Method and system for measuring and calculating potential of graded and classified disposal of idle homestead
By using a two-layer planning model that integrates multi-source site data and a nested iterative algorithm, the problems of lack of hierarchical classification and neglect of villagers' wishes in the disposal of idle homesteads have been solved, and the accurate hierarchical classification and disposal of homesteads and the efficient use of land resources have been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SICHUAN NUCLEAR GEOLOGICAL SURVEY INST
- Filing Date
- 2026-03-30
- Publication Date
- 2026-04-28
Smart Images
Figure CN121936864A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of land resource management technology, and more specifically, to a method and system for calculating the potential of idle homesteads for hierarchical and classified disposal. Background Technology
[0002] With the acceleration of urbanization, the phenomenon of rural residents leaving their homes vacant is becoming increasingly common. A large number of idle homesteads not only waste land resources but also lead to the deterioration of the rural living environment. However, existing methods for dealing with idle homesteads suffer from several key challenges: First, there is a lack of quantitative criteria for tiered and categorized management. Traditional evaluations often focus only on plot size or simple location factors, lacking a detailed assessment of the homestead's own quality (such as the degree of house damage and site construction conditions), making a one-size-fits-all approach difficult to implement. Second, the intentions of micro-entities are ignored. Homestead disposal is not only a government planning activity but also an economic activity for villagers or market entities. Existing methods rarely consider the villagers' willingness to repair, the interplay between renovation costs and market returns, leading to a disconnect between planning schemes and actual needs, resulting in ambitious plans that are poorly implemented. Third, the disposal path is singular. There is a lack of differentiated disposal strategies for different types of homesteads, such as those of good quality, poor quality, periodically idle, and permanently idle.
[0003] Therefore, there is an urgent need for a method that can integrate multi-source site data, simulate the game relationship between upper and lower levels through mathematical models, and accurately calculate the costs and benefits of different remediation paths, so as to provide scientific support for the hierarchical and classified disposal of idle homesteads. Summary of the Invention
[0004] To achieve the above objectives, this invention provides a method and system for calculating the potential for the graded and classified disposal of idle homestead land, which is specifically implemented through the following technical solutions:
[0005] A method for assessing the potential for the tiered and categorized disposal of idle homestead land, comprising the following steps: Collect surface vector data and quality data representing the idle status and construction conditions of N homestead plots in the target area, and construct a multi-source heterogeneous spatial attribute library; A two-tiered planning model for assessing the disposal potential of idle homesteads is established. The upper-tier planning model aims to maximize the comprehensive benefits of regional spatial utilization and outputs a vector of regional macro-control intensity. The lower-tier planning model aims to maximize the adaptability of individual plots under specific disposal modes and outputs a plot micro-response decision vector, simulating the matching process between villagers' wishes and disposal paths. Set constraints on site resource carrying capacity and regional spatial planning red lines, and construct a lower-level constraint system that includes a nonlinear comprehensive remediation cost function; The nested iterative algorithm based on KKT conditions is used to solve the two-level planning model for calculating the disposal potential of idle homesteads. Under the premise of satisfying the upper-level planning constraints, the optimal equilibrium solution of the response of individual plots in the lower-level planning is searched. Based on the optimal value of the lower-level objective function under equilibrium and the shadow price of the upper-level decision variables, the comprehensive disposal potential of each homestead is calculated, and a graded and classified disposal plan is generated based on the calculation degree.
[0006] Optionally, the upper-level planning model aims to maximize the comprehensive benefits of regional spatial utilization, and its calculation formula is as follows:
[0007] Where N represents the total number of residential land parcels within the target area; i is the parcel index; M represents the total number of disposal modes; and j is the disposal mode index. is the upper-level decision variable matrix, representing the macro-planning guidance intensity of the i-th plot adopting the j-th mode, with a value range of [0,1]. The lower-level response variable matrix represents the probability or state of the i-th plot actually choosing the j-th mode; This represents the economic value added per unit area for the i-th plot under the j-th mode; This represents the social security utility value of the i-th plot under the j-th mode; This represents the equivalent value of the ecosystem services of the i-th plot. This represents the ecological restoration coefficient. These are the normalized weighting coefficients for economic, social, and ecological benefits, respectively.
[0008] Optionally, the lower-level planning model aims to maximize the adaptability utility of individual land parcels under a specific disposal mode, and its calculation formula is as follows:
[0009] in, The expected market return after selecting the j-th mode for the i-th plot; Let be the comprehensive remediation cost function for the i-th plot; This is a policy deviation penalty factor used to constrain lower-level response variables. With upper layer guidance strength Consistency.
[0010] Optionally, the comprehensive remediation cost function The calculation formula is as follows:
[0011] in, Let $i$ be the unit cost of demolition and leveling of the foundation for the $i$ plot under the $j$ model. This serves as the benchmark cost coefficient for site remediation projects. The volume of waste, dilapidated buildings, or pits that need to be backfilled within the i-th plot identified through multi-source data is used to characterize the degree of quality damage to the homestead. Let be the projected area of the i-th plot; Characterizes the rate of site remediation projects; Let be the ground slope angle of the i-th plot; These are the difficulty coefficients for site repair and management and slope support, respectively.
