Optimization Method and System for Planning Index Allocation in the Context of Stock Planning

Abstract

The present application provides an optimization method and system for planning index allocation in the context of inventory planning, relating to the technical field of spatial planning, including: building a digital twin platform for multi-right confirmation, using an evaluation network to conduct three-dimensional value evaluation of spatial resources, and establishing evaluation results; performing resource allocation according to the evaluation results and multi-right confirmation results; using the resource allocation results to conduct spatial resource flow processing, and performing right adjustment according to the multi-right confirmation results; comparing the flow monitoring results and resource allocation results of the spatial resource flow processing, and establishing spatial resource damage marks; performing balance optimization under the spatial resource damage marks to complete the optimization of planning index allocation. The present application solves the technical problem that the existing technology leads to unreasonable spatial resource allocation due to the lack of dynamic monitoring and multi-dimensional value evaluation, improves the rationality of resource allocation, and enhances the adaptability and scientificity of planning index allocation.

Description

Technical Field

[0001] This application relates to the technical field of spatial planning, and particularly to an optimization method and system for planning index allocation in the context of stock planning environment. Background Art

[0002] Planning index allocation is an important link in spatial resource management, which determines the rational allocation and effective utilization of resources such as land, buildings, and infrastructure. In the context of stock planning environment, the allocation of spatial resources no longer depends on large-scale new development, but is based on the optimized allocation of existing resources.

[0003] The existing planning index allocation in the stock planning environment mainly relies on static functional zoning and use control, and forms the supply-demand relationship of resource elements through the combination of administrative means and market mechanisms. For example, resource flow is guided by setting use limits or formulating use control measures. These methods can meet the basic resource allocation needs to a certain extent, but have limitations in many aspects. First, the existing methods lack dynamic monitoring of spatial resources and real-time data support, and it is difficult to adapt to the complex and changing resource demands in stock planning. Second, traditional methods are mostly static blueprint-style planning in resource allocation, lacking flexibility, which makes it easy for resources to be mismatched, inefficiently circulated or even wasted during the market circulation and adjustment process. In addition, the existing methods have a relatively single evaluation of the value of spatial resources, and fail to fully consider the ownership relationship and multi-dimensional value, resulting in low resource allocation efficiency and difficulty in achieving the optimized utilization of resources and the scientific implementation of planning. Summary of the Invention

[0004] This application provides an optimization method and system for planning index allocation in the context of stock planning environment, which solves the technical problem that the existing technology leads to unreasonable allocation of spatial resources and difficulty in adapting to the dynamic changes of stock planning due to the lack of dynamic monitoring and multi-dimensional value evaluation, and achieves the technical effects of improving the rationality of resource allocation, enhancing the adaptability and scientificity of planning index allocation.

[0005] In view of the above problems, on the one hand, the present application provides an optimization method for planning index allocation in the context of stock planning. The method includes: building a digital twin platform for spatial resources, which is constructed through UAV aerial survey collection and data interaction; using the digital twin platform for spatial resources to conduct multi-right confirmation, and using an evaluation network to conduct three-dimensional value evaluation of spatial resources to establish an evaluation result; according to the evaluation result and the multi-right confirmation result, perform resource allocation to establish a resource allocation result, where resource allocation includes rigid allocation and flexible allocation; using the resource allocation result to conduct spatial resource flow processing in the tertiary market, and perform right adjustment according to the multi-right confirmation result; establish a flow monitoring for spatial resource flow processing to establish a flow monitoring result, and use a loss identification channel to compare the flow monitoring result and the resource allocation result to establish a spatial resource damage identification; configure a balance fitness function, perform balance optimization under the spatial resource damage identification, and complete the optimization of planning index allocation according to the balance optimization result and the flow monitoring result.

[0006] On the other hand, the present application also provides an optimization system for planning index allocation in the context of stock planning. The system includes: a twin platform module for building a digital twin platform for spatial resources, which is constructed through UAV aerial survey collection and data interaction; a three-dimensional value evaluation module for using the digital twin platform for spatial resources to conduct multi-right confirmation, and using an evaluation network to conduct three-dimensional value evaluation of spatial resources to establish an evaluation result; a resource allocation module for performing resource allocation according to the evaluation result and the multi-right confirmation result to establish a resource allocation result, where resource allocation includes rigid allocation and flexible allocation; a right adjustment module for using the resource allocation result to conduct spatial resource flow processing in the tertiary market, and perform right adjustment according to the multi-right confirmation result; a flow monitoring module for establishing a flow monitoring for spatial resource flow processing to establish a flow monitoring result, and using a loss identification channel to compare the flow monitoring result and the resource allocation result to establish a spatial resource damage identification; a balance optimization module for configuring a balance fitness function, performing balance optimization under the spatial resource damage identification, and completing the optimization of planning index allocation according to the balance optimization result and the flow monitoring result.

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

[0008] Build a digital twin platform for spatial resources through UAV aerial survey acquisition and data interaction, achieve precise modeling of spatial resources, form high-precision three-dimensional spatial data, and provide data support for subsequent evaluation, configuration, and optimization. Through multi-dimensional ownership confirmation and three-dimensional value assessment, accurately identify resource ownership and evaluate the multi-dimensional value of spatial resources, form a resource assessment result with clear ownership and value, and provide a decision-making basis for subsequent configuration and optimization. According to the evaluation results, conduct resource allocation. The combination of rigid and flexible allocation ensures that the resource allocation not only meets the rigid requirements of the plan but also has a certain degree of flexibility to adapt to dynamic changes, improving the adaptability of resource allocation. Through the spatial resource flow processing and ownership adjustment in the three-level market, realize the reasonable circulation of resources, and combine with ownership confirmation for adjustment, improve the market regulation ability, and reduce resource idleness or misallocation. By establishing a flow monitoring of spatial resource flow processing, combining the loss identification channel to compare the flow monitoring results with the resource allocation results, accurately identify possible resource losses or misallocation situations, ensure the rationality of spatial resources during the flow process, and avoid unnecessary losses caused by transactions or adjustments. Based on the damaged identification of resources, perform balance optimization under the damaged identification of spatial resources, improve the overall balance of resource allocation through optimization algorithms, achieve global optimization of planning indicators, and improve the scientificity of planning indicator allocation.

