Low-efficiency land use redevelopment priority evaluation method and system

Abstract

The invention belongs to the technical field of urban planning, and discloses a low-efficiency land redevelopment priority evaluation method and system, and the method comprises the steps: introducing the recognition of correlation characteristics between land parcels, and carrying out the iterative correction of the priority score of the land parcels based on the correlation characteristics and a preset correlation effect quantification rule, so as to generate a plurality of land parcel development sequences, and finally, an optimal development strategy is determined by calculating the overall benefit of each sequence, so that the problems that the evaluation result is unreasonable and combinatorial optimization cannot be performed due to the fact that the interaction relationship between the plots cannot be considered in the existing method are solved, and maximization of the regional value is realized.

Description

Technical Field

[0001] The present application relates to the field of urban planning technology, and in particular to a method and system for evaluating the priority of redevelopment of low-efficiency land. Background Art

[0002] In the daily work of urban management departments, inventorying, evaluating, and prioritizing inefficient land use within their jurisdictions for redevelopment is a core task in promoting the intensive use of land resources and optimizing and upgrading urban functions. To this end, management departments generally use a decision-making support system. This system inputs detailed data on various plots in the city, covering a wide range of dimensions, including multiple basic attributes, economic attributes, and social attributes.

[0003] When the system is operational, decision-makers first assign weights to various evaluation indicators based on the city's macro-strategic goals. Once the weights are set, the system scores each parcel of low-efficiency land and calculates its overall priority score through weighted summation or other multi-criteria decision-making models. Finally, the system outputs a list ranked from highest to lowest by score, providing data support for decision-makers to prioritize redevelopment plots. This approach, based on static data and a fixed weight model, can provide effective decision-making guidance during periods of stable urban development and slow external environmental change.

[0004] However, urban development is not static and is often impacted by major projects or policy adjustments. If decision-makers continue to rely on decision-support systems based on historical data and pre-defined weights, their assessment results will be severely distorted. Existing technologies typically rely on operators to manually adjust weights, but this method is crude and fails to accurately reflect the complex and ever-changing needs of urban development.

[0005] Existing decision-making support methods, whether static or heavily manually adjusted, rely on an isolated, independent evaluation of each plot. This assumes that the redevelopment value of each plot is a function of its own attributes, while ignoring the interplay between redevelopment projects. In reality, the redevelopment of a plot is not an isolated event but rather has radiating impacts on surrounding areas. The redevelopment of different plots can have synergistic or antagonistic effects. For example, the successful redevelopment of one plot may increase the value of surrounding plots or create favorable conditions for their subsequent development; conversely, inappropriate development may negatively impact the surrounding environment.

[0006] This kind of synergistic or antagonistic effect between projects is ubiquitous, but it is completely ignored by existing isolated evaluation methods. This results in the redevelopment priority evaluation results being unscientific and unreasonable, and unable to fully tap the overall value of the region. The essence of the problem has shifted from "which single plot is the best" to "which plot combination and development sequence is the best." Decision makers no longer need a simple priority list, but a holistic development strategy that can reveal the internal connections between projects and deduce the future urban form and benefits under different development combinations. Existing decision-making support tools are unable to handle this kind of combinatorial optimization problem, which may cause decision makers to miss the opportunity to maximize regional value through coordinated development, or make suboptimal decisions that lead to regional functional conflicts.

[0007] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0008] In order to address the shortcomings of the existing technology, this application provides a method and system for evaluating the redevelopment priority of low-efficiency land, which can effectively solve the problem of isolated evaluation of the redevelopment priority of low-efficiency land in the existing technology. By considering the correlation characteristics and mutual influence between plots, a plot development sequence based on overall benefit optimization is generated, thereby providing a more scientific and reasonable redevelopment priority, which helps to maximize regional value.

[0009] In a first aspect, the present application provides a method for evaluating the redevelopment priority of low-efficiency land in a city, and the method comprises the following steps: A1. Obtain attribute data for each plot to be evaluated to assess the initial priority score of each plot to be evaluated; the attribute data includes basic attribute data, economic attribute data, and social attribute data; A2. Identify the correlation characteristics between the parcels to be evaluated based on their geographic location, intended use, and transportation network connectivity. These correlation characteristics include spatial proximity, mutual influence, and transportation connectivity. A3. For each plot to be evaluated, determine a set of associated plots of the plot to be evaluated based on the associated features; A4. Based on the initial priority score, the associated parcel set, the associated characteristics, and the pre-set association effect quantification rules, iteratively select parcels to be evaluated and modify the priority scores of the associated parcels to generate multiple parcel development sequences sorted by priority score; A5. Calculate the overall benefits of the development sequences of each plot, and determine the priority of each plot to be evaluated based on the plot development sequence corresponding to the maximum overall benefit.

[0010] Secondly, the present application provides a low-efficiency land redevelopment priority evaluation system for evaluating the redevelopment priority of low-efficiency land in cities. The system includes: An initial evaluation module is used to obtain attribute data of each plot to be evaluated, so as to evaluate the initial priority score of each plot to be evaluated; the attribute data includes basic attribute data, economic attribute data and social attribute data; A correlation feature identification module is used to identify correlation features between the plots to be evaluated based on their geographical locations, intended uses, and transportation network connection information; the correlation features include spatial proximity, mutual influence, and transportation correlation; An associated plot determination module is used to determine, for each plot to be evaluated, a set of associated plots of the plot to be evaluated based on the associated features; a sequence generation module for iteratively selecting plots to be evaluated and modifying the priority scores of the associated plots based on the initial priority scores, the associated plot set, the associated features, and a preset association effect quantification rule, so as to generate a plurality of plot development sequences sorted by priority scores; The priority determination module is used to calculate the overall benefits of the development sequences of each plot, and determine the priority of each plot to be evaluated based on the plot development sequence corresponding to the maximum overall benefit.

[0011] In summary, the present application provides a method and system for evaluating the priority of redevelopment of low-efficiency land. By introducing the identification of correlation characteristics between plots, and based on these correlation characteristics and preset correlation effect quantification rules, the priority scores of plots are iteratively corrected to generate multiple plot development sequences. Finally, the optimal development strategy is determined by calculating the overall benefits of each sequence. This solves the problem that existing methods cannot consider the interaction relationship between plots, resulting in unreasonable evaluation results and inability to perform combination optimization, thereby maximizing regional value. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flowchart of a low-efficiency land redevelopment priority evaluation method provided for this application.

[0013] Figure 2 A schematic diagram of a low-efficiency land redevelopment priority evaluation system provided for this application.

