the entire data processing and analysis workflow, improving the platform's efficiency.

[0034] In some embodiments, the data acquisition module has a built-in large language model. The data acquisition module uses the large language model to collect development level evaluation index data based on the data in the original database and sends the collected index data to the data processing module.

[0035] Large language models possess powerful natural language understanding and generation capabilities, enabling them to understand and parse text data from raw databases, automatically identifying and extracting key indicators related to development level assessment. This feature significantly improves the intelligence level of data collection, reduces manual intervention, and automates data collection. Through deep learning and training, large language models can accurately understand the meaning and context of text data, thereby more accurately extracting and identifying development level assessment indicators. Compared to traditional manual collection methods, large language models can more comprehensively cover the information in text data, reducing omissions and errors, and improving the accuracy of data collection. The specific training process can utilize existing methods.

[0036] Large language models can rapidly process massive amounts of text data, enabling high-speed data acquisition. Compared to manual data collection, large language models can process more data in the same amount of time, significantly improving data acquisition efficiency. Simultaneously, by reducing manual operations, the data acquisition cycle is shortened, improving overall work efficiency. The data acquisition module uses large language models to ensure that the collected indicator data is more accurate, comprehensive, and structured. This high-quality data input provides a solid foundation for the data processing module, facilitating more efficient and accurate subsequent data processing and analysis. By applying large language models to the data acquisition module, the platform can collect richer and more accurate data, providing more comprehensive information support for the evaluation of rural development levels. Combined with other platform functions, such as data processing and display modules, intelligent evaluation and analysis of rural development levels can be achieved, providing more precise data support for the formulation and implementation of rural development policies.

[0037] By embedding a large language model into the data acquisition module for collecting development level evaluation index data, the platform not only improves the intelligence and automation of data acquisition, enhancing its accuracy and comprehensiveness, but also increases its efficiency and speed. It also optimizes the input quality of the data processing module, promoting the intelligent development of rural development level evaluation. These beneficial effects have collectively facilitated the platform's widespread application and sustainable development.

[0038] In some embodiments, the data processing module has a built-in comprehensive index calculation model. The data processing module calls the comprehensive index calculation model based on the received display processing instructions and calculates the comprehensive index value of each village based on the data in the standard library. The control module receives the comprehensive index calculation results of each village through the display area. The display module displays rural GIS points and three-level evaluation indicators based on information from the control component's display area.

[0039] Through its built-in comprehensive index calculation model, the data processing module can automatically and accurately calculate the comprehensive index value for each village without manual intervention, significantly improving the intelligence level of data processing and analysis. The comprehensive index calculation model is based on data from a standard library, ensuring data accuracy and consistency. Furthermore, the model has been professionally designed and validated to accurately reflect the actual situation of rural development, improving the accuracy and reliability of the comprehensive index calculation. The data processing module can instantly respond to display processing commands, quickly invoke the comprehensive index calculation model for calculation, and transmit the results to the management and control module. This process optimizes the data processing workflow, reduces delays and errors in intermediate steps, and improves overall work efficiency.

[0040] The management module receives and displays the comprehensive index calculation results of each village through the display area, enabling users to intuitively understand the overall status and differences in rural development. Simultaneously, the display module uses information from the management component's display area to show rural GIS points and three-level evaluation indicators, further enriching the data visualization content and improving data readability and comprehensibility.

[0041] Through comprehensive index calculation models and visualizations, users can more accurately grasp the actual situation and trends of rural development, providing strong data support for formulating and optimizing rural development policies. This helps promote the sustained and healthy development of the rural economy and improve the living standards of rural residents.

[0042] The comprehensive index calculation model provides a unified standard and methodology for evaluating rural development levels, which helps promote the standardization and normalization of rural development level evaluation. This facilitates comparison and exchange between different regions, jointly promoting the progress of rural development.

[0043] In some embodiments, the management platform is equipped with a data acquisition trigger button and a display trigger button connected to the management module; The control module receives data collection trigger commands from users via the data collection trigger button; The control module receives the display trigger command input by the user through the display trigger button.

