Intelligent quantitative evaluation method and system for rural development level

Abstract

The invention relates to the technical field of rural planning analysis and evaluation, discloses an intelligent quantitative evaluation method and system for a rural development level, and is used for solving the technical problem that objective quantitative evaluation cannot be comprehensively and dynamically performed on the rural development level from multi-dimensional indexes in the prior art. The method specifically comprises the following steps: acquiring different types of original data of a to-be-evaluated country from a plurality of data sources; calling a space-time alignment algorithm, and aligning the various types of rural original data in a space dimension and a time dimension to obtain aligned data; obtaining quantitative evaluation items, and based on the alignment data corresponding to each quantitative evaluation item, calling a weight distribution algorithm to calculate an evaluation weight of each quantitative evaluation item; and calling a quantitative evaluation model to output a rural development level evaluation score of each to-be-evaluated rural based on the alignment data corresponding to each quantitative evaluation item and the weight of each quantitative evaluation item.

Description

Technical Field

[0001] This invention relates to the field of rural planning analysis and evaluation technology, and in particular to an intelligent quantitative evaluation method, system, electronic device, computer storage medium, and computer program product for rural development level. Background Technology

[0002] The scientific and standardized assessment of rural development levels urgently requires the support of an effective technical system. In recent years, the assessment indicators for rural development levels have expanded from single economic indicators to multiple dimensions including industry, ecology, and living standards, which has brought new requirements and greater challenges to quantitative assessment technologies.

[0003] Currently, there are several quantitative assessment methods for rural areas, mainly categorized into three types: statistical report analysis, expert review, and basic model assessment. Statistical report analysis relies on institutionalized data (such as GDP, per capita income, and greening rate) from local statistical yearbooks and departmental reports, using weighted calculations to arrive at a score. While this method relies on authoritative data sources and has high information reliability, it suffers from drawbacks such as time lag (annual / quarterly report updates) and narrow coverage (lacking unstructured data like real-time ecological environment monitoring and villager satisfaction). Expert review involves organizing experts from various fields to conduct field research and score indicators based on experience (such as subjective indicators like governance effectiveness and rural civilization). This method can handle qualitative issues, but it is highly subjective, costly, and the results are difficult to compare across regions. Basic model assessment uses simple regression models or AHP (Analogous Hierarchical Analysis) to introduce multi-indicator fusion for batch scoring. However, the model is static and fixed, unable to comprehensively consider real-time data from multiple dimensions for integrated assessment, and the weights in the model depend on manual pre-setting.

[0004] Therefore, based on the problems in the existing technology, there is an urgent need for an assessment method that can shift the assessment of rural development level from a single economic indicator to a comprehensive development evaluation, and can conduct a comprehensive and dynamic quantitative assessment of information from multiple sources and multiple dimensions. Summary of the Invention

[0005] The main objective of this invention is to solve the technical problem that existing technologies cannot objectively assess the level of rural development by comprehensively and dynamically quantifying it from multiple dimensions of indicators.

[0006] The first aspect of this invention provides an intelligent quantitative assessment method for rural development levels, comprising: Obtain different types of raw data from multiple data sources for the villages to be evaluated; The spatiotemporal alignment algorithm is invoked to align the original rural data of each type in both spatial and temporal dimensions to obtain aligned data. Obtain the quantitative evaluation items, and based on the alignment data corresponding to each quantitative evaluation item, call the weight allocation algorithm to calculate the evaluation weight of each quantitative evaluation item; The quantitative assessment model is invoked to output the assessment score of the rural development level of each village to be assessed, based on the aligned data corresponding to each quantitative assessment item and the weight of each quantitative assessment item.

[0007] Optionally, in a first implementation of the first aspect of the present invention, the step of calculating the evaluation weight of each quantitative evaluation item based on the alignment data corresponding to each of the quantitative evaluation items by calling a weight allocation algorithm includes: The objective weight of each quantitative evaluation item is calculated based on the degree of dispersion of the aligned data corresponding to each quantitative evaluation item. A judgment matrix is ​​constructed based on expert evaluation experience, and the subjective weight of each quantitative evaluation item is calculated based on the judgment matrix. Based on the requirements of the quantitative assessment scenario, a weight configuration ratio is selected, and the objective weight and the subjective weight are weighted and calculated based on the weight configuration ratio to obtain the assessment weight of each quantitative assessment item.

