A system and method for predicting the size of an urban land reserve

Abstract

The application discloses a kind of urban land reserve scale prediction system and method, it is related to urban planning and land management technical field, the application is by collecting urban land reserve total, annual increase and consumption area, land structure distribution and planning target structure etc. Data, calculate the dynamic coefficient reflecting the dynamic change of reserve scale and the structure adaptation coefficient for measuring the adaptation degree of structure and historical target. After the dynamic coefficient and the structure adaptation coefficient are standardized, the comprehensive index is calculated by combining weight, according to which the annual adjustment ratio is determined, and then the benchmark forecast reserve is calculated. Combined with the historical actual adjustment ratio, generate the forecast reserve of conservative, benchmark and active three kinds of scenes, determine the final forecast reserve by comparing with the average value of history. The application comprehensively dynamic trend and structure adaptability, supports multi-scenario analysis, and can provide scientific, flexible decision-making basis for urban land reserve planning.

Description

Technical Field

[0001] This invention relates to the field of urban planning and land management technology, specifically to a system and method for predicting the scale of urban land reserves. Background Technology

[0002] The urban land reserve system is an important means for city governments to regulate the land market, safeguard public interests, and promote the rational allocation of land resources. The accurate prediction of land reserve size is crucial for optimizing land supply plans, preventing land financial risks, supporting urban planning implementation, and achieving sustainable urban development. Against the backdrop of accelerating urbanization, how to scientifically and accurately predict land reserve size has become a core issue urgently needing to be addressed in the fields of land management and urban planning.

[0003] Currently, methods for predicting land reserve size largely rely on historical data analysis, planning indicator extrapolation, or empirical judgment, lacking a comprehensive consideration of the dynamic changes and structural characteristics within the land reserve system. This fails to systematically assess the compatibility between the current reserve structure and historical or planned target structures. Consequently, predictions often deviate significantly from reality, failing to adapt to the complexities and uncertainties of land market fluctuations and urban development.

[0004] Furthermore, existing forecasting methods mostly output a single forecast value, lacking a flexible response mechanism for different development scenarios. They cannot support policymakers' scientific judgment in a changing policy environment and market conditions, and the reliability and stability of the forecast results are also insufficient.

[0005] There is a need for a land reserve size prediction method and system that can systematically integrate the dynamic changes and structural characteristics of land reserves, have multi-scenario output capabilities, and can be verified and adjusted in conjunction with historical data, so as to improve the scientificity, accuracy and practicality of the prediction, thereby providing reliable decision support for the sustainable management of urban land resources. Summary of the Invention

[0006] The purpose of this invention is to provide a system and method for predicting the scale of urban land reserves, so as to solve the problems raised in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the scale of urban land reserves, the method comprising the following steps: Step S100: Collect relevant data on urban land reserves; Step S100 includes the following steps: Step S101: Obtain relevant data on urban land reserves, including total land reserves, annual newly added reserve area, annual consumed reserve area, land reserve structure distribution data, and planning target structure data; The total land reserve refers to the current total urban land reserve L0, representing the total amount of land resources available for regulation in the city. It serves as the base for forecasting. The annual newly added reserve area refers to the current annual newly added reserve land area A. 新增 The annual consumption reserve area refers to the reserve land area A used to obtain the current year's supply and consumption. 消耗 These two types of data reflect the activity level and supply-demand relationship of the land market, and their ratio reveals the expansion or contraction trend of the scale. The land reserve structure distribution data refers to obtaining the proportion of the j-th type of land reserve area in the current urban land reserves. This data reveals the current allocation of land resources across different uses (such as residential, commercial, industrial, and public facilities). The planning target structure data is obtained by acquiring the percentage of the area of ​​the j-th type of land reserve at the end of each of the past n consecutive years. t∈[1, 2, 3, ..., n], the It contains the structural preferences and long-term trends of urban development planning and is an important reference for assessing whether the current structure is reasonable; Step S102: Calculate the planned target proportion of the j-th type of land reserve based on the planned target structure data. : This step, through smoothing historical structural data, provides an objective benchmark for subsequent assessment of the rationality of the current structure.