[0014] In the figure: 1. Initial assessment module; 2. Associated feature recognition module; 3. Associated plot determination module; 4. Sequence generation module; 5. Priority determination module. DETAILED DESCRIPTION

[0015] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0016] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0017] refer to Figure 1 This application provides a method for evaluating the redevelopment priority of low-efficiency land, which is used to evaluate the redevelopment priority of low-efficiency land in cities. The method includes the following steps: A1. Obtain attribute data for each plot to be evaluated to assess the initial priority score of each plot to be evaluated; the attribute data includes basic attribute data, economic attribute data, and social attribute data; A2. Identify the correlation characteristics between the parcels to be evaluated based on their geographic location, intended use, and transportation network connectivity. These correlation characteristics include spatial proximity, mutual influence, and transportation connectivity. A3. For each plot to be evaluated, determine a set of associated plots of the plot to be evaluated based on the associated features; A4. Based on the initial priority score, the associated parcel set, the associated characteristics, and the pre-set association effect quantification rules, iteratively select parcels to be evaluated and modify the priority scores of the associated parcels to generate multiple parcel development sequences sorted by priority score; A5. Calculate the overall benefits of the development sequences of each plot, and determine the priority of each plot to be evaluated based on the plot development sequence corresponding to the maximum overall benefit.

[0018] Among them, attribute data refers to various types of information used to evaluate the value and potential of land plots. It can be obtained through data collection, database query or geographic information system (GIS) integration. It is mainly used to conduct preliminary and independent value assessments of each land plot to be evaluated and obtain an initial priority score.

[0019] Among them, correlation characteristics refer to the characteristics of the interaction relationships between plots, which can be identified using technologies such as geographic spatial analysis, functional matching analysis or transportation network analysis. They are mainly used to quantify the synergistic or antagonistic effects between plot redevelopment projects and provide a basis for subsequent priority adjustments.

[0020] Among them, the associated plot set refers to a combination of other plots that have obvious interaction relationships with the specific plot to be evaluated. It can be determined by methods such as association strength threshold screening or network topology analysis. It is mainly used to clarify which plots’ priority will be affected by the development of the current plot, thereby achieving accurate priority correction.

[0021] Among them, the association effect quantification rule refers to a model or algorithm used to calculate the degree of influence of plot redevelopment on the priority of associated plots. It can be constructed using regression analysis models, expert system rules or machine learning algorithms. It is mainly used to quantify the positive or negative externalities generated by plot development and provide a calculation basis for iteratively correcting plot priorities.

[0022] Among them, the plot development sequence refers to a list of plots to be evaluated arranged in a specific order, representing a possible redevelopment timing plan. It can be generated using optimization strategies such as greedy algorithms, heuristic searches, or dynamic programming. It is mainly used to simulate the dynamic changes in plot priorities under different development paths and provide alternative plans for overall benefit evaluation.

[0023] Among them, overall benefit refers to the comprehensive value that a land development sequence can bring. It can be calculated by the weighted sum of economic benefits, social benefits and environmental benefits or multi-objective optimization function. It is mainly used to quantify the comprehensive value brought by different development combinations and time sequences, so as to determine the optimal redevelopment strategy.

[0024] The core innovation of this application lies in introducing the identification of correlation features between plots, and based on these correlation features and preset correlation effect quantification rules, iteratively correcting the priority scores of plots to generate multiple plot development sequences, and finally determining the optimal development strategy by calculating the overall benefits of each sequence. This solves the problem that existing methods cannot consider the interaction relationship between plots, resulting in unreasonable evaluation results and inability to perform combination optimization, thereby maximizing regional value.

[0025] Specifically, this method aims to address existing methods for prioritizing the redevelopment of low-efficiency land, which evaluate parcels in isolation, ignore interactions between parcels, and fail to optimize combinations. First, by obtaining the attribute data of the parcels to be evaluated, a preliminary value assessment is performed on each parcel, generating an initial priority score. This score provides the basis for subsequent dynamic corrections. Next, using the parcels' geographic location, intended use, and transportation network connectivity, correlation features such as spatial proximity, mutual influence, and transportation connectivity are identified between the parcels. These features are key to quantifying synergistic or antagonistic effects between parcels. Based on these identified correlation features, the set of associated parcels is determined for each parcel to clarify which parcels will be affected by the current parcel's development. Subsequently, this method introduces an iterative correction mechanism to simulate the impact of parcel redevelopment on the priority of surrounding associated parcels, using the initial priority score, the set of associated parcels, the correlation features, and pre-defined rules for quantifying the correlation effects. By iteratively selecting parcels and revising the priority scores of associated parcels, the impact of parcel development on the overall region can be dynamically reflected. Multiple different parcel development sequences can be generated to explore diverse development paths. Ultimately, the overall benefits of each parcel development sequence are calculated, quantifying the combined value of different development combinations and timings. The parcel development sequence with the greatest overall benefit is selected, and the final priority of each parcel to be evaluated is determined based on this sequence. This ensures that the evaluation results not only consider the value of individual parcels but, more importantly, maximize regional value.

[0026] Through the above scheme, this application solves the problem that the existing low-efficiency land redevelopment priority evaluation method cannot consider the interaction relationship between plots, making the evaluation results more reasonable. This method can handle the synergistic or antagonistic effects between plot redevelopment projects, avoiding the limitations of isolated evaluation. At the same time, through iterative correction and generation of plot development sequences, this method realizes the consideration of plot combination optimization and development timing, can reveal the internal connection between projects, and deduce the future urban form and benefits under different development combinations, thereby providing decision makers with a holistic development strategy, effectively avoiding suboptimal decisions, and promoting the maximization of regional value.

[0027] As a preferred embodiment, the solution of this application is implemented as follows: In practical applications, attribute data for the parcels to be evaluated can first be obtained through an urban geographic information system (GIS). This data is standardized and quantified, and then combined with preset weights to calculate an initial priority score for each parcel. Subsequently, the Euclidean distance or road distance between the parcels is calculated using their geographic coordinates to determine spatial proximity. The functional interactions between the parcels are assessed based on their planned uses (i.e., intended uses, such as residential, commercial, and industrial) and a predefined functional compatibility matrix. Furthermore, the transport accessibility between the parcels is assessed by analyzing their connectivity to transportation networks such as urban rail transit, bus routes, and arterial roads, thereby determining transport connectivity. For each parcel to be evaluated, a comprehensive connectivity strength threshold can be set. Parcels with a connectivity strength exceeding the threshold are then selected to form a set of associated parcels for that parcel. Optimization methods such as simulated annealing or genetic algorithms can be employed to generate the parcel development sequence. Finally, for each generated land parcel development sequence, a comprehensive benefit evaluation model can be constructed. This model considers multiple dimensions, including economic benefits (such as land appreciation and tax contributions), social benefits (such as employment opportunities and improved public services), and environmental benefits (such as greening rate and pollution control). These are weighted and summed to calculate the overall benefits of each sequence. The sequence with the highest overall benefits is selected as the optimal development plan, and the final redevelopment priority of each parcel to be evaluated is determined based on the development order or final score of the parcels in that sequence.

[0028] Preferably, the basic attribute data includes area, current use, property ownership and floor area ratio; The economic attribute data include benchmark land prices, surrounding real estate market prices and potential development costs; The social attribute data include surrounding population density, public transportation station coverage, educational facility accessibility score, and medical facility accessibility score.