[0008] Optionally, in a second implementation of the first aspect of the present invention, calculating the objective weight of each of the quantitative evaluation items based on the dispersion of the alignment data corresponding to each of the quantitative evaluation items includes: Alignment data corresponding to each of the quantitative assessment items for multiple villages to be assessed for intelligent quantitative assessment of development level are obtained respectively; Based on the alignment data of each of the plurality of villages to be evaluated, a first data matrix is ​​generated for each of the villages to be evaluated; Calculate the entropy value of the aligned data in each of the quantitative evaluation projects based on the first data matrix; Configure the dynamic weights of each quantitative evaluation item based on the entropy value of the aligned data in each quantitative evaluation item.

[0009] Optionally, in a third implementation of the first aspect of the present invention, the step of invoking the spatiotemporal alignment algorithm to align the original rural data of each type in both spatial and temporal dimensions to obtain aligned data includes: The collected rural raw data with spatial location information is unified into a spatial framework with the administrative village boundary as the basic evaluation unit through coordinate transformation and spatial matching algorithms. The original rural data collected at different times are interpolated over time or aggregated over time using the mean, so that the original rural data are aligned within the same time window.

[0010] Optionally, in a fourth implementation of the first aspect of the present invention, the step of calling the quantitative assessment model to output a rural development level assessment score for each village to be assessed based on the aligned data corresponding to each of the quantitative assessment items and the weights of each of the quantitative assessment items includes: Based on the aligned data of each village to be evaluated, a first virtual village is generated where all quantitative evaluation items are optimal, and the first virtual data of each quantitative evaluation item of the first virtual village is taken as the positive ideal solution. Based on the aligned data of each village to be evaluated, a second virtual village is generated where all quantitative evaluation items are the worst, and the second virtual data of each quantitative evaluation item of the second virtual village is used as the negative ideal solution. The quantitative evaluation model is called to calculate the alignment data of each village to be evaluated and the Euclidean distance between the positive ideal solution and the negative ideal solution based on the TOPSIS method, so as to obtain the ideal proximity of each village to be evaluated. Using the positive ideal solution as reference data, the grey relational degree between the quantitative assessment items of each village to be assessed and the reference data is calculated. The assessment score of the rural development level of each village to be evaluated is calculated based on the ideal proximity and the grey relational degree.

[0011] Optionally, in a fifth implementation of the first aspect of the present invention, the different types of raw data include remote sensing image data, Internet of Things data, and basic geographic information data.

[0012] A second aspect of the present invention provides an intelligent quantitative assessment system for rural development levels, comprising: The data acquisition module is used to acquire different types of raw data of the villages to be evaluated from multiple data sources; The data alignment module is used to call the spatiotemporal alignment algorithm to align the original rural data of various types in the spatial and temporal dimensions to obtain aligned data. The weight calculation module is used to obtain the quantitative evaluation items, and based on the alignment data corresponding to each quantitative evaluation item, call the weight allocation algorithm to calculate the evaluation weight of each quantitative evaluation item. The evaluation execution module is used to call the quantitative evaluation model to output the rural development level evaluation score of each village to be evaluated based on the alignment data corresponding to each quantitative evaluation item and the weight of each quantitative evaluation item.

[0013] A third aspect of the present invention provides an intelligent quantitative assessment device for rural development level, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the intelligent quantitative assessment device for rural development level to perform the steps of the above-described intelligent quantitative assessment method for rural development level.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the steps of the above-described intelligent quantitative assessment method for rural development levels.

[0015] A fifth aspect of the present invention provides a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the intelligent quantitative assessment method for rural development levels as described above.

[0016] The technical solution provided by this invention involves obtaining different types of raw data of villages to be evaluated from multiple data sources; calling a spatiotemporal alignment algorithm to align the raw data of each type of village in both spatial and temporal dimensions to obtain aligned data; obtaining quantitative evaluation items; and, based on the aligned data corresponding to each quantitative evaluation item, calling a weight allocation algorithm to calculate the evaluation weight of each quantitative evaluation item; and calling a quantitative evaluation model based on the aligned data corresponding to each quantitative evaluation item and the weight of each quantitative evaluation item to output the evaluation score of the rural development level of each village to be evaluated.