[0008] Step S200: Based on the land reserve-related data, calculate the land reserve dynamic coefficient and the land reserve structure adaptation coefficient, and characterize the state of the land reserve system from the two dimensions of land reserve change and land reserve structure, respectively. Step S200 includes the following steps: Step S201: Based on the annual newly added reserve area and the annual consumed reserve area, calculate the land reserve dynamic coefficient a1: a1=A 新增 / A 消耗 This is used to reflect the recent expansion or contraction trend of land reserve scale. When a1>1, it indicates that the increase is greater than the consumption, and the reserve scale is in an expansion channel; when a1<1, it means that the consumption is greater than the increase, and the reserve scale is contracting. Step S202: Based on the planned target proportion and reserve area proportion of the j-th type of land reserve, calculate the land reserve structure adaptation coefficient a2: In the formula, M represents the total number of land use types. This coefficient is used to quantitatively measure the degree of fit between the current land reserve structure and the historical average structure. The higher the value, the stronger the fit.

[0009] Step S300: Standardize the coefficients to ensure that data of different natures can participate in the comprehensive evaluation fairly. Combine the comprehensive index to calculate the annual adjustment ratio and obtain the predicted reserve amount, thus realizing the transformation from multi-dimensional indicators to a single decision parameter. Step S300 includes the following steps: Step S301: Normalize the dynamic coefficient a1 and the structural adaptation coefficient a2 of the land reserve respectively, map them to the [0,1] interval, eliminate the influence of different indicator dimensions and magnitudes, and obtain the standardized indicator values ​​A1 and A2; Step S302: Calculate the comprehensive index G based on the standardized index values ​​A1 and A2: G = W a ×A1+W b ×A2, in the formula, W a and W b These are the weights of the dynamic coefficient and the structural adaptation coefficient, respectively, satisfying W. a +W b =1, the index G integrates information from two dimensions: scale dynamics and structural adaptation. The closer its value is to 1, the healthier and more in line with expectations the overall land reserve status is. Step S303: Calculate the baseline projected reserve L1 based on the current total land reserve L0: In the formula, Q is the annual adjustment ratio, Q=Q0×(2G-1), and Q0 is the baseline adjustment ratio, which is set by professionals. When G=0.5 (intermediate state), the adjustment ratio is 0, and it is recommended to maintain the status quo; when G>0.5, the adjustment ratio is positive, and it is recommended to increase reserves; when G<0.5, the adjustment ratio is negative, and it is recommended to reduce reserves. The baseline adjustment ratio Q0 reflects the maximum adjustment range that the system can withstand or recommend under the ideal state (G=1).

[0010] Step S400: Generate three scenarios of predicted reserves based on the predicted reserves: conservative, baseline, and positive. Retrieve the actual annual adjustment ratios for the past n years in the city and compare them with the historical average to enhance the robustness of the prediction results. Determine the final predicted reserves to provide a reliable reference for policymakers to respond to external market fluctuations and policy changes.

[0011] Step S400 includes the following steps: Step S401: Based on the preset confidence offset Δ, generate a conservative predicted reserve L1. low , Actively predict reserve volume L1 high , ; Step S402: Retrieve the actual annual adjustment ratio data h1, h2, h3, h4, h5, ..., h for the city over the past n years. nCalculate the historical average value μ h The annual adjustment ratio R is compared with the historical average value μ. h The comparison is used to determine the final predicted reserve level, specifically: when R < μ h When -δ, the conservatively predicted reserve amount L1 is used. low As the final predicted reserve, when μ h -δ≤R≤μ h When +δ, the baseline predicted reserve amount L1 is used as the final predicted reserve amount. When R > μ h When +δ, the actively predicted reserve quantity L1 is used. high As the final predicted reserve, δ is a preset threshold parameter. By introducing an offset, a prediction interval is constructed to cover the uncertainty of future development, providing decision-makers with a space for different strategy choices.

