Optimal allocation method and system for national land space development rights

Abstract

The present invention provides a method and system for optimizing the configuration of land space development rights, which relates to the field of spatial resource configuration technology, including: obtaining K spatial nodes based on a spatial partitioning model of a target area; using a GIS platform to collect basic data and obtain a basic data set of K spatial nodes; identifying development rights constraint information and obtaining K spatial development rights constraint information; performing remote sensing monitoring and big data interaction to obtain a K spatial node monitoring data set; performing constraint anomaly probability analysis to obtain a first abnormal spatial node and an abnormal probability of the first abnormal spatial node; performing balanced collaborative optimization configuration to obtain a target optimization configuration solution. The present invention solves the technical problem that the existing 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, lack of flexibility and sustainability.

Description

Technical Field

[0001] The present invention relates to the technical field of spatial 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 and space resources have become an important driving force for social development. In this process, how to scientifically and rationally allocate land and 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 land space development rights, aiming to solve the technical problems that the existing land space planning and management methods are usually based on static data and single indicators to allocate spatial resources, resulting in low resource allocation efficiency, lack of flexibility and sustainability.

[0005] The first aspect disclosed in the present application provides a method for optimizing the configuration of land space development rights, which 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 to obtain 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, to obtain 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. The system is used for the above-mentioned method for optimizing the configuration of land space development rights. The system includes: a spatial node acquisition module for acquiring K spatial nodes based on the spatial partitioning model of the target area, where K is a positive integer; a basic data acquisition module for traversing the K spatial nodes using a GIS platform to perform basic data acquisition and obtain K spatial node basic data sets; a constraint information identification module for identifying development rights constraint information for the K spatial nodes based on the K spatial node basic data sets and obtaining K spatial development rights constraint information; a monitoring module for A measurement data acquisition module is used to traverse the K spatial nodes to perform remote sensing monitoring and big data interaction to obtain K spatial node monitoring data sets; an abnormality probability analysis module is used to perform constrained 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 to 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, as well as the K spatial node monitoring data sets to obtain a target optimization configuration solution.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects:

[0008] The target area is effectively divided through the spatial partitioning 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 collection is carried out 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 Continuous spatial development optimization provides real-time feedback, 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.

[0009] 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

[0010] Figure 1 A flow chart of the method for optimizing the configuration of land space development rights provided in the embodiment of this application.

[0011] Figure 2 Schematic diagram of the system structure for optimizing the configuration of land space development rights provided in the embodiment of this application.

[0012] Explanation of the accompanying symbols: spatial node acquisition module 10, basic data acquisition module 20, constraint information identification module 30, monitoring data acquisition module 40, abnormal probability analysis module 50, collaborative optimization configuration module 60. DETAILED DESCRIPTION

[0013] The embodiments of the present application solve the technical problems that the existing land space planning and management methods are usually based on static data and single indicators to allocate spatial resources, resulting in low resource allocation efficiency, lack of flexibility and sustainability, by providing an optimized configuration method and system for land space development rights.

[0014] After introducing the basic principles of this application, various non-limiting embodiments of this application will be specifically described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0015] Example 1, as Figure 1 As shown, the embodiment of the present application provides a method for optimizing the configuration of land space development rights, the method comprising:

[0016] Obtain K spatial nodes based on the spatial partition model of the target area, where K is a positive integer.

[0017] 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 areas, and urban planning areas. It can also be based on data-driven methods, such as cluster analysis using geographic information systems, to identify spatial nodes.

[0018] A spatial node is the core point of each partitioned area, representing a larger spatial development region. K is a positive integer representing 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 spatial partitioning model, K spatial nodes can be obtained, each representing a city, block, region, or geographic unit. The spatial partitioning model can determine the location, area, range, and other information of each spatial node, providing a foundation for subsequent data collection, monitoring, and analysis.

[0019] The K spatial nodes are traversed by using a GIS platform to collect basic data and obtain a basic data set of the K spatial nodes.