[0017] This assessment method can comprehensively and dynamically quantify and objectively evaluate the level of rural development from multiple dimensions, reducing subjectivity and increasing the credibility of the assessment results. Furthermore, the system, electronic device, computer-readable storage medium, and computer program product provided by this invention also solve the corresponding technical problems. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the overall process of an embodiment of the intelligent quantitative assessment method for rural development level in this invention. Figure 2 This is a schematic diagram of the data processing and alignment process in an embodiment of the intelligent quantitative assessment method for rural development level according to the present invention. Figure 3 This is a flowchart illustrating the quantitative assessment process of an embodiment of the intelligent quantitative assessment method for rural development level in this invention. Figure 4 This is a schematic diagram of an embodiment of the intelligent quantitative assessment system for rural development level in this invention. Figure 5 This is a schematic diagram of an embodiment of the intelligent quantitative assessment device for rural development level in this invention. Figure 6 This is a schematic diagram illustrating the principle of a computer-readable medium according to an embodiment of the present invention. Detailed Implementation

[0019] Exemplary embodiments of the invention will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limiting the invention to the embodiments set forth herein. Rather, these exemplary embodiments are provided to make the invention more comprehensive and complete, and to facilitate a full communication of the inventive concept to those skilled in the art. The same reference numerals in the drawings denote the same or similar elements, components, or parts, and therefore repeated descriptions of them will be omitted.

[0020] Subject to the technical concept of this invention, the features, structures, characteristics or other details described in a particular embodiment may be combined in one or more other embodiments in a suitable manner.

[0021] In the description of specific embodiments, the features, structures, characteristics, or other details described in this invention are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that those skilled in the art can practice the technical solutions of this invention without one or more of the specific features, structures, characteristics, or other details.

[0022] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0023] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0024] The terms “and / or” or “and / or” include all combinations of any one or more of the listed items.

[0025] Please see Figures 1-3 One embodiment of the intelligent quantitative assessment method for rural development level in this invention includes: S101. Obtain different types of raw data of the villages to be evaluated from multiple data sources; It is understood that the executing entity of this invention can be an intelligent quantitative assessment system for rural development levels, or it can be a terminal or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0026] In this embodiment, the server can perform quantitative assessments on multiple different villages in a batch and give specific scores. After receiving a request for intelligent quantitative assessment of the development level of a village, it first obtains the raw data required for the quantitative assessment.

[0027] In one specific implementation, please refer to Figure 2 In this embodiment, the method can access data from at least four different sources through a heterogeneous data adapter, including remote sensing image data, Internet of Things (IoT) data, open Internet data, and basic geographic information data. (1) Remote sensing image data: This can be remote sensing image data acquired by satellites or drones, which can be used to extract spatial information such as building density, water distribution, vegetation cover, administrative boundaries, and land use. Subsequently, based on this spatial information, projects and indicators such as the ecological livability of rural areas can be obtained. Specifically, after obtaining this spatial information, remote sensing features can be obtained based on the remote sensing image spatial data. After converting the obtained remote sensing features from raster data into vector form, land use classification information obtained from satellite imagery can be obtained as the final remote sensing image data information.

[0028] (2) Internet of Things (IoT) data: Information can be obtained based on sensors pre-installed in various environments or scenarios in rural areas. For example, sensors capable of testing water pH and turbidity can be deployed in water quality monitoring stations to obtain information on water distribution and specific water conditions; air quality sensors can be deployed in rural environments to test and control PM2.5 index information; real-time energy consumption monitoring data can be set up using smart meters, as well as other environmental and facility data. In addition, in scenarios where hardware is lacking, information can be supplemented by acquiring data through "meteorological data + government affairs platform". Specifically, after acquiring data from IoT channels, dynamic data aggregation and grid processing can be performed to obtain dynamic monitoring data that can be obtained based on IoT data. Subsequently, this IoT data can be used to calculate and obtain projects and indicators such as the ecological livability of rural areas. In addition, supplementary data obtained through "meteorological data + government affairs platform" can also be aggregated through a similar process.

[0029] (3) Open Internet data: This includes POIs (Points of Interest), such as the locations of factories, schools, and shops; social media information, such as the analysis of the popularity and sentiment of topics related to rural civilization; and public opinion information. Specifically, after obtaining these contents of open Internet data, the public opinion data is correlated, geocoded, and matched with POI spatial data points to obtain public opinion analysis data related to geographic information.

[0030] (4) Basic Geographic Information System (GIS): Provides basic spatial frameworks such as administrative boundaries, road networks, and topography. Specifically, after integrating the topographic data, road networks, and administrative boundaries of each village, the boundary topology is verified based on the specific division method of each village's administrative unit, and finally, spatial data of geographic information is obtained.