[0044] The platform also includes a parameter setting trigger button and a parameter update module that are connected to the management and control module; The control module has a built-in parameter setting component, which includes a parameter setting interface; The control module receives the parameter setting trigger command input by the user through the setting trigger button, and in response to the parameter setting trigger command, displays the parameter setting interface, and converts the user's operation on the parameter setting interface into a parameter update command and sends it to the parameter update module; The parameter update module updates the data resource directory, data standards, and database structure. Specifically, this includes: dividing the original data resources into multiple data resource directories according to their business attributes; defining the data standards for each original data source; and storing all data resources under the multiple data resource directories in a table and naming them according to a set format. In this embodiment of the invention, the naming follows the format "resource directory first letter lowercase_data name first letter lowercase".

[0045] The parameter setting component allows users to flexibly configure data resource catalogs, data standards, and database structures according to actual needs, without relying on technical personnel or performing secondary system development. This feature significantly enhances the system's flexibility and configurability, enabling it to better adapt to changing scenarios and requirements. The parameter setting component allows users to define data standards for each raw data source, ensuring consistent format and quality throughout the acquisition, processing, and analysis processes. Simultaneously, by storing all data resources from multiple data resource catalogs in a table and naming them according to a set format, data chaos and errors can be effectively avoided, improving data accuracy and reliability. The collaborative work of the parameter setting component and the parameter update module makes the data management process clearer and more efficient. Users can quickly complete the partitioning of data resource catalogs, the formulation of data standards, and the updating of database structures through simple interface operations, reducing tedious manual operations and improving work efficiency.

[0046] The parameter setting component allows users to customize configurations according to their own business needs, enabling the system to better meet the requirements of specific scenarios. This feature helps improve user satisfaction and the application value of the system.

[0047] In summary, integrating the parameter setting component into the management module, and designing it to receive user-input parameter setting trigger commands, display the parameter setting interface in response, and convert user actions into parameter update commands sent to the parameter update module, not only enhances the system's flexibility and configurability, ensures data consistency and accuracy, but also optimizes data management processes, improves work efficiency, enhances system scalability and maintainability, supports customized needs, and promotes data governance and compliance. These beneficial effects collectively drive the system's widespread application and continuous development.

[0048] Based on data management needs, the original data resources are divided into multiple data resource catalogs according to their business attributes. Based on data management needs, the original data resources used to calculate the above indicators are divided into multiple data resource catalogs according to their business attributes, such as administrative divisions, population, topography, climate conditions, land resources, economic level, collective assets, and family farms.

[0049] In some embodiments, the data processing module converts the data according to the established data standard and stores it in the standard library; specifically, it includes: determining whether the acquired raw data meets the input data type requirements of the comprehensive index calculation model for each village, and converting the data of data that does not meet the data type requirements; determining whether the acquired raw data meets the input data floating-point number requirements of the comprehensive index calculation model for each village, and performing data calculation on the data that does not meet the requirements.

[0050] In some embodiments, the data processing module is further provided with a model generation module; The model generation module standardizes each indicator; calculates the... i The village accounted for the first j The weight of each indicator; based on the first i The village accounted for the first j Calculation of the weight of each indicator j The entropy value of the index; calculate the first... j The coefficient of difference for each indicator; the calculation of the first indicator based on the coefficient of difference. j The weight of each indicator is assigned to all indicators; the deviation maximization method is used to measure the weight by the proportion of the deviation of a single indicator to the total deviation of the indicators, and the deviation proportion is calculated; the deviation proportion is normalized by applying a Lagrange function and taking the partial derivative; the comprehensive index of each village is calculated by combining the various indicators, and the comprehensive index calculation model of each village is output.

[0051] Specifically, the working process of the model generation module includes: S21. Standardize all indicators and denegate positive and negative indicators. The specific methods are as follows: Negative indicators:

[0052] Positive indicators:

[0053] For the processed first i The first village j The value of each indicator. Among them, i =1,2,3,…,n; j=1,2,3,…,m.

[0054] S22. Calculate the first... i The village accounted for the first j The proportion of each indicator.

[0055]

[0056] S23. Calculate the first j The entropy value of the indicator.

[0057]

[0058] S24. Calculate the... j The coefficient of difference for each indicator.