[0020] GIS platforms can process and display geospatial data, supporting spatial analysis, mapping, and connections to external data sources. GIS platforms traverse K spatial nodes and collect relevant information about them. The collected basic data includes, but is not limited to, geographic location, land use type, climate conditions, economic data, environmental data, and infrastructure distribution. Data can also cover social, demographic, and traffic flow information. The collected data from each spatial node is integrated to form K data sets. Each spatial node has its own data set, which contains basic information about the area where the node is located, providing basic data support for subsequent analysis.

[0021] Based on the K spatial node basic data sets, development right constraint information of the K spatial nodes is identified to obtain K pieces of spatial development right constraint information.

[0022] Using the collected basic data set, the resource status of each spatial node is identified. For example, whether there is sufficient land for development, whether there are ecological protection constraints, and whether the population density is too high. Land use types (such as agricultural land, urban land, and ecological protection areas) are classified and development restrictions for these land uses are analyzed. For example, some areas may be ecological protection areas and unsuitable for excessive development, while others may have low development potential due to limited transportation conditions. Based on the resource data of each spatial node, it is determined which areas are subject to rigid control. For example, using the "three lines and one list" rule, such as the ecological red line, the environmental protection red line, and the basic farmland protection line, these areas are subject to strict development restrictions. Within these spatial nodes, the development intensity of the area is identified based on factors such as land nature, resource carrying capacity, and environmental protection requirements. For example, the maximum allowable development density and construction intensity are determined. The floor area ratio requirements, such as the developable building area per square meter of land, are also specified. Based on these constraints, the spatial nodes are divided into different development zones, such as those where development is permitted, restricted, and prohibited. 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 resource carrying capacity and the bottom line of environmental protection.

[0023] The K spatial nodes are traversed to perform remote sensing monitoring and big data interaction to obtain a set of monitoring data of the K spatial nodes.

[0024] Traversing K spatial nodes, the system regularly monitors land use and resource conditions at these nodes using technologies such as satellite remote sensing, drone imagery, and ground monitoring. For example, remote sensing imagery can be used to identify changes in land use types, such as the transition from agricultural land to urban construction land, to monitor land development. A big data platform integrates various data sources, such as government data, environmental data, traffic data, and climate data, and conducts interactive analysis. Through big data mining, the system assesses resource allocation and development trends at different spatial nodes. During the monitoring process, timely feedback data is collected to assess regional development, resource consumption, and environmental pressures. Based on this data, development rights constraints are updated to ensure that development activities are aligned with resource carrying capacity.

[0025] The monitoring data obtained from the interaction of remote sensing monitoring and big data 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.

[0026] 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.

[0027] 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. Exceeding this range is considered abnormal. Combined with 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.

[0028] Based on each node's monitoring data and constraints, the probability of each node exceeding the preset range is calculated. This can be done using probabilistic models, such as normal distribution and Bayesian inference, to calculate the likelihood of anomalies. For example, if the development intensity of a spatial node should normally be 1.2, but the actual monitored development intensity is 1.8, exceeding the expected value by 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 greatest risk or development default.

[0029] 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.

[0030] 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 node is 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 node, but also to comprehensively consider the overall layout of the other K spatial nodes to ensure that the optimization plan minimizes negative impacts while achieving the goal.

[0031] Based on the first optimized allocation plan, random adjustments or refined optimizations are performed. For example, the development intensity of certain nodes may be appropriately increased, or certain spatial nodes with excessive resource consumption may be reduced. The allocation plan is repeatedly adjusted through an iterative optimization algorithm until the minimum adjustment cost is achieved. This adjustment cost can include various factors such as economic costs, resource consumption, and environmental damage. After multiple adjustments and optimizations, a globally optimal spatial development rights allocation plan is ultimately determined. This plan takes into account various factors such as spatial development needs, resource carrying capacity, and environmental protection, ensuring optimal spatial development within the region.

[0032] Furthermore, 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:

[0033] Based on the K spatial development rights constraint information, tolerance thresholds are identified on 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 degree 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.

