Rural industry revitalization effect evaluation system based on multi-source geographic data

Abstract

The present application relates to the technical field of industrial performance evaluation, and discloses a rural industry revitalization performance evaluation system based on multi-source geographic data, which comprises a data acquisition module, a data processing module, a spatial feature acquisition module, an evaluation index determination module and a report generation module, wherein the data processing module is used for defining a unified spatial reference framework for the multi-source geographic data set, performing coordinate system conversion based on the spatial reference framework, and implementing multi-temporal time alignment on the converted data to obtain a spatio-temporally consistent geographic data cube; the spatial feature acquisition module is used for identifying the spatial aggregation form of agricultural land and the distribution evolution trend of industrial buildings from the spatio-temporally consistent geographic data cube, and simultaneously analyzing the topological connection relationship of the traffic network; the present application can comprehensively and accurately evaluate the performance of rural industry revitalization, and clearly present the internal correlation among the industrial spatial layout, evolution trend and economic performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial performance evaluation, in particular to a rural industry revitalization performance evaluation system based on multi-source geographic data. BACKGROUND

[0002] As the core carrier of activating rural economic vitality, improving the income level of farmers and optimizing the allocation of regional resources, the revitalization performance of rural industry is directly related to the overall quality and sustainability of rural economic development. In order to avoid problems such as blind resource investment, development direction deviation and regional development imbalance in the process of industrial development, it is necessary to accurately grasp the actual situation of industrial development in different rural areas through scientific evaluation methods, and to clarify the advantage fields and weak links of industrial revitalization, so as to provide data support and decision basis for subsequent adjustment of development strategy, optimization of resource input direction and promotion of high-quality development of rural industry.

[0003] In the current rural industry revitalization performance evaluation process, the core problems of insufficient data utilization and imperfect evaluation logic are often encountered. Some evaluation methods only rely on non-spatial data such as statistical reports, and fail to tap the industrial spatial distribution and evolution information contained in satellite remote sensing, land use and other geographic data. Even if multi-source geographic data is introduced, due to the heterogeneous characteristics of spatial data itself, such as different formats and different precision, there is a lack of unified spatial reference framework and standardized time alignment mechanism, resulting in deviations in spatial coordinates and time dimensions of data from different sources and different time phases, which cannot form a spatio-temporally consistent data set to support in-depth analysis. At the same time, in the evaluation index construction link, it is difficult to establish a deep correlation between industrial spatial dynamic characteristics and economic performance data, and it is difficult to quantify the spatial dependence relationship and collaborative evolution law between the two, making it difficult for the evaluation results to fully reflect the actual impact of industrial spatial layout on revitalization performance, and unable to provide sufficient fine decision basis for rural industry development. SUMMARY

[0004] The present application provides a rural industry revitalization performance evaluation system based on multi-source geographic data to solve the problems raised in the background art.

[0005] To achieve the above purpose, the rural industry revitalization performance evaluation system based on multi-source geographic data provided by the present application is characterized in that the system comprises a data acquisition module 101, a data processing module 102, a spatial feature acquisition module 103, an evaluation index determination module 104 and a report generation module 105, wherein:

[0006] The data acquisition module is used to acquire satellite remote sensing images, land use classification data and population statistical distribution information of the target rural area, and form a multi-source geographic data set.

[0007] a data processing module, configured to define a unified spatial reference framework for the multi-source geographic data set, perform coordinate system conversion based on the spatial reference framework, and implement multi-temporal temporal alignment on the converted data to obtain a spatio-temporally consistent geographic data cube;

[0008] a spatial feature acquisition module, configured to identify spatial aggregation patterns of agricultural land and distribution evolution trends of industrial buildings from the spatio-temporally consistent geographic data cube, and analyze topological connection relationships of the traffic network, and form an industrial spatial dynamic feature set by comprehensively integrating the spatial aggregation patterns, the distribution evolution trends and the topological connection relationships;

[0009] an evaluation index determination module, configured to couple the industrial spatial dynamic feature set with external economic performance data in a spatial dependence relationship, evaluate the correlation strength between the features and the performance based on the coupling result, and perform weighted fusion on the correlation strength to obtain a comprehensive evaluation index set;

[0010] a report generation module, configured to divide the effectiveness level according to the numerical distribution of the comprehensive evaluation index set, and output a village industry revitalization effectiveness evaluation report.

[0011] Preferably, when the data acquisition module acquires satellite remote sensing images, land use classification data and population statistical distribution information of a target rural area to form a multi-source geographic data set, it is specifically configured to:

[0012] acquire satellite remote sensing images of the target rural area, and perform radiation correction on the satellite remote sensing images to obtain radiation-corrected remote sensing images;

[0013] acquire land use classification data of the target rural area, and perform format unification processing on the land use classification data to obtain land use data in a unified format;

[0014] acquire population statistical distribution information of the target rural area, and perform spatial interpolation on the population statistical distribution information to obtain population statistical data with complete spatial coverage;

[0015] fuse the radiation-corrected remote sensing images, the land use data in the unified format and the population statistical distribution information with complete spatial coverage to obtain the multi-source geographic data set.

[0016] Preferably, when the data processing module defines a unified spatial reference framework for the multi-source geographic data set and performs coordinate system conversion based on the spatial reference framework, it is specifically configured to:

[0017] identify spatial aggregation patterns of agricultural land and industrial buildings in the multi-source geographic data set, and divide the range of an industrial core area according to the spatial aggregation patterns;

[0018] select an equirectangular conic projection system according to the range of the industrial core area, and construct an industrial-adapted spatial reference framework.

[0019] Based on the spatial reference framework of industrial adaptation, coordinate system conversion is implemented on multi-source geographic data sets;

[0020] The spatial consistency of the projected coordinate system data is verified, and the identified coordinate deviation is corrected to obtain a coordinate-unified and spatially consistent data set.

[0021] Preferably, the data processing module, when implementing multi-temporal temporal alignment on the converted data to obtain a spatio-temporally consistent geographic data cube, is specifically used for:

[0022] Extract the time attribute of the coordinate-unified and spatially consistent data set, and define the temporal alignment reference point combined with the rural agricultural production cycle;

[0023] According to the temporal alignment reference point, the time axis synchronization operation is implemented on the multi-temporal data;

[0024] Detect the time sequence gap of the synchronized data, compensate for the missing phase based on the continuity of industrial activities, and obtain a spatio-temporally consistent geographic data cube.