[0012] Optionally, the specific calculation formula for setting site resource carrying capacity constraints and regional spatial planning red line constraints is as follows:
[0013] in, Let k be the inequality constraint function for the i-th plot. This is a collection of construction land use patterns; This is a set of farmland reclamation models; For development of class patterns; For design loads; To establish a safety reserve coefficient; Let be the characteristic bearing capacity of the foundation soil layer of the i-th idle homestead plot; Let represent the current effective soil layer thickness of the i-th idle residential land plot. The minimum effective soil layer thickness threshold required for qualified farmland reclamation; This is a red line indicator function. It takes a value of 1 when the i-th plot of land is located within the ecological protection red line, and a value of 0 otherwise.
[0014] Optionally, the nested iterative algorithm based on KKT conditions is used to solve the two-level planning model for calculating the disposal potential of idle homesteads. Specifically, this includes using the penalty function method to construct a single-level equivalent fitness function containing KKT violation degrees. The solution is minimized using a genetic algorithm, and the calculation formula is as follows:
[0015] in, This represents the fitness function value of the genetic algorithm; This represents the value of the upper-level objective function. This is a pre-defined large number penalty factor used to eliminate solutions that do not meet the KKT conditions; This represents the total number of inequality constraints in the lower-level programming. Let k be the value of the inequality constraint function. For the corresponding Lagrange multipliers; This represents the total number of equation constraints in the lower-level planning; Let q be the value of the equality constraint function; The total dimension of the lower-level decision variable Y; For the lower-level Lagrangian function For the d-th decision variable The partial derivatives of are used to characterize the degree of violation of the stationarity condition.
[0016] Optionally, the calculation formula for the comprehensive disposal potential assessment of each residential land parcel is as follows:
[0017] in, This is the equilibrium net utility value obtained by the i-th plot in the lower-level planning model when the model reaches equilibrium. This represents the theoretically maximum possible net utility value for all plots within the region, used for normalization. This is the set of constraints related to the i-th plot of land. The shadow price of the k-th constraint corresponding to the equilibrium solution represents the degree of marginal restriction that constraint places on the objective function. Let be the sensitivity of the k-th constraint function to the volume of mass loss; This is a preset site quality risk reduction factor; The optimal guidance intensity for the upper-level planning model to adopt the j-th processing mode for the i-th plot under equilibrium conditions; Weighting coefficients for macroeconomic policy guidance; The preset policy priority weight or model value weight corresponding to the j-th processing mode is used to characterize the relative priority of different processing modes in regional development strategies.
[0018] Optionally, the step of generating a hierarchical classification and treatment scheme based on the calculated degree specifically includes: Set the first potential grading threshold Second potential grading threshold ; like And the optimal mode index corresponding to the equilibrium solution If so, the i-th plot of land is determined to be a first-level land use, and a first disposal plan is generated accordingly; like And the optimal mode index corresponding to the equilibrium solution If so, the i-th plot is determined to be a second-level land use, and a second disposal plan is generated accordingly; like If so, the i-th plot is determined to be a third-level land use, and a third disposal plan is generated accordingly.
[0019] Optionally, the calculation formula for the difficulty coefficient of site repair and management is as follows:
[0020] in, The average depth at which waste needs to be cleared or foundation treatment needs to be carried out for the i-th plot; As a reference depth; The fluidity index for repair filling materials.
[0021] A potential calculation system for the graded and classified disposal of idle homesteads. The system includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements a method for calculating the potential of the graded and classified disposal of idle homesteads.
[0022] The technical solution of the present invention has at least the following advantages and beneficial effects: This invention is the first to introduce a master-slave game-theoretic bi-level programming model into the field of idle homestead land disposal, unifying macro-level planning goals with micro-level subjectivity through a mathematical model, overcoming the subjectivity and ambiguity of traditional evaluation methods. By solving for the Nash equilibrium, the disposal solution found is theoretically the optimal solution that balances collective interests and individual wishes, transforming the decision-making process from guesswork to model-based calculation, greatly enhancing its scientific rigor and rationality. This invention constructs a comprehensive disposal potential measurement system, providing each plot with a clear quantitative score. This score integrates the plot's intrinsic value (net utility), the constraints and risks of site construction (shadow price), and the matching degree of macro-policies, objectively reflecting the plot's true potential. Based on the grading thresholds set by this score, massive amounts of homestead land can be automatically and accurately classified into different types such as priority revitalization, stable reclamation, and risk management, perfectly adapting to the management needs of good-quality, poor-quality, periodically idle, and permanently idle land, providing a key basis for precise policy implementation. This invention innovatively proposes an exponential comprehensive remediation cost function linked to the volume of site waste. This function accurately simulates the real-world economic law that the worse the quality, the higher the remediation cost, and the lower the willingness to deal with it. The design that deeply couples the physical state of the site with economic costs makes the potential calculation results closer to reality and effectively avoids planning failures caused by underestimating the difficulty of remediation.