[0031] S102. Call the spatiotemporal alignment algorithm to align the original rural data of each type in the spatial and temporal dimensions to obtain aligned data. In this embodiment, the original rural data with spatial location information collected in the previous step is unified into a spatial framework with the administrative village boundary as the basic evaluation unit through coordinate transformation and spatial matching algorithms; the original rural data collected at different time frequencies are interpolated or aggregated by means of average to align the original rural data within the same time window.

[0032] Specifically, in one implementation, when aligning various types of raw rural data, a pre-built spatiotemporal alignment engine is invoked to perform the alignment work. This spatiotemporal alignment engine processes the collected information uniformly in both the spatial and temporal dimensions, specifically including: (1) Spatial Alignment: This section unifies the spatial reference of the collected information. All collected data with spatial location information (such as remote sensing image data, sensor location data, map point of interest data, basic geographic information data, etc.) are unified into a spatial framework with administrative village boundaries as the basic evaluation unit through coordinate transformation and spatial matching algorithms. The spatial matching algorithm can be based on geocoding or spatial overlay analysis. Please continue reading Figure 2 When performing specific spatial alignment, administrative unit aggregation, attribute field association, raster vector data fusion, and coordinate system unification can be performed to finally obtain a standardized index matrix after spatial alignment.

[0033] (2) Time alignment: This part unifies the time base of the collected information. For example, time interpolation is performed on data of different frequencies (real-time, daily, monthly, yearly) to divide data with excessively long collection periods into data with shorter periods; or data with shorter collection periods are aggregated, such as by taking daily averages or monthly averages, to aggregate them to the required period length, thereby ensuring that all indicator data required for evaluation are within the same time window.

[0034] Furthermore, in one specific implementation, the data is further cleaned and structured. This includes invoking an intelligent cleaning model, based on a cleaning rule engine (such as range validation, outlier removal, etc.) and a machine learning model (such as using the pre-trained natural language processing model BERT for cleaning and analyzing sentiment in social media text), to clean, denoise, and fill in missing values ​​in the raw data, and then convert it into structured, numerical aligned data.

[0035] S103. Obtain the quantitative evaluation items, and based on the alignment data corresponding to each quantitative evaluation item, call the weight allocation algorithm to calculate the evaluation weight of each quantitative evaluation item. The objective weights of each quantitative evaluation item are calculated based on the dispersion of the aligned data corresponding to each quantitative evaluation item; a judgment matrix is ​​constructed based on expert evaluation experience, and the subjective weights of each quantitative evaluation item are calculated based on the judgment matrix; a weight configuration ratio is selected based on the requirements of the quantitative evaluation scenario, and the objective weights and subjective weights are weighted and calculated based on the weight configuration ratio to obtain the evaluation weights of each quantitative evaluation item.

[0036] Based on this approach, a structured quantitative assessment project is pre-constructed before performing the specific rural development quantitative assessment in this step. The quantitative assessment project system described in this embodiment includes pre-constructed quantitative assessment projects and dynamically calculated weights for each project indicator. The final assessment criteria are determined by comprehensively considering the quantitative assessment project and indicator weight information. One specific method for pre-constructing the quantitative assessment projects is as follows: To illustrate with a specific example, please refer to Table 1. A multi-level quantitative basic indicator system is predefined, covering seven dimensions: industrial prosperity, ecological livability, rural civilization, effective governance, affluent life, suitable for work, and livability. Each basic indicator contains multiple measurable secondary indicators.

[0037] Table 1: Scoring System for Basic Indicators The multi-level quantitative indicator system includes three levels: basic indicators, recommended indicators, and negative indicators. The system will then use these indicators to determine whether the village being evaluated meets the scoring criteria. If it does, the corresponding indicator score will be calculated. The recommended indicators are set based on the basic indicators; if they include additional points, the scoring system will use those meeting the requirements of the recommended indicators as bonus points. The negative indicators can be scored or penalized. If the scoring system identifies a village containing negative indicators, it will either deduct points or directly mark the village containing negative indicators as non-compliant.

[0038] Please refer to Table 2 for the specific scoring rules of the basic indicators for the identification of rural pilot projects, which adopts a point system.