[0025] Preferably, the spatial feature acquisition module, when identifying the spatial aggregation pattern of agricultural land and the distribution evolution trend of industrial buildings, is specifically used for:

[0026] Extract the spatial distribution information of agricultural land from the spatio-temporally consistent geographic data cube, and implement spatial aggregation evaluation on the spatial distribution information to obtain an agricultural land aggregation index;

[0027] Based on the agricultural land aggregation index, the core area range of the spatial aggregation pattern of agricultural land is identified;

[0028] Extract the multi-temporal distribution information of industrial buildings from the spatio-temporally consistent geographic data cube, and implement time trend evaluation on the multi-temporal distribution information to obtain the distribution evolution trend of industrial buildings;

[0029] Verify the spatial coordination of the core area range of the spatial aggregation pattern of agricultural land and the distribution evolution trend of industrial buildings to obtain an industrial spatial dynamic feature subset.

[0030] Preferably, the spatial feature acquisition module, when analyzing the topological connection relationship of the traffic network, forms an industrial spatial dynamic feature set by integrating the spatial aggregation pattern, the distribution evolution trend, and the topological connection relationship, and is specifically used for:

[0031] Extract the spatial elements of the traffic network from the spatio-temporally consistent geographic data cube, and implement topological structure identification on the spatial elements to obtain a traffic network topological connection graph;

[0032] Based on the traffic network topology connection diagram, the accessibility relationship between the key nodes and the industrial regions is evaluated, and an industrial-oriented traffic connectivity index is obtained.

[0033] The spatial coupling evaluation is implemented by fusing the spatial agglomeration form of agricultural land, the distribution evolution trend of industrial buildings, and the industrial-oriented traffic connectivity index, and the dynamic characteristics set of industrial space is formed.

[0034] Preferably, when the evaluation index determination module couples the dynamic characteristics set of industrial space with external economic performance data based on spatial dependence relationship, and evaluates the correlation strength between the characteristics and the performance based on the coupling result, it is specifically used for:

[0035] A spatial dependence strength quantification mechanism of the dynamic characteristics set of industrial space and the external economic performance data is established, and a characteristic-performance correlation matrix is constructed based on spatial adjacency relationship.

[0036] The industrial synergy effect region is identified through the characteristic-performance correlation matrix, the resonance strength of spatial agglomeration form and economic performance is evaluated, and an industrial synergy effect index is obtained.

[0037] The temporal and spatial coordination evolution relationship between the characteristics and the performance is quantified by introducing the time sequence dimension of industrial evolution, constructing a dynamic coupling coefficient, and obtaining the correlation strength evaluation result.

[0038] Preferably, when the evaluation index determination module introduces the time sequence dimension of industrial evolution, constructs a dynamic coupling coefficient, and quantifies the temporal and spatial coordination evolution relationship between the characteristics and the performance, the correlation strength evaluation result is obtained, and it is specifically used for:

[0039] The stage characteristics in the distribution evolution trend of industrial buildings are extracted, and the industrial evolution stages are divided according to the periodicity of infrastructure construction.

[0040] A mapping relationship between different industrial evolution stages and spatial dependence strength is established, and a stage coupling strength correspondence table is formed.

[0041] Based on the stage coupling strength correspondence table, a coupling relationship graph reflecting the temporal and spatial coordination evolution relationship between the characteristics and the performance is constructed.

[0042] The coordination evolution degree of the characteristics and the performance is quantified through the coupling relationship graph, and the correlation strength evaluation result is obtained.

[0043] Preferably, the evaluation index determination module weights and fuses the correlation strength to obtain a comprehensive evaluation index set, and it is specifically used for:

[0044] Based on the coupling relationship graph, the dominant influence path in the temporal and spatial coordination evolution relationship is identified, and a weight distribution scheme is determined according to the path influence.

[0045] The weight distribution scheme is applied to adjust the numerical distribution of the correlation strength evaluation result, and a weighted and optimized correlation strength is obtained.

[0046] The weighted and optimized correlation strength is integrated with the industry space dynamic characteristic set to form a comprehensive evaluation index set.

[0047] Preferably, when the report generation module divides the effectiveness level according to the numerical distribution of the comprehensive evaluation index set and outputs the village industry revitalization effectiveness evaluation report, it is specifically used for:

[0048] Identifying the numerical distribution characteristics of the comprehensive evaluation index set, determining the effectiveness level division threshold according to the distribution inflection point;

[0049] Mapping each evaluation index to a unified level system based on the effectiveness level division threshold to obtain an effectiveness level set;

[0050] Integrating the effectiveness level set and the industry space dynamic characteristic set to construct a structured report element;

[0051] According to the structured report element, a rural industry revitalization effectiveness evaluation report containing effectiveness levels and spatial characteristics is compiled.

[0052] Advantages

[0053] Compared with the prior art, the present application has the following advantages:

[0054] 1. By multi-source geographic data collection, unified spatial reference framework construction and multi-temporal time alignment to form a spatio-temporal consistent geographic data cube, combined with the spatial dependence relationship coupling and correlation strength weighted fusion of industry space dynamic characteristics and external economic performance data, the comprehensive and accurate evaluation of the effectiveness of rural industry revitalization can be realized, and the internal relationship between the industrial space layout, evolution trend and economic performance can be clearly presented, providing reliable data support and evaluation basis for accurately grasping the industrial development situation and clearly optimizing the direction.

[0055] 2. By implementing radiation correction on satellite remote sensing images and performing spatial interpolation on population statistical distribution information, the integrity and accuracy of multi-source geographic data can be significantly improved; based on the selection of adaptive projection system for industry core area, the consistency of spatial data can be effectively guaranteed; combined with the aggregation evaluation of agricultural land, the analysis of industrial building evolution trend and the analysis of traffic network topology, the industry space dynamic characteristics can be further refined; the introduction of industry evolution time dimension to construct dynamic coupling coefficient and optimize weight distribution can enhance the scientificity and pertinence of evaluation index, and the final output evaluation report can better reflect the actual law of rural industry development, providing more detailed and effective reference for the accurate adjustment of industry development strategy. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1A system architecture diagram of a rural industry revitalization effectiveness evaluation system based on multi-source geographic data provided by an embodiment of the present application is shown in the figure.

[0057] The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments belong to some of the embodiments of the present application but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0059] The terms used in the embodiments of the present application are merely for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. "Plural" generally includes at least two.

[0060] Depending on the context, the word "if" or "if" as used herein can be interpreted as "when" or "when" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0061] In addition, the step sequence in each of the following method embodiments is only an example and is not strictly limited.