[0009] In summary, this application builds a digital twin platform to obtain accurate data, combines multi-dimensional ownership confirmation and three-dimensional value assessment for reasonable resource allocation, considers resource flow and ownership adjustment under market factors, and establishes a monitoring and balance optimization mechanism, realizing the reasonable allocation of spatial resources and the optimal allocation of planning indicators in the context of stock planning environment, thus significantly improving the utilization efficiency of spatial resources, enhancing the adaptability and scientificity of planning indicator allocation, and providing new ideas and methods for urban spatial management in the stock planning environment.

[0010] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0011] Figure 1 It is a flow diagram of the method for optimizing the allocation of planning indicators in the stock planning environment provided by the embodiment of this application.

[0012] Figure 2 It is a flow diagram of establishing the damaged identification of spatial resources in the method for optimizing the allocation of planning indicators in the stock planning environment provided by the embodiment of this application.

[0013] Figure 3This is a schematic structural diagram of the planning index allocation optimization system in the stock planning environment provided by the embodiments of the present application.

[0014] Explanation of reference numerals: Twin platform module 10, three-dimensional value evaluation module 20, resource allocation module 30, ownership adjustment module 40, flow monitoring module 50, balance optimization module 60. Specific embodiments

[0015] The embodiments of the present application provide a planning index allocation optimization method and system in the stock planning environment, solving the technical problem in the prior art that due to the lack of dynamic monitoring and multi-dimensional value evaluation, the spatial resource allocation is unreasonable and it is difficult to adapt to the dynamic changes of stock planning, achieving the technical effects of improving the rationality of resource allocation and enhancing the adaptability and scientificity of planning index allocation.

[0016] Embodiment 1, as Figure 1 shown, the embodiments of the present application provide a planning index allocation optimization method in the stock planning environment, and the method includes:

[0017] Step S1: Build a digital twin platform for spatial resources, which is constructed through UAV aerial survey collection and data interaction.

[0018] Specifically, first use a UAV to conduct aerial photography in the target area. The UAV flies according to a predetermined route and obtains information such as the terrain, landform, and surface cover of spatial resources through the equipment it carries. Then, these data are interactively integrated with other relevant data (such as existing land planning data, building distribution data, etc.) and imported into the digital twin platform to construct a virtual space model, obtaining a digital twin platform for spatial resources. Through a network interface, the digital twin platform for spatial resources is connected to external data sources such as urban databases and sensor networks to achieve real-time data update and interaction. The built digital twin platform for spatial resources can reflect the attributes and states of the physical space in real time and support data analysis and simulation, providing an accurate and rich data basis for subsequent multi-dimensional ownership confirmation, value evaluation, and other operations.

[0019] Step S2: Use the digital twin platform for spatial resources to conduct multi-dimensional ownership confirmation, and use an evaluation network to conduct three-dimensional value evaluation of spatial resources to establish an evaluation result.

[0020] Specifically, there may be various ownership relationships for spatial resources, such as the ownership, right of use, and management right of land. Using the data in the digital twin platform of spatial resources constructed in step S1, these different ownership relationships are comprehensively and accurately defined. For example, the different ownership situations of a certain piece of land are determined based on data such as land registration information and lease contracts. Then, using the evaluation network, the spatial resources are evaluated from three dimensions: economic value (such as land price, development potential), social value (such as public service facilities, population density), and ecological value (such as green area, ecological sensitivity), an evaluation result including economic, ecological, and social values is established, and the evaluation result is stored in the digital twin platform to form a multi-dimensional value evaluation database. This evaluation network is an algorithm or model for evaluation and can be constructed based on technologies such as machine learning and multi-criteria decision-making.

[0021] Through multi-dimensional ownership confirmation, the ownership relationship of spatial resources is comprehensively and accurately determined, ensuring the clarity of the ownership of spatial resources. Through three-dimensional value evaluation, a comprehensive scientific basis is provided for resource allocation, making the planning more scientific and reasonable.

[0022] Step S3: According to the evaluation result and multi-dimensional ownership confirmation result, perform resource allocation to establish a resource allocation result. The resource allocation includes rigid allocation and flexible allocation.

[0023] Specifically, according to the evaluation result and multi-dimensional ownership confirmation result obtained in step S2, resource allocation is carried out according to pre-set rules, including rigid allocation and flexible allocation. For the rigid allocation part, the amount of resources that must be allocated is determined according to urban planning standards, such as the land for public service facilities. In addition, the protection of the ecological value of spatial resources and the needs of social fairness are also considered. For example, in order to protect the ecological value of a certain area, a certain area of ecological protection area land will be rigidly allocated. For the flexible allocation part, factors such as market demand and development potential are comprehensively considered, and the resource allocation is carried out with reference to the market change trend of economic value and the dynamic change of social demand. For example, according to the local economic development trend and the demand for different types of housing, the ratio of residential land and commercial land is flexibly adjusted. The resource allocation process can be completed using resource allocation planning software.

[0024] Through the combination of rigid allocation and flexible allocation, reasonable resource allocation is achieved. It not only ensures the fixed allocation of necessary resources but also can flexibly adjust some resources according to the actual situation, improving the rationality and adaptability of resource allocation.

[0025] Step S4: Use the resource allocation result to perform spatial resource flow processing in the tertiary market and perform ownership adjustment according to the multi-dimensional ownership confirmation result.

[0026] Specifically, in the market for spatial resources such as land, the primary market is the market where landowners transfer land use rights; the secondary market is the market where land users transfer land use rights; and the tertiary market is the market where land users re-transfer, lease, mortgage, etc. land use rights. Using the resource allocation results obtained in step S3, simulate the flow of spatial resources in the tertiary market on the digital twin platform and analyze the market supply and demand relationship. According to the resource allocation results and the market flow situation, adjust the ownership relationship of spatial resources (such as the ownership and scope of use rights) to ensure the rationality of resource allocation.

[0027] Through the resource flow processing and ownership adjustment in the tertiary market, the resource allocation is optimized, the resource utilization efficiency is improved, and the plan is made more adaptable to market demands.

[0028] Step S5: Establish a flow monitoring for the processing of spatial resource flow, establish the flow monitoring results, and use the loss identification channel to compare the flow monitoring results and the resource allocation results to establish an identification of damaged spatial resources.

