A method and system for monitoring and evaluating national land space planning data

Abstract

The present invention relates to the field of data evaluation technology, and in particular to a method and system for monitoring and evaluating land space planning data. The method comprises the following steps: obtaining land space GIS remote sensing images; performing regional plot segmentation on the land space GIS remote sensing images to generate a land space segmentation plot map; screening the land space segmentation plot map for infrastructure spatial layout to obtain a land space infrastructure spatial layout map; collecting intelligent device operation data on the land space infrastructure spatial layout map to obtain a regional intelligent device operation data set; calculating the intelligent device coverage rate on the regional intelligent device operation data set to obtain intelligent device coverage rate data. The present invention realizes comprehensive, real-time and accurate land space planning data monitoring and evaluation by integrating GIS remote sensing images, intelligent device data, environmental data and digital twin technology, and significantly improves the scientificity and effectiveness of planning optimization.

Description

Technical Field

[0001] The present invention relates to the field of data evaluation technology, and in particular to a national land space planning data monitoring and evaluation method and system. Background Art

[0002] Traditional planning methods mainly rely on manual drawing and statistical data. With the rise of geographic information system (GIS) technology, digital planning has gradually become the mainstream. Entering the 21st century, the application of remote sensing technology, global positioning system (GPS) and big data has made the acquisition and processing of spatial data more accurate and efficient. In recent years, the introduction of cloud computing and artificial intelligence has further improved the intelligence level of monitoring and evaluation. In particular, dynamic monitoring and real-time evaluation have become possible in ecological protection, urban expansion and infrastructure construction. In addition, with the maturity of Internet of Things technology, sensor networks are increasingly used in national land space monitoring, realizing real-time capture of environmental changes. Nowadays, the monitoring and evaluation methods of national land space planning data are developing in the direction of integration, intelligence and automation, and the scientificity and timeliness of decision-making support have been significantly improved. However, the traditional national land space planning is still based on static data or periodic updates, and lacks multi-dimensional analysis in the assessment of ecological restoration capacity, which leads to the lack of comprehensiveness in the prediction of spatiotemporal change trends in spatial planning. Summary of the invention

[0003] Based on this, it is necessary to provide a national land space planning data monitoring and evaluation method and system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for monitoring and evaluating land space planning data is provided, the method comprising the following steps:

[0005] Step S1: Acquire a national land space GIS remote sensing image; segment the national land space GIS remote sensing image into regional plots to generate a national land space segmentation plot map; perform infrastructure spatial layout screening on the national land space segmentation plot map to obtain a national land space infrastructure spatial layout map;

[0006] Step S2: collecting intelligent device operation data on the spatial layout map of the national space infrastructure to obtain a regional intelligent device operation data set; calculating the intelligent device coverage rate on the regional intelligent device operation data set to obtain intelligent device coverage rate data; performing signal base station density analysis on the regional intelligent device operation data set to generate national space communication base station density data; calculating the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent device operation data set to obtain the integration degree of national space intelligent transportation facilities; evaluating the adaptability of intelligent infrastructure based on the intelligent device coverage rate data, the density data of national space communication base stations and the integration degree of national space intelligent transportation facilities, thereby generating an intelligent infrastructure adaptability index;

[0007] Step S3: Perform ecological space layout screening on the land space segmentation plot map to obtain a land space ecological layout map; perform green plant land occupation calculation on the land space ecological layout map to obtain green plant land occupation data; perform regional environmental data collection on the land space ecological layout map to obtain regional environmental data; perform environmental change peak and valley spectrum extraction on the regional environmental data to obtain regional environmental change peak and valley spectrum data; perform multi-dimensional ecological restoration capacity assessment on the green plant land occupation data through the regional environmental change peak spectrum data to generate a land space ecological restoration capacity index;

[0008] Step S4: extracting the plot contour features of the land space segmentation plot map to obtain spatial plot contour feature data; performing digital twin modeling on the spatial plot contour feature data to generate a spatial plot digital twin model; performing spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index;

[0009] Step S5: Based on the smart infrastructure adaptability index, the national space ecological resilience index and the digital twin space consistency index, the national space comprehensive evaluation indicators are used to predict the spatiotemporal change trend of national space planning, and a spatiotemporal prediction matrix of national space planning changes is generated; the spatiotemporal prediction matrix of national space planning changes is used to optimize spatial planning and generate a national space planning optimization plan.

[0010] By acquiring and segmenting GIS remote sensing images, the present invention can accurately identify and divide different areas and plots of national space, generate infrastructure spatial layout maps, and help to fully understand the spatial distribution and layout of existing infrastructure, and provide basic data for subsequent analysis and planning. By collecting intelligent device operation data, calculating coverage and signal base station density, and calculating the integration of transportation facilities, the coverage, communication capabilities and layout of intelligent infrastructure can be evaluated in detail, thereby generating an intelligent infrastructure adaptability index to provide data support for optimizing and improving infrastructure services. By screening the ecological space layout, calculating the land occupation of green plants and environmental data collection, and extracting the peak and valley spectrum of environmental changes, the distribution of green plants and environmental changes in the ecological space can be evaluated, and then the ecological restoration capacity index can be generated. This helps to better understand the health status and recovery potential of the ecosystem and provide a scientific basis for ecological protection and restoration. By extracting the contour features of the plot and performing digital twin modeling, an accurate digital model of the spatial plot can be created, and a distributed sensor network can be used to perform spatial consistency assessment to generate a digital twin spatial consistency index. This improves the ability to understand and manage the consistency of spatial plots and supports more accurate spatial planning and monitoring. Based on the adaptability index of intelligent infrastructure, the ecological resilience index and the spatial consistency index of digital twins, the spatiotemporal change trend of national land space planning is predicted, a spatiotemporal prediction matrix of planning changes is generated, and the matrix is ​​used to optimize the spatial planning scheme. This makes planning decisions more scientific and forward-looking, and can effectively respond to future changes and optimize space utilization. Therefore, the present invention realizes comprehensive, real-time and accurate monitoring and evaluation of national land space planning data by integrating GIS remote sensing images, intelligent device data, environmental data and digital twin technology, which significantly improves the scientificity and effectiveness of planning optimization.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: Acquire GIS remote sensing images of national land space;

[0013] Step S12: performing image preprocessing on the national land space GIS remote sensing image to generate a standard national land space GIS remote sensing image, wherein the image preprocessing includes image filtering, image brightness enhancement and image color enhancement;

[0014] Step S13: segmenting the standard land space GIS remote sensing image into regional plots to generate a land space segmentation plot map;

[0015] Step S14: Screen the spatial layout of infrastructure on the land space segmentation plot map to obtain a spatial layout map of the land space infrastructure.

[0016] The present invention filters, enhances brightness and enhances color of original remote sensing images through image preprocessing to generate standardized image data. These processing steps can improve the quality of images, enhance the visibility of key features, reduce noise and image distortion, thereby ensuring the accuracy and reliability of subsequent analysis. By segmenting standardized remote sensing images into regional plots, different plots in the national land space can be accurately identified and divided. This precise segmentation helps to gain a deep understanding of the spatial distribution characteristics of the plots, and provides support for more accurate national land planning and resource management. Infrastructure spatial layout screening can match and screen the segmented plot map with existing or planned infrastructure to generate an optimized infrastructure layout map. This layout map can provide a scientific basis for urban planning, transportation network design, and other infrastructure construction, avoid blind construction, and improve resource utilization efficiency. The spatial layout map of national land space infrastructure generated in the end not only provides the current layout of the infrastructure, but also can be used as basic data for future planning and adjustment. Through these data, decision makers can better understand the spatial structure, optimize the layout, reduce development risks, and improve the management level and utilization efficiency of national land resources.

[0017] Preferably, step S2 comprises the following steps:

[0018] Step S21: using the IoT data collection platform to collect smart device operation data on the spatial layout map of the national space infrastructure to obtain a regional smart device operation data set; performing smart device distribution density analysis on the regional smart device operation data set to generate smart device distribution density data; performing infrastructure topology analysis on the spatial layout map of the national space infrastructure to generate infrastructure topology data; calculating smart device coverage using smart device distribution density data and infrastructure topology data to obtain smart device coverage data;

[0019] Step S22: measuring the network signal strength of the regional smart device operation data set to generate national land space network signal strength data; using the national land space network signal strength data to perform signal base station density analysis on the national land space infrastructure spatial layout map to generate national land space communication base station density data;

[0020] Step S23: calculating the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent device operation data set to obtain the integration degree of the national space intelligent transportation facilities;

[0021] Step S24: Based on the smart device coverage data, the land space communication base station density data and the land space intelligent transportation facility integration data, the smart infrastructure adaptability index is generated.

[0022] The present invention can calculate the coverage data of smart devices by collecting the operation data of smart devices, analyzing the distribution density and analyzing the infrastructure topology. This helps to understand the distribution of smart devices in the national space, ensure the reasonable layout of smart infrastructure, and thus improve the coverage effect and use efficiency of smart devices. By measuring the network signal strength of the operation data of smart devices and generating the national space network signal strength data, combined with the signal base station density analysis, the layout of the communication infrastructure can be evaluated and optimized. This can help improve the breadth and strength of network signal coverage and ensure the stable operation of the communication network in the national space. By calculating the integration of intelligent transportation facilities, the degree of intelligence of transportation facilities in the national space can be evaluated. This provides data support for further improving the efficiency and safety of the transportation network, and helps to optimize traffic flow management and intelligent upgrading of transportation facilities. By integrating key indicators such as smart device coverage, communication base station density and intelligent transportation facility integration, the adaptability evaluation of intelligent infrastructure is carried out to generate an intelligent infrastructure adaptability index. This index provides decision makers with comprehensive analysis results, helping them to better understand the adaptability and development potential of intelligent infrastructure, and provide a scientific basis for future planning and optimization. Through multi-dimensional data analysis and evaluation, an intelligent infrastructure adaptability index is generated. This index can not only reflect the current layout and adaptability of smart infrastructure, but also provide a reference for future urban planning, infrastructure construction and optimization, and improve the scientific nature and foresight of smart city planning.