[0062] In fact, the server equipment deployed by the rural industry revitalization effectiveness evaluation system based on multi-source geographic data can be composed of one or more devices. The rural industry revitalization effectiveness evaluation system based on multi-source geographic data can be implemented as a business instance, a virtual machine, or a hardware device. For example, the rural industry revitalization effectiveness evaluation system based on multi-source geographic data can be implemented as a business instance deployed on one or more devices in a cloud node. In short, the rural industry revitalization effectiveness evaluation system based on multi-source geographic data can be understood as a software deployed on a cloud node, which provides a rural industry revitalization effectiveness evaluation system based on multi-source geographic data for each user terminal. Alternatively, the rural industry revitalization effectiveness evaluation system based on multi-source geographic data can also be implemented as a virtual machine deployed on one or more devices in a cloud node. The virtual machine has application software for managing each user terminal. Alternatively, the rural industry revitalization effectiveness evaluation system based on multi-source geographic data can also be implemented as a server composed of a plurality of same or different types of hardware devices, and one or more hardware devices are provided to provide a rural industry revitalization effectiveness evaluation system based on multi-source geographic data for each user terminal.

[0063] In terms of implementation, the rural industry revitalization effectiveness evaluation system based on multi-source geographic data and the user terminal are mutually adapted. That is, the rural industry revitalization effectiveness evaluation system based on multi-source geographic data is installed as an application on a cloud service platform, and the user terminal is a client that establishes a communication connection with the application; or the rural industry revitalization effectiveness evaluation system based on multi-source geographic data is implemented as a website, and the user terminal is implemented as a webpage; or the rural industry revitalization effectiveness evaluation system based on multi-source geographic data is implemented as a cloud service platform, and the user terminal is implemented as an applet in an instant messaging application.

[0064] As shown in Figure 1 FIG. 1 is a system architecture diagram of the rural industry revitalization effectiveness evaluation system based on multi-source geographic data according to an embodiment of the present application.

[0065] The rural industry revitalization effectiveness evaluation system based on multi-source geographic data 100 of the present application can be set in a cloud server, and in terms of implementation, it can be one or more service devices, or it can be installed as an application on a cloud (such as a server of a mobile service operator, a server cluster, etc.), or it can be developed as a website. According to the functions implemented, the rural industry revitalization effectiveness evaluation system based on multi-source geographic data 100 can include a data acquisition module 101, a data processing module 102, a spatial feature acquisition module 103, an evaluation index determination module 104, and a report generation module 105. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, and are stored in the memory of the electronic device.

[0066] In the embodiment of the present application, each of the above modules can be independently implemented and called by other modules in the rural industry revitalization effectiveness evaluation system based on multi-source geographic data. The calling here can be understood as that a module can connect multiple modules of another type and provide corresponding services for the connected multiple modules. The rural industry revitalization effectiveness evaluation system based on multi-source geographic data provided by the embodiment of the present application can adjust the application scope of the rural industry revitalization effectiveness evaluation system based on multi-source geographic data by increasing modules and directly calling without modifying program codes, realize cluster horizontal expansion, and achieve the purpose of quickly and flexibly expanding the rural industry revitalization effectiveness evaluation system based on multi-source geographic data. In actual application, the above modules can be arranged in the same device or different devices, or in a virtual device, such as a service instance in a cloud server.

[0067] The following will describe the components and specific work flow of the rural industry revitalization effectiveness evaluation system based on multi-source geographic data with reference to specific embodiments:

[0068] The data acquisition module 101 is configured to acquire satellite remote sensing images, land use classification data and population statistical distribution information of a target rural area to form a multi-source geographic data set.

[0069] The data processing module 102 is configured to define a unified spatial reference framework for the multi-source geographic data set, perform coordinate system conversion based on the spatial reference framework, and implement multi-temporal temporal alignment on the converted data to obtain a spatio-temporally consistent geographic data cube.

[0070] The spatial feature acquisition module 103 is configured to identify the spatial aggregation form of agricultural land and the distribution evolution trend of industrial buildings from the spatio-temporally consistent geographic data cube, analyze the topological connection relationship of the traffic network, and form an industry spatial dynamic feature set by comprehensively integrating the spatial aggregation form, the distribution evolution trend and the topological connection relationship.

[0071] The evaluation index determination module 104 is configured to couple the industry spatial dynamic feature set with external economic performance data in a spatial dependence relationship, evaluate the correlation strength between the features and the performance based on the coupling result, and perform weighted fusion on the correlation strength to obtain a comprehensive evaluation index set.

[0072] The report generation module 105 is configured to divide the effectiveness level according to the numerical distribution of the comprehensive evaluation index set, and output a rural industry revitalization effectiveness evaluation report.

[0073] In a preferred embodiment, when the data acquisition module 101 acquires satellite remote sensing images, land use classification data and population statistical distribution information of a target rural area to form a multi-source geographic data set, it is specifically configured to:

[0074] Satellite remote sensing images of the target rural area are collected, and radiation correction is performed on the satellite remote sensing images to obtain radiation-corrected remote sensing images.

[0075] Land use classification data of the target rural area are collected, and format unification processing is performed on the land use classification data to obtain land use data in a unified format.

[0076] Population statistical distribution information of the target rural area is collected, and spatial interpolation is performed on the population statistical distribution information to obtain population statistical data with complete spatial coverage.

[0077] The radiation-corrected remote sensing images, the land use data in a unified format, and the population statistical distribution information with complete spatial coverage are fused to obtain a multi-source geographic data set.

[0078] Specifically, satellite remote sensing images of the target rural area are obtained from a satellite image database, and digital quantization values of the images are converted into real ground reflectivity through sensor calibration parameters to eliminate radiation interference caused by sensor response errors and atmospheric scattering and absorption, complete the radiation correction operation, and obtain radiation-corrected remote sensing images.

[0079] Different formats of land use classification data of the target rural area are obtained from the land department, vector file format land use data is converted into raster file format, and the national geodetic coordinate system is uniformly used. The "land class name" and "area in hectares" fields in the attribute table are uniformly named as "land type" and "area", and land use data in a unified format is obtained.

[0080] Township-level population census data of the target rural area are obtained from the statistical department, and the coordinates of the township government sites are used as sample points. The weights are determined according to the straight-line distances between the sample points, the closer the distance, the greater the weight. The township population is distributed to each grid cell in its jurisdiction, and the spatial area without population statistical data is filled to obtain population statistical data with complete spatial coverage.

[0081] The radiation-corrected remote sensing images, the land use data in a unified format, and the population statistical data with complete spatial coverage are imported into a geographic information system, and spatial registration is performed based on the national geodetic coordinate system to completely match the geographic positions of each data layer. All layers are superimposed and the association between attribute fields is established to obtain a multi-source geographic data set.

