Optimal configuration method and system for territorial space development right

Abstract

The invention provides an optimal configuration method and system for territorial space development right, and relates to the technical field of space resource allocation, and the method comprises the steps: obtaining K space nodes based on a space partitioning model of a target region; the method comprises the following steps: acquiring basic data by using a GIS platform to obtain K space node basic data sets; carrying out development right constraint information identification to obtain K pieces of space development right constraint information; performing remote sensing monitoring and big data interaction to obtain K space node monitoring data sets; performing constraint anomaly probability analysis to obtain a first abnormal space node and an anomaly probability of the first abnormal space node; and carrying out balanced collaborative optimization configuration to obtain a target optimization configuration scheme. According to the invention, the technical problems of low resource allocation efficiency and lack of flexibility and sustainability due to the fact that the land space planning and management method in the prior art generally allocates space resources based on static data and a single index are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of space resource allocation, and in particular to a method and system for optimizing the allocation of national land space development rights. Background Art

[0002] With the rapid development of the economy and society and the acceleration of urbanization, the development and utilization of land space resources have become an important driving force for social development. In this process, how to scientifically and rationally allocate land space development rights, especially in the optimal allocation of space development rights, has become an important topic in research and practice.

[0003] Traditional land space planning and management methods are usually based on static data and single indicators to allocate spatial resources, which leads to low resource allocation efficiency, lack of flexibility and sustainability, and difficulty in coping with complex and changing socio-economic environments and rapidly changing natural conditions; moreover, existing resource optimization allocation methods, especially in the implementation process of optimization allocation, usually lack flexible adjustment mechanisms and iterative optimization processes, making it difficult to cope with dynamic changes in complex environments. Summary of the invention

[0004] This application provides a method and system for optimizing the allocation of national land space development rights, aiming to solve the technical problems that the existing national land space planning and management methods are usually based on static data and a single indicator to allocate spatial resources, resulting in low resource allocation efficiency and lack of flexibility and sustainability.

[0005] The first aspect disclosed in the present application provides a method for optimizing the configuration of national land space development rights, and the method includes: obtaining K spatial nodes based on a spatial partitioning model of a target area, where K is a positive integer; using a GIS platform to traverse the K spatial nodes to collect basic data and obtain K spatial node basic data sets; identifying development rights constraint information of the K spatial nodes based on the K spatial node basic data sets and obtaining K spatial development rights constraint information; traversing the K spatial nodes to perform remote sensing monitoring and big data interaction and obtain K spatial node monitoring data sets; performing constraint anomaly probability analysis on the K spatial nodes based on the K spatial development rights constraint information and the K spatial node monitoring data sets and obtaining a first abnormal spatial node and a first abnormal spatial node anomaly probability; performing balanced collaborative optimization configuration of the K spatial nodes based on the first abnormal spatial node and the first abnormal spatial node anomaly probability, as well as the K spatial node monitoring data sets and obtaining a target optimization configuration plan.

[0006] The second aspect disclosed in the present application provides a system for optimizing the configuration of land space development rights, and the system is used for the above-mentioned method for optimizing the configuration of land space development rights. The system includes: a space node acquisition module, which is used to acquire K space nodes based on the spatial partition model of the target area, where K is a positive integer; a basic data acquisition module, which is used to use the GIS platform to traverse the K space nodes to collect basic data and obtain K space node basic data sets; a constraint information identification module, which is used to identify the development rights constraint information of the K space nodes based on the K space node basic data sets and obtain K space development rights constraint information; a monitoring module A data acquisition module is used to traverse the K spatial nodes to perform remote sensing monitoring and big data interaction, and obtain K spatial node monitoring data sets; an abnormality probability analysis module is used to perform constraint abnormality probability analysis on the K spatial nodes based on the K spatial development rights constraint information and the K spatial node monitoring data sets, and obtain the first abnormal spatial node and the abnormal probability of the first abnormal spatial node; a collaborative optimization configuration module is used to perform balanced collaborative optimization configuration on the K spatial nodes based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets, and obtain a target optimization configuration plan.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects: The target area can be effectively divided through the spatial partition model to ensure that the spatial nodes of the area have reasonable representativeness and spatial distribution characteristics. By subdividing the target area into multiple spatial nodes, the spatial development can be finely managed and optimized in the local area; the basic data can be collected by traversing each spatial node through the GIS platform, and various types of data in the target area can be collected efficiently and accurately. The GIS platform can realize the visualization and analysis of geographic spatial data, making the data collection process more accurate and automated, and providing data support for subsequent analysis and decision-making; by analyzing the basic data of spatial nodes, the constraints of each node in the development process can be identified, and the necessary constraint information can be provided for the subsequent spatial optimization configuration; through remote sensing monitoring and big data interaction, the actual development status of spatial nodes can be collected dynamically and in real time, and various data can be obtained, which will be used for subsequent It provides real-time feedback for continuous spatial development optimization, ensuring that planning and decision-making can be adjusted according to the latest actual situation, thereby improving the accuracy and timeliness of decision-making; through comprehensive analysis of development rights constraint information and monitoring data, it can identify spatial nodes that deviate from expectations in the actual development process and calculate their abnormal probability, which helps to find potential risk areas, discover problems in advance, and provide a basis for subsequent adjustments and optimizations; through balanced collaborative optimization configuration, the spatial development rights configuration of the entire region can be optimized according to the actual needs of each spatial node, the deviation of abnormal nodes, and the carrying capacity of resources and the environment. The target optimization configuration plan can ensure that resources are reasonably distributed among spatial nodes, especially make necessary adjustments to abnormal spatial nodes, so as to achieve sustainable development in the region, improve resource utilization efficiency, and reduce development imbalances or resource waste.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of a method for optimizing the configuration of land space development rights provided in an embodiment of the present application.