[0023] Preferably, step S23 includes the following steps:

[0024] Step S231: using the Internet of Vehicles communication technology to collect traffic network and vehicle communication data from the regional smart device operation data set to obtain traffic network operation data and smart vehicle communication data;

[0025] Step S232: Performing spatial network responsiveness analysis on traffic network operation data and intelligent vehicle communication data through national land space communication base station density data to generate intelligent traffic feedback responsiveness data;

[0026] Step S233: marking the state of the spatial layout diagram of the national space infrastructure according to the traffic network operation data and the intelligent vehicle communication data to generate a national space traffic network diagram; performing traffic simulation on the national space traffic network diagram to generate traffic flow simulation data;

[0027] Step S234: Use the intelligent traffic feedback responsiveness data and traffic flow simulation data to evaluate the integration of intelligent traffic facilities and generate the integration of national land space intelligent traffic facilities.

[0028] The present invention can obtain detailed traffic network operation data and intelligent vehicle communication data by using vehicle network communication technology to collect regional intelligent devices and vehicle communication data. This provides basic data support for subsequent traffic analysis and optimization, and improves the real-time and accuracy of traffic data. Through the density data of national space communication base stations, the collected traffic network and vehicle communication data are analyzed for spatial network responsiveness, and intelligent traffic feedback responsiveness data is generated. This analysis can evaluate the real-time response capability of the traffic network, and provides an important reference for optimizing the layout of traffic facilities and improving the flexibility of the traffic network. By analyzing and simulating the traffic network operation data and intelligent vehicle communication data, a national space traffic network diagram and traffic flow simulation data are generated. These simulation data can simulate the actual traffic flow situation, help identify potential traffic bottlenecks and optimization opportunities, and enhance the predictability and planning capabilities of traffic management. By combining the intelligent traffic feedback responsiveness data with the traffic flow simulation data, an intelligent traffic facility integration evaluation is performed to generate the national space intelligent traffic facility integration. This integration evaluation can help decision makers identify the intelligence level of current traffic facilities, find deficiencies, promote the upgrading and optimization of traffic facilities, and improve the operation efficiency of the overall traffic network. Through the comprehensive application of the above steps, the design of step S23 helps to generate accurate intelligent transportation facility integration data, which provides a scientific basis for urban traffic management. Decision makers can formulate more accurate traffic planning based on these data, improve the intelligence of the traffic system, optimize resource allocation, and thus achieve more efficient urban traffic management.

[0029] Preferably, step S3 comprises the following steps:

[0030] Step S31: screening the ecological space layout of the land space segmentation plot map to obtain the land space ecological layout map; calculating the green plant area on the land space ecological layout map to obtain green plant area data;

[0031] Step S32: using environmental sensors to collect regional environmental data on the national land space ecological layout map to obtain regional environmental data; performing environmental change analysis on the regional environmental data to generate regional environmental change data;

[0032] Step S33: performing change amplitude spectrum conversion on the regional environment change data to generate regional environment change spectrum data; performing peak and valley spectrum extraction on the regional environment change spectrum data to obtain regional environment change peak and valley spectrum data;

[0033] Step S34: Conduct a multi-dimensional ecological restoration capacity assessment on the green plant land area data through the regional environmental change peak spectrum data to generate a national land space ecological restoration capacity index.

[0034] The present invention screens the ecological space layout of the land space segmentation plot map, and the generated land space ecological layout map can effectively identify the ecological functional area, especially the distribution of green plant land. This provides basic data for subsequent ecological protection and planning, can help optimize the layout of the ecological space, and enhance the sustainability of the ecosystem. The ecological layout map is used to collect regional environmental data in real time by environmental sensors, and regional environmental change data is generated through environmental change analysis. These data help to track changes in environmental conditions, identify potential environmental risks, and provide timely feedback for ecological management. By performing spectrum conversion and peak-valley spectrum extraction on environmental change data, detailed regional environmental change spectrum data and peak-valley spectrum data can be generated. These data can reveal the dynamic characteristics and trends of environmental changes, help understand the stability and change range of the ecosystem, and provide a scientific basis for subsequent ecological restoration measures. Through the combination of regional environmental change peak spectrum data and green plant land data, the ecological restoration capacity is evaluated in multiple dimensions, and the generated land space ecological restoration capacity index can quantify the restoration potential of the ecosystem. This index provides decision makers with a comprehensive ecological restoration capacity assessment result, helping to formulate more effective ecological protection and restoration strategies. Through detailed ecological spatial layout analysis, environmental change monitoring and restoration capacity assessment, a complete set of scientific ecological management methods is provided.

[0035] Preferably, step S34 includes the following steps:

[0036] Step S341: Calculate the mean of the peak and valley spectrum data of regional environmental changes to obtain mean data of regional environmental changes;

[0037] Step S342: taking the regional environmental change mean data as the environmental change slow period, and calculating the change amplitude of the regional environmental change peak and valley spectrum data based on the environmental change slow period to obtain the regional environmental change amplitude data;

[0038] Step S343: performing disturbance time series analysis on the green plant area data according to the regional environment change amplitude data to generate green plant area time series change data, wherein the green plant area time series change data includes the green plant area time series change data before the disturbance and the green plant area time series change data after the disturbance;

[0039] Step S344: Calculate the ecological restoration difference based on the time-series change data of green plant area before and after the disturbance to obtain the green plant area disturbance difference data; perform a multi-dimensional ecological restoration capacity assessment based on the green plant area disturbance difference data to generate the national land space ecological restoration capacity index.

[0040] The present invention calculates the mean value of the peak and valley spectrum data of regional environmental changes, and the regional environmental change mean data obtained can accurately capture the stable stage of environmental changes. This helps to identify the gentle period of environmental changes and lays the foundation for subsequent amplitude calculation and ecological restoration assessment. By taking the regional environmental change mean data as a benchmark, the amplitude of environmental changes is calculated in detail to generate regional environmental change amplitude data. This process can quantify the intensity and frequency of environmental fluctuations, and provides a scientific basis for evaluating the dynamic changes and potential impacts of the ecosystem. The green plant land area data is disturbed by time series analysis using the regional environmental change amplitude data, and the generated green plant land area time series change data includes detailed change information before and after the disturbance. This time series analysis can reveal the dynamic changes of green plant coverage under different environmental conditions, and provides key data for understanding the recovery capacity of the ecosystem. By performing difference calculation on the green plant land area time series change data before and after the disturbance, the generated green plant area disturbance difference data can quantify the recovery capacity of the ecosystem after being disturbed. This difference provides a specific quantitative indicator for the subsequent multi-dimensional ecological restoration capacity assessment, ensuring the accuracy of the ecological restoration assessment. Based on the multi-dimensional ecological restoration capacity assessment of green plant area disturbance difference data, the final national land space ecological restoration capacity index can fully reflect the restoration potential of the ecosystem under different environmental change conditions. This index provides a scientific reference for ecological management and planning, and helps to formulate more effective ecological protection and restoration strategies.

[0041] Preferably, step S4 comprises the following steps:

[0042] Step S41: extracting the plot contour features from the land space segmentation plot map to obtain spatial plot contour feature data;

[0043] Step S42: performing point cloud conversion on the spatial plot outline feature data to generate spatial plot outline point cloud data; performing three-dimensional gridding on the spatial plot outline point cloud data to generate spatial plot three-dimensional grid data;

[0044] Step S43: using digital twin technology to perform digital twin modeling on the three-dimensional grid data of the spatial plot to generate a digital twin model of the spatial plot;

[0045] Step S44: Perform spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index.

[0046] The present invention extracts the contour features of the plots from the land space segmentation plot map, and the generated spatial plot contour feature data can accurately describe the boundaries and shapes of the plots. This provides high-precision basic data for subsequent point cloud conversion and three-dimensional modeling, which helps to ensure the accuracy of spatial analysis. By converting the spatial plot contour feature data into point cloud data and performing three-dimensional grid processing on it, the generated spatial plot three-dimensional grid data can fully reflect the three-dimensional structure of the plot. This process helps to expand the two-dimensional information into a three-dimensional model, lays the foundation for digital twin modeling, and improves the authenticity and detail expression of the plot model. The digital twin technology is used to model the spatial plot three-dimensional grid data, and the generated spatial plot digital twin model can truly simulate the physical characteristics and behavior of the plot. This model is not only a virtual mapping of the plot, but also a complete replica of it in a digital environment, which helps to achieve accurate virtual experiments and simulation operations, and promotes the intelligentization of land space management. The digital twin model is spatially consistent through a distributed sensor network, and the generated digital twin spatial consistency index can quantify the consistency between the virtual model of the plot and the real physical characteristics. This index is a key indicator for evaluating the quality of digital twin models, which helps to ensure the accuracy and reliability of the model and improve the application effect of digital twin technology in land and space management. Through plot feature extraction, 3D modeling, digital twin modeling and spatial consistency assessment, it provides strong technical support for the refined management of land and space. These steps ensure high-precision mapping from physical plots to digital models, and improve the scientificity and accuracy of land and space management and planning.

[0047] Preferably, step S44 includes the following steps:

[0048] Step S441: collect spatial block real-time data through a distributed sensor network to obtain a spatial block real-time integrated data set;

[0049] Step S442: synchronizing the spatial plot real-time integrated data set and the spatial plot digital twin model to generate a spatial plot real-time integrated synchronized data set; performing multi-dimensional information mapping on the spatial plot digital twin model using the spatial plot real-time integrated synchronized data set to generate a digital twin multi-dimensional mapping data set;

[0050] Step S443: Perform spatial dimension consistency evaluation on the digital twin multidimensional mapping data set to generate a spatial consistency analysis data set; perform error correction on the spatial consistency analysis data set to generate a spatial consistency correction data set;

[0051] Step S444: Use the multidimensional consistency index calculation technology to calculate the multidimensional spatial consistency index of the spatial consistency correction data set, so as to generate the digital twin spatial consistency index.