[0082] In general, collecting satellite remote sensing images, land use classification data and population distribution information of the target rural area and forming a multi-source geographic data set can comprehensively cover the spatial pattern and population distribution core elements related to rural industrial development. Radiometric correction of satellite remote sensing images can eliminate sensor errors and atmospheric interference, improving the accuracy of images reflecting the true state of the earth's surface. Format unification of land use classification data can solve the heterogeneity problem of data from different sources. Spatial interpolation of population distribution information can fill in the data spatial coverage gaps. The fusion of multi-source data not only retains the unique value of each data source, but also achieves information complementation and synergy, providing high-quality data support for subsequent unified spatial reference framework construction and multi-temporal alignment, laying a solid foundation for accurately extracting industrial spatial dynamic characteristics and establishing a deep correlation with economic performance data, and helping to improve the comprehensiveness and accuracy of rural industrial revitalization performance evaluation.

[0083] In a preferred embodiment, the data processing module 102 defines a unified spatial reference framework for the multi-source geographic data set, and performs coordinate system conversion based on the spatial reference framework, specifically for:

[0084] Identifying the spatial aggregation patterns of agricultural land and industrial buildings in the multi-source geographic data set, and delineating the industrial core area range according to the spatial aggregation patterns;

[0085] Selecting an equirectangular conic projection system according to the industrial core area range, and constructing an industry-adapted spatial reference framework;

[0086] Performing coordinate system conversion on the multi-source geographic data set based on the industry-adapted spatial reference framework;

[0087] Verifying the spatial consistency of the projection coordinate system data, correcting the identified coordinate deviation, and obtaining a coordinate-unified and spatially-consistent data set.

[0088] Specifically, through land use data and radiation-corrected remote sensing images in the multi-source geographic data set, the patch boundaries of agricultural land distribution and the outline range of industrial building concentration distribution are extracted, the continuous area of agricultural land patches and the aggregation density of industrial buildings are counted, and the industrial core area range is delineated according to the peripheral boundary line of the continuous patch and the peripheral contour line of the building aggregation area.

[0089] According to the latitude and longitude range of the industrial core area, an equirectangular conic projection system is selected, the meridian passing through the center of the industrial core area is determined as the central meridian, and the latitudes on the north and south sides of the industrial core area are selected as the standard latitudes. Combined with the deformation control requirements of the projection system, the projection parameters are set to construct an industry-adapted spatial reference framework.

[0090] The original coordinate information of each layer in the multi-source geographic data set is extracted, and the geographic coordinates of each layer are converted into planar rectangular coordinates according to the projection parameters of the spatial reference framework adapted to the industry. The coordinate values of each ground feature element are adjusted to match the coordinate rules of the projection system, and the coordinate system conversion is completed.

[0091] The boundary coordinates and position relationship of the same ground features in each data layer after coordinate conversion are compared, the spatial connection of the ground features in the core area of the industry is checked, the coordinate values with position deviation are adjusted point by point, the ground feature boundaries of different layers are completely overlapped, and the data set with unified coordinates and consistent space is obtained.

[0092] In this embodiment, the data processing module 102 is used for:

[0093] Extracting the time attributes of the data set with unified coordinates and consistent space, and defining the time sequence alignment reference point in combination with the rural agricultural production cycle;

[0094] According to the time sequence alignment reference point, the time axis synchronization operation is performed on the multi-time phase data;

[0095] Detecting the time sequence gap of the synchronized data, compensating for the missing time phase based on the continuity of industrial activities, and obtaining the spatio-temporal consistent geographic data cube.

[0096] Specifically, the remote sensing image acquisition time, the land use data update time, the population statistics data statistics time and other time attributes are extracted from the metadata of each layer of the data set with unified coordinates and consistent space, and the first day of each month is determined as the time sequence alignment reference point in combination with the three months of spring ploughing as the starting point of the annual time sequence and the eleven months of autumn harvest as the end point of the annual time sequence in the rural agricultural production cycle.

[0097] According to the defined time sequence alignment reference point, the collection time of the multi-time phase data is matched with the reference point, the data with collection time in late February is adjusted to the first day of March, the data with collection time in mid-November is adjusted to the first day of November, the time labels of all time phase data are unified to the corresponding reference point position, and the time axis synchronization operation is completed.

[0098] According to the time sequence, the data of each reference point after synchronization is traversed, the time nodes without corresponding data are marked as time sequence gaps, the agricultural land vegetation coverage state, industrial building operation data and other industrial activity information of adjacent time phases before and after the gap are referred to, the industrial activity feature data of the gap period is filled, and the spatio-temporal consistent geographic data cube is obtained.

[0099] In general, defining a unified spatial reference framework for multi-source geographic data sets, delineating the core area of the industry according to the spatial aggregation pattern of agricultural land and industrial buildings, and selecting an appropriate equirectangular conic projection system can solve the problem of heterogeneity of multi-source geographic data with different formats and different precision. It provides a unified spatial reference for data from different sources, performs coordinate system conversion based on the framework, and verifies the spatial consistency of the projected coordinate system data, corrects the identified coordinate deviation, eliminates the spatial coordinate deviation between satellite remote sensing and land use data, ensures the accurate matching of ground feature elements in each data layer, avoids analysis errors caused by coordinate disorder, and implements multi-temporal alignment of the converted data. Combined with the definition of the synchronous time axis of the time sequence reference point based on the continuity of industrial activities, the missing time phase can be compensated based on the continuity of industrial activities, the gaps in the time sequence of the data can be filled, the continuity and integrity of the data in the time dimension can be ensured, and the time sequence correlation of the industrial development can be avoided. Inconsistent, the final spatio-temporal consistent geographic data cube can provide high-quality data support for extracting the aggregation pattern of agricultural land, the evolution trend of industrial buildings, and the topological relationship of the transportation network, lay the foundation for establishing the deep correlation between industrial spatial characteristics and economic performance data, and effectively improve the data reliability and analysis accuracy of the evaluation of rural industrial revitalization.

[0100] In a preferred embodiment, the spatial feature acquisition module 103, when identifying the spatial aggregation pattern of agricultural land and the distribution evolution trend of industrial buildings, is specifically used for:

[0101] extracting the spatial distribution information of agricultural land from the spatio-temporal consistent geographic data cube, performing spatial aggregation evaluation on the spatial distribution information, and obtaining an agricultural land aggregation index;

[0102] identifying the core area range of the spatial aggregation pattern of agricultural land based on the agricultural land aggregation index;

[0103] extracting the multi-temporal distribution information of industrial buildings from the spatio-temporal consistent geographic data cube, performing time trend evaluation on the multi-temporal distribution information, and obtaining an industrial building distribution evolution trend;

[0104] verifying the spatial coordination of the core area range of the spatial aggregation pattern of agricultural land and the distribution evolution trend of industrial buildings, and obtaining an industrial spatial dynamic feature subset.