[0010] Figure 2 A schematic diagram of the system structure for optimizing the configuration of land space development rights provided in an embodiment of the present application.

[0011] Explanation of the accompanying drawings: spatial node acquisition module 10, basic data collection module 20, constraint information identification module 30, monitoring data acquisition module 40, abnormal probability analysis module 50, collaborative optimization configuration module 60. DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a method and system for optimizing the configuration of national land space development rights, thereby solving the technical problems that the national land space planning and management methods in the prior art are usually based on static data and a single indicator to allocate spatial resources, resulting in low resource allocation efficiency and lack of flexibility and sustainability.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a method for optimizing the configuration of land space development rights, and the method includes: Based on the spatial partition model of the target area, K spatial nodes are obtained, where K is a positive integer.

[0015] Define the target area. The target area depends on the needs of research or application. Based on the characteristics of the target area, construct a spatial zoning model. The design of the spatial zoning model is determined by combining multiple factors such as the region's geography, economy, and environment. For example, zoning can be based on different geographical units, such as administrative divisions, natural scenic spots, and urban planning areas. It can also be based on data-driven methods, such as using geographic information systems for cluster analysis to identify spatial nodes.

[0016] A spatial node refers to the core point of each partitioned area, representing a larger spatial development area. Here, K is a positive integer, indicating the number of spatial nodes obtained after partitioning. The specific value of K depends on the complexity of the spatial partitioning and the size of the target area. Through the division of the spatial partitioning model, K spatial nodes can be obtained, and each node represents a city, block, region or a geographical unit. Through the spatial partitioning model, the location, area, range and other information of each spatial node can be determined, providing a basis for subsequent data collection, monitoring and analysis.

[0017] The K spatial nodes are traversed by using a GIS platform to collect basic data, and a basic data set of the K spatial nodes is obtained.

[0018] The GIS platform can process and display geospatial data, support spatial analysis, map drawing, and connection with external data sources. The GIS platform is used to traverse K spatial nodes and collect relevant information of the nodes. The basic data collected includes but is not limited to geographic location, land use type, climate conditions, economic data, environmental data, infrastructure distribution, etc. The data can also cover relevant information in the fields of society, population, and traffic flow. The collected data of each spatial node is integrated to form K data sets. Each spatial node has its own data set. These data sets contain basic information about the area where the node is located, providing basic data support for subsequent analysis.

[0019] Based on the K spatial node basic data sets, development right constraint information of the K spatial nodes is identified to obtain K spatial development right constraint information.

[0020] Using the collected basic data set, identify the resource situation of each spatial node, for example, whether there are enough land resources for development, whether there are ecological protection restrictions, whether the population density is too high, etc., classify the land use types (such as agricultural land, urban land, ecological protection area, etc.), and analyze the development restrictions of these lands. For example, some areas may be ecological protection areas and are not suitable for overdevelopment. Some areas may have low development potential due to traffic conditions. According to the resource data of each spatial node, determine which areas belong to rigid control areas. For example, using the three-line and one-single rule, such as ecological red line, environmental protection red line, basic farmland protection line, etc., the development of these areas will be strictly restricted. In these spatial nodes, based on factors such as land nature, resource carrying capacity, and environmental protection requirements, identify the development intensity of the area, such as the maximum allowable development density, construction intensity, etc., and clarify the floor area ratio requirements, such as the developable building area per square meter of land. According to the constraints, divide the spatial nodes into different development areas, for example, they can be divided into areas where development is allowed, restricted development, and prohibited development. For each spatial node, a corresponding set of constraint information is generated, including development intensity, floor area ratio requirements, etc. This information will serve as the basis for subsequent analysis to ensure that spatial development does not exceed the bottom line of resource carrying capacity and environmental protection.

[0021] The K spatial nodes are traversed to perform remote sensing monitoring and big data interaction to obtain a monitoring data set of the K spatial nodes.