[0029] Specifically, deploy intelligent terminals and edge computing nodes on the digital twin platform to establish a flow monitoring system for the processing of spatial resource flow, and monitor the flow of spatial resources in real time. This system can obtain relevant information on the flow of spatial resources through methods such as collecting market transaction data and on-site investigations. For example, for economic value, monitor the price fluctuations of land or real estate in the market and the changes in rental income; for ecological value, monitor the changes in ecological environment indicators during the use of spatial resources, such as changes in forest coverage rate and water pollution indicators; for social value, monitor the public's satisfaction and usage frequency of spatial resources. Then, use the loss identification channel to comprehensively compare the flow monitoring results with the resource allocation results from the perspectives of economic, ecological, and social values, identify and quantify damaged spatial resources, and establish an identification of damaged spatial resources to provide a basis for subsequent optimization. For example, during the renovation of a certain community, it is monitored that some historical buildings are demolished. Using the loss identification channel, it is identified that the social value is damaged, and an identification of damaged spatial resources is established to adjust the resource allocation. Among them, the loss identification channel is an algorithm model used to identify whether there are losses (such as value losses, functional losses, etc.) in the process of spatial resource flow.

[0030] By conducting flow monitoring and establishing an identification of damaged spatial resources, the damaged situation of spatial resources during the flow process can be discovered in a timely manner, providing a basis for subsequent adjustments, and helping to ensure the effective utilization and value maintenance of spatial resources.

[0031] Step S6: Configure a balance fitness function, perform balance optimization under the identification of damaged spatial resources, and complete the optimization of the allocation of planning indicators according to the balance optimization results and the flow monitoring results.

[0032] Specifically, the balance fitness function is a mathematical function used to measure the balance degree of spatial resources in different states. First, configure the balance fitness function to determine the variables it contains (such as resource supply and demand volume, ownership ratio, etc.) and their weights. When configuring the balance fitness function, relevant factors of economic, ecological, and social values are incorporated into the function variables. For example, in terms of economic value, it may include the input-output ratio of land development; in terms of ecological value, it may include the maintenance level of ecosystem service functions; in terms of social value, it may include the accessibility of the public to spatial resources, etc. Then, perform balance optimization under the identification of damaged spatial resources. According to the economic, ecological, and social value losses reflected by the damage identification, use optimization algorithms (such as genetic algorithms, simulated annealing algorithms) to adjust the values of relevant variables to make the balance fitness function reach the optimum. For example, if it is found that the ecological value of a certain area is damaged due to overdevelopment (such as a reduction in green space), during the balance optimization process, the ecological value-related variables in the balance fitness function will be adjusted, increasing the green space planning index and reducing the indicators of some development projects with greater ecological impacts. Finally, according to the optimization results, adjust the distribution of planning indicators to form the final optimized planning indicator scheme.

[0033] Through balance optimization, the distribution of planning indicators is optimized according to the actual situation of spatial resources (especially the damaged situation), improving the rationality and effectiveness of the plan in multiple aspects such as economy, ecology, and society, and promoting the sustainable development of spatial resources.

[0034] Furthermore, in step S5, establish a flow monitoring for the processing of spatial resource flow, and establish the flow monitoring results, including:

[0035] Step S51: Deploy intelligent terminals and use the intelligent terminals to conduct flow monitoring of spatial resources to establish a real-time data set.

[0036] Step S52: After preprocessing the data of the real-time data set through the edge computing nodes distributed in the intelligent terminals, it is used as the first flow data.

[0037] Step S53: Conduct resource interaction monitoring on spatial resources and establish the second flow data based on the resource interaction monitoring results.

[0038] Step S54: Construct the flow monitoring results according to the first flow data and the second flow data.

[0039] Specifically, an intelligent terminal refers to a device with data acquisition, processing, and transmission functions, such as Internet of Things sensors, intelligent cameras, mobile terminals, etc. Intelligent terminals are deployed at key locations (such as traffic nodes, building entrances, near public facilities) to monitor the flow of spatial resources. The intelligent terminal collects data in real time through sensors (such as cameras, infrared sensors, RFID readers, etc.), such as information on the flow of people, vehicles, and logistics. The real-time data collected is stored locally or in the cloud to form a real-time data set.

[0040] After the intelligent terminal collects the real-time data set, the edge computing node will preprocess this data, including data cleaning (removing noise data), data compression (reducing the amount of data), feature extraction (extracting key information), etc. For example, perform preliminary screening, format conversion, etc. on the collected personnel flow data, remove invalid data, and unify the data format into a form convenient for subsequent analysis to obtain the first flow data.

[0041] Resource interaction refers to the interaction situation between spatial resources, such as the impact of land development on the surrounding ecology, the impact of commercial activities on residents' lives, etc. Through the sensor network or data analysis model, monitor the interaction situation between spatial resources, such as land development intensity, ecological footprint, etc. Organize the monitoring results into the second flow data and store it in the digital twin platform. Through resource interaction monitoring, the dynamic changes in the flow of spatial resources can be comprehensively understood, providing richer data for subsequent analysis.

[0042] Fuse the first flow data (real-time monitoring data) and the second flow data (interaction monitoring data) to obtain a complete flow monitoring data set and generate a flow monitoring result. By integrating data from different sources, a comprehensive and accurate flow monitoring result can be constructed, providing a complete data basis for the subsequent establishment of spatial resource damage identification, thereby better managing and optimizing the spatial resource flow process.

[0043] Furthermore, as Figure 2 shown, in step S5, use the loss identification channel to compare the flow monitoring result and the resource allocation result to establish a spatial resource damage identification, including:

[0044] Step S55: Invoke the planning loss identification layer of the loss identification channel to establish an identification loss result according to the resource allocation result.

[0045] Step S56: Invoke the comparison identification layer of the loss identification channel, use the comparison identification layer to perform a planning comparison of the resource allocation result and the second flow data, and establish a planning comparison identification result.

[0046] Step S57: Invoke the intensity recognition layer of the loss recognition channel, perform the development intensity trigger recognition of the second flow data, and establish the development intensity trigger recognition result.