[0105] Specifically, the spatial distribution information of the patch boundary, area, and connection relationship of adjacent patches of agricultural land is extracted from the spatio-temporal consistent geographic data cube, the number of adjacent patches and the total area of connected patches of each agricultural land patch are counted, and the agricultural land aggregation index is determined by calculating the concentration distribution degree of the patch.

[0106] Based on the high-value area range of the agricultural land aggregation index, the agricultural land patch group with the largest contiguous area and the closest connection between adjacent patches is selected, and the boundary line of the peripheral patch is used as the boundary to delineate the core area range of the spatial aggregation form of agricultural land.

[0107] From the spatio-temporally consistent geographic data cube, the multi-temporal distribution information such as the location, quantity and area of industrial buildings at different time nodes is extracted, the newly added locations and disappeared areas of industrial buildings at adjacent time nodes are compared, the distribution range changes of industrial buildings at each period are counted, and the distribution evolution trend of industrial buildings is obtained.

[0108] The core area range of the spatial aggregation form of agricultural land is superimposed and compared with the spatial range of the distribution evolution trend of industrial buildings, the spatial conflict between the evolution area of industrial buildings and the agricultural core area is checked, the spatial layout matching degree of the two is confirmed, and the dynamic feature subset of industrial space is obtained.

[0109] In this embodiment, the spatial feature acquisition module 103 analyzes the topological connection relationship of the traffic network, integrates the spatial aggregation form, the distribution evolution trend and the topological connection relationship to form the industrial space dynamic feature set, and is specifically used for:

[0110] From the spatio-temporally consistent geographic data cube, the spatial elements of the traffic network are extracted, and the topological structure of the spatial elements is identified to obtain a traffic network topological connection graph;

[0111] Based on the traffic network topological connection graph, the accessibility relationship between the key nodes and the industrial area is evaluated to obtain an industrial-oriented traffic connectivity index;

[0112] The spatial aggregation form of agricultural land, the distribution evolution trend of industrial buildings and the industrial-oriented traffic connectivity index are fused to implement spatial coupling evaluation, and an industrial space dynamic feature set is formed.

[0113] Specifically, the road center line, intersection node and other spatial elements of the traffic network are extracted from the spatio-temporally consistent geographic data cube, the connection relationship between each intersection node and the road center line is marked, the connection order and direction between roads are sorted out, and a traffic network topological connection graph containing node connection mode and road association relationship is drawn.

[0114] Based on the traffic network topological connection graph, the number of direct connection roads from the key traffic nodes to the core area of agricultural land and the evolution area of industrial buildings is counted, the grades and traffic capacity of the connection roads are checked, and the industrial-oriented traffic connectivity index is determined according to the connection closeness between the nodes and the industrial area.

[0115] The space aggregation form range of agricultural land, the distribution evolution trend area of industrial buildings, and the industry-oriented traffic connectivity index layer are spatially overlapped, the spatial matching degree of the agricultural aggregation area and the high connectivity traffic node is analyzed, the connection degree of the industrial evolution area and the traffic network is checked, the spatial coupling evaluation is completed by evaluating the spatial correlation state of each element, and the industrial space dynamic characteristic set is formed.

[0116] In general, the spatial aggregation form of agricultural land is identified from the spatio-temporally consistent geographic data cube, the core area range is determined by extracting spatial distribution information for aggregation evaluation, the continuous distribution characteristics and core development area of the agricultural industry can be accurately captured, the fuzzy cognition of agricultural layout is avoided, the multi-temporal distribution information is extracted for time trend evaluation when identifying the distribution evolution trend of industrial buildings, the expansion, contraction or stable state of the industrial industry over time can be clearly presented, the dynamic change trajectory of industrial development is reflected, the topological structure identification is performed on the spatial elements when analyzing the topological connection relationship of the traffic network, and the accessibility of the key nodes and the industrial area is evaluated, the support ability of the traffic infrastructure to the industrial development can be clearly determined, the external connection efficiency of the industrial area is judged, the three form an industrial space dynamic characteristic set, the spatial correlation information of agriculture, industry and traffic is integrated, the limitations of single element analysis are broken, and the overall situation of the rural industrial space development is completely presented, comprehensive feature support is provided for subsequent establishment of spatial dependence relationship coupling with external economic performance data, and the internal correlation between industrial space layout and economic performance is more accurately mined, and the scientificity and pertinence of rural industrial revitalization effect evaluation are improved.

[0117] In a preferred embodiment, the evaluation index determination module 104 is specifically used for:

[0118] establishing a spatial dependence strength quantification mechanism of the industrial space dynamic characteristic set and the external economic performance data, constructing a feature-performance correlation matrix based on spatial adjacency relationship;

[0119] identifying an industrial synergy effect area through the feature-performance correlation matrix, evaluating the resonance strength of the spatial aggregation form and the economic performance, and obtaining an industrial synergy effect index;

[0120] introducing an industrial evolution time sequence dimension, constructing a dynamic coupling coefficient, quantifying the spatio-temporal synergistic evolution relationship between features and performance, and obtaining an association strength evaluation result.

[0121] Specifically, the target rural area is divided into spatial units, and the characteristic values in the set of industrial space dynamic characteristics, such as the agricultural agglomeration range, the industrial evolution trend, and the traffic connectivity, are corresponded to the performance values in the external economic performance data, such as the industrial output value and the number of employees, one by one. The characteristic and performance correlation state of adjacent units is marked according to the adjacency relationship of the spatial units, and a spatial dependence strength quantification mechanism of the set of industrial space dynamic characteristics and the external economic performance data is established. The characteristic-performance correlation matrix is constructed based on the spatial adjacency relationship.

[0122] The characteristic values and performance values of each spatial unit in the characteristic-performance correlation matrix are traversed, and the spatial units with characteristic values in the high interval and performance values in the high interval at the same time are filtered out. The contiguous range of these units is delineated as the industrial synergy effect area. The matching proportion of the spatial agglomeration form index and the economic performance data in the synergy effect area is counted, the resonance strength of the spatial agglomeration form and the economic performance is evaluated, and the industrial synergy effect index is obtained.

[0123] The time nodes of industrial evolution are divided according to years, and the industrial space dynamic characteristics and economic performance data of each time node are extracted. The change direction and amplitude of the characteristic index and the performance data at each time node are compared, the dynamic coupling coefficient is constructed according to the synchronous change degree of the characteristics and the performance, the synergistic relationship of the characteristics and the performance with time is quantified through the dynamic coupling coefficient, and the correlation strength evaluation result is obtained.