[0022] Traverse K spatial nodes, use satellite remote sensing, drone images, ground monitoring and other technologies to regularly monitor the land use and resource conditions of spatial nodes. For example, obtain changes in land use types through remote sensing images, such as from agricultural land to urban construction land, and monitor land development; integrate various data sources through the big data platform, such as government data, environmental data, traffic data, climate data, etc., and conduct interactive analysis. Through big data mining, evaluate the resource allocation and development trends of different spatial nodes. During the monitoring process, timely collect feedback data, evaluate regional development, resource consumption, environmental pressure, etc., and update development rights constraint information based on these data to ensure that development activities match resource carrying capacity.

[0023] The monitoring data obtained from remote sensing monitoring and big data interaction are integrated into monitoring data sets for each spatial node. These data sets include not only the current land use status, but also indicators such as environmental quality and resource utilization. These monitoring data will provide important basis for subsequent anomaly analysis, spatial development optimization configuration, etc.

[0024] Based on the K spatial development rights constraint information and the K spatial node monitoring data sets, constraint abnormality probability analysis is performed on the K spatial nodes to obtain a first abnormal spatial node and an abnormal probability of the first abnormal spatial node.

[0025] According to the constraints of each spatial node, the normal development progress or development degree of each node is set. For example, based on historical data and planning requirements, the development intensity of a spatial node is set to be within a certain range. If it exceeds this range, it is considered abnormal. Combined with the monitoring data and constraints, each spatial node is compared in real time. If the development intensity or floor area ratio of a spatial node exceeds the preset normal range, such as exceeding the maximum floor area ratio or development density, the node is considered to have an abnormal situation.

[0026] Based on the monitoring data and constraints of each node, the probability of each node exceeding the preset range is calculated. This can be done through probability models, such as normal distribution, Bayesian inference, etc., to calculate the possibility of abnormality. For example, the development intensity of a certain spatial node should be 1.2 under normal circumstances, but the actual monitored development intensity is 1.8, which exceeds the expected value of 0.6. The abnormal probability of this abnormal behavior is calculated. Finally, among all K spatial nodes, the node with the highest abnormal probability is selected as the first abnormal spatial node, and its abnormal probability value is calibrated. This node is the spatial node with the most risk or development default.

[0027] Based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets, the K spatial nodes are balanced and collaboratively optimized to obtain a target optimization configuration solution.

[0028] The goal of the optimization configuration is to achieve balanced development of spatial nodes, that is, to reduce the number of abnormal nodes and balance the pressures of development intensity, resource utilization and environmental protection. Specifically, based on the first abnormal spatial node and its abnormal probability, a preliminary optimization configuration plan is first formulated. In this plan, the abnormal nodes are adjusted, such as reducing the development intensity, adjusting the floor area ratio or strengthening environmental protection measures, to ensure that the development of the node meets the constraints. When generating the first optimization configuration plan, it is necessary not only to consider the adjustment of the abnormal nodes, but also to comprehensively consider the overall layout of the other K spatial nodes to ensure that the optimization plan can achieve its goals while minimizing negative impacts.

[0029] According to the first optimization configuration plan, random adjustments or refinement optimization are carried out, for example, the development intensity of some nodes is appropriately increased, or some spatial nodes with excessive resource consumption are reduced, and the configuration plan is repeatedly adjusted through iterative optimization algorithms until the minimum adjustment cost is reached. The adjustment cost may include economic costs, resource consumption, environmental damage and other costs. After multiple adjustments and optimizations, a global optimal spatial development rights configuration plan is finally determined, which takes into account various factors such as spatial development needs, resource carrying capacity and environmental protection, to ensure that the spatial development in the region reaches the optimal state.

[0030] Further, based on the K spatial development rights constraint information and the K spatial node monitoring data sets, a constraint abnormality probability analysis is performed on the K spatial nodes to obtain a first abnormal spatial node and an abnormal probability of the first abnormal spatial node, including: Based on the K spatial development rights constraint information, tolerance thresholds are identified for the K spatial node monitoring data sets to obtain K spatial node tolerance thresholds; big data matching is performed using the K spatial node monitoring data sets and the K spatial node tolerance thresholds as search items to obtain K matching spatial node anomaly degree sets; centralized retrieval is performed on the K matching spatial node anomaly degree sets to determine K centralized matching spatial node anomaly degrees; the ratio of the K centralized matching spatial node anomaly degrees to the preset anomaly degrees is used as the K spatial node anomaly probabilities of the K spatial nodes; the maximum value of the K spatial node anomaly probabilities is used as the first abnormal spatial node anomaly probability, and the corresponding spatial node is used as the first abnormal spatial node.

[0031] The tolerance threshold refers to the deviation or degree of excess that can be tolerated in the development activities of a certain spatial node during the development process. In other words, it is a limit value. Beyond this range, it will be regarded as abnormal and require further analysis and processing. For example, if the maximum volume ratio of an area is 2.5, and the tolerance threshold can be set to ±0.2, then the development intensity between 2.3 and 2.7 is regarded as the normal range, and exceeding this range is abnormal. Based on the specific constraints of the spatial node, a reasonable tolerance threshold is formulated. For example, according to the resource status and land nature of the spatial node, a tolerance for development intensity is defined. It can be based on statistical calculations of historical data or expert evaluation standards. Different types of spatial nodes may have different tolerance thresholds, depending on their planning goals and resource conditions.