[0052] The present invention collects real-time data of spatial plots through a distributed sensor network, and the generated real-time integrated data set of spatial plots can dynamically reflect the actual situation of the plots. This real-time data integration not only ensures the synchronization of the digital twin model with the real environment, but also enhances the real-time response capability of the model, which helps to cope with rapidly changing environmental conditions. By synchronizing the real-time data with the digital twin model, the generated real-time integrated synchronized data set of spatial plots ensures high consistency between the model and reality. Subsequently, the digital twin multidimensional mapping data set generated by the multidimensional information mapping technology further enhances the complexity and realism of the model, so that the model can accurately reflect the multidimensional characteristics of the plot. The spatial dimension consistency evaluation of the digital twin multidimensional mapping data set is performed, and the generated spatial consistency analysis data set can accurately identify the subtle differences between the model and reality. The spatial consistency correction data set generated by error correction further improves the accuracy of the model and ensures that the model can be highly consistent with the actual environment. Using the multidimensional consistency index calculation technology, the multidimensional spatial consistency index calculation is performed on the corrected data set, and the generated digital twin spatial consistency index can comprehensively reflect the degree of spatial consistency of the model. This index provides a scientific basis for evaluating and optimizing the digital twin model, which helps to ensure the accuracy and consistency of the model in various dimensions. Through real-time data collection and synchronization, multi-dimensional information mapping, error correction, and consistency index calculation, the accuracy and reliability of the digital twin model have been significantly improved. This not only enhances the authenticity and responsiveness of the model, but also improves its application value in land and space management, making it a powerful tool for decision support and simulation experiments.

[0053] Preferably, step S5 comprises the following steps:

[0054] Step S51: Integrate the evaluation indicators based on the smart infrastructure adaptability index, the national land space ecological restoration capacity index, and the digital twin space consistency index to generate a comprehensive national land space evaluation index;

[0055] Step S52: predicting the spatiotemporal change trend of national space planning for the national space comprehensive evaluation index through a preset time series model, and generating a spatiotemporal prediction matrix for national space planning changes;

[0056] Step S53: Utilize the spatiotemporal prediction matrix of national land space planning changes to optimize spatial planning and generate a national land space planning optimization plan.

[0057] The present invention generates a comprehensive evaluation index of national space by integrating the adaptability index of intelligent infrastructure, the ecological restoration capacity index of national space and the spatial consistency index of digital twins. This comprehensive evaluation can comprehensively evaluate the key indicators of national space from multiple dimensions, provide a solid data foundation for subsequent planning and decision-making, and ensure the comprehensiveness and scientificity of the planning scheme. Through the preset time series model, the temporal and spatial change trend of the comprehensive evaluation index of national space is predicted, and the temporal and spatial prediction matrix of national space planning change is generated. This prediction can accurately reflect the changing trend of national space planning at different time nodes in the future, help decision makers identify potential planning problems in advance, formulate response strategies, and ensure the foresight and sustainability of national space planning. By optimizing spatial planning using the spatiotemporal prediction matrix, the generated national space planning optimization scheme can improve spatial utilization efficiency in multiple dimensions and promote the rational allocation of resources. The optimization scheme can not only effectively respond to future changes and challenges, but also be dynamically adjusted according to real-time data and trends to ensure the scientificity and adaptability of the planning scheme. Through comprehensive evaluation, spatiotemporal prediction and optimization, a scientific national space planning process is constructed. This process not only improves the accuracy and operability of national land space planning, but also ensures the sustainability of the planning scheme through forward-looking prediction and optimization, and promotes the long-term development of national land space.

[0058] In this specification, a national land space planning data monitoring and evaluation system is provided, which is used to execute the above-mentioned national land space planning data monitoring and evaluation method, and the national land space planning data monitoring and evaluation system includes:

[0059] The land parcel segmentation module is used to obtain the national land space GIS remote sensing image; perform regional land parcel segmentation on the national land space GIS remote sensing image to generate a national land space segmentation land parcel map; perform infrastructure spatial layout screening on the national land space segmentation land parcel map to obtain a national land space infrastructure spatial layout map;

[0060] The equipment coverage calculation module is used to collect intelligent equipment operation data from the spatial layout map of the national space infrastructure to obtain a regional intelligent equipment operation data set; to calculate the intelligent equipment coverage rate of the regional intelligent equipment operation data set to obtain intelligent equipment coverage rate data; to analyze the signal base station density of the regional intelligent equipment operation data set to generate national space communication base station density data; to calculate the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent equipment operation data set to obtain the integration degree of national space intelligent transportation facilities; to evaluate the adaptability of intelligent infrastructure based on the intelligent equipment coverage rate data, the density data of national space communication base stations and the integration degree of national space intelligent transportation facilities, thereby generating an intelligent infrastructure adaptability index;

[0061] The ecological restoration calculation module is used to screen the ecological space layout of the land space segmentation plot map to obtain the national space ecological layout map; calculate the green plant area on the national space ecological layout map to obtain the green plant area data; collect regional environmental data on the national space ecological layout map to obtain regional environmental data; extract the environmental change peak and valley spectrum of the regional environmental data to obtain the regional environmental change peak and valley spectrum data; conduct a multi-dimensional ecological restoration capacity assessment on the green plant area data through the regional environmental change peak spectrum data to generate the national space ecological restoration capacity index;

[0062] The spatial consistency calculation module is used to extract the plot contour features of the national land space segmentation plot map to obtain the spatial plot contour feature data; to perform digital twin modeling on the spatial plot contour feature data to generate a spatial plot digital twin model; to perform spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index;

[0063] The planning optimization module is used to predict the spatiotemporal change trend of national space planning based on the intelligent infrastructure adaptability index, the national space ecological resilience capacity index and the digital twin space consistency index of the national space comprehensive evaluation indicators, and generate a spatiotemporal prediction matrix for national space planning changes; use the spatiotemporal prediction matrix for national space planning changes to optimize spatial planning and generate a national space planning optimization plan.

[0064] The beneficial effect of the present invention is that by acquiring and processing the GIS remote sensing images of the national space, the accurate depiction of the spatial layout of the infrastructure is ensured. This provides an accurate spatial data basis for subsequent analysis. The coverage and density of smart devices and signal base stations are analyzed, so that the adaptability and distribution of the infrastructure are comprehensively evaluated, and the layout and efficiency of smart facilities in the national space can be accurately grasped. Combined with the ecological space layout and environmental data, the ecological restoration capacity is evaluated, providing a scientific basis for ecological protection and restoration. Through digital twin modeling and spatial consistency evaluation, the consistency of spatial data with reality is ensured, and the authenticity and reliability of the digital model are improved. Comprehensively considering the adaptability of smart infrastructure, ecological restoration capacity and digital twin consistency, the trend of spatiotemporal changes is predicted, so as to generate an optimized spatial planning scheme. This comprehensive analysis method can improve the scientificity and accuracy of planning in multiple dimensions, ensuring that future planning can effectively respond to different challenges and needs. The adaptability assessment of smart infrastructure helps identify the advantages and disadvantages of infrastructure and provides specific data support for resource allocation and facility optimization. The ecological restoration capacity assessment guides the implementation of ecological protection measures. The digital twin spatial consistency assessment provides a basis for model verification, ensuring the consistency of the virtual model with the actual situation. The prediction of spatiotemporal change trends can identify possible future changes and problems in advance, provide forward-looking guidance for decision makers, and help make reasonable adjustments and optimizations in the planning stage. By optimizing planning based on comprehensive evaluation indicators, it is possible to better balance the needs and goals of various aspects in the optimization plan and promote the scientific management of national space. Therefore, the present invention integrates GIS remote sensing images, intelligent device data, environmental data and digital twin technology to achieve comprehensive, real-time and accurate national space planning data monitoring and evaluation, significantly improving the scientificity and effectiveness of planning optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the steps of a method for monitoring and evaluating national land space planning data;

[0066] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0067] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0068] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0069] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0072] To achieve this, please refer to Figures 1 to 3 , a national land space planning data monitoring and evaluation method, the method comprising the following steps:

[0073] Step S1: Acquire a national land space GIS remote sensing image; segment the national land space GIS remote sensing image into regional plots to generate a national land space segmentation plot map; perform infrastructure spatial layout screening on the national land space segmentation plot map to obtain a national land space infrastructure spatial layout map;

[0074] Step S2: collecting intelligent device operation data on the spatial layout map of the national space infrastructure to obtain a regional intelligent device operation data set; calculating the intelligent device coverage rate on the regional intelligent device operation data set to obtain intelligent device coverage rate data; performing signal base station density analysis on the regional intelligent device operation data set to generate national space communication base station density data; calculating the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent device operation data set to obtain the integration degree of national space intelligent transportation facilities; evaluating the adaptability of intelligent infrastructure based on the intelligent device coverage rate data, the density data of national space communication base stations and the integration degree of national space intelligent transportation facilities, thereby generating an intelligent infrastructure adaptability index;

[0075] Step S3: Perform ecological space layout screening on the land space segmentation plot map to obtain a land space ecological layout map; perform green plant land occupation calculation on the land space ecological layout map to obtain green plant land occupation data; perform regional environmental data collection on the land space ecological layout map to obtain regional environmental data; perform environmental change peak and valley spectrum extraction on the regional environmental data to obtain regional environmental change peak and valley spectrum data; perform multi-dimensional ecological restoration capacity assessment on the green plant land occupation data through the regional environmental change peak spectrum data to generate a land space ecological restoration capacity index;

[0076] Step S4: extracting the plot contour features of the land space segmentation plot map to obtain spatial plot contour feature data; performing digital twin modeling on the spatial plot contour feature data to generate a spatial plot digital twin model; performing spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index;

[0077] Step S5: Based on the smart infrastructure adaptability index, the national space ecological resilience index and the digital twin space consistency index, the national space comprehensive evaluation indicators are used to predict the spatiotemporal change trend of national space planning, and a spatiotemporal prediction matrix of national space planning changes is generated; the spatiotemporal prediction matrix of national space planning changes is used to optimize spatial planning and generate a national space planning optimization plan.