[0029] Among them, basic attribute data refers to a data set that reflects the inherent physical characteristics, current usage, and legal status of a land parcel. This data can be obtained through on-site measurements of the land parcel, analysis of planning drawings, and real estate registration information inquiries. Economic attribute data refers to a data set that measures the market value, potential returns, and development investment of a land parcel. This data can be obtained through market research, land valuation reports, and project budget analysis. Social attribute data refers to a data set that assesses the impact of a land parcel on surrounding communities and residents' lives. This data can be obtained through demographic statistics, analysis of public service facility distribution maps, and analysis of transportation network data.

[0030] In some embodiments, step A1 comprises: A101. Obtain attribute data for each plot to be evaluated; the attribute data includes basic attribute data, economic attribute data, and social attribute data; A102. Convert the current usage and ownership of the basic attribute data into corresponding quantitative scores according to the preset classification and scoring criteria; A103. Convert the area and floor area ratio in the basic attribute data, the benchmark land price, surrounding real estate market price, and potential development cost in the economic attribute data, and the surrounding population density, public transportation station coverage, educational facility accessibility score, and medical facility accessibility score in the social attribute data into corresponding quantitative scores according to pre-set quantitative rules. A104. Calculate the initial priority score of each plot to be evaluated based on the quantitative scores and the preset weights.

[0031] The pre-defined classification scoring criteria refers to a set of pre-defined rules or tables for data with categorical or qualitative characteristics, such as the current use or ownership of a plot of land. These rules are used to convert this non-numerical information into a quantitative score with specific meaning. For example, different scores can be assigned based on the impact of different uses on redevelopment, or based on the clarity of ownership rights, thus allowing qualitative data to be included in the numerical calculation.

[0032] Pre-set quantification rules refer to a set of pre-defined conversion methods for numerical attribute data that may have different dimensions or ranges, such as plot area, floor area ratio, land price, or population density. These rules can include linear normalization, logarithmic transformation, and piecewise function mapping. Their purpose is to eliminate dimensional differences between different data and map them to a comparable numerical range to ensure fair evaluation.

[0033] Preset weights refer to the importance coefficients assigned to the quantitative scores of different categories or items when comprehensively calculating the initial priority score. These weights can be flexibly set based on the city's macro-strategy for development, specific policy guidelines, or expert experience to reflect the relative importance of different attributes in the evaluation system, thereby ensuring that the final priority score more accurately reflects decision-making needs.

[0034] This approach systematically assesses the initial priority scores of each parcel to be evaluated through a series of sequential and logically rigorous steps. First, by obtaining basic, economic, and social attribute data for each parcel to be evaluated, a comprehensive data foundation is laid for subsequent quantification and calculation. Second, qualitative information contained in the attribute data, such as current use and ownership, is converted into a unified quantitative score by applying pre-defined classification and scoring criteria. This conversion process effectively addresses the difficulty of directly incorporating qualitative data into numerical calculations, ensuring the objectivity and operability of the assessment. Furthermore, numerical attribute data such as area, floor area ratio, benchmark land price, surrounding real estate market prices, potential development costs, surrounding population density, public transportation station coverage, and accessibility scores for educational and medical facilities are standardized or normalized into a unified quantitative score by applying pre-defined quantification rules. This eliminates dimensional differences between different data types and ensures comparability of all data in subsequent calculations. Finally, after all attribute data are converted into unified quantitative scores, these quantitative scores are comprehensively calculated according to the preset weights to obtain the initial priority score of each plot to be evaluated.

[0035] The entire process ensures that the initial priority score assessment process is systematic, standardized, and objective, avoiding subjective judgment and inconsistency. This provides a solid and reliable starting point for the subsequent identification of associated plot characteristics, the determination of associated plot sets, and the iterative generation and revision of plot development sequences. This ensures that the entire low-efficiency land redevelopment priority assessment method is based on accurate and reliable data, significantly improving the scientific nature and effectiveness of the evaluation results.

[0036] In a specific embodiment, the method can be implemented as follows: When acquiring attribute data for each parcel to be evaluated, data can be collected from various sources, including urban planning databases, land and resources management systems, statistical yearbooks, and on-site surveys. For example, basic attribute data such as area and floor area ratio can be directly obtained from cadastral information; benchmark land prices and surrounding real estate market prices in economic attribute data can be obtained from land trading markets or appraisal agencies; surrounding population density in social attribute data can be obtained from census data, while public transportation station coverage, educational facility accessibility scores, and medical facility accessibility scores can be obtained based on spatial analysis results from a geographic information system (GIS). A detailed scoring table can be established to convert the current use and ownership of the basic attribute data into corresponding quantitative scores based on preset classification and scoring criteria. For example, for "current use," "industrial land" can be assigned a low quantitative score, "commercial land" a medium quantitative score, and "vacant or inefficient residential land" a high quantitative score to encourage its redevelopment. Regarding "property ownership," "exclusive state ownership" can be assigned a high score because it presents less resistance to development, while "collectively owned" or "shared by multiple parties" can be assigned a lower score because development coordination is more challenging. For basic attribute data such as area and floor area ratio, economic attribute data such as benchmark land prices, surrounding real estate market prices, and potential development costs, and social attribute data such as surrounding population density, public transportation station coverage, and accessibility scores for educational facilities and medical facilities, different standardization or normalization methods can be used when converting these data into corresponding quantitative scores based on pre-set quantification rules. For example, for positive indicators such as area and floor area ratio, linear normalization can be used to map them to a range between 0 and 1. For benchmark land prices and surrounding real estate market prices, positive quantification can be performed based on their contribution to development value. For potential development costs, inverse quantification can be used, meaning that the higher the cost, the lower the quantitative score. Surrounding population density, public transportation station coverage, accessibility scores for educational facilities, and accessibility scores for medical facilities are generally numerical data and can be directly normalized or appropriately range-mapped. When calculating the initial priority score for each parcel to be evaluated based on the quantitative scores and preset weights, a weighted summation model can be used. For example, the weight of the basic attribute data can be set to a1, the weight of the economic attribute data to a2, and the weight of the social attribute data to a3. Then, the initial priority score of a parcel can be calculated as: Initial Priority Score = (Basic Attribute Quantitative Score * a1) + (Economic Attribute Quantitative Score * a2) + (Social Attribute Quantitative Score * a3).The basic attribute score can be calculated by taking the weighted average of the quantitative scores for area, floor area ratio, current use, and ownership; the economic attribute score can be calculated by taking the weighted average of the quantitative scores for benchmark land price, surrounding real estate market price, and potential development cost; and the social attribute score can be calculated by taking the weighted average of the quantitative scores for surrounding population density, public transportation station coverage, accessibility to educational facilities, and accessibility to medical facilities. This approach comprehensively reflects the value of a land parcel across different dimensions, resulting in an objective and comparable initial priority score.

[0037] In some embodiments, step A2 comprises: A201. Obtaining geographic location information of each plot to be evaluated, for calculating the distance between the center points of each plot to be evaluated, to determine the spatial proximity between each plot to be evaluated; the spatial proximity is negatively correlated with the distance; A202. Determine the mutual influence between each pair of plots to be evaluated based on the intended use of each plot and the pre-set functional relationship rules; A203. Obtain the traffic network connection information of each plot to be evaluated, which is used to evaluate the degree of traffic connection between each plot to be evaluated, so as to determine the traffic correlation between each plot to be evaluated; the traffic correlation is positively correlated with the degree of traffic connection; the traffic network connection information of the plot to be evaluated includes the connection information between the plot to be evaluated and the traffic network.