[0032] Using big data analysis technology, the monitoring data of each spatial node is matched with the corresponding tolerance threshold. The matching goal is to identify which spatial nodes’ development activities or resource consumption exceed their tolerance range. Through data matching, the abnormality of each spatial node is calculated. The abnormality represents the gap between the actual situation of the spatial node and the expected development or resource utilization. For example, if the development intensity of a spatial node is significantly higher than the predetermined allowable range, its abnormality will increase accordingly. For each spatial node, an abnormality value is calculated to reflect the degree of deviation between the development activities of the node and the expected plan. The calculation of the abnormality can be based on the deviation between the monitoring data and the threshold. Finally, a set of abnormalities of K spatial nodes is obtained. These abnormality values ​​will be used for the next abnormality analysis to help identify which spatial nodes have problems in their development progress or may cause future risks.

[0033] The purpose of centralized retrieval is to summarize the abnormality of each spatial node, further analyze the distribution of these abnormalities, and identify spatial nodes with extremely abnormal performance. Centralized retrieval usually adopts clustering, mean calculation, weighted scoring and other methods to combine the abnormality of multiple spatial nodes and find the most outstanding abnormal nodes. According to the retrieval results, the centralized abnormality of each spatial node is determined, that is, the degree to which the node is abnormal in all indicators, and finally a score representing the overall abnormality of the spatial node is obtained.

[0034] The preset abnormality is a standard value derived from historical data, empirical rules or expert evaluation, which is used to indicate the maximum abnormality that a spatial node can accept under normal circumstances. If the abnormality of a spatial node exceeds this preset value, it means that the development activities of the node may be risky or violate planning requirements. The ratio of the concentrated abnormality of each spatial node to the preset abnormality is calculated. For example, if the concentrated abnormality of a node is 0.8 and the preset abnormality is 0.5, the abnormal probability of the node is 0.8 / 0.5=1.6. The ratio represents the degree of deviation between the actual abnormality and the expected abnormality. Generally speaking, the larger the ratio, the more serious the abnormality.

[0035] From the abnormal probabilities of K spatial nodes, select the node with the largest abnormal probability value. This node is considered to be the most abnormal spatial node, which means that its development behavior or progress deviates most from expectations and there may be major risks or development errors. The spatial node with the largest abnormal probability is marked as the first abnormal spatial node, and its corresponding abnormal probability value is recorded. This node is the node that currently needs the most attention and its development process needs to be adjusted or further investigated and analyzed.

[0036] Furthermore, performing centralized retrieval on the K matching space node abnormality sets to determine the K centralized matching space node abnormality degrees includes: The means of the K matching space node anomaly degree sets are calculated respectively to obtain the means of the K matching space node anomaly degrees; the means of the K matching space node anomaly degrees are used as the initial iteration centers respectively, and the meanshift algorithm is used to perform centralized retrieval in the K matching space node anomaly degree sets to obtain K centralized matching space node anomaly degrees.

[0037] For each spatial node, the mean of its anomaly set is calculated. The mean is obtained by adding all the anomaly values ​​of the node and dividing it by the number of values. The mean reflects the overall degree of anomaly of each spatial node. The higher the mean, the greater the degree of anomaly of the spatial node.

[0038] MeanShift is a non-parametric clustering algorithm, commonly used in density estimation and pattern recognition. It iteratively adjusts the position of data points by finding the local mean of data points, thereby aggregating areas with higher density, and uses the mean of the abnormality of each spatial node as the initial iteration center. In other words, each initial center point of the MeanShift algorithm is the mean of each spatial node. In each round of iteration of the MeanShift algorithm, the algorithm calculates the mean of the surrounding neighborhood and moves the current center point toward the mean until convergence, that is, the change in the center point position is less than the preset threshold. In the set of spatial node abnormality, the MeanShift algorithm updates the centralized abnormality of each node based on the abnormality of the current node and the abnormality of the adjacent nodes. Through multiple iterations, the algorithm will concentrate the abnormality of the node to areas with higher or lower abnormality, thereby helping to find areas with higher abnormality density. After iteration, K centralized matching spatial node abnormalities are obtained. These centralized matching abnormalities reflect the position of each spatial node in its abnormality distribution, and finally determine the abnormality value of the centralized area.

[0039] Further, based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets, the K spatial nodes are balanced and collaboratively optimized to obtain a target optimization configuration scheme, including: Based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets, a first optimization configuration scheme is generated, wherein the first optimization configuration scheme is a scheme for optimizing the spatial development rights configuration of the K spatial nodes, including the spatial development rights configuration of the K spatial nodes; according to the first optimization configuration scheme, a target optimization configuration scheme is determined, and the target optimization configuration scheme is a spatial development rights configuration scheme with the minimum overall adjustment cost obtained after adjustment based on the first optimization configuration scheme.