[0012] A system for predicting the scale of urban land reserves, the system comprising a data collection and processing module, a coefficient calculation module, a standardization and prediction calculation module, and a scenario generation and final determination module; The data collection and processing module collects data related to urban land reserves, cleans, integrates and performs preliminary calculations to ensure the data quality and consistency of subsequent analysis. Specifically, it obtains the total land reserve, annual newly added and consumed area, current structural distribution and historical planning structural targets, providing standardized data input for subsequent dynamic and structural analysis. The coefficient calculation module constructs a land reserve dynamic coefficient reflecting the dynamic changes of land reserves and a land reserve structure adaptation coefficient reflecting the structure of land reserves based on the collected data. The aim is to quantify the trend health and structural rationality of the reserve system and provide a scientific basis for prediction. The coefficient calculation module includes a dynamic coefficient calculation unit and a structural adaptation coefficient calculation unit. The dynamic coefficient calculation unit generates a land reserve dynamic coefficient by calculating the ratio of the annual newly added reserve area to the annual consumed reserve area. This indicator directly reflects the intensity of the recent expansion or contraction of land reserves. A ratio greater than 1 indicates a net growth trend in reserve scale, while a ratio less than 1 indicates net consumption, providing a basis for judging the direction of subsequent adjustments. The structure adaptation coefficient calculation unit calculates the land reserve structure adaptation coefficient by comparing the current land reserve structure with the historical average structure. The coefficient ranges from 0 to 1. The closer it is to 1, the higher the matching degree between the current reserve structure and the long-term planning goal, reflecting the strategic compliance of land resource allocation.

[0013] The standardization and prediction calculation module constructs a comprehensive index by standardizing and weighting the coefficients, and calculates the predicted reserve scale under the baseline scenario based on the comprehensive index and the current total reserve. The standardization and prediction calculation module includes an indicator standardization unit, a comprehensive index calculation unit, and a benchmark prediction calculation unit. The standardization unit of the indicators normalizes the dynamic coefficient of land reserves and the adaptation coefficient of land reserve structure, mapping them to the interval [0,1] to obtain standardized indicators A1 and A2. This step aims to eliminate the influence of dimensions, so that indicators of different natures can be comprehensively evaluated on the same scale. The comprehensive index calculation unit calculates the land reserve comprehensive index based on the standardized indicators A1 and A2 and their preset weights. This index comprehensively reflects the overall performance of land reserves in terms of both "dynamic trend" and "structural adaptation". The benchmark forecast calculation unit calculates the benchmark forecast reserve under the benchmark scenario based on the current total land reserves and the annual adjustment ratio derived from the comprehensive index.

[0014] The scenario generation and final determination module constructs multi-scenario prediction results, verifies them with historical data, and outputs land reserve prediction values ​​with practical reference significance, thereby improving the robustness and applicability of the prediction results.

[0015] The scenario generation and final determination module includes a scenario prediction quantity generation unit and a final prediction quantity determination unit; The scenario forecast generation unit introduces a confidence bias based on the baseline forecast value to generate conservative forecast reserve amounts and aggressive forecast reserve amounts, forming three scenarios: "low-medium-high" to cover the possible reserve scale under different policy intensities or market fluctuations. The final prediction unit retrieves historical data on the actual annual adjustment ratios over the past n years, calculates their average value, compares the current adjustment ratio with the historical average, and makes a decision based on a preset threshold: when it is lower than the historical level, a conservative prediction reserve is adopted; if it is within the historical fluctuation range, a baseline scenario is adopted; when it is higher than the historical level, an aggressive prediction reserve is adopted. This mechanism makes the prediction results both historically consistent and realistically adaptable.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. Improve the scientific nature and accuracy of prediction results: Through the dual calculation of dynamic coefficient and structural adaptation coefficient, the system can not only reflect the trend of land reserve scale changes, but also measure its structural rationality, making the prediction more comprehensive and in line with reality.

[0017] 2. Support for multi-scenario decision-making and flexible response: Based on the baseline forecast results, three scenario reserve quantities—conservative, benchmark, and proactive—are generated, and historical data is used for comparison and selection, which enhances the adaptability and decision-making flexibility of land reserve management in different market and policy environments.

[0018] 3. Promote the structural optimization and refinement of land reserve management: Introduce a structural adaptation coefficient and weighting mechanism to promote the transformation of land reserves from simple scale control to "scale-structure" synergistic optimization, so as to achieve the rational allocation of land resources. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of applying the present invention to a system for predicting the scale of urban land reserves; Figure 2 This is a schematic diagram of the structure of a method for predicting the scale of urban land reserves, which is based on the present invention. Detailed Implementation