[0039] Table 2: Scoring Rules for Basic Indicators Please see Table 3 below for the detailed scoring rules of the recommended indicators for the identification of rural pilot projects. It includes 20 additional scoring items. The scoring will be based on these recommended indicators, and items that meet the indicator requirements will be considered as bonus items.

[0040] Table 3: Scoring Rules for Recommended Indicators Please see Table 4 below for the specific scoring rules for the negative indicators identified in the village pilot project. A total of 10 items are proposed, and a veto system is adopted. That is, if any one of the negative control indicators meets the veto conditions, the village will not be recognized as a qualified pilot project.

[0041] Table 4: Scoring Details for Negative Indicators In this embodiment, please refer to Figure 3The weighting process involves both objective and subjective weighting paths, which are then fused using a weight fusion tool. This weight fusion tool is an algorithm module for weighted fusion of objective and subjective weights. When acquiring objective weights, alignment data corresponding to each quantitative assessment item for multiple villages undergoing intelligent quantitative assessment of their development levels is obtained. Based on the alignment data of each village, a first data matrix is ​​generated for each village. The entropy value of the alignment data in each quantitative assessment item is calculated based on the first data matrix. The dynamic weights of each quantitative assessment item are configured according to the entropy values ​​of the alignment data in each quantitative assessment item. The entropy calculation can be implemented using the entropy weighting method.

[0042] To illustrate this with a concrete example, let's take the development level assessment of 70 administrative villages as an example. We input a standardized matrix of 70 project data from each administrative village and calculate the information entropy of each project. At this point, the objective weights are... The calculation expression is: In one specific implementation, the information entropy The formula used to measure the uncertainty of the j-th item indicator is as follows: in, Indicates the first j The project indicators in the first i The proportion (standardized value) in a sample. n Indicates the number of samples.

[0043] Obtain information entropy Subsequently, based on information entropy Calculate information utility value The information utility value Defined as the complement of information entropy, it is used to measure the amount of useful information in the project's indicators, and its calculation expression is: When information entropy The smaller the value, the lower the information utility value. The larger the value, the more useful the information provided by the project indicator, meaning the greater the impact of the information provided by the project indicator on the results.

[0044] Obtain information utility value Then, through the information utility value Normalization is performed to obtain the final objective weight of each project indicator. The final objective weight The expression is: in, m The total number of project indicators. It is the first j Objective weights of each project indicator.

[0045] Furthermore, in this embodiment, subjective weights can be obtained based on the AHP (Analytic Hierarchy Process). Specifically, experts in the field can pre-input importance comparison information through a UI interface to construct a judgment matrix (e.g., "Industrial Prosperity" to "Civilized Rural Customs" = 5:1), and finally solve for the subjective weights. .

[0046] After obtaining the objective and subjective weights, a weight configuration ratio is selected based on the quantitative evaluation scenario requirements. The objective weights are then adjusted according to this weight configuration ratio. and the subjective weights Weighted calculations are performed to obtain the evaluation weights of each quantitative assessment item. Under normal circumstances, the weighted calculations can be performed according to a weighting ratio of objective:subjective = 7:3 to obtain the final weight set.

[0047] S104. Call the quantitative assessment model to output the rural development level assessment score of each village to be assessed based on the aligned data corresponding to each quantitative assessment item and the weight of each quantitative assessment item.

[0048] In this step, based on the alignment data of each village to be evaluated, a first virtual village is generated where all quantitative evaluation items are optimal, and the first virtual data of each quantitative evaluation item in the first virtual village is taken as the positive ideal solution; based on the alignment data of each village to be evaluated, a second virtual village is generated where all quantitative evaluation items are worst, and the second virtual data of each quantitative evaluation item in the second virtual village is taken as the negative ideal solution; the quantitative evaluation model is called to calculate the Euclidean distance between the alignment data of each village to be evaluated and the positive and negative ideal solutions using the TOPSIS method (Technique for Order Preference by Similarity to an IdealSolution), to obtain the ideal proximity of each village to be evaluated; using the positive ideal solution as reference data, the grey relational degree between the quantitative evaluation items of each village to be evaluated and the reference data is calculated; the rural development level evaluation score of each village to be evaluated is calculated based on the ideal proximity and the grey relational degree.