[0040] The goal of training a neural network is to optimize the configuration of spatial nodes by learning historical data and the status of current nodes. When training a neural network, a set of labeled historical data is used as a training set to learn how to adjust the configuration of development rights according to the characteristics of the spatial nodes (monitoring data, abnormal probability, etc.). The network model adjusts the weights through the back propagation algorithm to minimize the error. Using the trained neural network model, the monitoring data of the current K spatial nodes and their abnormal probability are input. The model will output the first optimal configuration plan. This plan comprehensively considers the abnormality of each spatial node to ensure that the configuration of the abnormal node is appropriately adjusted. At the same time, it optimizes the resource allocation of other nodes to balance the development of the entire region.

[0041] According to the first optimization configuration plan, a spatial node is selected to reduce resource allocation. Usually, a node with a low degree of development or less resource demand is selected for adjustment. The configuration of this node will be removed, that is, the resource allocation of the node will be reduced. The resources obtained from the removed spatial node, such as land, development intensity, floor area ratio, etc., will be randomly allocated to other spatial nodes. This can be done through a random number generator to ensure that resource allocation is uniform and not biased towards a specific node. During the allocation process, some constraints need to be followed, such as the total amount of resources remains unchanged, the development intensity and floor area ratio of each node do not exceed their maximum allowable values, etc.

[0042] Adjustment cost is a criterion for measuring the quality of optimization schemes, which usually includes the cost of resource redistribution, the impact on the ecological environment, and the cost of economic benefits. Adjustment cost can be calculated in a weighted manner, including the cost of resource redistribution, the impact on the environment, and the changes in social and economic benefits brought about by the adjustment of development intensity. Through different random resource allocation and adjustment schemes, the cost of each scheme is calculated, and then the scheme with the lowest cost is selected as the target optimization configuration scheme. The goal is to ensure the optimal allocation of resources in the entire region and reduce the imbalance of development and the impact of abnormal nodes.

[0043] Furthermore, the difference between the abnormal probability of the first abnormal spatial node and the preset expected abnormal probability is calculated, and minimizing the difference is used as the balancing goal. The first optimization configuration scheme based on the first abnormal spatial node is generated in combination with the K spatial node monitoring data sets.

[0044] The abnormal probability of the first abnormal spatial node indicates the degree of deviation between the development activities of the corresponding spatial node and the expectations, reflecting the degree of abnormality in the execution of the development plan; the preset expected abnormal probability is a predetermined standard value, representing the expected abnormal probability. Ideally, the abnormal probability of each spatial node should be close to this expected value. For example, the preset expected abnormal probability can be set to 0.1, indicating that the abnormal probability of the expected spatial node does not exceed this value.

[0045] The difference refers to the difference between the actual abnormal probability of the first abnormal space node and the preset expected abnormal probability. This difference reflects the deviation between the abnormal degree of the current development progress of the node and the expected target.

[0046] Taking minimizing the difference as the equilibrium goal, that is, by adjusting the configuration of the spatial nodes, the abnormal probability of the first abnormal spatial node is made as close as possible to the preset expected abnormal probability, thereby reducing the abnormal deviation of its development activities and achieving balanced development. The monitoring data of each spatial node is used to support optimization decisions. By analyzing the monitoring data of K spatial nodes, the resource utilization, development intensity and environmental impact of each node can be evaluated. Combined with these monitoring data, adjustments are made to the first abnormal spatial node. For example, if the development intensity of the first abnormal spatial node is too high, its abnormal probability can be reduced by adjusting the resource allocation of the node, such as reducing the development intensity or improving land use efficiency. After optimization, the first optimal configuration scheme based on the first abnormal spatial node is finally generated.

[0047] Furthermore, according to the first optimization configuration scheme, determining a target optimization configuration scheme includes: The first optimization configuration scheme is randomly adjusted according to a preset adjustment range to obtain a second optimization configuration scheme; it is determined whether the adjustment cost of the second optimization configuration scheme is less than or equal to the adjustment cost of the first optimization configuration scheme, and if so, the second optimization configuration scheme is used as the stage optimization configuration scheme; the second optimization configuration scheme is randomly adjusted again according to the preset adjustment range, and after multiple iterations, the iteration is stopped until the preset number of iterations is met or the adjustment cost is lower than the preset adjustment cost threshold, so as to obtain the target optimization configuration scheme.

[0048] The preset adjustment range refers to the maximum range or amplitude of each adjustment during the optimization process. This amplitude is set in advance and is usually defined based on the specific conditions of the area. For example, the development intensity can be increased or decreased by a certain percentage, such as 5%, and the change in floor area ratio may also be within a preset range. The setting of the adjustment range needs to balance the optimization search space with the actual constraints to ensure that overly aggressive adjustments are not made while effectively exploring new configurations.

[0049] Random adjustment is achieved by randomly selecting the configuration of certain spatial nodes, such as development intensity, floor area ratio, etc., and adjusting them within the set adjustment range. The configuration after each adjustment will be different from the previous configuration, thus generating different configuration schemes. This process is to explore different configuration schemes and find possible better resource configurations. After random adjustment, the new configuration scheme obtained is the second optimized configuration scheme, which reflects the result of the first optimized configuration scheme after random adjustment.