[0047] Step S58: Perform damaged value quantification calculation based on the identified loss result, the planned comparison recognition result, and the development intensity trigger recognition result, and establish the damaged identification of spatial resources.

[0048] Specifically, the loss recognition channel is an algorithm model with a multi-layer structure, which is used to identify and quantify the losses that may occur during the flow, adjustment, or configuration of spatial resources. It mainly includes three main functional layers: the planned loss recognition layer, the comparison recognition layer, and the intensity recognition layer. Among them, the planned loss recognition layer takes the resource configuration results (including rigid configuration and elastic configuration) as input data, and by analyzing the resource configuration results, identifies potential loss risks, such as ecological damage and reduction of social value, and outputs the identified loss result (i.e., potential loss information), which can be trained and generated based on rule engines, threshold models, risk assessment models, etc. The comparison recognition layer takes the resource configuration results and the second flow data (such as actual development intensity, resource interaction situation) as input data, and by comparing the resource configuration results with the actual flow monitoring data, identifies the differences between the two, and outputs the planned comparison recognition result (i.e., specific difference information). The intensity recognition layer takes the second flow data (such as development intensity, resource interaction frequency) as input data, and by analyzing whether the resource development intensity exceeds the preset threshold, identifies the situation where the development intensity exceeds the standard, and outputs the development intensity trigger recognition result (i.e., whether the resource development intensity exceeds the standard).

[0049] When training the loss recognition channel, first collect data related to spatial resources, including resource configuration data, such as planned uses, land development intensity, infrastructure layout, etc.; and the corresponding flow monitoring data of the resource configuration data, including the first flow data and the second flow data. Label the collected data. According to professional knowledge and actual situations, determine which resource configuration results have loss situations with the flow monitoring data, as well as the types and degrees of losses. For example, if the planned ecological land in the resource configuration is occupied for commercial development, it is labeled as having an ecological value loss situation, and the degree of loss (such as slight, medium, severe, etc.) is determined. These labeled data will be used as the target values for training.

[0050] Next, construct the basic framework of the loss recognition channel and use the collected data for training. Exemplarily, for the planning loss recognition layer, use the labeled resource configuration data as training data and employ supervised learning algorithms (such as decision trees, support vector machines SVM) to train the model so that it can learn how to identify the differences between resource configuration and planning goals based on the training data. For the comparison recognition layer, use the labeled resource configuration results and flow monitoring data as training data, and adopt regression models or K-nearest neighbor algorithms to discover the deviation problems that occur during the resource flow process, and continuously optimize the matching accuracy through training the model. For the intensity recognition layer, use the development intensity (such as building area, building height, etc.) in the second flow data and the planned development standards as training data, and use deep learning models (such as convolutional neural networks or recurrent neural networks) to identify whether the development behavior meets the planning requirements. During the training process, adjust the model weights so that it can accurately identify the over-standard development behaviors.

[0051] During the model training process, use a part of the data that has not participated in training (test data) to evaluate the trained model. The evaluation metrics can include accuracy, recall rate, F1 value, etc. If the evaluation result of the model is not satisfactory, then optimize the model. The optimization methods include adjusting the model structure (such as increasing the number of neurons, adjusting the number of layers, etc.), adjusting the training parameters (such as learning rate, number of iterations, etc.), and then retraining until the model reaches satisfactory performance.

[0052] Integrate the planning loss recognition layer, the comparison recognition layer, and the intensity recognition layer into the loss recognition channel to ensure data flow and collaborative work between layers. Finally, deploy the constructed loss recognition channel to the digital twin platform, configure the flow monitoring data interface and the resource configuration data interface, and receive data in real time and output the damaged identifier.

[0053] During the process of establishing the spatial damaged identifier, first call the planning loss recognition layer of the loss recognition channel, input the resource configuration result into the planning loss recognition layer, identify the losses caused by the resource configuration not meeting the expected plan, and establish the marked loss result. For example, if the resource configuration result stipulates the ecological land ratio of a certain area, the planning loss recognition layer will judge whether there is a deviation from this plan according to the actual situation (such as land use change, development activities, etc.), and the losses of ecological value, economic value, or social value that may be caused by this deviation, etc., and generate the marked loss result.

[0054] Next, call the comparison and recognition layer of the loss recognition channel to compare the resource allocation result with the second flow data, identify unreasonable adjustments that may occur during the resource flow process, and establish a planning comparison and recognition result. For example, compare the building density of a certain commercial area in the plan with the actual building construction situation monitored. If the actual building density exceeds the planned value, the comparison and recognition layer will record this difference situation and organize it into a planning comparison and recognition result.

[0055] Then, call the intensity recognition layer of the loss recognition channel to analyze the development-related data (such as the speed and scale of building development) in the second flow data, check whether the second flow data meets the development intensity required by the plan, and establish a development intensity trigger recognition result. For example, for a real estate project under development, the intensity recognition layer will judge whether the actual development speed exceeds the set development intensity threshold (such as the upper limit of the annual building area growth rate, etc.) according to the set threshold, and record this situation as a development intensity trigger recognition result if it exceeds.

[0056] Finally, the loss recognition channel conducts a comprehensive analysis based on the recognition results of the three recognition layers (identification loss result, planning comparison and recognition result, and development intensity trigger recognition result), and quantifies the impact of each type of loss on spatial resources. For example, if the identification loss result shows that there is a loss in the ecological land planning of a certain area, the planning comparison and recognition result indicates that the actual building floor area exceeds the planned value, and the development intensity trigger recognition result shows that the development speed is too fast, then when calculating the quantification of the damaged value, the impact degrees of these factors on ecological value, economic value, and social value will be comprehensively considered, and they will be converted into quantifiable numerical values or levels, so as to establish an identification of damaged spatial resources, which can provide a basis for subsequent resource adjustment and optimization.

[0057] Through the recognition functions of each level of the loss recognition channel, various losses existing in the process of spatial resource flow, allocation, and development can be comprehensively recognized. This multi-level and all-round loss recognition mechanism can accurately capture potential problems in resource allocation and flow, and comprehensively evaluate possible resource damage situations, providing accurate reference and basis for subsequent resource adjustment, and promoting the adjustment of resource allocation and the optimization of planning indicators.