[0049] Specifically, in this embodiment, when calculating the assessment score for rural development level, a standardized decision matrix is ​​first constructed to determine the positive and negative ideal solutions. For example, in a real quantitative assessment project, "the optimal PM2.5 value = 10 μg / m³, and the worst value = 150 μg / m³", then the PM2.5 value of the first virtual village, constructed as the positive ideal solution, is the optimal value of 10 μg / m³, and the PM2.5 value of the second virtual village, constructed as the negative ideal solution, is the worst value of 150 μg / m³. Other project indicators follow the same principle and will not be elaborated further here. Subsequently, the Euclidean distance between each village and the positive / negative ideal solution is calculated (…). / Then calculate the ideal closeness. C The specific expression is: Next, using the positive ideal solution as a reference sequence, the quantitative evaluation model is called to calculate the grey relational coefficient of each rural project indicator based on the grey relational analysis (GRA) algorithm. The mean of the grey relational coefficients is then calculated to obtain the relational degree. R .

[0050] Achieving ideal closeness C and correlation R Then, the quantitative evaluation model is called to calculate the final score. S The expression is: in, and They represent the ideal closeness. C and correlation R The weights, and In one specific implementation, The value is 0.6. The value is 0.4.

[0051] Please continue reading. Figure 3 In this embodiment, the output results will include not only the static score (i.e., ideal closeness) obtained from the Euclidean distance calculated using the TOPSIS method, but also... C It will also output correlation information obtained from grey relational trend analysis. Furthermore, in one specific implementation, based on the final score, it can also generate charts such as score radar charts for visualization according to the dimensions of the evaluation items; for example, a radar chart can be output based on seven specific evaluation criteria.

[0052] The technical solution provided by this invention can acquire different types of raw data from multiple data sources, thereby achieving deep integration of multi-source data. It breaks through the spatial and temporal limitations of traditional statistical reports, covering various types of dynamic project indicators, expanding the evaluation dimensions from single economic data to multiple items across seven dimensions, and reducing evaluation blind spots by more than 60%. Furthermore, it can reduce the cost of manual surveys by intelligently acquiring evaluation standards and weights, shortening the time required for a single evaluation from 3-6 weeks using expert review methods to within 24 hours, making it suitable for batch evaluations of rural areas at the provincial level. It can comprehensively and dynamically quantify and objectively evaluate the level of rural development from multiple dimensions of project indicators, reducing the subjectivity of evaluation, improving the credibility of evaluation results, and enhancing the comprehensiveness and real-time effectiveness of quantitative evaluation.

[0053] The above describes the intelligent quantitative assessment method for rural development level in the embodiments of the present invention. The following describes the intelligent quantitative assessment system for rural development level in the embodiments of the present invention. Please refer to [link / reference]. Figure 4 One embodiment of the intelligent quantitative assessment system for rural development level in this invention includes: The data acquisition module 401 is used to acquire different types of raw data of the villages to be evaluated from multiple data sources; Data alignment module 402 is used to call a spatiotemporal alignment algorithm to align the original rural data of various types in the spatial and temporal dimensions to obtain aligned data; The weight calculation module 403 is used to obtain the quantitative evaluation items and, based on the alignment data corresponding to each quantitative evaluation item, call the weight allocation algorithm to calculate the evaluation weight of each quantitative evaluation item. The evaluation execution module 404 is used to call the quantitative evaluation model to output the rural development level evaluation score of each village to be evaluated based on the alignment data corresponding to each quantitative evaluation item and the weight of each quantitative evaluation item.

[0054] The system provided by this invention can comprehensively and dynamically quantify and objectively assess the level of rural development from multiple dimensions, thereby reducing the subjectivity of the evaluation and improving the credibility of the evaluation results.

[0055] In another embodiment of this application, the weight calculation module 403 is specifically used for: The objective weight of each quantitative evaluation item is calculated based on the degree of dispersion of the aligned data corresponding to each quantitative evaluation item. A judgment matrix is ​​constructed based on expert evaluation experience, and the subjective weight of each quantitative evaluation item is calculated based on the judgment matrix. Based on the requirements of the quantitative assessment scenario, a weight configuration ratio is selected, and the objective weight and the subjective weight are weighted and calculated based on the weight configuration ratio to obtain the assessment weight of each quantitative assessment item.