[0020] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example: Figures 1-2 As shown, this invention provides a technical solution: a system and method for predicting the scale of urban land reserves. Assuming City A adopts a method for predicting the scale of urban land reserves, the method includes the following steps: Step S100: Collect relevant data on urban land reserves; Step S100 includes the following steps: Step S101: Obtain relevant data on urban land reserves, including total land reserves, annual newly added reserve area, annual consumed reserve area, land reserve structure distribution data, and planning target structure data; The total land reserve refers to the current total urban land reserve L0, representing the total amount of land resources available for regulation in the city. It serves as the base for forecasting. The annual newly added reserve area refers to the current annual newly added reserve land area A. 新增 The annual consumption reserve area refers to the reserve land area A used to obtain the current year's supply and consumption. 消耗 These two types of data reflect the activity level and supply-demand relationship of the land market, and their ratio reveals the expansion or contraction trend of the scale. The land reserve structure distribution data refers to obtaining the proportion of the j-th type of land reserve area in the current urban land reserves. This data reveals the current allocation of land resources across different uses (such as residential, commercial, industrial, and public facilities). The planning target structure data is obtained by acquiring the percentage of the area of ​​the j-th type of land reserve at the end of each of the past n consecutive years. t∈[1, 2, 3, ..., n], the It contains the structural preferences and long-term trends of urban development planning and is an important reference for assessing whether the current structure is reasonable; Step S102: Calculate the planned target proportion of the j-th type of land reserve based on the planned target structure data. : This step, through smoothing historical structural data, provides an objective benchmark for subsequent assessment of the rationality of the current structure.

[0022] Example 1: Set the current land reserve data for City A: Current total land reserve L0 = 100 square kilometers, annual increase in reserve area A 新增 =12 square kilometers, annual consumption of reserve area A 消耗 =10 square kilometers; the land reserve structure is divided by land use type, assuming M=3, with residential land reserves accounting for B1. 储备 =0.4, commercial land reserves account for B2 储备 =0.3, industrial land reserves account for B3 储备 =0.3; set n=5, set the average proportion of various land uses over the past 5 consecutive years for the planning target structure data: b1 1 =0.38、b1 2 =0.39、b1 3 =0.41、b1 4 =0.42、b1 5 =0.40、b2 1 =0.32、b2 2 =0.31、b2 3 =0.29、b2 4 =0.28、b2 5 =0.30、b3 1 =0.30、b3 2 =0.30、b3 3 =0.30、b3 4 =0.30、b3 5 =0.30.

[0023] Calculate the percentage of the planning target: B1 目标 = (0.38+0.39+0.41+0.42+0.40) / 5=0.40, B2 目标 = (0.32+0.31+0.29+0.28+0.30) / 5=0.30, B3 目标 = (0.30+0.30+0.30+0.30+0.30) / 5=0.30.

[0024] Step S200: Based on the land reserve-related data, calculate the land reserve dynamic coefficient and the land reserve structure adaptation coefficient, and characterize the state of the land reserve system from the two dimensions of land reserve change and land reserve structure, respectively. Step S200 includes the following steps: Step S201: Based on the annual newly added reserve area and the annual consumed reserve area, calculate the land reserve dynamic coefficient a1: a1=A 新增 / A 消耗 This is used to reflect the recent expansion or contraction trend of land reserve scale. When a1>1, it indicates that the increase is greater than the consumption, and the reserve scale is in an expansion channel; when a1<1, it means that the consumption is greater than the increase, and the reserve scale is contracting. Step S202: Based on the planned target proportion and reserve area proportion of the j-th type of land reserve, calculate the land reserve structure adaptation coefficient a2: In the formula, M represents the total number of land use types. This coefficient is used to quantitatively measure the degree of fit between the current land reserve structure and the historical average structure. The higher the value, the stronger the fit.

[0025] Example 2: The calculation results are as follows: land reserve dynamic coefficient a1=12 / 10=1.2, land reserve structure adaptation coefficient a2=1-0.5×0=1.

[0026] Step S300: Standardize the coefficients to ensure that data of different natures can participate in the comprehensive evaluation fairly. Combine the comprehensive index to calculate the annual adjustment ratio and obtain the predicted reserve amount, thus realizing the transformation from multi-dimensional indicators to a single decision parameter. Step S300 includes the following steps: Step S301: Normalize the dynamic coefficient a1 and the structural adaptation coefficient a2 of the land reserve respectively, map them to the [0,1] interval, eliminate the influence of different indicator dimensions and magnitudes, and obtain the standardized indicator values ​​A1 and A2; Step S302: Calculate the comprehensive index G based on the standardized index values ​​A1 and A2: G = W a ×A1+W b ×A2, in the formula, W a and W b These are the weights of the dynamic coefficient and the structural adaptation coefficient, respectively, satisfying W. a +W b =1, the index G integrates information from two dimensions: scale dynamics and structural adaptation. The closer its value is to 1, the healthier and more in line with expectations the overall land reserve status is. Step S303: Calculate the baseline projected reserve L1 based on the current total land reserve L0: In the formula, Q is the annual adjustment ratio, Q=Q0×(2G-1), and Q0 is the baseline adjustment ratio, which is set by professionals. When G=0.5 (intermediate state), the adjustment ratio is 0, and it is recommended to maintain the status quo; when G>0.5, the adjustment ratio is positive, and it is recommended to increase reserves; when G<0.5, the adjustment ratio is negative, and it is recommended to reduce reserves. The baseline adjustment ratio Q0 reflects the maximum adjustment range that the system can withstand or recommend under the ideal state (G=1).