[0034] The tolerance threshold refers to the degree of deviation or 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. Exceeding this range will be considered an anomaly 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 considered normal, and exceeding this range is abnormal. Based on the specific constraints of the spatial node, a reasonable tolerance threshold is formulated. For example, based on the resource status and land nature of the spatial node, a tolerance for development intensity is defined. This 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.

[0035] 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 anomaly degree of each spatial node is calculated. The anomaly degree 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 anomaly degree will increase accordingly. For each spatial node, an anomaly value is calculated to reflect the degree of deviation between the development activities of the node and the expected plan. The calculation of the anomaly degree can be based on the deviation between the monitoring data and the threshold. Finally, a set of anomaly degrees of K spatial nodes is obtained. These anomaly values ​​will be used for the next anomaly analysis to help identify which spatial nodes have problems with their development progress or may lead to future risks.

[0036] 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.

[0037] The preset anomaly degree is a standard value derived from historical data, empirical rules, or expert assessments. It represents the maximum acceptable anomaly degree for a spatial node under normal circumstances. If the anomaly degree of a spatial node exceeds this preset value, it means that the development activities at that node may be risky or violate planning requirements. The ratio of the concentrated anomaly degree of each spatial node to the preset anomaly degree is calculated. For example, if the concentrated anomaly degree of a node is 0.8 and the preset anomaly degree is 0.5, the probability of an anomaly for that node is 0.8 / 0.5=1.6. This ratio represents the degree of deviation between the actual anomaly degree and the expected anomaly degree. Generally speaking, the larger the ratio, the more severe the anomaly.

[0038] 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.

[0039] Furthermore, performing centralized retrieval on the K matching space node abnormality sets to determine the K centralized matching space node abnormality degrees includes:

[0040] Calculate the means of the K matching space node anomaly degree sets respectively to obtain the means of the K matching space node anomaly degrees; use the means of the K matching space node anomaly degrees as the initial iteration center, use the meanshift algorithm to perform centralized search in the K matching space node anomaly degree sets, and obtain K centralized matching space node anomaly degrees.

[0041] 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 anomaly degree of each spatial node. The higher the mean, the greater the anomaly degree of the spatial node.

[0042] MeanShift is a nonparametric clustering algorithm commonly used in density estimation and pattern recognition. It iteratively adjusts the positions of data points by finding their local mean, thereby clustering areas of high density. The mean of the outlier scores of each spatial node is used as the initial iteration center. In other words, each initial center point in the MeanShift algorithm is the mean of each spatial node. In each iteration of the MeanShift algorithm, the algorithm calculates the mean of the surrounding neighborhood and moves the current center point toward this mean until convergence occurs (i.e., the change in the center point position is less than a preset threshold). Within the set of spatial node outlier scores, the MeanShift algorithm updates the concentrated outlier score of each node based on the outlier score of the current node and the outlier scores of its neighboring nodes. Over multiple iterations, the algorithm concentrates node outliers toward areas with higher or lower outlier scores, thereby helping to identify areas of high outlier density. After iteration, K concentrated matching spatial node outlier scores are obtained. These concentrated matching outlier scores reflect the position of each spatial node in its outlier score distribution, ultimately determining the outlier score value of the concentrated area.

[0043] Furthermore, 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:

[0044] 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 K spatial nodes, including the spatial development rights configuration of K spatial nodes; according to the first optimization configuration scheme, a target optimization configuration scheme is determined, and the target optimization configuration scheme is the spatial development rights configuration scheme with the minimum overall adjustment cost obtained after adjustment based on the first optimization configuration scheme.

[0045] 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 based on the characteristics of the spatial nodes (monitoring data, abnormality probability, etc.). The network model adjusts the weights through the backpropagation algorithm to minimize the error. Using the trained neural network model, the monitoring data of the current K spatial nodes and their abnormality probabilities are input. The model will output the first optimized 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.

[0046] According to the first optimization configuration scheme, 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.