[0023] The differentiated resource allocation strategy of this invention avoids ineffective or negative investments in low-potential land parcels, and concentrates limited funds and policy resources on high-potential land parcels, thereby maximizing the overall efficiency of regional land resource allocation. Attached Figure Description
[0024] Figure 1 A flowchart illustrating the potential assessment method for the graded and classified disposal of idle homesteads provided by this invention; Figure 2 This is a schematic diagram illustrating the principle of the potential calculation system for the graded and classified disposal of idle homesteads provided by the present invention. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0026] Example 1 Reference Figure 1 This document illustrates a flowchart of a method for calculating the potential of tiered and categorized disposal of idle homestead land according to an embodiment of the present invention. The core of this invention lies in constructing and solving a two-level programming model that can simulate the game process between government macro-control and farmers' micro-decision-making, and scientifically calculating the comprehensive disposal potential of each idle homestead land parcel based on the equilibrium solution of the model, ultimately generating a refined tiered and categorized disposal plan. The specific steps of this method will be described in detail below: Step S101: Construct a multi-source heterogeneous spatial attribute library.
[0027] The goal of this step is to prepare comprehensive and accurate foundational data for subsequent model calculations. First, a target study area needs to be identified, such as an administrative village, a township, or a county. Then, for all homestead plots initially identified as idle within this area, the following types of data are systematically collected and integrated: Surface vector data acquisition: This mainly involves obtaining the spatial location, geometric shape, and surrounding environment information of the land parcels. Specifically, high-resolution satellite imagery and UAV oblique photogrammetry are used to accurately extract vector data such as the land parcel boundaries, building outlines, and courtyard boundaries for each residential land parcel. Simultaneously, information on the location of surrounding road networks, water systems, and public service facilities (such as schools and clinics) is collected. This data will serve as the basis for location condition evaluation in subsequent models.
[0028] Quality data collection: This is the key difference between this invention and traditional methods, aiming to comprehensively depict the idle status of homesteads and the site construction conditions.
[0029] Idle status identification: By analyzing water and electricity usage data in recent years and combining interviews with village officials and neighbors, we accurately profiled the idle types of each plot of land, distinguishing them into periodic idleness (such as when the homeowner goes out to work and returns to live in the village during holidays or busy farming seasons) and permanent idleness (such as when the homeowner has moved to the city and the house is uninhabited and unmaintained for many years).
[0030] Site Construction Conditions Survey: This survey includes both above-ground and underground levels. Above-ground, through on-site investigation or remote sensing image interpretation, the structural safety of the building, the extent of wall cracks, and the condition of the roof are assessed, and the accumulation of weeds and waste in the yard is recorded. Underground and site-wide, for key plots or areas with complex geological conditions, geophysical exploration (such as ground-penetrating radar) or light dynamic probing methods can be used to determine the physical and mechanical properties of the foundation soil layers and identify the presence of weak underlying layers, shallow cavities, or other adverse geological phenomena.
[0031] Quantifying the Degree of Quality Damage: All negative physical entities identified in the above investigation are quantified. Specifically, a key indicator is defined—the volume of waste requiring removal, dilapidated buildings, or pits requiring backfilling. This indicator integrates the demolition volume of dilapidated buildings on the ground, the volume of construction waste cleared from the yard, and the volume of pits or subsidence areas requiring backfilling within the site. For example, if a house has partially collapsed, and the estimated construction waste requiring demolition and removal is 50 cubic meters, and there is a 20-cubic-meter abandoned water cellar in the yard requiring backfilling, then the total volume for this plot is 70 cubic meters. This indicator directly represents the degree of quality damage to the homestead and is a core input for subsequent remediation cost calculations.
[0032] Socioeconomic and willingness data collection: Through the design of a special questionnaire, door-to-door interviews were conducted with homestead owners to understand their family population structure, income sources, willingness to return to the village, and their preferences and expected compensation standards for different disposal models such as reclamation into farmland, transfer to the market for commercial development, and preservation and self-repair.
[0033] After completing the above data collection, using each homestead plot as the basic unit, Geographic Information System (GIS) technology is employed to integrate and correlate all collected spatial, qualitative, and socioeconomic data, constructing a structured, multi-source, heterogeneous spatial attribute database. Each record in the database corresponds to a plot and includes fields such as: unique plot code, spatial coordinates, plot area, ground slope angle, vacancy type, homeowner's intention, volume of quality damage, characteristic bearing capacity of foundation soil layer, and effective soil layer thickness.
[0034] Step S102: Establish a two-tiered planning model for assessing the potential for disposal of idle homesteads.
[0035] The disposal of idle homestead land is essentially a multi-stakeholder decision-making game. On the one hand, regional managers hope to optimize land resource allocation at a macro level to maximize comprehensive social, economic, and ecological benefits; on the other hand, each homestead holder will choose the disposal method that best suits their own interests. To simulate this decision-making mechanism that combines top-down guidance with bottom-up response, this invention constructs a two-tiered planning model.