[0058] Furthermore, step S58 includes:

[0059] Step S581: Obtain the damaged area according to the identification loss result, the planning comparison and recognition result, and the development intensity trigger recognition result, and calculate the ecological loss value by using the damaged area and the unit ecological integral.

[0060] Step S582: Conduct an analysis of the impact on population and quality of life losses based on the identification loss result, the planning comparison identification result, and the development intensity trigger identification result, and establish a social value loss value.

[0061] Step S583: Complete the quantification calculation of the damaged value based on the ecological loss value and the social value loss value.

[0062] Specifically, based on the identification loss result, the planning comparison identification result, and the development intensity trigger identification result, determine the specific damaged areas and areas. For example, obtain the area of the actual building floor area exceeding the planned value from the planning comparison identification result, and obtain the area of unplanned land additionally occupied due to overdevelopment from the development intensity trigger identification result, etc., and sum up these areas to obtain the damaged area. Then, use the pre-determined unit ecological score to calculate the ecological loss value. Assuming that the unit ecological score is set to a certain ecological value score corresponding to each square meter of ecological area, then the ecological loss value = damaged area × unit ecological score. By calculating the ecological loss value, the ecological value loss caused by development activities can be quantified, providing a scientific basis for subsequent compensation and optimization.

[0063] Based on the identification loss result and the planning comparison identification result, determine the specific population affected due to the damage of spatial resources (such as reduction of public space, deterioration of living environment, etc.), and evaluate the impact degree of development activities on the quality of life of residents, such as noise level, traffic convenience, availability of public facilities, etc. Combining the impact on population and quality of life losses, calculate the social value loss value through a preset weight model. For example, in the renovation of a certain community, the identification loss result shows a reduction in public space, the affected population is 1000 people, and the quality of life loss weight (evaluated according to the impact degree of the reduction of public space) is 0.5 social value units per person, then the social value loss value is calculated as: social value loss value = 1000 × 0.5 = 500 social value units. By establishing the social value loss value, the loss of spatial resources damaged in terms of social value (especially the impact on population and quality of life) can be quantified, providing a quantitative basis for comprehensively evaluating the damage of spatial resources in terms of social value.

[0064] Integrate the ecological loss value and the social value loss value, and perform weighted calculation through a preset weight model to obtain the total damaged value. Total damaged value = α × ecological loss value + β × social value loss value, where α and β are weighting coefficients representing the relative importance of ecological loss and social value loss, and can be user-defined. Then, according to the quantitative calculation results, generate a spatial resource damage identifier to clarify the type and degree of loss. By comprehensively considering the ecological loss value and the social value loss value, a comprehensive quantitative calculation of the damaged value of spatial resources is completed. The established spatial resource damage identifier can accurately reflect the damage situation of spatial resources in terms of ecological and social values, providing an important basis for subsequent resource management and optimization.

[0065] Further, in step S6, configure a balance fitness function and perform balance optimization under the spatial resource damage identifier, including:

[0066] Step S61: The balance fitness function is as follows:

[0067] ; where represents the balance fitness value, represents the economic loss measure, represents the social impact coefficient, represents the ecological environment cost, represents the resource recovery elasticity, 、 、 、 are the weight coefficients of the corresponding parameter items.

[0068] Step S62: After establishing the initial parameter set, use the balance fitness function to evaluate the fitness of the initial parameter set, and establish cumulative update constraints according to the fitness evaluation results.

[0069] Step S63: Iterate the initial parameter set through the cumulative update constraints, and complete the balance optimization according to the iteration results.

[0070] Specifically, the above balance fitness function comprehensively considers multiple dimensions such as economic loss, social impact, ecological environment cost, and resource recovery elasticity. By performing weighted summation of economic loss, social impact, ecological cost, and resource recovery elasticity, a balance fitness value is generated to evaluate the comprehensive performance of resource allocation. When calculating the balance fitness, first, according to the planning requirements, quantify the initial parameter values of the economic loss measure 、the social impact coefficient 、the ecological environment cost and the resource recovery elasticity , as well as the weight coefficients 、 、 、 , generate an initial parameter set. Exemplarily, the economic loss metric can be determined by comparing the economic values (such as land transfer price, rental income, etc.) before and after the spatial resources are damaged; the social impact coefficient can be obtained through methods such as questionnaires and social welfare indicators; the ecological environment cost can be calculated with the help of an ecological value assessment model; the resource recovery elasticity can be evaluated based on historical data or simulation experiments; the weight coefficient , , , can be assigned according to the importance of each dimension through methods such as expert scoring, the analytic hierarchy process (AHP), or other multi-criteria decision-making methods. Substitute these initial parameter values into the balance fitness function for calculation to determine the fitness evaluation result of the initial parameter set, that is, the balance fitness value . According to this fitness evaluation result, analyze which parameters have a greater impact on fitness and establish cumulative update constraints. For example, if it is found that the economic loss metric has a greater impact on fitness and the current value results in a lower fitness, then an update constraint for the economic loss metric can be established, such as restricting the increase range or specifying the adjustment direction. Through fitness evaluation and establishing cumulative update constraints, an initial evaluation and constraint framework is provided for the subsequent iterative optimization process, which helps to improve the efficiency and accuracy of the optimization process and makes the optimization process more targeted towards improving the overall balance fitness of spatial resources.

[0071] Iterate the initial parameter set using an optimization algorithm (such as a genetic algorithm, simulated annealing algorithm, etc.) according to the conditions of the cumulative update constraints. For example, if the cumulative update constraint stipulates that the economic loss metric can only be reduced by a certain proportion in each iteration, then in each iteration, adjust the value according to this constraint condition. At the same time, the social impact coefficient , the ecological environment cost and the resource recovery elasticity as well as the weight coefficient , , , values can also be adjusted according to other constraint conditions. Then substitute the adjusted parameter set into the balance fitness function again for calculation to obtain a new fitness value, and adjust the resource allocation according to the update result to find the one that makes The optimal resource allocation plan that maximizes. Repeat this process until a certain stop condition is met (such as the fitness value reaching a predetermined target value, the number of iterations reaching the upper limit, etc.).