[0124] It should be noted that, as a preferred embodiment, the dynamic coupling coefficient can be calculated by the following formula:

[0125]

[0126] In the formula, the dynamic coupling coefficient is represented by the normalized value of the th industrial space dynamic characteristic at the th stage, the normalized value of the th external economic performance data at the th stage, and the total number of paired indexes of the industrial space dynamic characteristics and the external economic performance data.

[0127] Specifically, the normalized value of the industrial space dynamic characteristic at the th stage is from the set of industrial space dynamic characteristics, which is formed by the fusion of the agricultural land spatial agglomeration form, the industrial building distribution evolution trend, and the traffic connectivity index oriented to industry. The normalization is completed by adjusting the original values of each characteristic to the interval of 0 to 1; the normalized value of the external economic performance data at the The normalized values come from the economic data such as industrial output value and number of employees published by the statistical department, and the original performance values are also adjusted to the interval of 0 to 1 to complete normalization; the total number of matched indicators of industrial space dynamic characteristics and external economic performance data is determined by matching the number of characteristic items in the characteristic set with the number of economic performance data items, and each matched characteristic and performance is an indicator.

[0128] Specifically, the dynamic coupling coefficient is used to measure the spatiotemporal evolution correlation between the industrial space dynamic characteristics and the external economic performance in the stage The sum of the products of each characteristic and the corresponding performance value is calculated, and then divided by the square root of the product of the square sum of the characteristic value and the square sum of the performance value, to quantify the spatiotemporal evolution correlation between the two.

[0129] In general, when the change direction of the industrial space dynamic characteristics and the external economic performance is consistent and the numerical matching degree is high, the dynamic coupling coefficient value tends to 1; when the change direction of the two is opposite or the numerical matching degree is low, the dynamic coupling coefficient value tends to 0; the stronger the synergy between the industrial space dynamic characteristics and the external economic performance, the higher the dynamic coupling coefficient value, and the weaker the synergy, the lower the value.

[0130] In this embodiment, the evaluation index determination module 104 introduces the industrial evolution time dimension to construct the dynamic coupling coefficient, quantify the spatiotemporal evolution relationship between the characteristics and the performance, obtain the correlation strength evaluation result, and is specifically used for:

[0131] extracting the stage characteristics in the evolution trend of industrial building distribution, and dividing the industrial evolution stage according to the periodicity of infrastructure construction;

[0132] establishing a mapping relationship between different industrial evolution stages and space dependence strengths to form a stage coupling strength correspondence table;

[0133] based on the stage coupling strength correspondence table, constructing a coupling relationship graph reflecting the spatiotemporal evolution relationship between the characteristics and the performance;

[0134] quantifying the synergistic evolution degree of the characteristics and the performance through the coupling relationship graph to obtain the correlation strength evaluation result.

[0135] Specifically, the change characteristics of industrial buildings from scattered addition to concentrated expansion to stable operation in the evolution trend of industrial building distribution are extracted, and the industrial evolution is divided into three industrial evolution stages of start-up stage, growth stage and mature stage according to the rule that the roads, water supply and other facilities in rural infrastructure construction have a construction cycle of 3 years.

[0136] The division results of the start-up stage, growth stage and mature stage are sorted out, the spatial dependence strength of the dynamic characteristics of the industrial space and the external economic performance data in each stage is counted, the corresponding relationship of the start-up stage corresponding to low spatial dependence strength, the growth stage corresponding to medium spatial dependence strength and the mature stage corresponding to high spatial dependence strength is sorted out, and a stage coupling strength corresponding table is formed.

[0137] Taking the industrial evolution stage in the stage coupling strength corresponding table as the horizontal axis and the spatial dependence strength as the vertical axis, the matching data of each stage and the spatial dependence strength in the corresponding table are marked as coordinate points, a smooth curve is connected to each coordinate point, and a coupling relationship graph reflecting the spatio-temporal coordination evolution relationship of characteristics and performance is constructed.

[0138] The change amplitude and trend of the curve in the coupling relationship graph are observed, the matching number of the spatial dependence strength and the characteristic-performance coordination state corresponding to the curve in each industrial evolution stage is counted, the coordination evolution degree of characteristics and performance is quantified according to the matching number ratio, and finally the correlation strength evaluation result is obtained.

[0139] In this embodiment, the evaluation index determination module 104 performs weighted fusion on the correlation strength to obtain a comprehensive evaluation index set, which is specifically used for:

[0140] Based on the coupling relationship graph, the dominant influence path in the spatio-temporal coordination evolution relationship is identified, and the weight distribution scheme is determined according to the path influence;

[0141] The numerical distribution of the correlation strength evaluation result is adjusted by applying the weight distribution scheme to obtain the weighted and optimized correlation strength;

[0142] The weighted and optimized correlation strength and the industrial space dynamic characteristic set are integrated to form a comprehensive evaluation index set.

[0143] Specifically, the curve change amplitude corresponding to each influence path in the coupling relationship graph is observed, the traffic connectivity-industrial evolution-economic performance path with the largest curve slope is selected as the dominant influence path in the spatio-temporal coordination evolution relationship, the contribution proportion of the dominant path and other auxiliary paths to the coordination evolution is counted, and the weight distribution scheme in which the weight of the dominant path is higher than that of the auxiliary path is determined according to the contribution proportion.

[0144] The weight values of each path in the weight distribution scheme are matched one by one with the corresponding correlation strength evaluation results, the correlation strength evaluation result corresponding to the dominant path is multiplied by a higher weight value, and the correlation strength evaluation result corresponding to the auxiliary path is multiplied by a lower weight value. In this way, the numerical distribution of the correlation strength evaluation result is adjusted to obtain the weighted and optimized correlation strength.

[0145] The weighted and optimized correlation strength is added as a new indicator field to the attribute table of the industrial space dynamic feature set. It is then merged with the original feature indicators such as the spatial agglomeration pattern of agricultural land, the distribution and evolution trend of industrial buildings, and the transportation connectivity of industry orientation. The names and numerical formats of all indicators are then standardized to form a comprehensive evaluation indicator set.

[0146] It should be noted that, as a preferred implementation method, the comprehensive evaluation index set can be weighted and fused using the following formula:

[0147]

[0148] In the formula, Indicates the first Comprehensive evaluation indicators for each spatial unit; Indicates the first The weight of the dominant influence path, Indicates the first The spatial unit in the first The results of the correlation strength assessment on the dominant influence path, This indicates the total number of dominant influence paths.