[0036] The goal of the upper-level planning model is to maximize the comprehensive benefits of regional spatial utilization. Decision-makers seek an optimal regional development blueprint by setting macro-level guidance intensities for different treatment models of different land parcels. Its mathematical expression is constructed as a weighted summation form containing a logarithmic utility function:
[0037] In this function, the first term represents economic benefits. This represents the economic value added per unit area for the i-th plot under the j-th development model. As a concrete example, for the reclamation model, It can be estimated based on grain yield and price; regarding the market-entry construction model, It can be assessed through land transfer fees or future commercial rents. Upper-level variables. In this context, the multiplier implies that the stronger the guidance, the greater the expected contribution to economic output.
[0038] The second item represents social benefits. This represents the social security utility value of the i-th plot under the j-th mode. For example, land reclamation ensures food security, while market development may provide employment or improve living conditions. The natural logarithm function is used here, a commonly used utility function form in economics, reflecting the diminishing marginal utility effect. That is, when the level of social security is low, the increase in social satisfaction from increasing social security by one unit is significant, but as the level of social security increases, the increase in satisfaction from the same increment gradually decreases. The independent variable of this term is the actual response at the lower level. This means that social benefits can only be realized when a plot of land is actually disposed of.
[0039] The third item represents ecological benefits. This refers to the quantification of the ecological service value of a land parcel, such as its value in water conservation and biodiversity protection. (This is the method used here.) This is an S-shaped saturation function, characterizing the properties of ecological restoration: when the ecological value of a plot is low, a small amount of effort invested in ecological restoration will rapidly increase its ecological benefits; however, when its ecological value is already high, the incremental ecological benefits from further investment will tend to saturate. γ is an ecological restoration coefficient used to adjust the shape of the saturation curve.
[0040] These are three dimensionless weighting coefficients that sum to 1. For example, they can be set to... The weighting settings reflect the overall direction of regional development; for example, in ecological protection zones, the weighting can be increased. The value can be adjusted upwards in key development areas; The value of .
[0041] The objective function of the lower-level planning model aims to maximize the net utility of individual plots, and its mathematical expression is constructed in the form of an exponential geological cost penalty term:
[0042] In this function, the first term is the expected market return. The expected market return after selecting the j-th model for the i-th plot of land. For example, for farmers, it's the income from grain cultivation after reclamation or the land transfer rent. For developers, it's the sales profit or rental income after construction is completed.
[0043] The second item is the comprehensive remediation cost function. This is a core consideration in lower-level decision-making, as it directly links the physical quality of the land parcel to the cost of revitalizing it.
[0044] The third item is the penalty for policy deviation. This is a penalty factor, and its value can be set to a positive number of 100 or higher. Understandably, this applies to the actual choices made by lower-level market participants. The intensity of guidance from higher-level regional planning managers Inconsistency will result in a quadratic penalty.
[0045] Simultaneously, the lower-level planning must satisfy logical constraints: for each plot i, the sum of the response variables for all its disposal modes must be 1, combined with... The binary attribute, which is either 0 or 1, ensures that each plot of land has one and only one final disposal mode.
[0046] Through this two-layer model structure, the present invention can dynamically simulate the matching process between villagers' wishes and disposal paths, and find an equilibrium solution that satisfies both macro-planning goals and respects the choices of micro-entities.
[0047] Step S103: Construct a lower-level constraint system that includes a nonlinear comprehensive remediation cost function.
[0048] To make the decisions of the lower-level model more realistic, a series of strict constraints must be set for it to ensure that the final solution is safe, feasible and compliant.
[0049] As mentioned earlier, the comprehensive remediation cost function This is the core of the hierarchical and categorized policy implementation concept of this invention, and its formula is:
[0050] in, This represents the unit cost of basic demolition and leveling for the i-th plot under the j-th mode. Specifically, it is the cost of conventional basic remediation, including the removal of surface attachments, the removal of construction waste, and the initial leveling of the site. The core issue is the cost of geological remediation. It is the benchmark cost coefficient for site improvement projects, used to adjust for regional differences in project costs. This is the site remediation project rate indicator, which is the ratio of the volume of quality damage quantified in step S101 to the area of the plot. The higher the ratio, the more severe the damage to the plot, and the greater the amount of remediation work required. These are the difficulty coefficients for site remediation and slope protection, respectively. The exponential function exp defined in this embodiment means that the remediation cost does not increase linearly with the increase in the site remediation project rate, but rather increases exponentially. A well-maintained plot of land ( (Approaching 0), its remediation cost mainly consists of basic costs. However, for a plot of land with severe subsidence or a large number of dilapidated buildings ( (The cost of its remediation would rise sharply, thus automatically suppressing the possibility of high-cost development, making it naturally inclined in the model to adopt low-cost models such as reclamation or ecological restoration.)