[0072] By iterating the initial parameter set and completing the balance optimization according to the iteration results, a set of optimal parameter values (including economic loss metrics, social impact coefficients, ecological environment costs, resource recovery elasticity, and weight coefficients, etc.) can be found, making the value of the balance fitness function reach the optimal or close to the optimal, thus providing the best parameter setting plan for the management and optimization of spatial resources, and helping to improve the overall balance and sustainable development capabilities of spatial resources in multiple aspects such as economy, society, and ecology.

[0073] Furthermore, in step S6, completing the optimization of the planning index allocation according to the balance optimization result and the flow monitoring result further includes:

[0074] Step S64: Establish a feedback data set for the planning index allocation based on the spatial resource damage identifier and the flow monitoring result.

[0075] Step S65: Perform a feedback impact analysis on the feedback data set. When the feedback impact analysis result triggers a preset impact threshold, generate a configuration feedback for the resource allocation according to the feedback impact analysis result.

[0076] Step S66: Reconstruct the resource allocation result according to the configuration feedback, and complete the optimization of the planning index allocation according to the reconstructed resource allocation result.

[0077] Specifically, first, extract relevant information from the spatial resource damage identifier, such as data on the damaged area and the quantified result of the damaged value. At the same time, obtain data such as the resource flow rate and the resource flow direction from the flow monitoring result. Then integrate these data together according to certain rules and formats to form a feedback data set, providing a comprehensive information source for comprehensively understanding the state of spatial resources.

[0078] The feedback data set is analyzed using data analysis methods. For example, statistical analysis methods can be used to analyze the correlation between various indicators in the data set, or model analysis methods (such as regression models based on multiple factors, etc.) can be used to evaluate the impact degree of each piece of data on resource allocation. The result of the impact degree obtained from the analysis is compared with a preset impact threshold, which is a standard value set in advance to measure the impact degree. When the result of the feedback impact analysis reaches or exceeds this threshold, corresponding measures need to be taken to generate a configuration feedback for resource allocation. For example, if the analysis result shows that due to damaged spatial resources, the ecological environment cost in a certain area is too high, affecting the balance of overall resource allocation, when this impact exceeds the preset threshold, the configuration feedback may be to increase the investment in ecological resources in this area or adjust the development plan of this area, etc. Through feedback impact analysis, the significant impact of the spatial resource status on resource allocation can be discovered in a timely manner. When the impact reaches a certain level (triggering the preset impact threshold), the generated configuration feedback can provide a scientific basis for the adjustment of resource allocation and further optimize the spatial resource allocation.

[0079] According to the adjustment suggestions in the configuration feedback, each indicator in the resource allocation result is modified. For example, if the configuration feedback suggests increasing the ecological land use indicator in a certain area, then when reconstructing the resource allocation result, the ecological land use ratio and other indicators in the land use plan are adjusted accordingly. On the basis of reconstructing the resource allocation result, the planning indicators are reallocated according to the optimized resource allocation plan. By completing the optimization of the planning indicator allocation through reconstructing the resource allocation result, the scientificity and rationality of resource allocation can be improved, better adapting to the actual status of spatial resources and promoting the sustainable utilization and development of spatial resources.

[0080] Furthermore, after completing the optimization of the planning indicator allocation according to the balance optimization result and the flow monitoring result, it further includes:

[0081] Step S71: Establish the flow monitoring result and the spatial resource damage identifier as a time series data set.

[0082] Step S72: Conduct a time series development prediction on the time series data set to establish a trend warning result.

[0083] Step S73: Match an emergency response mechanism according to the trend warning result, and perform warning optimization processing according to the emergency response mechanism.

[0084] Specifically, first, determine the recording unit of time (such as day, month, year, etc.). Then, integrate the data in the mobile monitoring results and the spatial resource damage identification according to this time unit. For example, if the mobile monitoring result is to monitor the flow rate and flow volume of resources once a day, and the spatial resource damage identification is also updated and evaluated once a day, then the mobile monitoring results and the spatial resource damage identification data on the same day can be combined to form a time series data point. Arrange multiple such time series data points in chronological order to construct a time series data set, which can reflect the dynamic changes of spatial resources over time.

[0085] Based on the time series data set, use methods such as statistical analysis or machine learning to predict the development trend of spatial resources in a future period of time, including the change trends in aspects such as the flow state and damage situation of spatial resources. Multiple methods can be used for time series development prediction. For example, use traditional statistical methods such as the moving average method and the exponential smoothing method to predict future values by performing operations such as weighted averaging on the data in the time series data set. Machine learning methods such as long short-term memory networks can also be used. Take the time series data set as the input and train the model to predict the future development trend of spatial resources. According to the prediction results, establish a trend warning result. For example, if the prediction result shows that the resource development intensity in a certain area will exceed the ecological carrying capacity in the next three months, record this result as the trend warning result. Establishing a trend warning result through time series development prediction can detect potential problems of spatial resources in advance, provide warning information for taking corresponding measures, and help improve the foresight of the management and protection of spatial resources.

[0086] According to the trend warning result, search for the matching emergency response mechanism in the pre-set emergency response mechanism library. The emergency response mechanism library stores pre-developed response plans for different trend warning results, including measures to be taken in case of emergencies (such as ecological crises, overdevelopment of resources, etc.) of resources, such as adjusting the resource development plan and starting a resource restoration project. Optimize the warning information according to the matching emergency response mechanism. For example, according to the requirements of the emergency response mechanism, adjust parameters such as the warning level, scope, and time to ensure that the warning information can be accurately conveyed to relevant departments and personnel, so that the response measures can be implemented more effectively.

[0087] Through the warning optimization process of matching the emergency response mechanism, the effectiveness of the warning information and the pertinence of the response measures can be improved, enabling the spatial resource management to respond more timely and effectively when facing potential risks and protecting the sustainable development of spatial resources.