[0078] By acquiring and segmenting GIS remote sensing images, the present invention can accurately identify and divide different areas and plots of national space, generate infrastructure spatial layout maps, and help to fully understand the spatial distribution and layout of existing infrastructure, and provide basic data for subsequent analysis and planning. By collecting intelligent device operation data, calculating coverage and signal base station density, and calculating the integration of transportation facilities, the coverage, communication capabilities and layout of intelligent infrastructure can be evaluated in detail, thereby generating an intelligent infrastructure adaptability index to provide data support for optimizing and improving infrastructure services. By screening the ecological space layout, calculating the land occupation of green plants and environmental data collection, and extracting the peak and valley spectrum of environmental changes, the distribution of green plants and environmental changes in the ecological space can be evaluated, and then the ecological restoration capacity index can be generated. This helps to better understand the health status and recovery potential of the ecosystem and provide a scientific basis for ecological protection and restoration. By extracting the contour features of the plot and performing digital twin modeling, an accurate digital model of the spatial plot can be created, and a distributed sensor network can be used to perform spatial consistency assessment to generate a digital twin spatial consistency index. This improves the ability to understand and manage the consistency of spatial plots and supports more accurate spatial planning and monitoring. Based on the adaptability index of intelligent infrastructure, the ecological resilience index and the spatial consistency index of digital twins, the spatiotemporal change trend of national land space planning is predicted, a spatiotemporal prediction matrix of planning changes is generated, and the matrix is ​​used to optimize the spatial planning scheme. This makes planning decisions more scientific and forward-looking, and can effectively respond to future changes and optimize space utilization. Therefore, the present invention realizes comprehensive, real-time and accurate monitoring and evaluation of national land space planning data by integrating GIS remote sensing images, intelligent device data, environmental data and digital twin technology, which significantly improves the scientificity and effectiveness of planning optimization.

[0079] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for monitoring and evaluating land space planning data of the present invention. In this example, the method for monitoring and evaluating land space planning data includes the following steps:

[0080] Step S1: Acquire a national land space GIS remote sensing image; segment the national land space GIS remote sensing image into regional plots to generate a national land space segmentation plot map; perform infrastructure spatial layout screening on the national land space segmentation plot map to obtain a national land space infrastructure spatial layout map;

[0081] In an embodiment of the present invention, a high-resolution GIS remote sensing image of the required area is obtained by using a remote sensing satellite or an unmanned aerial vehicle. The image should have sufficient spatial resolution to clearly distinguish different plots and infrastructure. The acquired image is subjected to geometric correction, radiation correction and atmospheric correction to ensure the accuracy and consistency of the image. The remote sensing image is divided into different regional plots by applying an image segmentation algorithm (such as K-means clustering, regional growth, semantic segmentation model based on deep learning, etc.). Commonly used deep learning models include U-Net and SegNet. The segmentation results are converted into a plot map, different regional categories are marked, and the boundaries and attribute information of each plot are recorded. Relevant features such as roads, buildings, water bodies, etc. can be extracted from the land space segmentation plot map. These features can be obtained by image analysis technology or spatial data analysis tools. Areas with infrastructure are screened out by spatial analysis methods (such as buffer analysis, overlay analysis, etc.). Spatial analysis tools in GIS software can be used, or deep learning models can be used to identify and mark infrastructure. The screened infrastructure information is visualized to generate a spatial layout map of land space infrastructure. The map should show the location of all infrastructure and their relative distribution. Verify the generated infrastructure spatial layout map to ensure its accuracy and completeness. Verification can be performed by comparing it with actual ground survey data or other reliable data sources. Based on the verification results, adjust and optimize the segmentation and screening process to improve the accuracy and practicality of the final map. Generate the final national spatial infrastructure spatial layout map and output it to commonly used GIS data formats (such as Shapefile, GeoJSON, etc.).

[0082] Step S2: collecting intelligent device operation data on the spatial layout map of the national space infrastructure to obtain a regional intelligent device operation data set; calculating the intelligent device coverage rate on the regional intelligent device operation data set to obtain intelligent device coverage rate data; performing signal base station density analysis on the regional intelligent device operation data set to generate national space communication base station density data; calculating the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent device operation data set to obtain the integration degree of national space intelligent transportation facilities; evaluating the adaptability of intelligent infrastructure based on the intelligent device coverage rate data, the density data of national space communication base stations and the integration degree of national space intelligent transportation facilities, thereby generating an intelligent infrastructure adaptability index;

[0083] In an embodiment of the present invention, the operation data of smart devices in the region are collected, including sensors, cameras, smart traffic lights, etc. The data can be obtained through the sensors, management systems or network interfaces of the devices. The monitoring system or Internet of Things platform of the smart devices is used for data collection. Ensure that the frequency and timeliness of data collection meet the analysis requirements. The collected smart device operation data are integrated into a regional smart device operation data set, including information such as the type, location, operation status, and transmission signal of the device. Based on the device location and coverage information in the smart device operation data set, the spatial coverage of the devices in the region is calculated. The smart device coverage rate of each region is calculated using spatial analysis tools or algorithms (such as spatial interpolation, coverage models, etc.). The calculation formula may include the ratio of the total area of ​​the device coverage area to the target area, generate smart device coverage data, and display the device coverage of each region. The results can be output in the form of a GIS map or a data table. Extract the information of the signal base station from the smart device operation data set, including the location and coverage radius of the base station. Use spatial statistical analysis methods (such as kernel density estimation, point density analysis, etc.) to calculate the base station density of each region. The calculation formula includes the number of base stations per unit area, generates the density data of communication base stations in the national space, and displays the distribution density of base stations in a graphical form. A heat map or density map can be used. Combine the smart device operation data set with the spatial layout map of the national space infrastructure to identify the traffic facilities in the area (such as traffic lights, traffic signs, etc.). The integration of traffic facilities can be calculated by analyzing the distribution of traffic facilities, coverage areas, and the degree of association with smart devices. The calculation methods include facility density, distribution uniformity, etc., to obtain data on the integration of national space intelligent transportation facilities, and generate relevant charts or maps to display the integration of facilities. Summarize the data on smart device coverage, communication base station density, and intelligent transportation facility integration. Use a comprehensive evaluation model (such as weighted average method, multidimensional evaluation method, etc.) to evaluate the above data. The evaluation formula can include the weighted sum of different indicators to calculate the adaptability index of the intelligent infrastructure. Calculate the adaptability index of the intelligent infrastructure based on the weights and scores of each indicator. The following formula can be used: I = α·C+β·B+γ·T; where I represents the adaptability index of intelligent infrastructure, C represents the coverage rate of intelligent devices, B represents the base station density, T represents the integration degree of transportation facilities, α represents the coverage weight coefficient, β represents the density weight coefficient, and γ represents the integration weight coefficient. The output is the adaptability index of intelligent infrastructure and its related data, which can be a chart, report or GIS map.

[0084] Step S3: Perform ecological space layout screening on the land space segmentation plot map to obtain a land space ecological layout map; perform green plant land occupation calculation on the land space ecological layout map to obtain green plant land occupation data; perform regional environmental data collection on the land space ecological layout map to obtain regional environmental data; perform environmental change peak and valley spectrum extraction on the regional environmental data to obtain regional environmental change peak and valley spectrum data; perform multi-dimensional ecological restoration capacity assessment on the green plant land occupation data through the regional environmental change peak spectrum data to generate a land space ecological restoration capacity index;

[0085] In an embodiment of the present invention, by using a land space segmentation plot map as basic data, the attribute information of the plot, such as land use type, geomorphic features, etc., is collected. Ecological space screening algorithms or tools (such as remote sensing image classification, geographic information system (GIS) analysis, etc.) are applied to identify areas with ecological functions. The screening criteria include ecological spaces such as green space, wetland, and forest. A land space ecological layout map is generated based on the screening results, and the distribution of ecological function areas is marked. The map can be visualized through a GIS system, and the attribute information of each ecological area is recorded. Green plant areas can be identified in the land space ecological layout map, and vegetation indices (such as NDVI) in remote sensing images can be used for detection. The total area of ​​the green plant area is calculated. The spatial analysis function in the GIS tool can be used to calculate the area of ​​each green plant area, generate green plant land occupation data, and display the green plant coverage in different areas. The results can be output in the form of a data table or a graphical map. Collect regional environmental data, including air quality, soil moisture, water quality, etc. It can be obtained through sensors, environmental monitoring stations, or existing environmental data sources. Integrate the collected environmental data to ensure the integrity and consistency of the data. Data cleaning and standardization methods can be used for preprocessing. Perform time series analysis on regional environmental data to extract peaks and valleys of environmental changes. This can be achieved through spectrum analysis methods (such as Fourier transform, wavelet transform, etc.). Calculate the peak and valley spectrum data of environmental changes to show the periodicity and amplitude of environmental changes. Generate a spectrum graph or data table to record the main characteristics of environmental changes. Combine the green plant land area data with the regional environmental change peak and valley spectrum data to analyze the relationship between green plant coverage and environmental changes. Use a multidimensional evaluation model to evaluate ecological restoration capacity. Regression analysis, correlation analysis and other methods can be used to calculate the resilience of green plant land to environmental changes. The evaluation formula can be as follows: K = ρ·L+μ·V; where K represents the ecological restoration capacity index, L represents the green plant land area data, V represents the environmental change peak and valley spectrum data, ρ represents the land area weight coefficient, μ represents the valley spectrum weight coefficient, and outputs the ecological restoration capacity index and related data, which can be charts, reports or GIS maps.

[0086] Step S4: extracting the plot contour features of the land space segmentation plot map to obtain spatial plot contour feature data; performing digital twin modeling on the spatial plot contour feature data to generate a spatial plot digital twin model; performing spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index;

[0087] In an embodiment of the present invention, a land space segmentation plot map is used as basic data. Ensure that the boundaries of the plots in the map are clear and the attribute information is complete. Apply a plot contour feature extraction algorithm (such as edge detection, contour tracking algorithm, shape recognition, etc.) to identify and extract the contour features of each plot. These features may include boundary shape, side length, angle, curvature, etc. The extracted contour feature data is structured to generate spatial plot contour feature data. The data can be stored in a vector graphics format or a data table to record the contour details of each plot. According to the spatial plot contour feature data, a suitable digital twin modeling method is selected. Common modeling tools include CAD software, 3D modeling tools, or a dedicated digital twin platform. The plot contour feature data is converted into a digital twin model using 3D modeling technology. This process may include generating a 3D plot model, adding attribute information (such as material, texture, etc.) to the model, and performing spatial positioning. The generated digital twin model is optimized to ensure its high consistency with the actual plot contour. Optimization may include precision adjustment, detail repair, and model simplification. Deploy a distributed sensor network to cover the plots in the target area. These sensors can include GPS, LiDAR, cameras, etc., which are used to capture the spatial data of the plot in real time. The real-time spatial data of the plot is collected through the sensor network. This data is matched and integrated with the digital twin model to form a dynamic spatial data set of the actual plot. A spatial consistency analysis is performed to compare the spatial data of the digital twin model with the actual plot. An algorithm is used to calculate the degree of match between the model and the actual situation. Common methods include error analysis, deformation detection, spatial registration, etc. Based on the evaluation results, the digital twin spatial consistency index is calculated. The index can be expressed by the following formula: Among them, M represents the spatial consistency index, P i Represented as the i-th spatial point data of the digital twin model, S i It represents the i-th spatial point data collected by the sensor, N represents the total number of spatial points evaluated, outputs the digital twin spatial consistency index, and generates relevant reports and charts. This index can be used to evaluate the accuracy and practicality of the digital twin model.