[0051] Furthermore, the difficulty of site repair and management is... The value of is defined for calculation:
[0052] The formula for the difficulty coefficient of underground cavity remediation describes the variation of remediation difficulty with burial depth and the properties of filling materials. It is the average depth at which waste needs to be cleared or foundation treatment needs to be carried out on the i-th plot. It is the reference depth, for example, 0.5 meters. It is the fluidity index of the filling material. The difficulty of filling with highly fluid self-compacting concrete is higher than that of backfilling with ordinary soil.
[0053] Two types of rigid constraints are set for the model: site resource carrying capacity constraints and regional spatial planning boundary constraints. These constraints are bottom-line requirements that must be met unconditionally, and they are expressed as the following set of inequalities:
[0054] First constraint It refers to the foundation bearing capacity constraint. It stipulates that if a plot of land is selected for construction, i.e., j belongs to the set of construction land use patterns... Then, the bearing capacity of its foundation soil and rock characteristics It must be able to support the expected building load. And consider a safety margin factor. (For example, 1.5). If this condition is not met, the value of the inequality will be greater than 0, constituting a violation.
[0055] Second constraint It is a constraint on the thickness of the reclaimed soil layer. It stipulates that if a plot of land is selected for reclamation, i.e., j belongs to the set of reclamation patterns... So, what is the effective soil layer thickness after treatment? The minimum soil thickness threshold required for agricultural planting must be met. (e.g., 0.5 meters).
[0056] The third constraint It is constrained by the ecological protection red line. This is a red line indicator function, which takes the value 1 when plot i is within the red line, and 0 otherwise. This constraint means that any plot located within the ecological protection red line is absolutely prohibited from selecting any development mode.
[0057] By constructing the aforementioned cost function and constraint system, the lower-level model can fully incorporate multi-dimensional factors such as quality, cost, safety, and compliance when simulating farmers' decision-making, thus ensuring the scientific and realistic nature of the decision-making.
[0058] Step S104: Solve the model using a nested iterative algorithm based on KKT conditions.
[0059] Bilevel programming is an NP-hard problem, which is very difficult to solve directly. This invention adopts an efficient solution strategy, which first uses the Cartesian-Kun-Tucker (KKT) conditions to transform the bilevel model into an equivalent single-level nonlinear programming problem, and then uses a heuristic algorithm to solve it.
[0060] The specific process is as follows: The lower-level programming model is a constrained optimization problem. According to optimization theory, its optimal solution must satisfy its KKT conditions, which include stationarity, primal feasibility, dual feasibility, and complementary relaxation conditions. We add the KKT conditions of the lower-level model as a new set of constraints to the upper-level model. In this way, the entire problem is transformed from a two-level model into a single-level model that only requires solving a single objective function, but with very complex constraints.
[0061] To facilitate the use of intelligent optimization algorithms such as genetic algorithms, we employ the penalty function method to transform this complex single-layer constrained optimization problem into an unconstrained minimization problem. We construct a single-layer equivalent fitness function that includes KKT violation degrees, the specific expression of which is:
[0062] In this function, the first term is Since genetic algorithms typically perform minimization operations, minimizing the negative value of the upper-level objective function is equivalent to maximizing the upper-level objective function itself.
[0063] The second item is the penalty item, which is... The penalty term consists of a very large positive number multiplied by the sum of violations within parentheses. This penalty ensures that any solution that does not meet the optimality conditions of the lower level will have its fitness function value maximized, thus being naturally eliminated during the selection process of the genetic algorithm.
[0064] The brackets contain three types of violations. The first type is the violation of complementary relaxation conditions. For each inequality constraint at the lower level... Its complementary relaxation condition requires ,in These are the corresponding Lagrange multipliers. It measures the degree of deviation from this condition.
[0065] The second category is the degree of violation of equality constraints. For each equality constraint at the lower level... Its absolute value It measures the degree of deviation from the constraint.
[0066] The third category is the degree of violation of the stationarity condition. The stationarity condition requires the lower-level Lagrangian function... For each decision variable The partial derivative of is 0 at the optimal point. It measures the degree of deviation from this condition.
[0067] The execution process of the genetic algorithm is as follows: Step 1: Randomly generate an initial population containing a number of individuals (chromosomes). Each individual represents a complete solution. ,in, For continuous variables, It is a binary variable.
[0068] Step 2: For each individual in the population, calculate its fitness value according to the fitness function Ψ described above.
[0069] Step 3: Based on the fitness value, strategies such as roulette wheel selection or tournament selection are used. Individuals with lower fitness values (i.e., better solutions) have a higher probability of being selected and entering the next generation.
[0070] Step 4: Selected individuals are paired up and exchange some gene fragments with a certain probability, simulating biological cross-recombination to generate new offspring individuals.
[0071] Step 5: Randomly alter the genes of offspring individuals with a tiny probability to simulate biological mutation. This helps to escape local optima and enhances global search capabilities.