[0149] Specifically, in Eq. The comprehensive evaluation index of each spatial unit is the result of weighted fusion calculation; the first The weight of each dominant influence path is derived from the influence of the spatiotemporal co-evolution dominant influence paths identified in the coupling relationship graph, and is determined based on the proportion of the path's contribution to the co-evolution; The spatial unit in the first The correlation strength assessment results on the dominant influence paths are derived from the correlation strength assessment stage, reflecting the degree of correlation between the characteristics and performance of the spatial unit under the corresponding path; the total number of dominant influence paths is the statistical result of the number of all dominant influence paths identified in the coupling relationship map.

[0150] Specifically, the formula is used to sum the correlation strength assessment results under different dominant influence paths according to their corresponding weights, and finally obtain the comprehensive assessment index of each spatial unit, quantifying the comprehensive effectiveness of industrial revitalization of the spatial unit.

[0151] In general, the higher the weight of a dominant influence path and the higher the correlation strength assessment result of the corresponding spatial unit on that path, the larger the comprehensive assessment index value of that spatial unit. As the weight or correlation strength assessment result of the dominant influence path increases, the comprehensive assessment index value also increases, and vice versa.

[0152] In general, the coupling of the industry space dynamic feature set and the external economic performance data is coupled with the spatial dependence relationship, the feature-performance correlation matrix is constructed by establishing a quantitative mechanism, the industry synergy effect area can be accurately identified, the limitation that the industry space features and economic performance data are disconnected in the past is broken, and the correlation state of the two in the spatial dimension is clearly presented. Based on the coupling results, the correlation strength between features and performance is evaluated, the dynamic coupling coefficient is introduced based on the time sequence dimension of industrial evolution, the spatio-temporal synergistic evolution relationship between features and performance is quantified, the evaluation deviation caused by relying only on static data is avoided, and the periodicity of rural industry development is more in line with the periodicity. When the correlation strength is weighted and fused, the dominant influence path is identified according to the coupling relationship graph and the weight distribution scheme is determined, which can highlight the key role of the key path to industrial revitalization and avoid the weakening of important information caused by average weighting. Finally, the comprehensive evaluation index set obtained integrates the industry space dynamic features and the weighted correlation strength, which not only retains the detailed information in the spatial dimension, but also integrates the correlation analysis of economic performance, provides comprehensive and accurate index support for subsequent designation of effectiveness level and generation of evaluation report, and effectively improves the scientificity and pertinence of rural industry revitalization effectiveness evaluation.

[0153] In a preferred embodiment, the report generation module 105 is specifically used for:

[0154] identifying the numerical distribution characteristics of the comprehensive evaluation index set, determining the effectiveness level division threshold according to the distribution inflection point;

[0155] mapping each evaluation index to a unified level system based on the effectiveness level division threshold to obtain a set of effectiveness levels;

[0156] integrating the set of effectiveness levels and the set of industry space dynamic features to construct structured report elements;

[0157] compiling a rural industry revitalization effectiveness evaluation report containing effectiveness levels and spatial features according to the structured report elements.

[0158] Specifically, the numerical values of all indexes in the comprehensive evaluation index set are counted, arranged in order from small to large, and the turning point where the numerical growth rate changes from fast to slow or from slow to fast is found as the distribution inflection point. The numerical value corresponding to the inflection point is taken as the division point to determine the effectiveness level division threshold of the excellent, good, general and poor four levels.

[0159] The specific values of each evaluation index are compared with the threshold values of the performance level one by one. The index with a value higher than the highest threshold value is mapped to the excellent level, the index between the highest threshold value and the intermediate high threshold value is mapped to the good level, the index between the intermediate high threshold value and the intermediate low threshold value is mapped to the general level, and the index lower than the intermediate low threshold value is mapped to the poor level. The mapping results of all indexes are collected to obtain a performance level set.

[0160] The spatial feature information such as the spatial aggregation form of agricultural land and the evolution trend of industrial building distribution in the industrial space dynamic feature set is extracted. Each spatial feature is associated with the corresponding evaluation index level in the performance level set one by one to clearly show the performance of each spatial feature. A structured data combination containing the spatial feature name, corresponding evaluation index and performance level is formed to construct a structured report element.

[0161] According to the classification order of spatial features, the contents in the structured report element are arranged in chapters. The specific performance of each industrial space dynamic feature is first described, and then the performance level is presented. The index value corresponding to each level is supplemented. Finally, the overall industrial revitalization performance is summarized, and a rural industrial revitalization performance evaluation report containing performance levels and spatial features is compiled.

[0162] In summary, the performance level is determined according to the numerical distribution of the comprehensive evaluation index set. By identifying the numerical distribution characteristics and determining the performance level threshold value based on the distribution inflection point, the bias of relying on subjective experience to set the level standard can be avoided, and the level division is more in line with the actual industrial development status reflected by the data. Based on the threshold value, each evaluation index is mapped to a unified level system, which can eliminate the differences in evaluation scales of different dimensions such as agricultural land aggregation, industrial building evolution and traffic connectivity, so that the performance of each index has direct comparability. Integrating the performance level set and the industrial space dynamic feature set to construct a structured report element can make the evaluation report contain not only quantitative performance level results, but also concrete information such as industrial space layout form and evolution trend, avoiding the lack of spatial dimension interpretation when the report only presents abstract numerical values. The final output of the rural industrial revitalization performance evaluation report can clearly present the advantages and weaknesses of the target rural area industrial revitalization, provide intuitive and accurate reference for relevant departments to adjust development strategies and optimize resource input direction, and help to scientifically promote the high-quality development of rural industries.