[0088] Step S5: Based on the smart infrastructure adaptability index, the national space ecological resilience index and the digital twin space consistency index, the national space comprehensive evaluation indicators are used to predict the spatiotemporal change trend of national space planning, and a spatiotemporal prediction matrix of national space planning changes is generated; the spatiotemporal prediction matrix of national space planning changes is used to optimize spatial planning and generate a national space planning optimization plan.

[0089] In the embodiment of the present invention, the three main indexes generated by the first three steps are collected: the smart infrastructure adaptability index, the national space ecological restoration capacity index, and the digital twin space consistency index. The three indexes are standardized to ensure that different indexes are compared and integrated on the same scale. The standardization method may include normalization, z-score, etc., and the standardized index is weighted and integrated to generate a comprehensive evaluation index of the national space. Use historical national space data and combine comprehensive evaluation indicators to analyze the spatiotemporal change trend of national space planning. This can be achieved by methods such as spatiotemporal statistical analysis, regression analysis, and machine learning prediction models. Generate a spatiotemporal prediction matrix for national space planning changes based on the analysis results. The matrix contains multidimensional data such as spatial dimensions (such as plot location), time dimensions (such as years and quarters), and prediction dimensions (such as land use changes and infrastructure evolution). According to the spatiotemporal prediction matrix, a spatial planning optimization model is designed. The model design should consider optimization objectives (such as maximizing land use efficiency and giving priority to ecological protection) and constraints (such as policies and regulations, resource constraints). Calculate using the optimization model to generate multiple optimization schemes. Linear programming, genetic algorithms, multi-objective optimization, and other methods can be used for calculation to obtain the optimal solution. Select the best solution that meets actual needs and goals from multiple optimization solutions to generate the final national land space planning optimization solution. Evaluate the generated national land space planning optimization solution to ensure its feasibility and effectiveness. The evaluation can include simulation, expert review, and stakeholder feedback. Based on the evaluation results, implement the optimization solution. During the implementation process, monitor the implementation effect of the national land space planning in real time, and adjust the solution when necessary to adapt to the dynamically changing environment and needs.

[0090] Preferably, step S1 comprises the following steps:

[0091] Step S11: Acquire GIS remote sensing images of national land space;

[0092] Step S12: performing image preprocessing on the national land space GIS remote sensing image to generate a standard national land space GIS remote sensing image, wherein the image preprocessing includes image filtering, image brightness enhancement and image color enhancement;

[0093] Step S13: segmenting the standard national land space GIS remote sensing image into regional plots to generate a national land space segmentation plot map;

[0094] Step S14: Screen the spatial layout of infrastructure on the land space segmentation plot map to obtain a spatial layout map of the land space infrastructure.

[0095] In an embodiment of the present invention, a GIS remote sensing image covering the target land space is obtained from a remote sensing platform such as a satellite, an unmanned aerial vehicle or an aerial device. The acquired image should have a high spatial resolution and spectral resolution to ensure the accuracy of subsequent analysis. The image data can be preprocessed by multi-source remote sensing data fusion technology to synthesize a basic image data set containing rich information. The acquired land space GIS remote sensing image is image preprocessed to generate a standard land space GIS remote sensing image. Image preprocessing mainly includes the following sub-steps: Gaussian filtering, median filtering and other techniques are used to smooth the image, remove noise and clutter, and retain important spatial information. The brightness distribution of the image is adjusted by using methods such as histogram equalization or adaptive histogram equalization (CLAHE) to improve the contrast and detail expression ability of the image. The color of the image is corrected and enhanced by using methods such as color balance and gamma correction to make different objects in the image more distinct and improve the accuracy of subsequent segmentation and analysis. Advanced image segmentation algorithms (such as U-Net, DeepLab, etc.) are used to segment the standard land space GIS remote sensing image into regional plots. First, by training a supervised deep learning model, different types of objects in the image (such as buildings, roads, farmland, forests, etc.) are identified. Then, the image is segmented according to the category labels of different objects to generate an accurate land space segmentation plot map. During the segmentation process, multi-dimensional information such as the spectral characteristics, texture characteristics and spatial characteristics of the image can be combined to ensure the accuracy and consistency of the segmentation results. According to the planning needs and specifications of the target area, set the spatial layout rules of infrastructure (such as roads, bridges, water supply pipelines, etc.), including spatial distance, coverage, connectivity, etc. Use rule-based screening algorithms or spatial analysis tools to screen out infrastructure spatial layout areas that meet the set rules. The screening process can be combined with geographic information system (GIS) analysis to ensure the rationality and effectiveness of the layout. After screening, the spatial layout map of the land space infrastructure is finally obtained.

[0096] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0097] Step S21: using the IoT data collection platform to collect smart device operation data on the spatial layout map of the national space infrastructure to obtain a regional smart device operation data set; performing smart device distribution density analysis on the regional smart device operation data set to generate smart device distribution density data; performing infrastructure topology analysis on the spatial layout map of the national space infrastructure to generate infrastructure topology data; calculating smart device coverage using smart device distribution density data and infrastructure topology data to obtain smart device coverage data;

[0098] Step S22: measuring the network signal strength of the regional smart device operation data set to generate national land space network signal strength data; using the national land space network signal strength data to perform signal base station density analysis on the national land space infrastructure spatial layout map to generate national land space communication base station density data;

[0099] Step S23: calculating the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent device operation data set to obtain the integration degree of the national space intelligent transportation facilities;

[0100] Step S24: Based on the smart device coverage data, the land space communication base station density data and the land space intelligent transportation facility integration data, the smart infrastructure adaptability index is generated.

[0101] In an embodiment of the present invention, various intelligent devices (such as sensors, surveillance cameras, intelligent street lights, etc.) arranged in the national space are used to collect real-time data using the Internet of Things data collection platform. The platform can collect information such as the operating status and location of the equipment through technologies such as wireless sensor networks (WSN) and low-power wide area networks (LPWAN) to form a regional intelligent device operation data set. The collected regional intelligent device operation data set is subjected to intelligent device distribution density analysis. By statistically analyzing the distribution of devices in different regions, the density of intelligent devices in each plot is calculated, and intelligent device distribution density data is generated. The spatial layout map of the national space infrastructure is topologically analyzed using GIS tools to identify the connection relationship and spatial structure between the infrastructures and generate infrastructure topological structure data. The topological analysis includes indicators such as connectivity and proximity of the infrastructure to ensure the coordination between the layout of the intelligent device and the infrastructure network. Combined with the intelligent device distribution density data and the infrastructure topological structure data, the coverage rate of the intelligent device to the infrastructure in the national space is calculated. The specific method is to analyze its distribution in the infrastructure layout based on the coverage radius and density data of the intelligent device, calculate the coverage rate of the intelligent device, and generate the coverage rate data of the intelligent device. The network signal strength (such as Wi-Fi, cellular network, LoRa and other signals) at different locations is measured by smart devices in the region (such as smart phones, IoT nodes, etc.). These devices are used to generate national space network signal strength data and draw network signal strength distribution maps for subsequent analysis. The national space network signal strength data is used to analyze the signal base station density of the national space infrastructure spatial layout map. By analyzing the correlation between signal strength distribution and base station distribution, the communication base station density in each area is calculated to generate national space communication base station density data. The integration of transportation facilities (such as roads, traffic lights, parking lots, etc.) is analyzed using the operation data of smart devices (such as traffic monitoring equipment, vehicle-mounted sensors, etc.). The integration reflects the interconnection and coordination capabilities between transportation facilities. By calculating the integration of transportation facilities in each region, the integration data of national space intelligent transportation facilities is generated. Based on the smart device coverage data, national space communication base station density data and national space intelligent transportation facility integration data, a comprehensive assessment of the adaptability of infrastructure in the national space is conducted. The evaluation indicators include the distribution and coverage capabilities of smart devices, the density and signal coverage capabilities of communication base stations, and the integration and coordination capabilities of transportation facilities. The adaptability index of intelligent infrastructure is calculated by using multi-index fusion methods (such as weighted average, fuzzy comprehensive evaluation, etc.). The adaptability index reflects the support capacity of regional infrastructure for intelligent devices, network signals and transportation systems. Finally, the adaptability index of intelligent infrastructure is generated to provide a scientific basis for infrastructure planning and optimization.

[0102] Preferably, step S23 includes the following steps:

[0103] Step S231: using the Internet of Vehicles communication technology to collect traffic network and vehicle communication data from the regional smart device operation data set to obtain traffic network operation data and smart vehicle communication data;

[0104] Step S232: Performing spatial network responsiveness analysis on traffic network operation data and intelligent vehicle communication data through national land space communication base station density data to generate intelligent traffic feedback responsiveness data;

[0105] Step S233: marking the state of the spatial layout diagram of the national space infrastructure according to the traffic network operation data and the intelligent vehicle communication data to generate a national space traffic network diagram; performing traffic simulation on the national space traffic network diagram to generate traffic flow simulation data;

[0106] Step S234: Use the intelligent traffic feedback responsiveness data and traffic flow simulation data to evaluate the integration of intelligent traffic facilities and generate the integration of national land space intelligent traffic facilities.