[0050] The adjustment cost of the first optimization configuration scheme is the adjustment cost of the scheme calculated in the initial stage, which serves as a reference standard; the adjustment cost of the second optimization configuration scheme is the cost of the scheme after random adjustment, which may be lower or higher, depending on the effect of the adjustment. Comparing the two, if the adjustment cost of the second optimization configuration scheme is smaller or equal, it is considered to be a better choice, and the second scheme is accepted as the stage optimization configuration scheme, which means that the random adjustment optimizes the overall resource allocation and cost of the scheme. Otherwise, keep the first optimization configuration scheme and continue to make adjustments on this basis.

[0051] According to the preset adjustment range, the second optimization configuration scheme continues to be randomly adjusted. Each adjustment is based on the scheme adjusted last time, and further optimization is performed. In this way, the scheme is gradually improved and the adjustment cost is reduced. Set the maximum number of iterations, that is, if a satisfactory result is still not achieved after a certain number of adjustments, the optimization process is stopped. This number can be set based on experience or the complexity of the problem; set the minimum adjustment cost threshold, that is, if the cost change during the optimization process is lower than this threshold, it means that the optimization effect brought by further adjustment is not obvious, and the iteration can be stopped at this time. After multiple adjustments and optimizations, until the preset number of iterations is met or the adjustment cost is lower than the preset adjustment cost threshold, the final solution is the target optimization configuration scheme. This scheme is usually the configuration scheme with the lowest adjustment cost and the most reasonable resource allocation. It can effectively balance the needs of all spatial nodes, especially reduce the development risks of abnormal nodes, and ensure balanced regional spatial development.

[0052] Furthermore, based on the K spatial node basic data sets, the development right constraint information of the K spatial nodes is identified to obtain K spatial development right constraint information, including: Constructing a development right constraint network layer; using the development right constraint network layer to identify the K spatial node basic data sets, and obtaining the K spatial development right constraint information.

[0053] Construct a development right constraint network layer to process various constraint information of spatial nodes. This network layer will provide modeling of various restrictions and conditions faced by each spatial node in the development process, and provide a basis for the subsequent identification of constraint information. Specifically, the development right constraint network is an abstract model used to describe the resource, environmental, planning and other restrictions of each spatial node. It can contain a variety of constraints, such as development intensity restrictions, floor area ratio upper limit, land use type, ecological protection requirements, etc. Through this network layer, the constraint information of spatial nodes can be analyzed in multiple dimensions to ensure that the subsequent spatial development plan is feasible and in line with the principles of sustainable development.

[0054] The basic data of each spatial node include land use type, resource status, population density, infrastructure, environmental protection needs, etc. These data are used as input and processed by the development rights constraint network layer to identify the development restrictions of each node. For example, a node is restricted because it is an ecological protection area, or its development intensity is limited due to resource scarcity. For each spatial node, the corresponding spatial development rights constraint information is output through the identification process of the constraint network layer. This constraint information will serve as an important basis for the subsequent optimization and allocation of spatial development rights.

[0055] In summary, the method for optimizing the configuration of land space development rights provided in the embodiments of the present application has the following technical effects: The target area can be effectively divided through the spatial partition model to ensure that the spatial nodes of the area have reasonable representativeness and spatial distribution characteristics. By subdividing the target area into multiple spatial nodes, the spatial development can be finely managed and optimized in the local area; the basic data can be collected by traversing each spatial node through the GIS platform, and various types of data in the target area can be collected efficiently and accurately. The GIS platform can realize the visualization and analysis of geographic spatial data, making the data collection process more accurate and automated, and providing data support for subsequent analysis and decision-making; by analyzing the basic data of spatial nodes, the constraints of each node in the development process can be identified, and the necessary constraint information can be provided for the subsequent spatial optimization configuration; through remote sensing monitoring and big data interaction, the actual development status of spatial nodes can be collected dynamically and in real time, and various data can be obtained, which will be used for subsequent It provides real-time feedback for continuous spatial development optimization, ensuring that planning and decision-making can be adjusted according to the latest actual situation, thereby improving the accuracy and timeliness of decision-making; through comprehensive analysis of development rights constraint information and monitoring data, it can identify spatial nodes that deviate from expectations in the actual development process and calculate their abnormal probability, which helps to find potential risk areas, discover problems in advance, and provide a basis for subsequent adjustments and optimizations; through balanced collaborative optimization configuration, the spatial development rights configuration of the entire region can be optimized according to the actual needs of each spatial node, the deviation of abnormal nodes, and the carrying capacity of resources and the environment. The target optimization configuration plan can ensure that resources are reasonably distributed among spatial nodes, especially make necessary adjustments to abnormal spatial nodes, so as to achieve sustainable development in the region, improve resource utilization efficiency, and reduce development imbalances or resource waste.