[0027] Example 3: Let the range of a1 be [0.5, 2], normalized to [0, 1], A1 = (a1 - 0.5) / (2 - 0.5) = 0.467, a2 itself is already in [0, 1], A2 = 1; let the weight W a =0.6,W b =0.4, calculate the comprehensive index G=0.6×0.467+0.4×1=0.2802+0.4=0.6802; set the benchmark adjustment ratio Q0=5, Q=Q0×(2G-1)=1.802, and calculate L1=101.802 square kilometers.

[0028] Step S400: Generate three scenarios of predicted reserves based on the predicted reserves: conservative, baseline, and positive. Retrieve the actual annual adjustment ratios for the past n years in the city and compare them with the historical average to enhance the robustness of the prediction results. Determine the final predicted reserves to provide a reliable reference for policymakers to respond to external market fluctuations and policy changes.

[0029] Step S400 includes the following steps: Step S401: Based on the preset confidence offset Δ, generate a conservative predicted reserve L1. low , Actively predict reserve volume L1 high , ; Step S402: Retrieve the actual annual adjustment ratio data h1, h2, h3, h4, h5, ..., h for the city over the past n years. n Calculate the historical average value μ h The annual adjustment ratio R is compared with the historical average value μ. h The comparison is used to determine the final predicted reserve level, specifically: when R < μ h When -δ, the conservatively predicted reserve amount L1 is used. low As the final predicted reserve, when μ h -δ≤R≤μ h When +δ, the baseline predicted reserve amount L1 is used as the final predicted reserve amount. When R > μ h When +δ, the actively predicted reserve quantity L1 is used. highAs the final predicted reserve, δ is a preset threshold parameter. By introducing an offset, a prediction interval is constructed to cover the uncertainty of future development, providing decision-makers with a space for different strategy choices.

[0030] Example 4: Set the actual annual adjustment ratios for the past 5 years as: h1=1.5, h2=1.8, h3=2.0, h4=1.7, h5=1.6, and calculate the historical average value μ. h = (1.5 + 1.8 + 2.0 + 1.7 + 1.6) / 5 = 1.72, let the threshold δ = 0.3, μ h -δ=1.42, μ h +δ=2.02. Since 1.42≤1.802≤2.02, the baseline predicted reserve L1=101.802 square kilometers is adopted as the final predicted reserve.

[0031] A system for predicting the scale of urban land reserves, the system comprising a data collection and processing module, a coefficient calculation module, a standardization and prediction calculation module, and a scenario generation and final determination module; The data collection and processing module collects data related to urban land reserves, cleans, integrates and performs preliminary calculations to ensure the data quality and consistency of subsequent analysis. Specifically, it obtains the total land reserve, annual newly added and consumed area, current structural distribution and historical planning structural targets, providing standardized data input for subsequent dynamic and structural analysis. The coefficient calculation module constructs a land reserve dynamic coefficient reflecting the dynamic changes of land reserves and a land reserve structure adaptation coefficient reflecting the structure of land reserves based on the collected data. The aim is to quantify the trend health and structural rationality of the reserve system and provide a scientific basis for prediction. The coefficient calculation module includes a dynamic coefficient calculation unit and a structural adaptation coefficient calculation unit. The dynamic coefficient calculation unit generates a land reserve dynamic coefficient by calculating the ratio of the annual newly added reserve area to the annual consumed reserve area. This indicator directly reflects the intensity of the recent expansion or contraction of land reserves. A ratio greater than 1 indicates a net growth trend in reserve scale, while a ratio less than 1 indicates net consumption, providing a basis for judging the direction of subsequent adjustments. The structure adaptation coefficient calculation unit calculates the land reserve structure adaptation coefficient by comparing the current land reserve structure with the historical average structure. The coefficient ranges from 0 to 1. The closer it is to 1, the higher the matching degree between the current reserve structure and the long-term planning goal, reflecting the strategic compliance of land resource allocation.