[0047] Adjustment costs are a criterion for measuring the quality of optimization solutions. They typically include resource reallocation costs, ecological impacts, and economic benefits. Adjustment costs can be calculated using a weighted approach, including the costs of resource reallocation, environmental impacts, and changes in social and economic benefits resulting from adjustments to development intensity. By using different random resource allocation and adjustment schemes, the cost of each scheme is calculated. The scheme with the lowest cost is then selected as the target optimization configuration. The goal is to ensure optimal resource allocation across the entire region, reducing development imbalances and the impact of abnormal nodes.

[0048] 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 optimized configuration scheme based on the first abnormal spatial node is generated in combination with the K spatial node monitoring data sets.

[0049] 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.

[0050] 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.

[0051] Taking minimizing the difference as the equilibrium goal, that is, by adjusting the configuration of 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.

[0052] Furthermore, determining a target optimization configuration scheme based on the first optimization configuration scheme includes:

[0053] 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. 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, and the target optimization configuration scheme is obtained.

[0054] The preset adjustment range refers to the maximum range or amplitude of each adjustment during the optimization process. This range 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 and actual constraints to ensure that overly radical adjustments are not made while effectively exploring new configurations.

[0055] 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, thereby 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.

[0056] The adjusted cost of the first optimized configuration is the adjusted cost of the solution calculated in the initial stage and serves as a reference standard. The adjusted cost of the second optimized configuration is the cost of the solution after random adjustments, which may be lower or higher depending on the effectiveness of the adjustments. When comparing the two, if the adjusted cost of the second optimized configuration is lower or equal, it is considered the superior choice and is accepted as the optimized configuration for the current stage, indicating that the random adjustments have optimized the overall resource allocation and cost of the solution. Otherwise, the first optimized configuration is retained and adjustments are continued based on it.

[0057] According to the preset adjustment range, the second optimized configuration scheme continues to be randomly adjusted. Each adjustment is based on the scheme after the previous adjustment and further optimized. In this way, the scheme is gradually improved and the adjustment cost is reduced. Set the maximum number of iterations. If a satisfactory result is 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. If the cost change during the optimization process is lower than this threshold, it means that the optimization effect brought about by further adjustment is not obvious. At this time, the iteration can be stopped. 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 optimized 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 risk of abnormal nodes, and ensure balanced regional spatial development.

[0058] Furthermore, based on the K spatial node basic data sets, development right constraint information of the K spatial nodes is identified to obtain K pieces of spatial development right constraint information, including:

[0059] Constructing a development right constraint network layer; using the development right constraint network layer to identify the K spatial node basic data sets to obtain the K spatial development right constraint information.

[0060] 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 subsequent constraint information identification. 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 include multiple 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 subsequent spatial development plans are feasible and in line with the principles of sustainable development.

[0061] 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.

[0062] In summary, the method for optimizing the allocation of land space development rights provided in the embodiments of the present application has the following technical effects:

[0063] The target area is effectively divided through the spatial partitioning 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 collection is carried out 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 Continuous spatial development optimization provides real-time feedback, 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.

[0064] Example 2, based on the same inventive concept as the method for optimizing the allocation of land space development rights in the above-mentioned embodiment, Figure 2 As shown, the embodiment of the present application provides a system for optimizing the configuration of land space development rights, the system comprising:

[0065] The spatial node acquisition module 10 is configured to acquire K spatial nodes based on the spatial partition model of the target area, where K is a positive integer.

[0066] 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.

[0067] The constraint information identification module 30 is configured to identify the development right constraint information of the K spatial nodes based on the K spatial node basic data sets to obtain K pieces of spatial development right constraint information.

[0068] The monitoring data acquisition module 40 is used to traverse the K spatial nodes to perform remote sensing monitoring and big data interaction, and obtain a set of monitoring data of the K spatial nodes.

[0069] The abnormality probability analysis module 50 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 to obtain a first abnormal spatial node and an abnormal probability of the first abnormal spatial node.

[0070] 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.

