Urban development boundary optimization method

Abstract

The invention, which relates to the town planning field, discloses a town development boundary optimization method comprising the steps of collecting town information, and constructing a water balance feedback subsystem, a soil balance feedback subsystem, a human balance feedback subsystem, an ecological balance feedback subsystem and a resource industry balance feedback subsystem according to the town information. The urban development and development subsystem is used for predicting development and development directions of cities and towns, performing detailed planning processing on boundaries, performing adaptability evaluation and risk identification on the current cities and the cities after prediction of the development and development directions of the cities and towns, and adjusting the boundaries of the cities and towns; complicated action relations between a city and a water environment system, a land environment system, a manpower system, an ecological system and different resource industries are summarized, the development direction and degree of the city under different conditions in different time years in the future are predicted, and unified risk assessment is provided for prediction of each development of the city. And an optimization strategy is provided to adjust the boundary.

Description

Technical Field

[0001] The invention relates to the technical field of town planning, and in particular to a method for optimizing town development boundaries. Background Art

[0002] With the rapid urbanization in recent years, population growth and economic development have driven the demand for land, and predicting future urban development trends is crucial to developing reasonable boundaries. This helps ensure that the boundaries can adapt to future changes and needs.

[0003] At present, a Chinese invention patent with publication number CN202111218029.3, a method for spatial optimization configuration of urban land use based on Pareto frontier degradation, has established a fast method for spatial optimization configuration of urban land use, which can avoid the complex iterative process of traditional swarm intelligence multi-objective optimization, and has important practical significance for analyzing the optimal urban development boundary in national land space planning; however, the existing technology cannot summarize the complex interaction relationship between towns and water environment systems, land environment systems, human systems, ecosystems and different resource industries, and predict the development direction and degree of towns under different conditions at different times and years in the future, and cannot have a unified risk assessment for each development prediction of towns and make adjustments. Summary of the invention

[0004] The technical problem solved by the present invention is that the existing technology cannot summarize the complex interaction relationships between towns and water environment systems, land environment systems, human systems, ecosystems and different resource industries, and cannot predict the development direction and degree of towns under different conditions at different times and years in the future. It cannot have a unified risk assessment for the prediction of each development of the town and propose optimization strategies to adjust the boundaries.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for optimizing urban development boundaries, comprising the following steps:

[0006] Step S1: Collecting town information, including water resources information, land resources information, human resources information, ecological information and resource industry development information;

[0007] Step S2: constructing a water balance feedback subsystem, a soil balance feedback subsystem, a human balance feedback subsystem, an ecological balance feedback subsystem and a resource industry balance feedback subsystem according to the town information;

[0008] Step S3: combining the water balance feedback subsystem, soil balance feedback subsystem, human balance feedback subsystem, ecological balance feedback subsystem and resource industry balance feedback subsystem into a town development subsystem, performing qualitative and quantitative analysis and scenario analysis on the town development subsystem, and predicting the development direction of the town;

[0009] Step S4: taking the result of the town development direction prediction in step S3 as the dependent variable, inputting the dependent variable into the machine learning algorithm to output the optimal town planning, and performing detailed planning processing on the boundary according to the optimal town planning and the prediction of the town development direction;

[0010] Step S5: Conduct adaptability evaluation and risk identification on the current town and the town after the predicted town development direction, compare the risk of the current town with the risk of the town after the predicted town development direction, and adjust the town boundaries.

[0011] Preferably, the step S1 includes the following sub-steps:

[0012] Step S11: Collecting urban information includes using remote sensing technology to scan the current land landscape of the entire town, and collecting the total water consumption, land crop output, total human production value, total ecological species quantity and resource industry output value of the town, and using the grid method to measure the land use area and divide the land topography, and the land topography includes plains, hills, basins, mountains, coastal areas and rivers;

[0013] Step S12: Classify the land into water resource utilization land, land resource utilization land, human resource utilization land, ecological utilization land and resource industry development utilization land according to land utilization;

[0014] Step S13: Calculate the ratio of the total water consumption of the town to the number of grids of water resource utilization land as water resource information, calculate the ratio of land crop output to the number of grids of land resource utilization land as land resource information, calculate the ratio of total human production value to the number of grids of human resource utilization land as human resource information, and calculate the ratio of ecological species prosperity to ecological utilization land as ecological information;

[0015] Step S14: Calculate the ratio of the output value of the resource industry that accounts for the largest proportion of the total resource industry output of the town to the resource industry development and utilization area of ​​the corresponding resource industry as resource industry development information;

[0016] The ecological species prosperity degree is the ratio of the total number of ecological species in the town to the total number of ecological species in the country;

[0017] The resource industries include services, heavy industry, light industry, agriculture and tourism.

[0018] Preferably, the step S14 includes the following sub-steps:

[0019] Step S141: Collecting the service industry income and land area, heavy industry income and land area, light industry income and land area, agricultural income and land area, and tourism income and land area of ​​the town respectively;

[0020] Step S142: Divide the service industry income by the land area to obtain the service industry income ratio, divide the heavy industry income by the land area to obtain the heavy industry income ratio, divide the light industry income by the land area to obtain the light industry income ratio, divide the agricultural income by the land area to obtain the agricultural income ratio, and divide the tourism industry income by the land area to obtain the tourism industry income ratio;

[0021] Step S143: Draw a pie chart model of the service industry income ratio, heavy industry income ratio, light industry income ratio, agricultural income ratio and tourism industry income ratio, select the resource industry with the largest proportion in the pie chart model, and take the output value of the resource industry with the largest proportion as the quotient of the resource industry development and utilization area to obtain the resource industry development information.