[0032] The standardization and prediction calculation module constructs a comprehensive index by standardizing and weighting the coefficients, and calculates the predicted reserve scale under the baseline scenario based on the comprehensive index and the current total reserve. The standardization and prediction calculation module includes an indicator standardization unit, a comprehensive index calculation unit, and a benchmark prediction calculation unit. The standardization unit of the indicators normalizes the dynamic coefficient of land reserves and the adaptation coefficient of land reserve structure, mapping them to the interval [0,1] to obtain standardized indicators A1 and A2. This step aims to eliminate the influence of dimensions, so that indicators of different natures can be comprehensively evaluated on the same scale. The comprehensive index calculation unit calculates the land reserve comprehensive index based on the standardized indicators A1 and A2 and their preset weights. This index comprehensively reflects the overall performance of land reserves in terms of both "dynamic trend" and "structural adaptation". The benchmark forecast calculation unit calculates the benchmark forecast reserve under the benchmark scenario based on the current total land reserves and the annual adjustment ratio derived from the comprehensive index.

[0033] The scenario generation and final determination module constructs multi-scenario prediction results, verifies them with historical data, and outputs land reserve prediction values ​​with practical reference significance, thereby improving the robustness and applicability of the prediction results.

[0034] The scenario generation and final determination module includes a scenario prediction quantity generation unit and a final prediction quantity determination unit; The scenario forecast generation unit introduces a confidence bias based on the baseline forecast value to generate conservative forecast reserve amounts and aggressive forecast reserve amounts, forming three scenarios: "low-medium-high" to cover the possible reserve scale under different policy intensities or market fluctuations. The final prediction unit retrieves historical data on the actual annual adjustment ratios over the past n years, calculates their average value, compares the current adjustment ratio with the historical average, and makes a decision based on a preset threshold: when it is lower than the historical level, a conservative prediction reserve is adopted; if it is within the historical fluctuation range, a baseline scenario is adopted; when it is higher than the historical level, an aggressive prediction reserve is adopted. This mechanism makes the prediction results both historically consistent and realistically adaptable.

[0035] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for predicting the scale of urban land reserves, characterized in that: The method includes the following steps: Step S100: Collect relevant data on urban land reserves; Step S200: Based on the land reserve-related data, calculate the land reserve dynamic coefficient and the land reserve structure adaptation coefficient; Step S300: Standardize the coefficients and calculate the annual adjustment ratio by combining them with the comprehensive index to obtain the predicted reserve amount; Step S400: Generate three scenarios of predicted reserves based on the predicted reserves: conservative, baseline, and aggressive. Retrieve the actual annual adjustment ratios for the past n years in the city and compare them with the historical average to determine the final predicted reserves.

2. The method for predicting the scale of urban land reserves according to claim 1, characterized in that: Step S100 includes the following steps: Step S101: Obtain relevant data on urban land reserves, including total land reserves, annual newly added reserve area, annual consumed reserve area, land reserve structure distribution data, and planning target structure data; The total land reserve refers to the current total urban land reserve L0, and the annual newly added reserve area refers to the current annual newly added reserve land area A. 新增 The annual consumption reserve area refers to the reserve land area A used to obtain the current year's supply and consumption. 消耗 The land reserve structure distribution data refers to obtaining the proportion of the j-th type of land reserve area in the current urban land reserves. The planned target structure data is obtained by acquiring the percentage of the area of ​​the j-th type of land reserve at the end of each of the past n consecutive years. ,t∈[1, 2, 3,…,n]; Step S102: Calculate the planned target proportion of the j-th type of land reserve based on the planned target structure data. : .

3. The method for predicting the scale of urban land reserves according to claim 1, characterized in that: Step S200 includes the following steps: Step S201: Based on the annual newly added reserve area and the annual consumed reserve area, calculate the land reserve dynamic coefficient a1: a1=A 新增 / A 消耗 ; Step S202: Based on the planned target proportion and reserve area proportion of the j-th type of land reserve, calculate the land reserve structure adaptation coefficient a2: In the formula, M represents the total number of land use types.