[0107] In an embodiment of the present invention, the operation data of the traffic network in the region and the communication data of the intelligent vehicles are collected in real time through the vehicle-to-everything (V2X) communication technology, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-network (V2N). The collected content includes information such as vehicle position, speed, driving path, communication status, network delay, etc., to form traffic network operation data and intelligent vehicle communication data. The collected traffic network operation data and intelligent vehicle communication data are integrated to form a unified data set. The data can be stored in the edge computing node or the cloud for subsequent analysis. Combined with the density data of the national space communication base station, the communication quality and response speed of the traffic network and intelligent vehicles in different areas are analyzed. The analysis method includes spatial network coverage analysis, communication delay analysis, etc., to evaluate the interaction effect between the traffic network and vehicle communication. Through the spatial network responsiveness analysis, the intelligent traffic feedback responsiveness data is generated. The data reflects the communication efficiency and responsiveness of the traffic network in different areas, and provides a reference for the layout optimization of intelligent transportation facilities. The traffic network operation data and intelligent vehicle communication data are mapped to the spatial layout map of the national space infrastructure, and the status of traffic facilities such as roads, intersections, and traffic lights are marked. The status identification includes indicators such as traffic flow, vehicle density, and network delay, and a national space traffic network map is generated. Traffic flow simulation is performed on the national space traffic network map using traffic simulation tools (such as SUMO, VISSIM, etc.). The simulation process simulates traffic flow in different scenarios, including peak hours, accidents, etc., and generates traffic flow simulation data. The simulation data is used to analyze the carrying capacity and optimization plan of the traffic network. Combined with the intelligent traffic feedback responsiveness data and traffic flow simulation data, the integration of intelligent transportation facilities in the national space is evaluated. The evaluation content includes the interconnection capability, response speed, traffic mobility, and network coverage of transportation facilities. Through multi-dimensional evaluation, the integration data of intelligent transportation facilities in the national space is generated. This data reflects the integration and synergy effect of intelligent transportation facilities in the overall transportation network, providing decision support for urban planning and traffic management.

[0108] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0109] Step S31: screening the ecological space layout of the land space segmentation plot map to obtain the land space ecological layout map; calculating the green plant area on the land space ecological layout map to obtain green plant area data;

[0110] Step S32: using environmental sensors to collect regional environmental data on the national land space ecological layout map to obtain regional environmental data; performing environmental change analysis on the regional environmental data to generate regional environmental change data;

[0111] Step S33: performing change amplitude spectrum conversion on the regional environment change data to generate regional environment change spectrum data; performing peak and valley spectrum extraction on the regional environment change spectrum data to obtain regional environment change peak and valley spectrum data;

[0112] Step S34: Conduct a multi-dimensional ecological restoration capacity assessment on the green plant land area data through the regional environmental change peak spectrum data to generate a national land space ecological restoration capacity index.

[0113] In an embodiment of the present invention, the ecological space layout is screened by dividing the land space into plots. GIS tools and ecological indicators (such as land use type, ecological sensitive area division, nature reserve boundaries, etc.) are used to identify and screen areas suitable for ecological protection and restoration. After screening, a land space ecological layout map is generated, which identifies ecologically important green land, wetlands, water bodies and other areas. The area of ​​the green plant area in the land space ecological layout map is calculated. Using remote sensing images and high-resolution geographic data, the green plant coverage areas such as green land, forests, and grasslands are accurately measured, and the green plant area of ​​each area is calculated to generate green plant area data. The green plant area data reflects the distribution and total area of ​​green plants in the land space, providing basic data for subsequent ecological assessments. An environmental sensor network is deployed in the land space ecological layout map, and the sensor types include air quality sensors, temperature and humidity sensors, water quality sensors, etc. These sensors collect environmental data in the region in real time, including air pollution index, temperature changes, humidity levels, water quality, etc., to form a regional environmental data set. The collected regional environmental data is analyzed in time series to evaluate the changing trends and fluctuations of various environmental indicators. Through statistical analysis and regression models, significant areas and major influencing factors of environmental change are identified, and regional environmental change data are generated. This data reflects the dynamic changes of environmental conditions in the region and provides a basis for ecological management. The spectrum analysis method is used to convert regional environmental change data from the time domain to the frequency domain to generate regional environmental change spectrum data. Spectral analysis can reveal the periodic characteristics, frequency distribution and change amplitude in environmental change. The conversion process can use methods such as Fourier transform to convert continuous environmental changes into spectrum information for further analysis. In the spectrum data, the peak and valley spectrum data of environmental changes are identified and extracted. The peak and valley spectrum data reflect the most significant fluctuation points in environmental changes and can reveal extreme events of environmental fluctuations (such as temperature surges, pollution events, etc.). By analyzing the frequency and intensity of these extreme changes, the peak and valley spectrum data of regional environmental changes are obtained, which provides a key reference for ecological restoration assessment. Combining the peak and valley spectrum data of regional environmental changes with the green plant land area data, a comprehensive assessment of the ecological restoration capacity in the region is conducted from multiple dimensions (such as spatial dimension, temporal dimension, and ecological function dimension). The evaluation content includes the speed of green plant recovery, the correlation between green plant cover changes and environmental changes, and the resilience and self-recovery capacity of regional ecosystems. The national land space ecological restoration capacity index is calculated through multi-index fusion methods (such as weighted average, fuzzy comprehensive evaluation, etc.). This index reflects the recovery potential and capacity of regional ecosystems after environmental disturbances and is an important reference for guiding ecological restoration and protection. The resulting ecological restoration capacity index can be used to support the decision-making process of national land space ecological planning and management.

[0114] Preferably, step S34 includes the following steps:

[0115] Step S341: Calculate the mean of the peak and valley spectrum data of regional environmental changes to obtain mean data of regional environmental changes;

[0116] Step S342: taking the regional environmental change mean data as the environmental change slow period, and calculating the change amplitude of the regional environmental change peak and valley spectrum data based on the environmental change slow period to obtain the regional environmental change amplitude data;

[0117] Step S343: performing disturbance time series analysis on the green plant area data according to the regional environment change amplitude data to generate green plant area time series change data, wherein the green plant area time series change data includes the green plant area time series change data before the disturbance and the green plant area time series change data after the disturbance;

[0118] Step S344: Calculate the ecological restoration difference based on the time-series change data of green plant area before and after the disturbance to obtain the green plant area disturbance difference data; perform a multi-dimensional ecological restoration capacity assessment based on the green plant area disturbance difference data to generate the national land space ecological restoration capacity index.

[0119] In an embodiment of the present invention, the peak and valley spectrum data of regional environmental changes are processed by using a statistical analysis method to calculate the mean of the entire spectrum data. The mean data reflects the relatively stable period of regional environmental changes, that is, the period of time when the environmental conditions have not changed dramatically. The mean can be calculated by a simple average, a weighted average or other suitable statistical methods to finally generate the mean data of regional environmental changes. According to the mean data of regional environmental changes obtained in step S341, the period of time when the environmental changes are gentle, that is, the data segment in which the amplitude of change in the spectrum is close to the mean, is determined. These gentle periods will be used as a reference to help identify and separate the periods of significant changes. Based on the period of gentle environmental changes, the amplitude of change in the peak and valley spectrum data is calculated. By comparing the peak and valley spectrum data of environmental changes with the mean data, the amplitude of change in each period is calculated to generate the amplitude data of regional environmental changes. This data quantifies the intensity of environmental fluctuations and provides key parameters for subsequent green plant land analysis. Using the environmental change amplitude data, the green plant land data is subjected to disturbance analysis. Specifically, the green plant land data is divided into two periods before and after the disturbance by a time series analysis method, and the data is processed separately. The generated green plant area time series change data include the green plant area time series change data before the disturbance and the green plant area time series change data after the disturbance, which reflect the changes in green plant coverage at different time points. According to the green plant area time series change data obtained in step S343, the green plant area before and after the disturbance is compared to calculate the disturbance difference of the green plant area. The difference data can reflect the degree of influence of environmental disturbance on green plant coverage and provide basic data for ecological restoration assessment. Using the green plant area disturbance difference data, combined with the regional environmental change amplitude data, a multi-dimensional ecological restoration capacity assessment is carried out. The assessment content includes the self-recovery ability, recovery speed, environmental adaptability and other aspects of the ecosystem, and finally generates the national land space ecological restoration capacity index. This index is a key indicator for assessing the health status and sustainability of regional ecosystems, and can provide scientific guidance for ecological protection and restoration work.

[0120] Preferably, step S4 comprises the following steps:

[0121] Step S41: extracting the plot contour features from the land space segmentation plot map to obtain spatial plot contour feature data;

[0122] Step S42: performing point cloud conversion on the spatial plot outline feature data to generate spatial plot outline point cloud data; performing three-dimensional gridding on the spatial plot outline point cloud data to generate spatial plot three-dimensional grid data;

[0123] Step S43: using digital twin technology to perform digital twin modeling on the three-dimensional grid data of the spatial plot to generate a digital twin model of the spatial plot;

[0124] Step S44: Perform spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index.

[0125] In an embodiment of the present invention, the contours of each plot in the land space segmentation plot map are extracted by using image processing technology. Edge detection algorithms, contour tracking algorithms or other suitable image processing technologies can be used to identify and extract the contour lines of the plot. These contour feature data include information such as the shape, boundary, and geometric form of the plot, which are the basic data for subsequent three-dimensional modeling. The extracted plot contour information is digitized to generate spatial plot contour feature data. The data contains two-dimensional geometric features of the plot, such as boundary coordinates, contour curves, etc., which are convenient for subsequent point cloud conversion and three-dimensional modeling. The spatial plot contour feature data generated in step S41 is converted into point cloud. By discretizing the contour feature data into point cloud data, the two-dimensional contour features are expanded into three-dimensional space point clouds. Point cloud data retains the spatial distribution information of the plot contour and is an important input data for three-dimensional modeling. The generated spatial plot contour point cloud data is subjected to three-dimensional gridding. A grid generation algorithm, such as a Delaunay triangulation algorithm or a Poisson reconstruction algorithm, is used to convert discrete point cloud data into a continuous three-dimensional grid model. The spatial plot three-dimensional grid data finally generated represents the three-dimensional geometry of the plot, providing a basis for digital twin modeling. The spatial plot three-dimensional grid data generated in step S42 is input into the digital twin modeling platform. Through the digital twin technology, the three-dimensional geometric model of the plot is combined with real-time or historical data to create a digital twin model corresponding to virtual and reality. This model is not only a geometric replica, but also can simulate and predict the behavior and performance of the plot under different conditions. The generated spatial plot digital twin model contains multi-dimensional data such as the geometry, physical properties, and behavior simulation of the plot, providing a highly realistic virtual environment for the monitoring and analysis of the plot. Using a distributed sensor network, various environmental and state data (such as temperature, humidity, deformation, load, etc.) of the actual plot are collected. These actual data are compared and analyzed with the corresponding data in the digital twin model to evaluate the degree of consistency between the virtual model and reality. According to the evaluation results, the digital twin spatial consistency index is calculated. This index quantifies the degree of matching between the digital twin model and the actual plot in different dimensions, reflecting the accuracy and reliability of the digital twin model. A high consistency index indicates that the digital twin model can well reflect and predict the status and behavior of the actual plot, while a low consistency index suggests that the model needs further correction and optimization.