[0071] Furthermore, the abnormal probability analysis module 50 includes the following steps:

[0072] Based on the K spatial development rights constraint information, tolerance thresholds are identified on 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 degree 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.

[0073] Furthermore, the abnormal probability analysis module 50 includes the following steps:

[0074] Calculate the means of the K matching space node anomaly degree sets respectively to obtain the means of the K matching space node anomaly degrees; use the means of the K matching space node anomaly degrees as the initial iteration center, use the meanshift algorithm to perform centralized search in the K matching space node anomaly degree sets, and obtain K centralized matching space node anomaly degrees.

[0075] Furthermore, the collaborative optimization configuration module 60 includes the following steps:

[0076] 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 K spatial nodes, including the spatial development rights configuration of K spatial nodes; according to the first optimization configuration scheme, a target optimization configuration scheme is determined, and the target optimization configuration scheme is the spatial development rights configuration scheme with the minimum overall adjustment cost obtained after adjustment based on the first optimization configuration scheme.

[0077] 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 optimized configuration scheme based on the first abnormal spatial node is generated in combination with the K spatial node monitoring data sets.

[0078] Furthermore, the collaborative optimization configuration module 60 includes the following steps:

[0079] 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. 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, and the target optimization configuration scheme is obtained.

[0080] Furthermore, the constraint information identification module 30 includes the following steps:

[0081] Constructing a development right constraint network layer; using the development right constraint network layer to identify the K spatial node basic data sets to obtain the K spatial development right constraint information.

[0082] Through the detailed description of the optimization configuration method of land space development rights in the foregoing specification, those skilled in the art can clearly understand the optimization configuration system of land space development rights in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method section.

[0083] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one 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 is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The method for optimizing the allocation of 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; Identifying development right constraint information of the K spatial nodes based on the K spatial node basic data sets to obtain K pieces of spatial development right constraint information; Traversing 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; 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 the first abnormal spatial node and the abnormal probability of the first abnormal spatial node, including: based on the K spatial development rights constraint information, tolerance threshold identification is performed on 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 abnormality degree sets; centralized retrieval is performed on the K matching spatial node abnormality degree sets to determine the abnormality degrees of K centralized matching spatial nodes; the ratio of the abnormality degrees of the K centralized matching spatial nodes to the preset abnormality degree is used as the K spatial node abnormality probabilities of the K spatial nodes; the maximum value of the K spatial node abnormal probabilities is used as the abnormal probability of the first abnormal spatial node, and the corresponding spatial node is used as 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 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.

2. The method for optimizing the allocation of land space development rights according to claim 1, characterized in that: Performing a centralized search on the K matching space node abnormality sets to determine the K centralized matching space node abnormality degrees includes: Calculate the mean of the K matching space node abnormality sets respectively to obtain the mean of the K matching space node abnormality; The mean of the abnormality degrees of the K matching space nodes is respectively used as the initial iteration center, and the meanshift algorithm is used to perform centralized search in the abnormality degree set of the K matching space nodes to obtain the abnormality degrees of the K centralized matching space nodes.

3. The method for optimizing the allocation of land space development rights according to claim 1, 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. The first optimized configuration scheme based on the first abnormal spatial node is generated in combination with the K spatial node monitoring data sets.

4. The method for optimizing the allocation of land space development rights according to claim 3, characterized in that: Determining a target optimization configuration scheme based on the first optimization configuration scheme includes: Randomly adjusting the first optimized configuration scheme according to a preset adjustment range to obtain a second optimized 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; 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.

5. The method for optimizing the allocation of land space development rights according to claim 1, characterized in that: Identifying development right constraint information of the K spatial nodes based on the K spatial node basic data sets to obtain K pieces of spatial development right constraint information includes: 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.

6. The optimal allocation system of 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 5, wherein the system comprises: 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 is used to identify development right constraint information of the K spatial nodes based on the K spatial node basic data sets to obtain K pieces of 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 abnormality probability analysis module, configured to perform a 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 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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