[0072] Step 6: Repeat steps 2 to 5. As the population evolves generation by generation, its average fitness will continuously decrease, eventually converging to a globally optimal or near-optimal solution. The algorithm terminates when the preset maximum number of iterations is reached or the fitness value no longer significantly improves over multiple generations.
[0073] The best individual obtained when the algorithm terminates, its corresponding This is the optimal equilibrium solution we are looking for. X is the optimal macroeconomic guidance strategy for the region, and Y is the optimal market response under this strategy.
[0074] Step S105: Calculate the comprehensive treatment potential assessment degree.
[0075] After obtaining the equilibrium solution, the final step is to quantify and classify the potential of each plot. To this end, a comprehensive treatment potential measurement method is defined. The calculation formula is as follows:
[0076] in, In equilibrium, this represents the optimal net utility value obtained by the i-th plot of land, which can be understood as the optimal net income seen from the farmer's perspective. It is calculated by dividing by the theoretical maximum utility value of all plots in the region. Normalize it so that it falls between 0 and 1.
[0077] The second item is the risk reduction item, which is one of the core innovations of this invention. It is the set of site quality constraints associated with the i-th plot, such as the aforementioned foundation bearing capacity constraints. This is the shadow price of the k-th constraint corresponding to the equilibrium solution. The economic meaning of the shadow price is the amount by which the objective function (i.e., the farmer's net utility) increases when this constraint is relaxed by one unit. A very high shadow price... This means that the k-th constraint (such as insufficient foundation bearing capacity) is the bottleneck limiting the realization of the land's potential. It is a constraint function Damaged volume of the site The sensitivity measures the contribution of site quality deterioration to violations of the constraint. The shadow price is multiplied by the sensitivity, then multiplied by a comprehensive risk reduction factor. This results in a potential deduction due to site quality defects. Even if a site has a good location, if its site quality is poor (leading to a high shadow price for related constraints), its overall potential score will be significantly lowered.
[0078] The third item is the value-added item guided by policies. It represents the optimal guidance intensity of the upper layer for the j-th mode of the i-th plot under equilibrium conditions. These are the weights of different modes. This is a weighting coefficient for macroeconomic policy guidance. This term means that if the optimal disposal method for a plot of land is highly consistent with the government's macroeconomic guidance (i.e.,...) If it is very large, then its overall potential will also receive additional points.
[0079] This comprehensive potential measurement method can yield a scientific evaluation indicator that goes beyond simple economic assessments, integrating microeconomic benefits, site risks, and macroeconomic guidance.
[0080] Step S106: Generate a graded and classified treatment plan based on the calculated degree.
[0081] Based on the calculated comprehensive disposal potential measurement degree Two grading thresholds can be set, such as the first potential grading threshold. Second potential grading threshold Perform logical judgment: Level 1: High-efficiency utilization of potential areas (value activation type) Judgment criteria: If the comprehensive treatment potential of plot i is calculated to be... And the optimal mode index corresponding to its equilibrium solution. Belongs to the set of construction land use patterns .
[0082] Land characteristics: Typically, the land is in a superior location with convenient transportation, in good condition, and farmers have a strong willingness to transfer the land, and it is in line with the regional development plan.
[0083] The first proposed solution is to prioritize its revitalization. Specific strategies include: including it in the pilot program for the market entry of collectively owned commercial construction land, introducing social capital through the open market, and developing high-value-added industries such as rural tourism, health and wellness resorts, and cultural and creative industries to maximize the value of the land assets.
[0084] Level Two: Developing and reclaiming potential areas (functional optimization or ecological restoration). Judgment criterion: If the potential measurement degree of plot i is... And the optimal mode index corresponding to its equilibrium solution. This belongs to the set of farmland reclamation models. Or other appropriate utilization modes.
[0085] Site characteristics: The location may be average, or the site may have some quality issues but the cost of remediation is still within an acceptable range, or the farmers may prefer to retain some functions. This is especially true for homesteads that are periodically idle.
[0086] A second disposal plan should be generated. The disposal strategy should be more diverse and flexible. For areas suitable for reclamation, demolition and reclamation can be implemented, and the land occupation and compensation balance indicators can be obtained through the land increase-decrease linkage policy; for areas where houses still exist and there is a need to retain them, they can be guided to optimize their functions, such as transforming them into shared farms, short-term rental shared courtyards, or rural e-commerce service points, so as to flexibly revitalize the right to use.
[0087] Level 3: Risk Management and Avoidance Zone (Remediation, Restoration, or Sealing and Maintenance Type) Judgment criterion: If the potential measurement degree of plot i is... This is usually because the land has serious safety hazards (such as being located in a geologically hazardous area, or having extremely poor site quality leading to excessively high remediation costs), or is located within an ecological protection red line, or the homeowner has no intention of disposing of the land at all.