[0038] Spatial proximity is a quantitative measure of the spatial proximity between two parcels to be evaluated. Spatial proximity can be simply defined as the inverse of distance, or represented using an exponential decay function (e.g., e^(-k*L), where k is the decay coefficient and L is the distance).

[0039] Among them, the intended use refers to the functional positioning that the evaluated land may be given after future redevelopment, which can be expressed using a preset functional classification system, such as residential, commercial, industrial, public service or green space.

[0040] Among them, functional relationship rules refer to the preset logic or model that defines the functional interaction between different types of plots. They can be implemented using matrices, graphs or expert system rule sets to quantify the functional synergy or antagonism between plots.

[0041] Among them, the degree of mutual influence refers to a quantitative indicator of the impact of the redevelopment of one plot to be evaluated on the function or value of another plot to be evaluated. It can be expressed in numerical values, levels or percentages to reflect the positive or negative functional correlation between the plots.

[0042] Among them, transportation network connection information refers to data that describes the connection status between the land to be evaluated and the urban transportation infrastructure. It can be represented by road network data, public transportation route data or transportation hub location data, and is used to evaluate the accessibility of the land.

[0043] Among them, the degree of traffic connectivity refers to the quantitative assessment of the convenience or efficiency of mutual access between two evaluated plots through the transportation network, which can be measured by indicators such as the shortest path time, traffic flow or transportation facility density.

[0044] Among them, traffic correlation refers to a quantitative indicator of the impact of the redevelopment of one plot to be evaluated on the traffic accessibility or traffic load of another plot to be evaluated. It can be expressed in numerical values, levels or percentages to reflect the traffic interdependence between plots.

[0045] This solution provides a specific and quantitative method by refining the steps of identifying associated features, thereby solving the generalization problem of associated feature identification in the existing technology. By obtaining geographic location information and calculating distance to determine spatial proximity, the spatial proximity between plots can be objectively reflected. By determining the degree of mutual influence based on the intended use and functional relationship rules, the functional impact of plot redevelopment on surrounding plots can be accurately evaluated. By obtaining traffic network connection information and evaluating the degree of traffic connectivity to determine traffic correlation, the interaction between plots caused by traffic conditions can be accurately reflected. These quantitative and accurate association feature identifications provide a reliable data basis for the determination of associated plot sets, the generation of plot development sequences, and the correction of priority scores in subsequent steps, effectively improving the objectivity and effectiveness of the evaluation results of the priority of redevelopment of low-efficiency land.

[0046] Preferably, step A202 may include: According to the expected use of each plot of land to be evaluated and the preset functional relationship rules, obtaining initial functional interaction scores between each plot of land to be evaluated; Obtaining correction factors that affect the functional interaction score; the correction factors include a spatial correction factor determined based on the geographic location information of each plot to be evaluated, a traffic correction factor determined based on the traffic network connection information of each plot to be evaluated, and an environmental correction factor determined based on the attribute data of each plot to be evaluated; The final mutual influence degree between each pair of plots to be evaluated is calculated based on the initial functional interaction score and the correction factor.

[0047] Among them, the initial functional interaction score refers to the numerical value that preliminarily quantifies the degree of mutual promotion or inhibition of functions between plots based on the expected use of the plots and the preset functional relationship rules. It can be obtained using a predefined functional relationship matrix, an expert scoring table, or a functional association model based on historical data.

[0048] The correction factor refers to the parameter used to adjust the initial functional interaction score to reflect the influence of external or internal conditions on the actual interaction between plots. It can be expressed as a multiplicative factor or an additive term.

[0049] The spatial correction factor is a parameter that adjusts the functional interaction score based on the spatial relationship between plots. It can be determined using a distance decay function, a spatial weight matrix, or a correction model based on spatial topology. For example, the spatial correction factor can be determined based on the Euclidean distance between the center points of the plots. A distance decay function, such as an exponential decay function or a power law decay function, can be used to convert the distance into a correction coefficient between 0 and 1, where the longer the distance, the smaller the correction coefficient.

[0050] The traffic correction factor is a parameter that adjusts the functional interaction score based on the accessibility of transportation between plots. It can be determined using indicators such as the shortest path time of the transportation network, traffic flow data, or public transportation coverage. For example, the traffic correction factor can be determined based on the accessibility of the two plots through the transportation network. The shortest travel time between the two plots via public transportation or road network can be calculated and converted into a correction factor through an inverse mapping function. The shorter the travel time, the larger the correction factor.

[0051] The environmental correction factor is a parameter that adjusts the functional interaction score based on the land's own environmental attributes. It can be determined using indicators such as surrounding population density, green coverage, air quality index, and public service facility coverage. For example, a weighted summation or fuzzy comprehensive evaluation method can be used to calculate an environmental quality score and convert it into a correction coefficient. The higher the environmental quality score, the larger the correction coefficient.

[0052] The final mutual influence degree can be obtained by adding various correction factors to the initial functional interaction score or multiplying it with various correction factors.

[0053] This method first derives an initial functional interaction score between each parcel, based on its intended use and pre-defined functional relationship rules. This initial score serves as a basic quantification of the functional relevance between parcels, reflecting the potential synergy or antagonism between the parcels at the planning level. Based on this, the method further derives correction factors that influence the functional interaction score. These correction factors, the core of this approach, include a spatial correction factor based on the geographic location of each parcel, a transportation correction factor based on its transportation network connectivity, and an environmental correction factor based on its attribute data. The spatial correction factor quantifies the attenuation or enhancement effect of inter-parcel distance or spatial layout on functional interaction, ensuring that the degree of mutual influence conforms to actual spatial patterns. The transportation correction factor assesses the facilitation or hindrance of transportation conditions on functional interaction, ensuring that the degree of mutual influence takes into account actual accessibility. The environmental correction factor takes into account micro-environmental factors within the parcel itself, such as surrounding population density, environmental quality, and the level of infrastructure, which influence the actual effectiveness of parcel functional interaction. Finally, based on the initial functional interaction scores and the various correction factors mentioned above, the final mutual influence between each pair of evaluated plots is calculated. By combining the initial functional interaction scores with correction factors such as space, transportation, and environment, an accurate and comprehensive mutual influence can be obtained.

[0054] This multi-dimensionally revised mutual influence calculation method enables accurate reflection of the complex linkage effects during the redevelopment process when identifying inter-plot correlation characteristics. Compared with the traditional method of determining mutual influence based solely on intended use and functional relationship rules, this can improve the accuracy of inter-plot interaction assessments. This precise mutual influence, as an important component of inter-plot correlation characteristics, can provide a reliable basis for the subsequent determination of the associated plot set of a plot. Furthermore, when generating multiple plot development sequences ranked by priority scores, the revised priority scores of the associated plots will be closer to reality, making the final plot development sequence and priority evaluation results scientific and reasonable, avoiding suboptimal decisions caused by inaccurate inter-plot interaction assessments, and thus improving the overall accuracy and practicality of the redevelopment priority evaluation of low-efficiency land.