[0088] In summary, the planning index allocation optimization method in the inventory planning environment provided by the embodiments of the present application has the following technical effects:

[0089] Build a digital twin platform for spatial resources through UAV aerial survey collection and data interaction, providing a high-precision and visual digital model for existing spatial resources, realizing the comprehensive digital management of spatial resources, and thus providing accurate data support for subsequent ownership confirmation, value assessment, resource allocation, and dynamic monitoring. Use the digital twin platform for spatial resources to conduct multi-rights ownership confirmation, ensuring clear and definite property rights relationships for spatial resources and avoiding ownership disputes; use the evaluation network to comprehensively evaluate spatial resources from three dimensions: economy, society, and ecology, establish scientific evaluation results, provide an important basis for resource allocation, enable resource allocation to comprehensively consider various aspects of value, and avoid the irrationality brought by single-dimensional evaluation. According to the evaluation results and ownership confirmation, conduct resource allocation that combines rigidity and flexibility. Rigid allocation ensures the basic requirements and bottom line of the plan, while flexible allocation reserves space for future development and dynamic adjustment. Through the processing of spatial resource flow and ownership adjustment in the tertiary market, further optimize resource allocation to make it more adaptable to market demand and urban development dynamics. Real-time monitor the flow of spatial resources through intelligent terminals and edge computing nodes to establish flow monitoring results. At the same time, use the loss identification channel to conduct a comparative analysis of the resource allocation results and flow monitoring results, establish spatial resource damage identification, so as to be able to timely discover problems and potential risks in resource allocation and provide data support for subsequent optimization and adjustment. Quantify economic losses, social impacts, ecological environment costs, etc. through the configuration balance fitness function, dynamically adjust the resource allocation plan to achieve the optimal balance state, ensure the scientific nature and adaptability of the plan index allocation, and be able to effectively respond to the complex and changeable existing planning environment. Through feedback data set analysis and time series prediction, establish a trend warning mechanism, which can not only adjust resource allocation according to real-time data, but also predict future development trends, take optimization measures in advance, and further improve the forward-looking and scientific nature of the plan.

[0090] Overall, through the synergistic effect of the above-mentioned links in the embodiments of the present application, the efficient allocation and optimized management of spatial resources are realized, significantly improving the rationality of spatial resource allocation and spatial resources in the existing planning environment, enhancing the scientific nature, adaptability, and flexibility of plan index allocation, and providing strong support for the sustainable development of the city.

[0091] Embodiment 2, as Figure 3 shown, based on the same inventive concept as in the foregoing Embodiment 1, the embodiments of the present application provide an optimization system for plan index allocation in the existing planning environment, and the system includes:

[0092] A digital twin platform module 10, used to build a digital twin platform for spatial resources, and the digital twin platform for spatial resources is constructed through UAV aerial survey collection and data interaction.

[0093] The three-dimensional value evaluation module 20 is used to perform multi-right confirmation by using the spatial resource digital twin platform, and perform three-dimensional value evaluation of spatial resources by using the evaluation network to establish an evaluation result.

[0094] The resource allocation module 30 is used to perform resource allocation according to the evaluation result and the multi-right confirmation result, establish a resource allocation result, and the resource allocation includes rigid allocation and flexible allocation.

[0095] The right adjustment module 40 is used to perform spatial resource flow processing in the tertiary market by using the resource allocation result, and perform right adjustment according to the multi-right confirmation result.

[0096] The flow monitoring module 50 is used to establish flow monitoring for spatial resource flow processing, establish a flow monitoring result, and use the loss identification channel to compare the flow monitoring result and the resource allocation result to establish a spatial resource damage identification.

[0097] The balance optimization module 60 is used to configure a balance fitness function, perform balance optimization under the spatial resource damage identification, and complete the optimization of planning index allocation according to the balance optimization result and the flow monitoring result.

[0098] Furthermore, the flow monitoring module 50 in the embodiment of the present application is further used to perform the following steps:

[0099] Deploy intelligent terminals, use the intelligent terminals to perform flow monitoring of spatial resources, and establish a real-time data set; after preprocessing the data of the real-time data set through edge computing nodes distributed in the intelligent terminals, use it as the first flow data; perform resource interaction monitoring on spatial resources, and establish second flow data based on the resource interaction monitoring result; construct a flow monitoring result according to the first flow data and the second flow data.

[0100] Furthermore, the flow monitoring module 50 in the embodiment of the present application is further used to perform the following steps:

[0101] Call the planning loss identification layer of the loss identification channel, and establish an identification loss result according to the resource allocation result; call the comparison identification layer of the loss identification channel, and use the comparison identification layer to perform a planning comparison of the resource allocation result and the second flow data to establish a planning comparison identification result; call the intensity identification layer of the loss identification channel, perform development intensity trigger identification of the second flow data, and establish a development intensity trigger identification result; perform damaged value quantification calculation according to the identification loss result, the planning comparison identification result, and the development intensity trigger identification result, and establish a spatial resource damage identification.

[0102] Furthermore, the flow monitoring module 50 in the embodiment of the present application is further used to perform the following steps:

[0103] Obtain the damaged area based on the identification loss result, the planned comparison identification result, and the development intensity trigger identification result, and calculate the ecological loss value using the damaged area and the unit ecological integral; conduct an analysis of the affected population and the loss of quality of life based on the identification loss result, the planned comparison identification result, and the development intensity trigger identification result, and establish the social value loss value; complete the quantification calculation of the damaged value based on the ecological loss value and the social value loss value.

[0104] Furthermore, the balance optimization module 60 in the embodiment of the present application is further configured to perform the following steps:

[0105] The balance fitness function is as follows:

[0106] ; where represents the balance fitness value, represents the economic loss metric, represents the social impact coefficient, represents the ecological environment cost, represents the resource recovery elasticity, , , , are the weight coefficients of the corresponding parameter items; after establishing the initial parameter set, use the balance fitness function to evaluate the fitness of the initial parameter set, and establish the cumulative update constraint according to the fitness evaluation result; iterate the initial parameter set through the cumulative update constraint, and complete the balance optimization according to the iteration result.

[0107] Furthermore, the balance optimization module 60 in the embodiment of the present application is further configured to perform the following steps:

[0108] Establish a feedback data set for planning index allocation based on the spatial resource damage identification and the flow monitoring result; perform a feedback impact analysis on the feedback data set, and when the feedback impact analysis result triggers a preset impact threshold, generate a configuration feedback for resource allocation according to the feedback impact analysis result; reconstruct the resource allocation result according to the configuration feedback, and complete the optimization of the planning index allocation according to the reconstructed resource allocation result.