[0163] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0164] The embodiments can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by a digital computer, perceiving an environment, acquiring knowledge and using the knowledge to obtain optimal results.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A rural industry revitalization effectiveness evaluation system based on multi-source geographic data, characterized in that, The system comprises a data acquisition module, a data processing module, a spatial feature acquisition module, an evaluation index determination module and a report generation module, wherein: The data acquisition module is configured to acquire satellite remote sensing images, land use classification data and population statistical distribution information of a target rural area to form a multi-source geographic data set; The data processing module is configured to define a unified spatial reference framework for the multi-source geographic data set, perform coordinate system conversion based on the spatial reference framework, and implement multi-temporal temporal alignment on the converted data to obtain a spatio-temporally consistent geographic data cube; The spatial feature acquisition module is configured to identify the spatial aggregation form of agricultural land and the distribution evolution trend of industrial buildings from the spatio-temporally consistent geographic data cube, and analyze the topological connection relationship of the traffic network, and comprehensively form an industrial space dynamic feature set based on the spatial aggregation form, the distribution evolution trend and the topological connection relationship; The evaluation index determination module is configured to couple the industrial space dynamic feature set with external economic performance data based on spatial dependence relationship, evaluate the correlation strength between the features and the performance based on the coupling result, and weight and fuse the correlation strength to obtain a comprehensive evaluation index set; The report generation module is configured to divide the effectiveness level according to the numerical distribution of the comprehensive evaluation index set, and output a village industry revitalization effectiveness evaluation report; The evaluation index determination module is configured to: establish a spatial dependence strength quantification mechanism for the industrial space dynamic feature set and the external economic performance data, and construct a feature-performance correlation matrix based on the spatial adjacency relationship; identify industrial synergy effect regions through the feature-performance correlation matrix, evaluate the resonance strength of the spatial aggregation form and the economic performance, and obtain an industrial synergy effect index; introduce an industrial evolution time sequence dimension, construct a dynamic coupling coefficient, quantify the spatio-temporal synergistic evolution relationship between the features and the performance, and obtain the correlation strength evaluation result; The evaluation index determination module is configured to: extract the stage characteristics in the industrial building distribution evolution trend, and divide the industrial evolution stage according to the periodicity of infrastructure construction; establish a mapping relationship between different industrial evolution stages and spatial dependence strength to form a stage coupling strength correspondence table; construct a coupling relationship graph reflecting the spatio-temporal synergistic evolution relationship between the features and the performance based on the stage coupling strength correspondence table; quantify the synergistic evolution degree of the features and the performance through the coupling relationship graph to obtain the correlation strength evaluation result; The evaluation index determination module is configured to: identify the dominant influence path in the spatio-temporal synergistic evolution relationship based on the coupling relationship graph, and determine a weight distribution scheme according to the path influence; adjust the numerical distribution of the correlation strength evaluation result by applying the weight distribution scheme to obtain a weighted and optimized correlation strength; integrate the weighted and optimized correlation strength and the industrial space dynamic feature set to form a comprehensive evaluation index set. 2.The rural industry revitalization effectiveness evaluation system based on multi-source geographic data according to claim 1, wherein, The data collection module is specifically used for the following when collecting satellite remote sensing images, land use classification data and population statistical distribution information of a target rural area to form a multi-source geographic data set: collecting satellite remote sensing images of the target rural area, performing radiation correction on the satellite remote sensing images, and obtaining radiation corrected remote sensing images; collecting land use classification data of the target rural area, performing format unification processing on the land use classification data, and obtaining land use data in a unified format; collecting population statistical distribution information of the target rural area, performing spatial interpolation on the population statistical distribution information, and obtaining population statistical data with complete spatial coverage; fusing the radiation corrected remote sensing images, the land use data in the unified format and the population statistical distribution information with complete spatial coverage to obtain the multi-source geographic data set. 3.The rural industry revitalization effectiveness evaluation system based on multi-source geographic data of claim 1, wherein, The data processing module is specifically used for the following when defining a unified spatial reference framework for the multi-source geographic data set and performing coordinate system conversion based on the spatial reference framework: identifying the spatial aggregation form of agricultural land and industrial buildings in the multi-source geographic data set, and delimiting the range of an industrial core area according to the spatial aggregation form; selecting an equirectangular conic projection system according to the range of the industrial core area, and constructing an industry-adapted spatial reference framework; performing coordinate system conversion on the multi-source geographic data set based on the industry-adapted spatial reference framework; verifying the spatial consistency of the projection coordinate system data, correcting the identified coordinate deviation, and obtaining a data set with unified coordinates and spatial consistency. 4.The rural industry revitalization effectiveness evaluation system based on multi-source geographic data according to claim 3, wherein, The data processing module is specifically used for the following when performing multi-temporal time alignment on the converted data to obtain a spatio-temporally consistent geographic data cube: extracting the time attribute of the data set with unified coordinates and spatial consistency, and combining the rural agricultural production cycle to define a time alignment reference point; performing time axis synchronization operation on the multi-temporal data according to the time alignment reference point; detecting the time sequence gap of the synchronized data, compensating for the missing phase based on the continuity of industrial activities, and obtaining the spatio-temporally consistent geographic data cube. 5.The rural industry revitalization effectiveness evaluation system based on multi-source geographic data of claim 1, wherein, The spatial feature acquisition module is specifically used for the following when identifying the spatial aggregation form of agricultural land and the distribution evolution trend of industrial buildings: extracting the spatial distribution information of agricultural land from the spatio-temporally consistent geographic data cube, performing spatial aggregation evaluation on the spatial distribution information, and obtaining an agricultural land aggregation index; identifying the core area range of the spatial aggregation form of agricultural land based on the agricultural land aggregation index; extracting multi-temporal distribution information of industrial buildings from the spatio-temporally consistent geographic data cube, performing time trend evaluation on the multi-temporal distribution information, and obtaining an industrial building distribution evolution trend; verifying the spatial coordination of the core area range of the spatial aggregation form of agricultural land and the distribution evolution trend of industrial buildings, and obtaining an industrial spatial dynamic feature subset. 6.The rural industry revitalization effectiveness evaluation system based on multi-source geographic data according to claim 5, wherein, The spatial feature acquisition module is specifically used for the following when analyzing the topological connection relationship of the traffic network and forming an industrial spatial dynamic feature set by comprehensively considering the spatial aggregation form, the distribution evolution trend and the topological connection relationship: extracting spatial elements of the traffic network from the spatio-temporally consistent geographic data cube, performing topological structure identification on the spatial elements, and obtaining a traffic network topological connection graph; Based on the traffic network topology connection diagram, the accessibility relationship between the key nodes and the industrial areas is evaluated, and an industrial-oriented traffic connectivity index is obtained; Fusion of the spatial aggregation form of agricultural land, the distribution evolution trend of industrial buildings and the industrial-oriented traffic connectivity index, implement spatial coupling evaluation, form the dynamic characteristics set of industrial space. 7.The rural industry revitalization effectiveness evaluation system based on multi-source geographic data of claim 1, wherein, When the report generation module divides the effectiveness level according to the numerical distribution of the comprehensive evaluation index set and outputs the village industry revitalization effectiveness evaluation report, it is specifically used for: Identify the numerical distribution characteristics of the comprehensive evaluation index set, and determine the effectiveness level division threshold according to the distribution inflection point; Based on the effectiveness level division threshold, each evaluation index is mapped to a unified level system to obtain an effectiveness level set; Integrate the effectiveness level set and the dynamic characteristics set of industrial space to construct a structured report element; According to the structured report element, compile the rural industry revitalization effectiveness evaluation report containing the effectiveness level and the spatial characteristics.

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