4. The method for predicting the scale of urban land reserves according to claim 1, characterized in that: Step S300 includes the following steps: Step S301: Normalize the dynamic coefficient a1 and the structural adaptation coefficient a2 of the land reserve respectively, map them to the [0,1] interval, eliminate the influence of different indicator dimensions and magnitudes, and obtain the standardized indicator values ​​A1 and A2; Step S302: Calculate the comprehensive index G based on the standardized index values ​​A1 and A2: G = W a ×A1+W b ×A2, in the formula, W a and W b These are the weights of the dynamic coefficient and the structural adaptation coefficient, respectively, satisfying W. a +W b =1; Step S303: Calculate the baseline projected reserve L1 based on the current total land reserve L0: In the formula, Q is the annual adjustment ratio, Q=Q0×(2G-1), and Q0 is the benchmark adjustment ratio, which is set by professionals.

5. The method for predicting the scale of urban land reserves according to claim 4, characterized in that: Step S400 includes the following steps: Step S401: Based on the preset confidence offset Δ, generate a conservative predicted reserve L1. low , Actively predict reserve volume L1 high , ; Step S402: Retrieve the actual annual adjustment ratio data h1, h2, h3, h4, h5, ..., h for the city over the past n years. n Calculate the historical average value μ h The annual adjustment ratio R is compared with the historical average value μ. h The comparison is used to determine the final predicted reserve level, specifically: when R < μ h When -δ, the conservatively predicted reserve amount L1 is used. low As the final predicted reserve, when μ h -δ≤R≤μ h When +δ, the baseline predicted reserve amount L1 is used as the final predicted reserve amount. When R > μ h When +δ, the actively predicted reserve quantity L1 is used. high The δ is a preset threshold parameter used as the final predicted reserve amount.

6. A system for predicting the scale of urban land reserves, characterized in that: The system includes a data collection and processing module, a coefficient calculation module, a standardization and prediction calculation module, and a scenario generation and final determination module; The data collection and processing module collects data related to urban land reserves, cleans, integrates and performs preliminary calculations, specifically including obtaining the total land reserve, annual newly added and consumed area, current structural distribution and historical planning structural targets. The coefficient calculation module constructs a land reserve dynamic coefficient reflecting the dynamic changes of land reserves and a land reserve structure adaptation coefficient reflecting the structure of land reserves based on the collected data. The standardization and prediction calculation module constructs a comprehensive index by standardizing and weighting the coefficients, and calculates the predicted reserve scale under the baseline scenario based on the comprehensive index and the current total reserve. The scenario generation and final determination module constructs multi-scenario prediction results, verifies them using historical data, and outputs predicted land reserve values.

7. The urban land reserve scale prediction system according to claim 6, characterized in that: The coefficient calculation module includes a dynamic coefficient calculation unit and a structural adaptation coefficient calculation unit. The dynamic coefficient calculation unit generates a land reserve dynamic coefficient by calculating the ratio of the annual newly added reserve area to the annual consumed reserve area. The structure adaptation coefficient calculation unit calculates the land reserve structure adaptation coefficient by comparing the current land reserve structure with the historical average structure.

8. The urban land reserve scale prediction system according to claim 6, characterized in that: The standardization and prediction calculation module includes an indicator standardization unit, a comprehensive index calculation unit, and a benchmark prediction calculation unit. The index standardization unit normalizes the land reserve dynamic coefficient and the land reserve structure adaptation coefficient, mapping them to the [0,1] interval to obtain standardized indices A1 and A2. The comprehensive index calculation unit calculates the land reserve comprehensive index based on the standardized indicators A1 and A2 and their preset weights. The benchmark forecast calculation unit calculates the benchmark forecast reserve under the benchmark scenario based on the current total land reserves and the annual adjustment ratio derived from the comprehensive index.

9. The urban land reserve scale prediction system according to claim 6, characterized in that: The scenario generation and final determination module includes a scenario prediction quantity generation unit and a final prediction quantity determination unit; The scenario prediction generation unit introduces a confidence bias based on the baseline prediction value to generate conservative prediction reserves and aggressive prediction reserves, respectively. The final prediction unit retrieves historical data of the actual annual adjustment ratio over the past n years, calculates its average value, compares the current adjustment ratio with the historical average, and makes a decision based on a preset threshold: when it is lower than the historical level, a conservative prediction reserve is adopted; if it is within the historical fluctuation range, a baseline scenario is adopted; when it is higher than the historical level, an aggressive prediction reserve is adopted.

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