[0055] Preferably, step A203 may include: Obtaining connectivity information between each parcel to be evaluated and different types of transportation networks; the connectivity information includes accessibility, carrying capacity, and traffic efficiency; Determine the degree to which the intended use of each parcel of land to be evaluated depends on different types of transportation networks; Based on the connection information and the dependency level, evaluating the degree of transportation connectivity between each of the land parcels to be evaluated that matches the intended use; According to the traffic connection degree, the traffic correlation between each pair of the land parcels to be evaluated is determined; the traffic correlation is positively correlated with the traffic connection degree.

[0056] Among them, different types of transportation networks refer to the various types of transportation infrastructure existing in the city, which may include but are not limited to road networks, rail transit networks, bus line networks, water transport networks or air transport networks.

[0057] Among them, connection information refers to the quantitative indicators of establishing connections between plots and these transportation networks. Accessibility refers to the convenience of reaching or leaving a specific transportation network node from the plot to be evaluated, which can be measured by walking distance, commuting time or number of transfers; carrying capacity refers to the traffic flow or number of passengers that a specific transportation network can accommodate per unit time, which can be expressed by road grade, number of lanes or frequency density of public transportation vehicles; traffic efficiency refers to the smoothness and speed of traffic flow in the transportation network, which can be reflected by average vehicle speed, congestion index or delay time.

[0058] The degree of dependence of the intended use on different types of transportation networks refers to the degree of importance or intensity of demand for different types of transportation networks (such as roads, public transportation, freight rail, and water transportation) for the intended use. Specifically, expert scoring, hierarchical analysis, or fuzzy comprehensive evaluation methods can be used to assign a weight or dependence coefficient to each type of transportation network based on the transportation demand characteristics of different intended uses. For example, a plot planned for a commercial complex will have a high degree of dependence on the accessibility and carrying capacity of public transportation, but may have a low degree of dependence on freight rail or water transportation.

[0059] Evaluating the degree of transport connectivity between each parcel to be evaluated, based on connectivity information and dependency, is a process of analyzing the compatibility of a parcel's actual transport conditions (connectivity information) with its functional requirements (dependency) to produce a comprehensive transport connectivity score reflecting the degree of compatibility. This can be achieved through weighted summation, multi-objective optimization, or fuzzy matching. For example, various connectivity information (accessibility, carrying capacity, and traffic efficiency) can be weighted and combined with the determined dependency level to produce a highly customized transport connectivity assessment. The assessment is conducted between any two parcels to be evaluated, taking into account the accessibility from parcel A to parcel B, the accessibility from parcel B to parcel A, or any transport hubs on which both parcels rely.

[0060] Determining the traffic correlation between each pair of evaluated plots based on their degree of traffic connectivity involves quantifying the assessed degree of traffic connectivity into a traffic correlation index. Traffic correlation is positively correlated with the degree of traffic connectivity, meaning that higher traffic connectivity indicates a higher traffic correlation index. Specifically, the traffic correlation index can be represented by linear or nonlinear function mapping, or mapped to a specific numerical range using a monotonically increasing function.

[0061] This solution refines the determination of transportation connectivity during the step of identifying the interconnected characteristics between the parcels to be evaluated. First, the system obtains information on each parcel's connectivity to different types of urban transportation networks. This information goes beyond simply measuring connectivity strength to encompass multiple dimensions, including accessibility, carrying capacity, and efficiency. This multi-dimensional information collection enables the system to provide a more comprehensive and detailed picture of the parcel's transportation conditions, laying the foundation for subsequent evaluation. Furthermore, the system considers the intended use of each parcel. Because parcels with different intended uses have varying requirements for transportation networks—for example, commercial parcels have higher requirements for public transportation accessibility and road carrying capacity, while residential parcels prioritize a quiet living environment and walkability—the system determines the specific degree of dependence of the parcel on different types of transportation networks based on its intended use. This step introduces an inherent matching logic between parcel function and transportation network, making transportation assessments less general and more personalized for the parcel's future function. The system then combines the multi-dimensional connectivity information obtained above with the degree to which the intended use of the land parcel depends on the transportation network to assess the degree of transportation connectivity between each parcel to align with its intended use. This assessment process goes beyond simply measuring the physical strength of transportation connections to delve deeper into whether these connections truly meet the functional requirements of the land parcel after redevelopment. For example, even if a commercial parcel has numerous transportation routes nearby, if the carrying capacity or efficiency of these routes cannot meet the high passenger and logistics demands of commercial activities, its degree of transportation connectivity for its intended use will be considered low. This alignment assessment ensures that transportation connectivity considerations are targeted and practical, avoiding the bias of assessing high but inappropriate connectivity. Ultimately, based on this refined and personalized matching assessment, the system determines the degree of transportation correlation between each parcel to be evaluated, and the degree of transportation correlation and transportation connectivity show a positive correlation.

[0062] In this way, the resulting traffic correlation can more accurately reflect the mutual influence and synergy between plots due to the transportation network. This more precise traffic correlation, as an important component of identifying plot correlation characteristics, can provide a more reliable basis for subsequently determining the set of related plots and iteratively revising plot priority scores. This enables the entire low-efficiency land redevelopment priority evaluation method to more scientifically identify the inherent connections between plots, thereby improving the accuracy of the overall evaluation and decision-making support capabilities, and resolving the problems of insufficient precision and mismatch in traffic correlation assessment in traditional methods.

[0063] In some embodiments, step A3 comprises: A301. For each parcel to be evaluated, calculate the comprehensive strength of association between the parcel to be evaluated and the remaining parcels to be evaluated based on the spatial proximity, mutual influence, and traffic association between the parcel to be evaluated and the remaining parcels to be evaluated; A302. Filter out the remaining plots to be evaluated whose comprehensive correlation strength is higher than a preset correlation strength threshold to form a set of associated plots of the plot to be evaluated.

[0064] Among them, comprehensive correlation strength refers to the overall strength of the interaction between plots after comprehensively considering multiple correlation characteristics. It can be calculated using weighted summation, multi-attribute decision-making models, or machine learning algorithms. The correlation strength threshold refers to the strength critical value used to screen related plots. It can be determined based on empirical settings, statistical analysis, or optimization algorithms. For example, the spatial proximity, mutual influence, and traffic correlation between the plot to be evaluated and the remaining plots to be evaluated are first normalized. Then, the weighted sum of the normalized spatial proximity, normalized mutual influence, and normalized traffic correlation is calculated as the comprehensive correlation strength.

[0065] The association strength threshold may be set according to actual needs, for example, to 0.6, but is not limited thereto.