[0109] Furthermore, the system in the embodiment of the present application is further configured to perform the following steps:

[0110] Establish the flow monitoring result and the spatial resource damage identification as a time series data set; perform a time series development prediction on the time series data set to establish a trend warning result; match an emergency response mechanism according to the trend warning result, and perform warning optimization processing according to the emergency response mechanism.

[0111] Through the foregoing detailed description of the method for optimizing the allocation of planning indicators in the inventory planning environment, those skilled in the art can clearly know the system for optimizing the allocation of planning indicators in the inventory planning environment in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For the relevant parts, reference can be made to the description in the method section.

[0112] The foregoing 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 can 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 is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An optimization method for planning index allocation in the context of stock planning, characterized in that, The method includes: Building a digital twin platform for spatial resources, which is constructed by drone aerial survey collection and data interaction; Using the digital twin platform for spatial resources to conduct multi - right confirmation, and using an evaluation network to conduct three - dimensional value evaluation of spatial resources to establish an evaluation result; According to the evaluation result and multi - right confirmation result, perform resource allocation to establish a resource allocation result, where resource allocation includes rigid allocation and flexible allocation; Use the resource allocation result to conduct spatial resource flow processing in the tertiary market, and conduct right adjustment according to the multi - right confirmation result; Establish flow monitoring for spatial resource flow processing to establish a flow monitoring result, and use a loss identification channel to compare the flow monitoring result and the resource allocation result to establish a spatial resource damage identification; Configure a balance fitness function, perform balance optimization under the spatial resource damage identification, and complete the optimization of planning index allocation according to the balance optimization result and the flow monitoring result; Among them, the establishment of flow monitoring for spatial resource flow processing to establish a flow monitoring result includes: Deploy intelligent terminals, and use the intelligent terminals to conduct flow monitoring of spatial resources to establish a real - time data set; After pre - processing the data of the real - time data set through edge computing nodes distributed in the intelligent terminals, it is used as the first flow data; Conduct resource interaction monitoring on spatial resources, and establish second - flow data based on the resource interaction monitoring result; Construct a flow monitoring result according to the first flow data and the second flow data; Among them, the use of a loss identification channel to compare the flow monitoring result and the resource allocation result to establish a spatial resource damage identification includes: Call the planning loss identification layer of the loss identification channel to establish an identification loss result according to the resource allocation result; Call the comparison and identification layer of the loss identification channel, and use the comparison and identification layer to conduct a planning comparison of the resource allocation result and the second flow data to establish a planning comparison and identification result; Call the intensity identification layer of the loss identification channel to perform development intensity trigger identification of the second flow data to establish a development intensity trigger identification result; Conduct damaged value quantification calculation according to the identification loss result, the planning comparison and identification result, and the development intensity trigger identification result to establish a spatial resource damage identification.

2. The optimization method for planning index allocation in the inventory planning environment according to claim 1, characterized in that, The conduct of damaged value quantification calculation according to the identification loss result, the planning comparison and identification result, and the development intensity trigger identification result includes: Obtain the damaged area according to the identification loss result, the planning comparison and identification result, and the development intensity trigger identification result, and calculate the ecological loss value using the damaged area and the unit ecological integral; Conduct an analysis of the loss of affected population and quality of life according to the identification loss result, the planning comparison and identification result, and the development intensity trigger identification result to establish a social value loss value; Complete the damaged value quantification calculation according to the ecological loss value and the social value loss value.

3. The optimization method for planning index allocation in the inventory planning environment according to claim 1, wherein The configuration of the balance fitness function and the performance of balance optimization under the spatial resource damage identification includes: The balance fitness function is as follows: ； wherein, represents the balance fitness value, represents the economic loss measure, represents the social impact coefficient, represents the ecological environment cost, represents the resource recovery elasticity, and and and are the weight coefficients of the corresponding parameter terms; After establishing the initial parameter set, use the balanced fitness function to evaluate the fitness of the initial parameter set, and establish cumulative update constraints according to the fitness evaluation results; Iterate the initial parameter set through the cumulative update constraints, and complete the balanced optimization according to the iteration results.

4. The optimization method for planning index allocation in the inventory planning environment according to claim 1, wherein The completion of the optimization of the planning index allocation according to the balanced optimization result and the flow monitoring result further includes: Establish a feedback data set for the allocation of planning indicators based on the spatial resource damage identifier and the flow monitoring result; Execute the feedback impact analysis of the feedback data set. When the feedback impact analysis result triggers a preset impact threshold, generate a configuration feedback for resource allocation according to the feedback impact analysis result; Reconstruct the resource allocation result according to the configuration feedback, and complete the optimization of the planning index allocation according to the reconstructed resource allocation result.

5. The optimization method for planning index allocation in the inventory planning environment according to claim 1, wherein After the completion of the optimization of the planning index allocation according to the balanced optimization result and the flow monitoring result, it further includes: Establish the flow monitoring result and the spatial resource damage identifier as a time series data set; Perform time series development prediction on the time series data set to establish a trend warning result; Match the emergency response mechanism according to the trend warning result, and perform warning optimization processing according to the emergency response mechanism.

6. The planning index allocation optimization system in the inventory planning environment is characterized in that The system is used to execute the method for optimizing the allocation of planning indicators in the inventory planning environment described in any one of claims 1-5, including: A twin platform module for building a digital twin platform for spatial resources, which is constructed through UAV aerial survey collection and data interaction; A three-dimensional value evaluation module for using the digital twin platform for spatial resources to perform multi-dimensional ownership confirmation, and using an evaluation network to perform three-dimensional value evaluation of spatial resources to establish an evaluation result; A resource allocation module for performing resource allocation according to the evaluation result and the multi-dimensional ownership confirmation result, and establishing a resource allocation result, where the resource allocation includes rigid allocation and flexible allocation; An ownership adjustment module for using the resource allocation result to perform spatial resource flow processing in the tertiary market, and performing ownership adjustment according to the multi-dimensional ownership confirmation result; A flow monitoring module for establishing flow monitoring of spatial resource flow processing, establishing a flow monitoring result, and using a loss identification channel to compare the flow monitoring result and the resource allocation result to establish a spatial resource damage identifier; A balanced optimization module for configuring a balanced fitness function, performing balanced optimization under the spatial resource damage identifier, and completing the optimization of the planning index allocation according to the balanced optimization result and the flow monitoring result.

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