[0059]

[0060] S25. Calculate the first j The weight of this indicator among all indicators.

[0061]

[0062] S26. The deviation maximization method is used to measure the weight by the proportion of the deviation of a single indicator to the total deviation of the indicators, and the deviation proportion is calculated.

[0063]

[0064] In the formula, For the first j The weight of each indicator among all indicators. For the first i The first village j The value of the indicator.

[0065] S27. Plot the Lagrange function on the deviation proportion and calculate the partial derivative, normalize it, combine it with various indicators to calculate the comprehensive index of each village, and output the comprehensive index calculation model of each village.

[0066] The comprehensive index calculation model for each village: .

[0067] The platform utilizes its data reading, data transmission, and database writing components to synchronize raw data from the agricultural and rural big data platform base to the platform's raw database, with this task set to execute automatically once a year. The platform presents data in the form of a data visualization dashboard, including functions such as displaying rural GIS points, displaying 10 types of basic indicators, and displaying three-level evaluation indicators. Users can also select villages and indicators for data comparison. The rural development dynamic monitoring and evaluation system is developed based on the Spring Cloud + Vue2 front-end and back-end development framework and deployed in a JDK 1.8.0_202 and Node.js v20.17.0 environment.

[0068] Based on the raw data, various tertiary indicators are calculated and generated. These indicators may include per capita income, agricultural production efficiency, and infrastructure completeness, with specific indicators determined according to the actual situation of rural development and monitoring needs. After calculation, the tertiary indicator data is stored in the system's thematic library. The entropy method is used to objectively assign weights to each evaluation indicator. The entropy method calculates the information entropy of each indicator, reflecting its degree of variation and the amount of information, thereby determining the weights and avoiding the subjectivity of manual weighting. Based on the weighting results, a comprehensive index calculation model for the development level of each village is constructed. The model calculates and outputs the comprehensive index value for each village, reflecting its overall development level. The display module uses GIS technology to display the location information of each village on a map, facilitating a direct understanding of the geographical location and distribution of each village. A data resource catalog is provided to facilitate user querying and understanding of the data types and content included in the system. The tertiary evaluation indicator data for each village, including specific values ​​and trends, are displayed in the form of charts and tables, providing a basis for decision-making.

[0069] By connecting the data acquisition module with the agricultural and rural big data platform, real-time or periodic data synchronization is achieved, improving the timeliness and accuracy of the data. Simultaneously, the data processing module performs format conversion and indicator calculation on the raw data, providing rich data support for subsequent evaluation and analysis. The entropy method is used to objectively assign weights to each evaluation indicator, avoiding the subjectivity of human weighting and making the evaluation results more objective and fair. By calculating the comprehensive development level index of each village, the overall development level of each village can be accurately reflected, providing a scientific basis for rural development planning and decision-making. The display module uses GIS point-based displays and a data resource catalog to make the data more intuitive and easier to understand. Furthermore, the display of the three-level evaluation indicators provides decision-makers with detailed data support, helping to formulate more precise and effective rural development strategies.

[0070] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0071] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the present invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the present invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should also be covered within the protection scope of the present invention.

Claims

1. A digital display and control platform for evaluating rural development level, characterized in that: It includes a control module and a data acquisition module, a data processing module and a display module connected to the control module; the data acquisition module is connected to the data processing module; the display module is connected to the data processing module; The control module receives the data collection trigger instruction input by the user, generates a data collection instruction and sends it to the data collection module; the control module has a built-in display processing component, and the control module responds to the display trigger instruction input by the user, pops up the display processing component, and the display processing component includes a display area; at the same time, generates a display processing instruction and sends it to the data processing module; The data collection module synchronizes the original data from the original data source to the original library of the platform based on the received data collection instructions; collects the development level evaluation index data based on the data in the original library, and sends the collected index data to the data processing module; The data processing module converts the acquired indicator data into a format according to the set data standard and stores it in the standard library; and generates cascaded three-level indicators according to the attributes of the indicators and stores them in the platform theme library; The data processing module calculates the comprehensive index value of each village based on the received display processing instructions; The display module displays rural GIS points and three-level evaluation indicators based on the comprehensive index value of each village.