[0066] Through the above technical solution, the present application can objectively and quantitatively determine the associated plot set of each plot to be evaluated. Specifically, by comprehensively considering multi-dimensional correlation characteristics such as spatial proximity, mutual influence, and traffic correlation, and calculating the comprehensive correlation strength between plots, the problem of lack of quantitative basis and multi-dimensional consideration in the determination of associated plots in traditional methods is solved. Furthermore, by setting a clear correlation strength threshold and screening, it is ensured that the determined associated plot set is based on objective standards rather than subjective judgment, thereby avoiding the effectiveness of subsequent priority score corrections due to inaccurate or subjective determination of the associated plot set. This makes the redevelopment priority evaluation results more scientific and reasonable, can more accurately reflect the interaction relationship between plots, and provide a more reliable basis for urban low-efficiency land redevelopment decisions.

[0067] In some embodiments, step A4 comprises: A401. Initialize the plot development sequence and select a plot that has not been selected as the first development plot to be evaluated and add it to the plot development sequence as the first development plot; and initialize the current priority score of each plot to be evaluated to the corresponding initial priority score; A402. Initialize the list of plots to be developed, and add the other plots to be evaluated other than the first development plot to the list of plots to be developed; A403. Loop the following operations until the list of plots to be developed is empty, thereby generating a complete plot development sequence: B1. Select the plot with the highest current priority score from the list of plots to be developed as the selected plot; B2. Add the selected plot to the current plot development sequence and remove it from the list of plots to be developed; B3. For each associated land parcel in the associated land parcel set of the selected land parcel, calculating a priority correction value for the associated land parcel based on the intended use of the selected land parcel, the associated characteristics, and the pre-set association effect quantification rules, to correct the current priority score of the associated land parcel; A404. Repeat steps A401 to A403 until all plots to be evaluated have been selected as the first development plots, so as to generate multiple plot development sequences.

[0068] The "Pending Development Plot List" refers to the collection of pending plots that have not yet been selected for inclusion in the plot development sequence. The "Current Priority Score" refers to the real-time priority assessment of the pending plots during the iterative selection process. Selected plots refer to the plots selected from the pending development plot list based on specific criteria (e.g., the highest current priority score) and added to the plot development sequence during each iteration.

[0069] Among them, the correlation effect quantification rule refers to a pre-set mathematical model or logical rule used to calculate the degree of mutual influence between the evaluated plots, which can be implemented using a scoring matrix based on expert experience, a regression model or a machine learning algorithm.

[0070] The solution of this application aims to systematically generate multiple development sequences that take into account the correlation effects between plots, so as to overcome the limitations of isolated evaluation plots in the existing technology. The solution achieves this goal through a series of carefully designed iterative steps. First, in step A401, the system initializes a plot development sequence and strategically selects a plot to be evaluated that has not been the first development plot as the starting point of the current sequence. This operation ensures that each plot to be evaluated can be used as a starting point to fully explore different development time sequences, thereby avoiding the limitations that may be brought about by a single fixed starting point. The initial priority scores of all plots to be evaluated are used as the current priority evaluation values. This provides a clear initial state for subsequent iterative dynamic corrections.

[0071] Next, in step A402, a list of plots to be developed is initialized, and all plots to be evaluated except the first plot to be developed are included in the list. This provides a clear operating range for subsequent iterative selection.

[0072] Subsequently, in step A403, the system enters a core loop, executing a series of operations until the list of pending development plots is empty, thereby generating a complete plot development sequence. In this loop, first, in step B1, the system selects the currently selected plot from the list of pending development plots with the highest priority score. This reflects the strategy of prioritizing the currently most valuable plots at each step, guiding the sequence toward the current optimal direction. Next, in step B2, the selected plot is added to the current plot development sequence and removed from the list of pending development plots, ensuring that each pending development plot is selected only once, and gradually constructing a complete development sequence.

[0073] The key lies in step B3. After a parcel is selected for development and included in the development sequence, its impact on surrounding associated parcels is immediately assessed. Specifically, for each parcel in the selected parcel's associated set of parcels, a priority correction value is calculated based on the selected parcel's intended use and the associated characteristics identified in the previous step (such as spatial proximity, mutual influence, and transportation connectivity), combined with pre-set rules for quantifying the associated effects. These correction values ​​are then used to dynamically adjust the current priority scores of the associated parcels. This mechanism enables subsequent parcel selection to fully consider the dynamic interactions between parcels, such as synergistic or antagonistic effects, thereby generating a development sequence that is more realistic and more effective overall.

[0074] Finally, in step A404, the system repeats steps A401 to A403 until all parcels to be evaluated have been selected as the first development parcels. This repetitive mechanism ensures that the system can fully explore all possible development sequences and combinations, generating a diverse parcel development sequence.

[0075] By integrating the above steps, this approach leverages the previously acquired initial priority scores as a foundational assessment, and dynamically adjusts the priorities of parcels during the iterative selection process, combining the identified inter-parcel correlation characteristics and the defined set of associated parcels. This dynamic adjustment mechanism eliminates isolated parcel development decisions and instead fully considers their radiating impact on surrounding parcels, generating a development sequence that reflects the complex interactions between parcels. This not only provides a rich and diverse pool of candidate options for subsequent overall benefit evaluation, but also ensures that the final development strategy more comprehensively reflects the overall value of the urban area, effectively avoiding the suboptimal decisions that result from traditional approaches that ignore inter-parcel correlations.

[0076] Preferably, step B3 may include: B301. Based on the expected use of the selected land parcel, obtain an association effect model corresponding to the expected use from the preset association effect quantification rules; B302. Using the spatial proximity, mutual influence, and traffic correlation between the associated land parcel and the selected land parcel as input, and utilizing the correlation effect model, calculate the initial priority correction contribution of the associated land parcel; B303. Determine an attenuation coefficient of the initial priority correction contribution based on the distance between the selected plot and the associated plot; B304. Calculate the final priority correction value of the associated land parcel based on the initial priority correction contribution and the attenuation coefficient; B305. Add the final priority correction value to the current priority score of the associated land parcel to obtain a corrected current priority score.

[0077] Among them, the preset association effect quantification rules can be stored in a database or lookup table, which contains pre-trained or defined association effect models for different intended uses (for example, commercial, residential, industrial, public services, etc.). These models can be weighted functions based on expert experience to set a set of weights, or they can be functions trained using historical data through machine learning methods (such as regression analysis, neural networks). For example, the association effect model can be a weighted summation formula: initial priority correction contribution = w1*spatial proximity + w2*mutual influence + w3*traffic correlation, where w1, w2, and w3 are weights. These weights reflect the relative importance of different association features under specific intended uses. In addition, the association effect model can also be a more complex nonlinear function or decision tree model to capture the complex interactions of association features between plots.

[0078] The distance between the selected plot and the associated plots can refer to the Euclidean distance between the plot centers or the actual travel distance. The attenuation coefficient can be a distance-based function, such as an exponential attenuation function (e.g., exp(-K*l), where K is the attenuation constant and l is the distance between the selected plot and the associated plot) or a linear attenuation function (e.g., max(0,1-l / l_max), where l_max is a preset distance threshold). The specific form and parameters of the attenuation function can be determined based on actual urban planning experience or through data analysis.

[0079] The final priority correction value can be obtained by multiplying the initial priority correction contribution by the attenuation coefficient: Final Priority Correction Value = Initial Priority Correction Contribution * Attenuation Coefficient. This operation combines the multi-dimensional comprehensive impact (initial correction contribution) with the spatial attenuation effect to generate a final correction value that considers both functional connections and spatial distances. This correction value more accurately reflects the actual impact of the development of the selected plot on the priority of the associated plots.