[0056] Embodiment 2, based on the same inventive concept as the method for optimizing the allocation of land space development rights in the above embodiment, Figure 2 As shown, the embodiment of the present application provides an optimization configuration system for land space development rights, and the system includes: The spatial node acquisition module 10 is used to acquire K spatial nodes based on the spatial partition model of the target area, where K is a positive integer.

[0057] The basic data collection module 20 is used to use the GIS platform to traverse the K spatial nodes to collect basic data and obtain the basic data sets of the K spatial nodes.

[0058] The constraint information identification module 30 is used to identify the development right constraint information of the K spatial nodes based on the K spatial node basic data sets to obtain K spatial development right constraint information.

[0059] The monitoring data acquisition module 40 is used to traverse the K spatial nodes to perform remote sensing monitoring and big data interaction to obtain a set of monitoring data of the K spatial nodes.

[0060] The abnormal probability analysis module 50 is used to perform constraint abnormal probability analysis on the K spatial nodes based on the K spatial development rights constraint information and the K spatial node monitoring data sets to obtain the first abnormal spatial node and the abnormal probability of the first abnormal spatial node.

[0061] The collaborative optimization configuration module 60 is used to perform balanced collaborative optimization configuration on the K spatial nodes based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets to obtain a target optimization configuration solution.

[0062] Furthermore, the abnormal probability analysis module 50 includes the following operation steps: Based on the K spatial development rights constraint information, tolerance thresholds are identified for the K spatial node monitoring data sets to obtain K spatial node tolerance thresholds; big data matching is performed using the K spatial node monitoring data sets and the K spatial node tolerance thresholds as search items to obtain K matching spatial node anomaly degree sets; centralized retrieval is performed on the K matching spatial node anomaly degree sets to determine K centralized matching spatial node anomaly degrees; the ratio of the K centralized matching spatial node anomaly degrees to the preset anomaly degrees is used as the K spatial node anomaly probabilities of the K spatial nodes; the maximum value of the K spatial node anomaly probabilities is used as the first abnormal spatial node anomaly probability, and the corresponding spatial node is used as the first abnormal spatial node.

[0063] Furthermore, the abnormal probability analysis module 50 includes the following operation steps: The means of the K matching space node anomaly degree sets are calculated respectively to obtain the means of the K matching space node anomaly degrees; the means of the K matching space node anomaly degrees are used as the initial iteration centers respectively, and the meanshift algorithm is used to perform centralized retrieval in the K matching space node anomaly degree sets to obtain K centralized matching space node anomaly degrees.

[0064] Furthermore, the collaborative optimization configuration module 60 includes the following operation steps: Based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets, a first optimization configuration scheme is generated, wherein the first optimization configuration scheme is a scheme for optimizing the spatial development rights configuration of the K spatial nodes, including the spatial development rights configuration of the K spatial nodes; according to the first optimization configuration scheme, a target optimization configuration scheme is determined, and the target optimization configuration scheme is a spatial development rights configuration scheme with the minimum overall adjustment cost obtained after adjustment based on the first optimization configuration scheme.

[0065] Furthermore, the difference between the abnormal probability of the first abnormal spatial node and the preset expected abnormal probability is calculated, and minimizing the difference is used as the balancing goal. The first optimization configuration scheme based on the first abnormal spatial node is generated in combination with the K spatial node monitoring data sets.

[0066] Furthermore, the collaborative optimization configuration module 60 includes the following operation steps: The first optimization configuration scheme is randomly adjusted according to a preset adjustment range to obtain a second optimization configuration scheme; it is determined whether the adjustment cost of the second optimization configuration scheme is less than or equal to the adjustment cost of the first optimization configuration scheme, and if so, the second optimization configuration scheme is used as the stage optimization configuration scheme; the second optimization configuration scheme is randomly adjusted again according to the preset adjustment range, and after multiple iterations, the iteration is stopped until the preset number of iterations is met or the adjustment cost is lower than the preset adjustment cost threshold, so as to obtain the target optimization configuration scheme.

[0067] Furthermore, the constraint information identification module 30 includes the following operation steps: Constructing a development right constraint network layer; using the development right constraint network layer to identify the K spatial node basic data sets, and obtaining the K spatial development right constraint information.

[0068] Through the above-mentioned detailed description of the method for optimizing the allocation of land space development rights in this specification, those skilled in the art can clearly understand the system for optimizing the allocation of land space development rights in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0069] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. 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 application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The method for optimizing the allocation of national land space development rights is characterized by: The method comprises: Obtain K spatial nodes based on the spatial partition model of the target area, where K is a positive integer; Using the GIS platform to traverse the K spatial nodes to collect basic data, and obtain a basic data set of the K spatial nodes; Based on the K spatial node basic data sets, the development right constraint information of the K spatial nodes is identified to obtain K spatial development right constraint information; Traversing the K spatial nodes to perform remote sensing monitoring and big data interaction, and obtaining a monitoring data set of the K spatial nodes; Based on the K spatial development rights constraint information and the K spatial node monitoring data sets, a constraint abnormality probability analysis is performed on the K spatial nodes to obtain a first abnormal spatial node and an abnormal probability of the first abnormal spatial node; Based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets, the K spatial nodes are balanced and collaboratively optimized to obtain a target optimization configuration solution.