[0088] A third disposal plan is generated. For such plots, the core objective is no longer development and utilization, but risk management. Specific strategies include: for plots with safety hazards, demolition must be carried out and geological disaster management or engineering restoration must be implemented. After the hazards are eliminated, ecological transformation can be carried out; for plots located within the ecological red line, strict sealing and protection should be implemented, and any construction activities should be strictly prohibited to allow them to return to their natural ecological functions; for plots with no utilization value and no risk, simple demolition and greening can be carried out.
[0089] Through the complete implementation process described above, this invention can provide a scientific, quantitative, and operable hierarchical classification and disposal plan for each idle homestead in the region, thereby greatly improving the scientific nature of regional spatial planning decisions and the efficiency of land resource allocation.
[0090] Example 2 Reference Figure 2 As shown, this embodiment also provides a potential calculation system for the graded and classified disposal of idle homestead land, implementing the above method. (Refer to...) Figure 2 The system can be a server, a personal computer (PC), or other electronic device with computing capabilities. The system includes: a processor 101, a memory 102, and a system bus 103 for connecting the processor 101 and the memory 102.
[0091] The processor 101 is the control center of the device and can use a variety of suitable processors or microcontrollers, such as general-purpose central processing units (CPUs), graphics processing units (GPUs), or dedicated digital signal processors (DSPs).
[0092] Memory 102 is the storage component of the device, used to store data and program instructions. Memory 102 can be volatile memory, such as random access memory (RAM), or non-volatile memory, such as read-only memory (ROM), flash memory, or hard disk drive (HDD). Memory 102 stores a series of computer program instructions, which, when loaded and executed by processor 101, can implement all or part of the steps of the potential assessment method for the graded and classified disposal of idle homestead land described in Embodiment 1 above.
[0093] Specifically, the memory 102 may contain multiple functional modules, which are program code segments designed to perform specific tasks: Data acquisition and database construction module: responsible for the input, storage, management and spatial display of multi-source heterogeneous data in step S101, and building and maintaining the spatial attribute database.
[0094] Two-tier planning modeling module: Provides a user interface that allows decision-makers to input parameters, such as adjusting the weights of economic, social, and ecological benefits, and setting policy deviation penalty factors, in order to build a two-tier planning model that meets the needs of a specific region.
[0095] KKT Condition Transformation and Genetic Algorithm Solution Module: Built-in nested iterative algorithms (such as genetic algorithms) based on KKT condition transformation, execute the operation in step S104, and solve the equilibrium solution of the model.
[0096] Potential assessment and classification module: Based on the equilibrium solution and shadow price output by the solution engine, automatically calculate the comprehensive disposal potential of each plot and perform classification in step S106 according to the preset threshold.
[0097] The system bus 103 is responsible for transmitting data and control signals between the processor 101 and the memory 102, as well as with other possible hardware components such as input / output interfaces and network interfaces.
[0098] This embodiment also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps described in the foregoing method embodiments. The computer-readable storage medium can be any electronic, magnetic, optical, or other physical device capable of storing program code, such as a USB flash drive, portable hard drive, CD-ROM, or solid-state drive (SSD).
[0099] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for calculating the potential for the graded and classified disposal of idle homestead land, characterized in that, The steps of this method include: Collect surface vector data and quality data representing the idle status and construction conditions of N homestead plots in the target area, and construct a multi-source heterogeneous spatial attribute library; A two-tiered planning model for assessing the disposal potential of idle homesteads is established. The upper-tier planning model aims to maximize the comprehensive benefits of regional spatial utilization and outputs a vector of regional macro-control intensity. The lower-tier planning model aims to maximize the adaptability of individual plots under specific disposal modes and outputs a plot micro-response decision vector, simulating the matching process between villagers' wishes and disposal paths. Set constraints on site resource carrying capacity and regional spatial planning red lines, and construct a lower-level constraint system that includes a nonlinear comprehensive remediation cost function; The nested iterative algorithm based on KKT conditions is used to solve the two-level planning model for calculating the disposal potential of idle homesteads. Under the premise of satisfying the upper-level planning constraints, the optimal equilibrium solution of the response of individual plots in the lower-level planning is searched. Based on the optimal value of the lower-level objective function under equilibrium and the shadow price of the upper-level decision variables, the comprehensive disposal potential of each homestead is calculated, and a graded and classified disposal plan is generated based on the calculation degree.
2. The method for calculating the potential for the graded and classified disposal of idle homestead land according to claim 1, characterized in that, The upper-level planning model aims to maximize the comprehensive benefits of regional spatial utilization, and its calculation formula is as follows: Where N represents the total number of residential land parcels within the target area; i is the parcel index; M represents the total number of disposal modes; and j is the disposal mode index. is the upper-level decision variable matrix, representing the macro-planning guidance intensity of the i-th plot adopting the j-th mode, with a value range of [0,1]. The lower-level response variable matrix represents the probability or state of the i-th plot actually choosing the j-th mode; This represents the economic value added per unit area for the i-th plot under the j-th mode; This represents the social security utility value of the i-th plot under the j-th mode; This represents the equivalent value of the ecosystem services of the i-th plot. This represents the ecological restoration coefficient. These are the normalized weighting coefficients for economic, social, and ecological benefits, respectively.