[0056] In another embodiment of this application, calculating the objective weight of each quantitative evaluation item based on the dispersion of the alignment data corresponding to each quantitative evaluation item includes: Alignment data corresponding to each of the quantitative assessment items for multiple villages to be assessed for intelligent quantitative assessment of development level are obtained respectively; Based on the alignment data of each of the plurality of villages to be evaluated, a first data matrix is ​​generated for each of the villages to be evaluated; Calculate the entropy value of the aligned data in each of the quantitative evaluation projects based on the first data matrix; Configure the dynamic weights of each quantitative evaluation item based on the entropy value of the aligned data in each quantitative evaluation item.

[0057] In another embodiment of this application, the data alignment module 402 is specifically used for: The collected rural raw data with spatial location information is unified into a spatial framework with the administrative village boundary as the basic evaluation unit through coordinate transformation and spatial matching algorithms. The original rural data collected at different times are interpolated over time or aggregated over time using the mean, so that the original rural data are aligned within the same time window.

[0058] In another embodiment of this application, the evaluation execution module 404 is specifically used for: Based on the aligned data of each village to be evaluated, a first virtual village is generated where all quantitative evaluation items are optimal, and the first virtual data of each quantitative evaluation item of the first virtual village is taken as the positive ideal solution. Based on the aligned data of each village to be evaluated, a second virtual village is generated where all quantitative evaluation items are the worst, and the second virtual data of each quantitative evaluation item of the second virtual village is used as the negative ideal solution. The quantitative evaluation model is called to calculate the alignment data of each village to be evaluated and the Euclidean distance between the positive ideal solution and the negative ideal solution based on the TOPSIS method, so as to obtain the ideal proximity of each village to be evaluated. Using the positive ideal solution as reference data, the grey relational degree between the quantitative assessment items of each village to be assessed and the reference data is calculated. The assessment score of the rural development level of each village to be evaluated is calculated based on the ideal proximity and the grey relational degree.

[0059] In another embodiment of this application, the different types of raw data include remote sensing image data, Internet of Things data, and basic geographic information data.

[0060] The system provided by this invention can comprehensively and dynamically quantify and objectively assess the level of rural development from multiple dimensions, thereby reducing the subjectivity of the evaluation and improving the credibility of the evaluation results.

[0061] Based on the same inventive concept, this specification also provides an electronic device for intelligent quantitative assessment of rural development level. The following is a detailed description of the electronic device for intelligent quantitative assessment of rural development level in this embodiment of the invention from the perspective of hardware processing.

[0062] Figure 5 This is a schematic diagram of an electronic device provided as an embodiment of this specification. Refer to the following... Figure 5 To describe the electronic device 500 according to this embodiment of the invention. Figure 5 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0063] like Figure 5 As shown, the electronic device 500 is presented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), a display unit 540, etc.

[0064] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the processing method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 510 can perform, for example... Figures 1-3 The steps are shown.

[0065] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 5201 and / or a cache storage unit 5202, and may further include a read-only memory unit (ROM) 5203.

[0066] The storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0067] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0068] Electronic device 500 can also communicate with one or more external devices 100 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with the electronic device 500, and / or any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. Network adapter 560 can communicate with other modules of electronic device 500 via bus 530. It should be understood that, although... Figure 5 As not shown, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0069] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described in this invention can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the method described above according to this invention. When the computer program is executed by a data processing device, it enables the computer-readable medium to implement the method described above, i.e.: as... Figures 1-3 The method shown.

[0070] Figure 6 This is a schematic diagram of a computer-readable medium provided for embodiments of this specification.

[0071] accomplish Figures 1-3The computer program of the method shown can be stored on one or more computer-readable media. A computer-readable medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0072] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0073] In addition, the present invention also provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the intelligent quantitative assessment method for rural development level as described in any of the above embodiments.

[0074] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0075] In summary, the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that in practice, general-purpose data processing devices such as microprocessors or digital signal processors (DSPs) can be used to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0076] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0077] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0078] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0079] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for intelligent quantitative assessment of rural development level, characterized in that, include: Obtain different types of raw data from multiple data sources for the villages to be evaluated; The spatiotemporal alignment algorithm is invoked to align the original rural data of each type in both spatial and temporal dimensions to obtain aligned data. Obtain the quantitative evaluation items, and based on the alignment data corresponding to each quantitative evaluation item, call the weight allocation algorithm to calculate the evaluation weight of each quantitative evaluation item; The quantitative assessment model is invoked to output the assessment score of the rural development level of each village to be assessed, based on the aligned data corresponding to each quantitative assessment item and the weight of each quantitative assessment item.