2. The method for optimizing the allocation of land space development rights according to claim 1, characterized in that: Based on the K spatial development rights constraint information and the K spatial node monitoring data sets, a constraint abnormality probability analysis is performed on the K spatial nodes to obtain a first abnormal spatial node and an abnormal probability of the first abnormal spatial node, including: Based on the K spatial development rights constraint information, tolerance thresholds are identified for the K spatial node monitoring data sets to obtain K spatial node tolerance thresholds; Performing big data matching using the K spatial node monitoring data sets and the K spatial node tolerance thresholds as search items to obtain K matching spatial node abnormality degree sets; Centrally search the K matching space node abnormality sets to determine the K centralized matching space node abnormality degrees; The ratio of the abnormality degree of the K centralized matching spatial nodes to the preset abnormality degree is used as the abnormality probability of the K spatial nodes; The maximum value among the K spatial node abnormal probabilities is used as the first abnormal spatial node abnormal probability, and the corresponding spatial node is used as the first abnormal spatial node.

3. The method for optimizing the allocation of land space development rights according to claim 2, characterized in that: Centrally searching the K matching space node abnormality sets to determine the K centralized matching space node abnormality, including: Calculate the mean values ​​of the K matching space node abnormality sets respectively to obtain the mean values ​​of the K matching space node abnormality values; The mean of the abnormality degrees of the K matching space nodes is respectively taken as the initial iteration center, and the meanshift algorithm is used to perform centralized retrieval in the K matching space node abnormality sets to obtain K centralized matching space node abnormality degrees.

4. The method for optimizing the allocation of land space development rights according to claim 1, characterized in that: Based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets, the K spatial nodes are balanced and collaboratively optimized to obtain a target optimization configuration scheme, including: Based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets, a first optimization configuration scheme is generated, wherein the first optimization configuration scheme is a scheme for optimizing the spatial development rights of the K spatial nodes, including the spatial development rights configuration of the K spatial nodes; According to the first optimization configuration scheme, a target optimization configuration scheme is determined, and the target optimization configuration scheme is a spatial development rights configuration scheme with the minimum overall adjustment cost obtained after adjustment based on the first optimization configuration scheme.

5. The method for optimizing the allocation of land space development rights according to claim 4, characterized in that: The difference between the abnormal probability of the first abnormal spatial node and the preset expected abnormal probability is calculated, and minimizing the difference is used as the balancing goal. A first optimization configuration scheme based on the first abnormal spatial node is generated in combination with the K spatial node monitoring data sets.

6. The method for optimizing the allocation of land space development rights according to claim 5, characterized in that: According to the first optimization configuration scheme, determining a target optimization configuration scheme includes: Randomly adjusting the first optimization configuration scheme according to a preset adjustment range to obtain a second optimization configuration scheme; Determine whether the adjustment cost of the second optimization configuration scheme is less than or equal to the adjustment cost of the first optimization configuration scheme, and if so, use the second optimization configuration scheme as the stage optimization configuration scheme; The second optimization configuration scheme is randomly adjusted again according to the preset adjustment range. After multiple iterations, the iteration is stopped until the preset number of iterations is met or the adjustment cost is lower than the preset adjustment cost threshold, and the target optimization configuration scheme is obtained.

7. The method for optimizing the allocation of land space development rights according to claim 1, characterized in that: Based on the K spatial node basic data sets, the development right constraint information of the K spatial nodes is identified to obtain K spatial development right constraint information, including: Constructing a network layer of constraints on the right to development; The development right constraint network layer is used to identify the K spatial node basic data sets to obtain the K spatial development right constraint information.

8. The optimal allocation system of national land space development rights is characterized by: A method for optimizing the configuration of land space development rights according to any one of claims 1 to 7, the system comprising: A spatial node acquisition module is used to acquire K spatial nodes based on the spatial partition model of the target area, where K is a positive integer; A basic data collection module is used to use a GIS platform to traverse the K spatial nodes to collect basic data and obtain a basic data set of the K spatial nodes; A constraint information identification module, used to identify the development right constraint information of the K spatial nodes based on the K spatial node basic data sets, and obtain K spatial development right constraint information; A monitoring data acquisition module is used to traverse the K spatial nodes to perform remote sensing monitoring and big data interaction to obtain a set of monitoring data of the K spatial nodes; An abnormal probability analysis module is used to perform constraint abnormal probability analysis on the K spatial nodes based on the K spatial development rights constraint information and the K spatial node monitoring data sets to obtain a first abnormal spatial node and an abnormal probability of the first abnormal spatial node; The collaborative optimization configuration module is used to perform balanced collaborative optimization configuration on the K spatial nodes based on the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, and the K spatial node monitoring data sets to obtain a target optimization configuration solution.

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