3. The method for calculating the potential for graded and classified disposal of idle homestead land according to claim 2, characterized in that, The lower-level planning model aims to maximize the adaptability utility of individual land parcels under specific disposal modes, and its calculation formula is as follows: in, The expected market return after selecting the j-th mode for the i-th plot; Let be the comprehensive remediation cost function for the i-th plot; This is a policy deviation penalty factor used to constrain lower-level response variables. With upper layer guidance strength Consistency.
4. The method for calculating the potential for graded and classified disposal of idle homestead land according to claim 3, characterized in that, The comprehensive remediation cost function The calculation formula is as follows: in, Let $i$ be the unit cost of demolition and leveling of the foundation for the $i$ plot under the $j$ model. This serves as the benchmark cost coefficient for site remediation projects. The volume of waste, dilapidated buildings, or pits that need to be backfilled within the i-th plot identified through multi-source data is used to characterize the degree of quality damage to the homestead. Let be the projected area of the i-th plot; Characterizes the rate of site remediation projects; Let be the ground slope angle of the i-th plot; These are the difficulty coefficients for site repair and management and slope support, respectively.
5. The method for calculating the potential for graded and classified disposal of idle homestead land according to claim 4, characterized in that, The specific calculation formula for setting site resource carrying capacity constraints and regional spatial planning red line constraints is as follows: in, Let k be the inequality constraint function for the i-th plot. This is a collection of construction land use patterns; This is a set of farmland reclamation models; For development of class patterns; For design loads; To establish a safety reserve coefficient; Let be the characteristic bearing capacity of the foundation soil layer of the i-th idle homestead plot; Let represent the current effective soil layer thickness of the i-th idle residential land plot. The minimum effective soil layer thickness threshold required for qualified farmland reclamation; This is a red line indicator function. It takes a value of 1 when the i-th plot of land is located within the ecological protection red line, and a value of 0 otherwise.
6. The method for calculating the potential for graded and classified disposal of idle homestead land according to claim 5, characterized in that, The nested iterative algorithm based on KKT conditions solves the two-level planning model for calculating the disposal potential of idle homesteads. Specifically, it includes constructing a single-level equivalent fitness function containing KKT violation degrees using the penalty function method. The solution is minimized using a genetic algorithm, and the calculation formula is as follows: in, This represents the fitness function value of the genetic algorithm; The value of the upper-level objective function; This is a pre-defined large number penalty factor used to eliminate solutions that do not meet the KKT conditions; This represents the total number of inequality constraints in the lower-level programming. Let k be the value of the inequality constraint function. For the corresponding Lagrange multipliers; This represents the total number of equation constraints in the lower-level planning; Let q be the value of the equality constraint function; The total dimension of the lower-level decision variable Y; For the lower-level Lagrangian function For the d-th decision variable The partial derivatives of are used to characterize the degree of violation of the stationarity condition.
7. The method for calculating the potential for graded and classified disposal of idle homestead land according to claim 6, characterized in that, The formula for calculating the comprehensive disposal potential of each residential land parcel is as follows: in, This is the equilibrium net utility value obtained by the i-th plot in the lower-level planning model when the model reaches equilibrium. This represents the theoretically maximum possible net utility value for all plots within the region, used for normalization. This is the set of constraints related to the i-th plot of land. The shadow price of the k-th constraint corresponding to the equilibrium solution represents the degree of marginal restriction that constraint places on the objective function. Let be the sensitivity of the k-th constraint function to the volume of mass loss; This is a preset site quality risk reduction factor; The optimal guidance intensity for the upper-level planning model to adopt the j-th processing mode for the i-th plot under equilibrium conditions; Weighting coefficients for macroeconomic policy guidance; The preset policy priority weight or model value weight corresponding to the j-th processing mode is used to characterize the relative priority of different processing modes in regional development strategies.
8. The method for calculating the potential for graded and classified disposal of idle homestead land according to claim 7, characterized in that, The specific steps for generating a graded and classified disposal scheme based on this calculation are as follows: Set the first potential grading threshold Second potential grading threshold ; like And the optimal mode index corresponding to the equilibrium solution If so, the i-th plot of land is determined to be a first-level land use, and a first disposal plan is generated accordingly; like And the optimal mode index corresponding to the equilibrium solution If so, the i-th plot is determined to be a second-level land use, and a second disposal plan is generated accordingly; like If so, the i-th plot is determined to be a third-level land use, and a third disposal plan is generated accordingly.
9. The method for calculating the potential for graded and classified disposal of idle homestead land according to claim 4, characterized in that, The formula for calculating the difficulty coefficient of site repair and management is as follows: in, The average depth at which waste needs to be cleared or foundation treatment needs to be carried out for the i-th plot; As a reference depth; The fluidity index for repair filling materials.
10. A potential calculation system for the graded and classified disposal of idle homestead land, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the potential calculation method for the graded and classified disposal of idle homesteads as described in any one of claims 1 to 9.