2. The intelligent quantitative assessment method for rural development level according to claim 1, characterized in that, The step of calculating the evaluation weight of each quantitative evaluation item based on the alignment data corresponding to each of the quantitative evaluation items includes: The objective weight of each quantitative evaluation item is calculated based on the degree of dispersion of the aligned data corresponding to each quantitative evaluation item. A judgment matrix is ​​constructed based on expert evaluation experience, and the subjective weight of each quantitative evaluation item is calculated based on the judgment matrix. Based on the requirements of the quantitative assessment scenario, a weight configuration ratio is selected, and the objective weight and the subjective weight are weighted and calculated based on the weight configuration ratio to obtain the assessment weight of each quantitative assessment item.

3. The intelligent quantitative assessment method for rural development level according to claim 2, characterized in that, The calculation of the objective weight of each quantitative evaluation item based on the dispersion of the aligned data corresponding to each quantitative evaluation item includes: Alignment data corresponding to each of the quantitative assessment items for multiple villages to be assessed for intelligent quantitative assessment of development level are obtained respectively; Based on the alignment data of each of the plurality of villages to be evaluated, a first data matrix is ​​generated for each of the villages to be evaluated; Calculate the entropy value of the aligned data in each of the quantitative evaluation projects based on the first data matrix; Configure the dynamic weights of each quantitative evaluation item based on the entropy value of the aligned data in each quantitative evaluation item.

4. The intelligent quantitative assessment method for rural development level according to claim 1, characterized in that, The aforementioned spatiotemporal alignment algorithm aligns the various types of original rural data in both spatial and temporal dimensions, resulting in aligned data including: The collected rural raw data with spatial location information is unified into a spatial framework with the administrative village boundary as the basic evaluation unit through coordinate transformation and spatial matching algorithms. The original rural data collected at different times are interpolated over time or aggregated over time using the mean, so that the original rural data are aligned within the same time window.

5. The intelligent quantitative assessment method for rural development level according to claim 1, characterized in that, The quantitative assessment model, based on the aligned data corresponding to each quantitative assessment item and the weights of each quantitative assessment item, outputs a rural development level assessment score for each village to be assessed, including: Based on the aligned data of each village to be evaluated, a first virtual village is generated where all quantitative evaluation items are optimal, and the first virtual data of each quantitative evaluation item of the first virtual village is taken as the positive ideal solution. Based on the aligned data of each village to be evaluated, a second virtual village is generated where all quantitative evaluation items are the worst, and the second virtual data of each quantitative evaluation item of the second virtual village is used as the negative ideal solution. The quantitative evaluation model is called to calculate the alignment data of each village to be evaluated and the Euclidean distance between the positive ideal solution and the negative ideal solution based on the TOPSIS method, so as to obtain the ideal proximity of each village to be evaluated. Using the positive ideal solution as reference data, the grey relational degree between the quantitative assessment items of each village to be assessed and the reference data is calculated. The assessment score of the rural development level of each village to be evaluated is calculated based on the ideal proximity and the grey relational degree.

6. The intelligent quantitative assessment method for rural development level according to any one of claims 1-5, characterized in that, The different types of raw data include remote sensing image data, Internet of Things data, and basic geographic information data.

7. An intelligent quantitative assessment system for rural development level, characterized in that, The intelligent quantitative assessment system for rural development levels includes: The data acquisition module is used to acquire different types of raw data of the villages to be evaluated from multiple data sources; The data alignment module is used to call the spatiotemporal alignment algorithm to align the original rural data of various types in the spatial and temporal dimensions to obtain aligned data. The weight calculation module is used to obtain the quantitative evaluation items, and based on the alignment data corresponding to each quantitative evaluation item, call the weight allocation algorithm to calculate the evaluation weight of each quantitative evaluation item. The evaluation execution module is used to call the quantitative evaluation model to output the rural development level evaluation score of each village to be evaluated based on the alignment data corresponding to each quantitative evaluation item and the weight of each quantitative evaluation item.

8. An intelligent quantitative assessment device for rural development level, characterized in that, The intelligent quantitative assessment device for rural development level includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the intelligent quantitative assessment device for rural development level to perform the steps of the intelligent quantitative assessment method for rural development level as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program / instructions thereon, characterized in that, When the program / instruction is executed by the processor, it implements the steps of the intelligent quantitative assessment method for rural development level as described in any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, the steps of the intelligent quantitative assessment method for rural development level as described in any one of claims 1-6 are implemented.

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