[0080] The overlay operation can be a simple addition: Corrected Current Priority Score = Current Priority Score + Final Priority Correction. If the final priority correction is negative (indicating a negative impact), the overlay operation will reduce the current priority score accordingly. This operation is a key step in dynamically adjusting priorities. By applying the precisely calculated correction value to the current score of the associated parcel, real-time, dynamic updates of parcel priorities are achieved.

[0081] Through the above technical solution, this application can accurately and comprehensively quantify the correlation effect between plots, integrate multiple correlation characteristics such as spatial proximity, mutual influence, and traffic correlation, and consider the characteristics of their influence attenuation with distance, thereby generating a priority correction value that accurately reflects the actual impact. This allows the corrected priority score to fully reflect the complex interactions between plots, effectively solving the problem of the correction value calculation being not precise and comprehensive in the existing solution, thereby improving the optimization level of the final plot development sequence. In some embodiments, step A5 comprises: A501. Based on the final priority score (i.e., the final current priority score obtained after the iteration in step A403 ), the expected use, and the development ranking of each parcel to be evaluated in the parcel development sequence, obtain the expected benefits of each parcel to be evaluated in the parcel development sequence; A502. Identify the external impacts of each parcel to be evaluated in the parcel development sequence on the surrounding area; the external impacts include economic spillover effects, social spillover effects, and environmental spillover effects; A503. Quantify the overall externality contribution of the development sequence of the land parcel based on the externality impact; A504. Calculate the overall benefit of the land parcel development sequence based on the expected benefits of each parcel to be evaluated in the land parcel development sequence and the overall externality contribution; A505. Determine the priority of each plot to be evaluated based on the plot development sequence corresponding to the maximum overall benefit.

[0082] In step A501, a pre-defined benefit assessment model can be used to determine expected benefits. This model takes as input the parcel's final priority score (reflecting the parcel's individual value after accounting for interconnected effects), its planned future use, and its development ranking, and outputs the direct economic, social, or functional value that the parcel is likely to generate within a pre-defined planning period after development. For example, for a parcel planned for commercial use, its expected benefits can be calculated using a commercial value assessment model based on its area, floor area ratio, surrounding commercial activity, and its final priority score. For a parcel planned for residential use, its expected benefits can be calculated using a residential value assessment model based on its residential density, accessibility to public services, and its final priority score. By using the final priority score, which was iteratively revised in step A403, the assessment of individual parcel benefits is based on its interaction with interconnected parcels, rather than an isolated initial assessment, making the calculation of expected benefits more realistic.

[0083] In step A502, externality impacts include economic spillovers, social spillovers, and environmental spillovers. These can be assessed using a variety of methods, including expert assessment, geographic information system (GIS) analysis, and big data mining. For example, economic spillovers can be identified as the impact of land development on surrounding land prices, commercial activities, and employment opportunities; social spillovers can be identified as the impact of land development on accessibility to surrounding public services (such as schools and hospitals), community safety, and cultural activities; and environmental spillovers can be identified as the impact of land development on surrounding air quality, water quality, greenery coverage, and noise levels. These impacts can be positive (spillovers) or negative (negative externalities).

[0084] Step A503 can be implemented using multi-criteria decision analysis (MCDA), value engineering, or cost-benefit analysis. For example, economic spillover effects can be quantified as at least one of the following: increased value of surrounding land parcels, increased tax revenue, or the number of new jobs; social spillover effects can be quantified as at least one of the following: the increase in the population served by public service facilities or the improvement in the community satisfaction index; and environmental spillover effects can be quantified as at least one of the following: the air quality improvement index, the increase in green space area, and the reduction in pollutant emissions. These quantitative indicators can be weighted and summed according to preset weights to obtain a comprehensive externality contribution value. This quantification makes previously difficult-to-measure indirect benefits or costs calculable and comparable, thereby more accurately reflecting the comprehensive impact of a development sequence on the entire region and providing an important supplementary dimension for subsequent overall benefit calculations.

[0085] Step A504 can be implemented using a weighted summation model, where the sum of the expected benefits of all plots is weighted and superimposed with the overall externality contribution. This combines the expected benefits of the plot itself (internal benefits) with the overall externality contribution (external benefits) generated by its development. This approach yields a "total benefit" that not only captures the value of the plot itself but also more comprehensively reflects its positive or negative impact on surrounding areas and even the entire urban system, making comparisons of different development sequences more scientific and rational.

[0086] In step A505, a comparative ranking method can be used. Specifically, among all the generated plot development sequences, the sequence with the highest overall benefit is selected as the optimal development sequence. The order of the plots in this optimal development sequence is then determined as the final redevelopment priority.

[0087] Through the above technical solution, this application can accurately and comprehensively evaluate the overall benefits of the development sequence of each plot of land, solving the problem of ignoring the interaction and external effects between projects in traditional evaluation methods, thereby being able to more scientifically and accurately determine the redevelopment priority of the plot of land to maximize regional value.

[0088] refer to Figure 2 This application provides a low-efficiency land redevelopment priority evaluation system for evaluating the redevelopment priority of low-efficiency land in cities. The system includes: Initial Assessment Module 1 is used to obtain attribute data of each parcel to be assessed, in order to assess the initial priority score of each parcel to be assessed; the attribute data includes basic attribute data, economic attribute data, and social attribute data (the specific process can be referred to in step A1 above); Correlation feature identification module 2 is used to identify correlation features between the plots to be evaluated based on their geographic location, intended use, and transportation network connection information; the correlation features include spatial proximity, mutual influence, and transportation correlation (the specific process can be referred to in step A2 above); The associated plot determination module 3 is configured to determine, for each plot to be evaluated, a set of associated plots of the plot to be evaluated based on the associated features (the specific process can be referred to in step A3 above); Sequence generation module 4 is configured to iteratively select plots to be evaluated and modify the priority scores of the associated plots based on the initial priority scores, the associated plot set, the associated features, and a preset association effect quantification rule, so as to generate a plurality of plot development sequences sorted by priority scores (for details, refer to step A4 above); The priority determination module 5 is used to calculate the overall benefits of the development sequences of each plot, and determine the priority of each plot to be evaluated based on the plot development sequence corresponding to the maximum overall benefit (the specific process can be referred to step A5 above).

[0089] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for evaluating the redevelopment priority of low-efficiency land, which is used to evaluate the redevelopment priority of low-efficiency land in cities, characterized by: The steps of the method include: A1. Obtain attribute data for each plot to be evaluated to assess the initial priority score of each plot to be evaluated; the attribute data includes basic attribute data, economic attribute data, and social attribute data; A2. Identify the correlation characteristics between the parcels to be evaluated based on their geographic location, intended use, and transportation network connectivity. These correlation characteristics include spatial proximity, mutual influence, and transportation connectivity. A3. For each plot to be evaluated, determine a set of associated plots of the plot to be evaluated based on the associated features; A4. Based on the initial priority score, the associated parcel set, the associated characteristics, and the pre-set association effect quantification rules, iteratively select parcels to be evaluated and modify the priority scores of the associated parcels to generate multiple parcel development sequences sorted by priority score; A5. Calculate the overall benefits of the development sequences of each plot, and determine the priority of each plot to be evaluated based on the plot development sequence corresponding to the maximum overall benefit.