[0126] Preferably, step S44 includes the following steps:

[0127] Step S441: collect spatial block real-time data through a distributed sensor network to obtain a spatial block real-time integrated data set;

[0128] Step S442: synchronizing the spatial plot real-time integrated data set and the spatial plot digital twin model to generate a spatial plot real-time integrated synchronized data set; performing multi-dimensional information mapping on the spatial plot digital twin model using the spatial plot real-time integrated synchronized data set to generate a digital twin multi-dimensional mapping data set;

[0129] Step S443: Perform spatial dimension consistency evaluation on the digital twin multidimensional mapping data set to generate a spatial consistency analysis data set; perform error correction on the spatial consistency analysis data set to generate a spatial consistency correction data set;

[0130] Step S444: Use the multidimensional consistency index calculation technology to calculate the multidimensional spatial consistency index of the spatial consistency correction data set, so as to generate the digital twin spatial consistency index.

[0131] In an embodiment of the present invention, by arranging a distributed sensor network at different locations of a spatial plot, these sensors can collect real-time environmental and state data of the plot, including but not limited to multi-dimensional information such as temperature, humidity, stress, and displacement. The real-time collected data is transmitted to the data processing center through the sensor network, and the data of different sensors are integrated and processed to generate a real-time integrated data set of the spatial plot. The data set covers the multi-dimensional real-time information of the plot, providing a basis for subsequent synchronization and evaluation. The collected real-time data is synchronized with the existing data in the digital twin model in terms of time, space, and attributes to generate a real-time integrated synchronization data set of the spatial plot. This step ensures the consistency between the real-time data and the virtual model. The synchronized data set is used to perform multi-dimensional information mapping on the digital twin model, and the status, environment, and other information of the actual plot are mapped to the digital twin model. The generated digital twin multi-dimensional mapping data set contains various real-time information of the spatial plot, laying the foundation for consistency evaluation. The digital twin multi-dimensional mapping data set is subjected to consistency analysis of the spatial dimension to generate a spatial consistency analysis data set. By analyzing the degree of match between the actual data and the digital twin model in different spatial dimensions (such as X, Y, and Z coordinates), the difference between the model and the actual situation is evaluated. The errors found in the spatial consistency analysis data set are corrected to generate a spatial consistency correction data set. Through correction, the deviation between the digital twin model and the actual plot is reduced or eliminated, and the accuracy of the model is improved. The multidimensional consistency index calculation technology is applied to calculate the multidimensional spatial consistency index for the corrected spatial consistency correction data set. The calculation process takes into account the consistency between different spatial dimensions, including the multidimensional matching degree of spatial position, morphology, attributes, etc. The digital twin spatial consistency index finally generated is a comprehensive indicator that quantifies the consistency of the digital twin model in multidimensional space. The higher the index, the higher the degree of fit between the model and the actual plot, and the stronger the reliability.

[0132] Preferably, step S5 comprises the following steps:

[0133] Step S51: Integrate the evaluation indicators based on the smart infrastructure adaptability index, the national land space ecological restoration capacity index, and the digital twin space consistency index to generate a comprehensive national land space evaluation index;

[0134] Step S52: predicting the spatiotemporal change trend of national space planning for the national space comprehensive evaluation index through a preset time series model, and generating a spatiotemporal prediction matrix for national space planning changes;

[0135] Step S53: Utilize the spatiotemporal prediction matrix of national land space planning changes to optimize spatial planning and generate a national land space planning optimization plan.

[0136] In an embodiment of the present invention, the independent indexes (smart infrastructure adaptability index, national land space ecological restoration capacity index, digital twin space consistency index) generated in the previous steps are integrated. The integration process can use weighted average, principal component analysis or other multi-dimensional data fusion methods to generate a comprehensive evaluation index of national land space that comprehensively reflects the overall state of national land space. The final generated comprehensive evaluation index of national land space is an important data reflecting the overall quality and adaptability of national land space. This index integrates the consistency of smart infrastructure, ecological restoration capacity and digital twin model, and comprehensively evaluates the current status and potential development capacity of national land space. Using time series analysis technology, combined with the comprehensive evaluation index of national land space, a preset spatiotemporal change trend model is constructed. The model can use algorithms such as ARIMA and LSTM (long short-term memory network) to predict the change trend of national land space in the future. Substitute the comprehensive evaluation index of national land space into the time series model to predict the planning change trend of national land space in a certain time period in the future. The generated spatiotemporal prediction matrix of national land space planning change is a multidimensional data matrix that reflects the predicted values ​​of various spatial indicators at different time nodes. Based on the data results of the spatiotemporal prediction matrix, the existing national land space planning is optimized. The optimization process takes into account the temporal and spatial variation trends of the comprehensive evaluation indicators of national land space, and adjusts the spatial layout and resource allocation in combination with actual needs and development goals to improve the rationality and sustainability of spatial planning. The final national land space planning optimization plan is a planning strategy formulated after comprehensive evaluation and prediction. The plan not only takes into account the current status of spatial resources, but also can cope with possible future changes, thereby achieving efficient management and utilization of national land space.

[0137] In this specification, a national land space planning data monitoring and evaluation system is provided, which is used to execute the above-mentioned national land space planning data monitoring and evaluation method, and the national land space planning data monitoring and evaluation system includes:

[0138] The land parcel segmentation module is used to obtain the national land space GIS remote sensing image; perform regional land parcel segmentation on the national land space GIS remote sensing image to generate a national land space segmentation land parcel map; perform infrastructure spatial layout screening on the national land space segmentation land parcel map to obtain a national land space infrastructure spatial layout map;

[0139] The equipment coverage calculation module is used to collect intelligent equipment operation data from the spatial layout map of the national space infrastructure to obtain a regional intelligent equipment operation data set; to calculate the intelligent equipment coverage rate of the regional intelligent equipment operation data set to obtain intelligent equipment coverage rate data; to analyze the signal base station density of the regional intelligent equipment operation data set to generate national space communication base station density data; to calculate the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent equipment operation data set to obtain the integration degree of national space intelligent transportation facilities; to evaluate the adaptability of intelligent infrastructure based on the intelligent equipment coverage rate data, the density data of national space communication base stations and the integration degree of national space intelligent transportation facilities, thereby generating an intelligent infrastructure adaptability index;

[0140] The ecological restoration calculation module is used to screen the ecological space layout of the land space segmentation plot map to obtain the national space ecological layout map; calculate the green plant area on the national space ecological layout map to obtain the green plant area data; collect regional environmental data on the national space ecological layout map to obtain regional environmental data; extract the environmental change peak and valley spectrum of the regional environmental data to obtain the regional environmental change peak and valley spectrum data; conduct a multi-dimensional ecological restoration capacity assessment on the green plant area data through the regional environmental change peak spectrum data to generate the national space ecological restoration capacity index;

[0141] The spatial consistency calculation module is used to extract the plot contour features of the national land space segmentation plot map to obtain the spatial plot contour feature data; to perform digital twin modeling on the spatial plot contour feature data to generate a spatial plot digital twin model; to perform spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index;

[0142] The planning optimization module is used to predict the spatiotemporal change trend of national space planning based on the intelligent infrastructure adaptability index, the national space ecological resilience capacity index and the digital twin space consistency index of the national space comprehensive evaluation indicators, and generate a spatiotemporal prediction matrix for national space planning changes; use the spatiotemporal prediction matrix for national space planning changes to optimize spatial planning and generate a national space planning optimization plan.

[0143] The beneficial effect of the present invention is that by acquiring and processing the GIS remote sensing images of the national space, the accurate depiction of the spatial layout of the infrastructure is ensured. This provides an accurate spatial data basis for subsequent analysis. The coverage and density of smart devices and signal base stations are analyzed, so that the adaptability and distribution of the infrastructure are comprehensively evaluated, and the layout and efficiency of smart facilities in the national space can be accurately grasped. Combined with the ecological space layout and environmental data, the ecological restoration capacity is evaluated, providing a scientific basis for ecological protection and restoration. Through digital twin modeling and spatial consistency evaluation, the consistency of spatial data with reality is ensured, and the authenticity and reliability of the digital model are improved. Comprehensively considering the adaptability of smart infrastructure, ecological restoration capacity and digital twin consistency, the trend of spatiotemporal changes is predicted, so as to generate an optimized spatial planning scheme. This comprehensive analysis method can improve the scientificity and accuracy of planning in multiple dimensions, ensuring that future planning can effectively respond to different challenges and needs. The adaptability assessment of smart infrastructure helps identify the advantages and disadvantages of infrastructure, provides specific data support for resource allocation and facility optimization, and the ecological restoration capacity assessment guides the implementation of ecological protection measures to ensure the restoration of the ecosystem. The digital twin spatial consistency assessment provides a basis for model verification, ensuring the consistency of the virtual model with the actual situation. The prediction of spatiotemporal change trends can identify possible future changes and problems in advance, provide forward-looking guidance for decision makers, and help make reasonable adjustments and optimizations in the planning stage. By optimizing planning based on comprehensive evaluation indicators, it is possible to better balance the needs and goals of various aspects in the optimization plan and promote the scientific management of national space. Therefore, the present invention integrates GIS remote sensing images, intelligent device data, environmental data and digital twin technology to achieve comprehensive, real-time and accurate national space planning data monitoring and evaluation, significantly improving the scientificity and effectiveness of planning optimization.