2. The rural development level evaluation digital display and control platform according to claim 1 is characterized in that: The management and control module has a built-in data source extraction component, which includes a data source extraction interface; in response to a data collection trigger instruction input by a user, the data source extraction interface is displayed, and the user's selection operation on the data source extraction interface is converted into a data collection instruction and sent to the data collection module.

3. The rural development level evaluation digital display and control platform according to claim 2 is characterized in that: The data collection module has a built-in large language model. The data collection module uses the large language model to collect development level evaluation indicator data based on the data in the original library and sends the collected indicator data to the data processing module.

4. The rural development level evaluation digital display and control platform according to claim 3 is characterized in that: The data processing module has a built-in comprehensive index calculation model. The data processing module calls the comprehensive index calculation model based on the received display processing instruction and calculates the comprehensive index value of each village based on the data of the standard library; the management and control module receives the comprehensive index calculation results of each village through the display area; The display module displays rural GIS points and three-level evaluation indicators based on the information in the display area of ​​the control component.

5. The rural development level evaluation digital display and control platform according to claim 4 is characterized in that: The control platform is provided with a collection trigger button and a display trigger button connected to the control module; The control module receives the data collection trigger instruction input by the user through the collection trigger button; The management and control module receives a display trigger instruction input by the user through a display trigger button.

6. The rural development level evaluation digital display and control platform according to claim 5 is characterized in that: The platform also includes a parameter setting trigger button and a parameter update module connected to the control module; The control module has a built-in parameter setting component, and the parameter setting component includes a parameter setting interface; The control module receives the parameter setting trigger instruction input by the user through the setting trigger button, displays the parameter setting interface in response to the parameter setting trigger instruction, and converts the user's operation on the parameter setting interface into a parameter update instruction and sends it to the parameter update module; The parameter update module updates and sets the data resource directory, data standards and database structure; specifically includes: dividing the original data resources into multiple data resource directories according to their business attributes; formulating the data standards for each original data; Save all data resources under multiple data resource directories in a table and name them according to the set format.

7. The rural development level evaluation digital display and control platform according to claim 6 is characterized in that: The data processing module converts the data format according to the established data standards and stores it in the standard library; specifically, it includes: judging whether the obtained original data meets the input data type requirements of the comprehensive index calculation model of each village, and converting the data type that does not meet the requirements; judging whether the obtained original data meets the input data floating-point requirements of the comprehensive index calculation model of each village, and performing data calculation on the non-compliant data.

8. The rural development level evaluation digital display and control platform according to claim 7 is characterized in that: The data processing module is also provided with a model generation module; The model generation module performs standardization processing on various indicators; Calculate the i Villages account for j The weight of the indicators; Based on i Villages account for j The weight calculation of the index j The entropy value of each indicator; Calculate the j The coefficient of difference of the indicators is calculated based on the coefficient of difference. j The weight of each indicator in all indicators; the deviation maximization method is used to measure the weight through the proportion of single indicator deviation in the total indicator deviation, and the deviation proportion is calculated; the deviation proportion is made into a Lagrange function and the partial derivative is calculated, and normalized, and the comprehensive index of each village is calculated by combining various indicators, and the comprehensive index calculation model of each village is output.

9. The rural development level evaluation digital display and control platform according to claim 8 is characterized in that: The steps of standardizing various indicators in the model generation module include: non-negative processing of positive and negative indicators, as follows: Negative indicators: Positive indicators: In the formula, After processing i Village j The value of the indicator, where i =1,2,3,…,n; j=1,2,3,…,m.

10. The rural development level evaluation digital display and control platform according to claim 9 is characterized in that: The model generation module uses the deviation maximization method to measure the weight by the proportion of single indicator deviation to total indicator deviation and calculate the deviation proportion. The formula is as follows: In the formula, For the j The weight of each indicator accounts for all indicators. For the i Village j The value of the indicator; The model generation module uses the Lagrange function to calculate the partial derivative of the deviation ratio, and the formula for normalization is as follows: The calculation model of the comprehensive index of each village is as follows: .

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