2. The method for evaluating the priority of redevelopment of low-efficiency land according to claim 1, characterized in that: The basic attribute data include area, current use, ownership and volume ratio; The economic attribute data include benchmark land prices, surrounding real estate market prices and potential development costs; The social attribute data include surrounding population density, public transportation station coverage, educational facility accessibility score, and medical facility accessibility score.

3. The method for evaluating the priority of redevelopment of low-efficiency land according to claim 2, characterized in that: Step A1 includes: A101. Obtain attribute data for each plot to be evaluated; the attribute data includes basic attribute data, economic attribute data, and social attribute data; A102. Convert the current usage and ownership of the basic attribute data into corresponding quantitative scores according to the preset classification and scoring criteria; A103. Convert the area and floor area ratio in the basic attribute data, the benchmark land price, surrounding real estate market price, and potential development cost in the economic attribute data, and the surrounding population density, public transportation station coverage, educational facility accessibility score, and medical facility accessibility score in the social attribute data into corresponding quantitative scores according to pre-set quantitative rules. A104. Calculate the initial priority score of each plot to be evaluated based on the quantitative scores and the preset weights.

4. The method for evaluating the priority of redevelopment of low-efficiency land according to claim 1, characterized in that: Step A2 includes: A201. Obtaining geographic location information of each plot to be evaluated, for calculating the distance between the center points of each plot to be evaluated, to determine the spatial proximity between each plot to be evaluated; the spatial proximity is negatively correlated with the distance; A202. Determine the mutual influence between each pair of plots to be evaluated based on the intended use of each plot and the pre-set functional relationship rules; A203. Obtain the traffic network connection information of each plot to be evaluated, which is used to evaluate the degree of traffic connection between each plot to be evaluated, so as to determine the traffic correlation between each plot to be evaluated; the traffic correlation is positively correlated with the degree of traffic connection; the traffic network connection information of the plot to be evaluated includes the connection information between the plot to be evaluated and the traffic network.

5. The method for evaluating the priority of redevelopment of low-efficiency land according to claim 4, characterized in that: Step A202 includes: According to the expected use of each plot of land to be evaluated and the preset functional relationship rules, obtaining initial functional interaction scores between each plot of land to be evaluated; Obtaining correction factors that affect the functional interaction score; the correction factors include a spatial correction factor determined based on the geographic location information of each plot to be evaluated, a traffic correction factor determined based on the traffic network connection information of each plot to be evaluated, and an environmental correction factor determined based on the attribute data of each plot to be evaluated; The final mutual influence degree between each pair of plots to be evaluated is calculated based on the initial functional interaction score and the correction factor.

6. A method for evaluating the priority of redevelopment of low-efficiency land according to claim 4, characterized in that: Step A203 includes: Obtaining connectivity information between each parcel to be evaluated and different types of transportation networks; the connectivity information includes accessibility, carrying capacity, and traffic efficiency; Determine the degree to which the intended use of each parcel of land to be evaluated depends on different types of transportation networks; Based on the connection information and the dependency level, evaluating the degree of transportation connectivity between each of the land parcels to be evaluated that matches the intended use; According to the traffic connection degree, the traffic correlation between each pair of the land parcels to be evaluated is determined; the traffic correlation is positively correlated with the traffic connection degree.

7. The method for evaluating the priority of redevelopment of low-efficiency land according to claim 1, characterized in that: Step A3 includes: A301. For each parcel to be evaluated, calculate the comprehensive correlation strength between the parcel to be evaluated and the remaining parcels to be evaluated based on the spatial proximity, mutual influence, and traffic correlation between the parcel to be evaluated and the remaining parcels to be evaluated; A302. Filter out the remaining plots to be evaluated whose comprehensive correlation strength is higher than a preset correlation strength threshold to form a set of associated plots of the plot to be evaluated.

8. The method for evaluating the priority of redevelopment of low-efficiency land according to claim 1, characterized in that: Step A4 includes: A401. Initialize the plot development sequence and select a plot that has not been selected as the first development plot to be evaluated and add it to the plot development sequence as the first development plot; and initialize the current priority score of each plot to be evaluated to the corresponding initial priority score; A402. Initialize the list of plots to be developed, and add the other plots to be evaluated other than the first development plot to the list of plots to be developed; A403. Loop the following operations until the list of plots to be developed is empty, thereby generating a complete plot development sequence: B1. Select the plot with the highest current priority score from the list of plots to be developed as the selected plot; B2. Add the selected plot to the current plot development sequence and remove it from the list of plots to be developed; B3. For each associated land parcel in the associated land parcel set of the selected land parcel, calculating a priority correction value for the associated land parcel based on the intended use of the selected land parcel, the associated characteristics, and the pre-set association effect quantification rules, to correct the current priority score of the associated land parcel; A404. Repeat steps A401 to A403 until all plots to be evaluated have been selected as the first development plots, so as to generate multiple plot development sequences.

9. The method for evaluating the priority of redevelopment of low-efficiency land according to claim 8, characterized in that: Step B3 includes: B301. Based on the expected use of the selected land parcel, obtain an association effect model corresponding to the expected use from the preset association effect quantification rules; B302. Using the spatial proximity, mutual influence, and traffic correlation between the associated land parcel and the selected land parcel as input, and utilizing the correlation effect model, calculate the initial priority correction contribution of the associated land parcel; B303. Determine an attenuation coefficient of the initial priority correction contribution based on the distance between the selected plot and the associated plot; B304. Calculate the final priority correction value of the associated land parcel based on the initial priority correction contribution and the attenuation coefficient; B305. Add the final priority correction value to the current priority score of the associated land parcel to obtain a corrected current priority score.

10. A low-efficiency land redevelopment priority evaluation system for evaluating the redevelopment priority of low-efficiency urban land, characterized by: The system includes: An initial evaluation module is used to obtain attribute data of each plot to be evaluated, so as to evaluate the initial priority score of each plot to be evaluated; the attribute data includes basic attribute data, economic attribute data and social attribute data; A correlation feature identification module is used to identify correlation features between the plots to be evaluated based on their geographical locations, intended uses, and transportation network connection information; the correlation features include spatial proximity, mutual influence, and transportation correlation; An associated plot determination module is used to determine, for each plot to be evaluated, a set of associated plots of the plot to be evaluated based on the associated features; a sequence generation module for iteratively selecting plots to be evaluated and modifying the priority scores of the associated plots based on the initial priority scores, the associated plot set, the associated features, and a preset association effect quantification rule, so as to generate a plurality of plot development sequences sorted by priority scores; The priority determination module is used to calculate the overall benefits of the development sequences of each plot, and determine the priority of each plot to be evaluated based on the plot development sequence corresponding to the maximum overall benefit.

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