[0144] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0145] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for monitoring and evaluating national land space planning data, characterized in that: The following steps are involved: Step S1: Acquire a national land space GIS remote sensing image; segment the national land space GIS remote sensing image into regional plots to generate a national land space segmentation plot map; perform infrastructure spatial layout screening on the national land space segmentation plot map to obtain a national land space infrastructure spatial layout map; Step S2: Collecting smart device operation data on the spatial layout map of the national space infrastructure to obtain a regional smart device operation data set; calculating the smart device coverage rate on the regional smart device operation data set to obtain smart device coverage rate data; The signal base station density analysis is performed on the regional intelligent device operation data set to generate the national land space communication base station density data; the transportation facility integration degree is calculated on the national land space infrastructure spatial layout map through the regional intelligent device operation data set to obtain the national land space intelligent transportation facility integration degree; the intelligent infrastructure adaptability evaluation is performed based on the intelligent device coverage data, the national land space communication base station density data and the national land space intelligent transportation facility integration degree, thereby generating the intelligent infrastructure adaptability index; Step S3: Perform ecological space layout screening on the land space segmentation plot map to obtain a land space ecological layout map; perform green plant land occupation calculation on the land space ecological layout map to obtain green plant land occupation data; perform regional environmental data collection on the land space ecological layout map to obtain regional environmental data; perform environmental change peak and valley spectrum extraction on the regional environmental data to obtain regional environmental change peak and valley spectrum data; perform multi-dimensional ecological restoration capacity assessment on the green plant land occupation data through the regional environmental change peak spectrum data to generate a land space ecological restoration capacity index; Step S4: extracting the plot contour features from the land space segmentation plot map to obtain spatial plot contour feature data; Digital twin modeling is performed on the spatial plot contour feature data to generate a spatial plot digital twin model; spatial consistency evaluation is performed on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index; Step S5: Based on the smart infrastructure adaptability index, the national space ecological restoration capacity index, and the digital twin space consistency index, the national space comprehensive evaluation index is used to predict the spatiotemporal change trend of national space planning, and a spatiotemporal prediction matrix of national space planning changes is generated; The spatial planning is optimized by using the spatiotemporal prediction matrix of national land space planning changes to generate an optimized plan for national land space planning.

2. The national land space planning data monitoring and evaluation method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire GIS remote sensing images of national land space; Step S12: performing image preprocessing on the national land space GIS remote sensing image to generate a standard national land space GIS remote sensing image, wherein the image preprocessing includes image filtering, image brightness enhancement and image color enhancement; Step S13: segmenting the standard land space GIS remote sensing image into regional plots to generate a land space segmentation plot map; Step S14: Screen the spatial layout of infrastructure on the land space segmentation plot map to obtain a spatial layout map of the land space infrastructure.

3. The national land space planning data monitoring and evaluation method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: using the IoT data collection platform to collect smart device operation data on the spatial layout map of the national space infrastructure to obtain a regional smart device operation data set; performing smart device distribution density analysis on the regional smart device operation data set to generate smart device distribution density data; performing infrastructure topology analysis on the spatial layout map of the national space infrastructure to generate infrastructure topology data; calculating smart device coverage using smart device distribution density data and infrastructure topology data to obtain smart device coverage data; Step S22: measuring the network signal strength of the regional smart device operation data set to generate national land space network signal strength data; using the national land space network signal strength data to perform signal base station density analysis on the national land space infrastructure spatial layout map to generate national land space communication base station density data; Step S23: calculating the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent device operation data set to obtain the integration degree of the national space intelligent transportation facilities; Step S24: Based on the smart device coverage data, the land space communication base station density data and the land space intelligent transportation facility integration data, the smart infrastructure adaptability index is generated.

4. The method for monitoring and evaluating land space planning data according to claim 3 is characterized in that: Step S23 includes the following steps: Step S231: using the Internet of Vehicles communication technology to collect traffic network and vehicle communication data from the regional smart device operation data set to obtain traffic network operation data and smart vehicle communication data; Step S232: Performing spatial network responsiveness analysis on traffic network operation data and intelligent vehicle communication data through national land space communication base station density data to generate intelligent traffic feedback responsiveness data; Step S233: marking the state of the spatial layout diagram of the national space infrastructure according to the traffic network operation data and the intelligent vehicle communication data to generate a national space traffic network diagram; performing traffic simulation on the national space traffic network diagram to generate traffic flow simulation data; Step S234: Use the intelligent traffic feedback responsiveness data and traffic flow simulation data to evaluate the integration of intelligent traffic facilities and generate the integration of national land space intelligent traffic facilities.

5. The method for monitoring and evaluating land space planning data according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: screening the ecological space layout of the land space segmentation plot map to obtain the land space ecological layout map; calculating the green plant area on the land space ecological layout map to obtain green plant area data; Step S32: using environmental sensors to collect regional environmental data on the national land space ecological layout map to obtain regional environmental data; performing environmental change analysis on the regional environmental data to generate regional environmental change data; Step S33: performing change amplitude spectrum conversion on the regional environment change data to generate regional environment change spectrum data; performing peak and valley spectrum extraction on the regional environment change spectrum data to obtain regional environment change peak and valley spectrum data; Step S34: Conduct a multi-dimensional ecological restoration capacity assessment on the green plant land area data through the regional environmental change peak spectrum data to generate a national land space ecological restoration capacity index.

6. The national land space planning data monitoring and evaluation method according to claim 4 is characterized in that: Step S34 includes the following steps: Step S341: Calculate the mean of the peak and valley spectrum data of regional environmental changes to obtain mean data of regional environmental changes; Step S342: taking the regional environmental change mean data as the environmental change slow period, and calculating the change amplitude of the regional environmental change peak and valley spectrum data based on the environmental change slow period to obtain the regional environmental change amplitude data; Step S343: performing disturbance time series analysis on the green plant area data according to the regional environment change amplitude data to generate green plant area time series change data, wherein the green plant area time series change data includes the green plant area time series change data before the disturbance and the green plant area time series change data after the disturbance; Step S344: Calculate the ecological restoration difference based on the time-series change data of green plant area before and after the disturbance to obtain the green plant area disturbance difference data; perform a multi-dimensional ecological restoration capacity assessment based on the green plant area disturbance difference data to generate the national land space ecological restoration capacity index.

7. The method for monitoring and evaluating land space planning data according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: extracting the plot contour features from the land space segmentation plot map to obtain spatial plot contour feature data; Step S42: performing point cloud conversion on the spatial plot outline feature data to generate spatial plot outline point cloud data; performing three-dimensional gridding on the spatial plot outline point cloud data to generate spatial plot three-dimensional grid data; Step S43: using digital twin technology to perform digital twin modeling on the three-dimensional grid data of the spatial plot to generate a digital twin model of the spatial plot; Step S44: Perform spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index.

8. The method for monitoring and evaluating land space planning data according to claim 7 is characterized in that: Step S44 includes the following steps: Step S441: collect spatial block real-time data through a distributed sensor network to obtain a spatial block real-time integrated data set; Step S442: synchronizing the spatial plot real-time integrated data set and the spatial plot digital twin model to generate a spatial plot real-time integrated synchronized data set; performing multi-dimensional information mapping on the spatial plot digital twin model using the spatial plot real-time integrated synchronized data set to generate a digital twin multi-dimensional mapping data set; Step S443: Perform spatial dimension consistency evaluation on the digital twin multidimensional mapping data set to generate a spatial consistency analysis data set; perform error correction on the spatial consistency analysis data set to generate a spatial consistency correction data set; Step S444: Use the multidimensional consistency index calculation technology to calculate the multidimensional spatial consistency index of the spatial consistency correction data set, so as to generate the digital twin spatial consistency index.

9. The national land space planning data monitoring and evaluation method according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: Integrate the evaluation indicators based on the smart infrastructure adaptability index, the national land space ecological restoration capacity index, and the digital twin space consistency index to generate a comprehensive national land space evaluation index; Step S52: predicting the spatiotemporal change trend of national space planning for the national space comprehensive evaluation index through a preset time series model, and generating a spatiotemporal prediction matrix for national space planning changes; Step S53: Utilize the spatiotemporal prediction matrix of national land space planning changes to optimize spatial planning and generate a national land space planning optimization plan.

10. A national land space planning data monitoring and evaluation system, characterized in that: Used to execute the national land space planning data monitoring and evaluation method as claimed in claim 1, the national land space planning data monitoring and evaluation system comprises: The land parcel segmentation module is used to obtain the national land space GIS remote sensing image; perform regional land parcel segmentation on the national land space GIS remote sensing image to generate a national land space segmentation land parcel map; perform infrastructure spatial layout screening on the national land space segmentation land parcel map to obtain a national land space infrastructure spatial layout map; The equipment coverage calculation module is used to collect intelligent equipment operation data from the spatial layout map of the national space infrastructure to obtain a regional intelligent equipment operation data set; to calculate the intelligent equipment coverage rate of the regional intelligent equipment operation data set to obtain intelligent equipment coverage rate data; to analyze the signal base station density of the regional intelligent equipment operation data set to generate national space communication base station density data; to calculate the integration degree of transportation facilities on the spatial layout map of the national space infrastructure through the regional intelligent equipment operation data set to obtain the integration degree of national space intelligent transportation facilities; to evaluate the adaptability of intelligent infrastructure based on the intelligent equipment coverage rate data, the density data of national space communication base stations and the integration degree of national space intelligent transportation facilities, thereby generating an intelligent infrastructure adaptability index; The ecological restoration calculation module is used to screen the ecological space layout of the land space segmentation plot map to obtain the national space ecological layout map; calculate the green plant area on the national space ecological layout map to obtain the green plant area data; collect regional environmental data on the national space ecological layout map to obtain regional environmental data; extract the environmental change peak and valley spectrum of the regional environmental data to obtain the regional environmental change peak and valley spectrum data; conduct a multi-dimensional ecological restoration capacity assessment on the green plant area data through the regional environmental change peak spectrum data to generate the national space ecological restoration capacity index; The spatial consistency calculation module is used to extract the plot contour features of the national land space segmentation plot map to obtain the spatial plot contour feature data; to perform digital twin modeling on the spatial plot contour feature data to generate a spatial plot digital twin model; to perform spatial consistency evaluation on the spatial plot digital twin model through a distributed sensor network to generate a digital twin spatial consistency index; The planning optimization module is used to predict the spatiotemporal change trend of national space planning based on the intelligent infrastructure adaptability index, the national space ecological resilience capacity index and the digital twin space consistency index of the national space comprehensive evaluation indicators, and generate a spatiotemporal prediction matrix for national space planning changes; use the spatiotemporal prediction matrix for national space planning changes to optimize spatial planning and generate a national space planning optimization plan.

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