[0022] Preferably, step S2 includes the following sub-steps:

[0023] Step S21: construct a water balance feedback subsystem, which takes the water resource supply-demand ratio variable as the core element and forms multiple feedback loops including:

[0024] When the water resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, optimize the development and utilization of water resources. The optimization of water resource development and utilization includes strengthening the development and utilization of recycled water, seawater desalination and rainwater collection and utilization of water-saving technologies, and increasing the water supply of other water sources;

[0025] The causal feedback loop of the water balance feedback subsystem of the town includes:

[0026] Water resource supply-demand ratio → (+) total population → (+) annual water demand → (-) water resource supply-demand ratio;

[0027] Water resource supply-demand ratio → (+) urbanization rate → (+) annual water demand → (-) water resource supply-demand ratio;

[0028] Water resources supply-demand ratio → (+) GDP → (+) annual water demand → (-) water resources supply-demand ratio;

[0029] Water resource supply-demand ratio → (-) water supply from other water sources → (+) annual water supply of water resources → (+) water resource supply-demand ratio;

[0030] Among them, (-) is negative feedback, (+) is positive feedback, and → is the cause-effect relationship.

[0031] The other water sources include water source supply, rainwater recycling supply and seawater desalination supply;

[0032] Merge the textually identical parts of all feedback loops to obtain the water balance feedback subsystem;

[0033] Step S22: constructing a soil balance feedback subsystem, wherein the soil balance feedback subsystem takes the land resource supply-demand ratio variable as a core element and forms multiple feedback loops including:

[0034] When the land resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, the land resource development and utilization is optimized, and the methods of optimizing the development and utilization of land resources include strengthening land recycling, artificial land fertilization and land seasonal rotation land recuperation technology;

[0035] The town's soil balance feedback subsystem causal feedback loop includes:

[0036] Land resource supply-demand ratio → (+) total population → (+) annual land resource occupation → (-) land resource supply-demand ratio;

[0037] Land resource supply-demand ratio → (+) urbanization rate → (+) annual land resource occupation → (-) land resource supply-demand ratio;

[0038] Land resource supply-demand ratio → (+) gross domestic product → (+) annual land resource occupation → (-) land resource supply-demand ratio;

[0039] Land resource supply-demand ratio → (-) land optimization input → (+) annual land resource occupation → (+) land resource supply-demand ratio;

[0040] The land optimization inputs include technical inputs for land recycling, increasing the accuracy of land measurement, artificially increasing land fertilizer, and seasonal rotation of land utilization;

[0041] Merge the textually identical parts of all feedback loops to obtain the soil balance feedback subsystem;

[0042] Step S23: construct a human resource balance feedback subsystem, which takes the human resource supply-demand ratio variable as a core element and forms multiple feedback loops including:

[0043] When the human resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, optimize the development and utilization of human resources, and the methods of optimizing the development and utilization of human resources include career development planning, talent recruitment and introduction plans, and talent retention methods that strengthen personalized training;

[0044] The causal feedback loop of the manpower balance feedback subsystem of the town includes:

[0045] Human resource supply-demand ratio → (+) total population → (+) annual demand for human resources → (-) human resource supply-demand ratio;

[0046] Human resource supply-demand ratio → (+) urbanization rate → (+) annual demand for human resources → (-) human resource supply-demand ratio;

[0047] Human resource supply-demand ratio → (+) gross domestic product → (+) annual demand for human resources → (-) human resource supply-demand ratio;

[0048] Human resource supply-demand ratio → (-) Talent retention method investment → (+) Annual supply and demand of human resources → (+) Human resource supply-demand ratio;

[0049] Merge the textually identical parts of all feedback loops to obtain the manpower balance feedback subsystem;

[0050] Step S24: construct an ecological balance feedback subsystem, wherein the ecological balance feedback subsystem takes the ecological resource increase / decrease ratio variable as a core element and forms multiple feedback loops including:

[0051] When the increase / decrease ratio of ecological resources is greater than or equal to the upper carrying limit threshold, the ecological resource protection is optimized, and the optimized ecological resource protection methods include the establishment of protected areas, the introduction and breeding and research and protection of animals and plants, the recording of ecological increase / decrease species, and the ecological balance method of detecting and controlling environmental deterioration data technology;

[0052] The causal feedback loop of the ecological balance feedback subsystem of the town includes:

[0053] Ecological resource increase / decrease ratio → (+) environmental ecological status → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio;

[0054] Ecological resource increase / decrease ratio → (+) urbanization rate → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio;

[0055] Ecological resource increase / decrease ratio → (+) gross domestic product → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio;

[0056] Ecological resource increase / decrease ratio → (-) ecological balance method input → (+) annual value of ecological resources to be maintained → (+) ecological resource increase / decrease ratio;

[0057] Merge the textually identical parts of all feedback loops to obtain the ecological balance feedback subsystem;

[0058] Step S25: construct a resource industry balance feedback subsystem, wherein the resource industry balance feedback subsystem takes the resource industry profit and loss ratio variable as a core element and forms multiple feedback loops including:

[0059] When the profit-loss ratio of a resource industry is greater than or equal to the carrying upper limit threshold, the resource industry development and utilization is optimized, and the method of optimizing the resource industry development and utilization includes implementing a talent introduction plan, technology upgrade, sustainable development and regional interactive cooperation resource industry development planning for the resource industry;

[0060] The causal relationship feedback loop of the resource industry balance feedback subsystem of the town includes:

[0061] Resource industry profit and loss ratio → (+) total population → (+) annual development of resource industry → (-) resource industry profit and loss ratio;

[0062] Resource industry profit and loss ratio → (+) urbanization rate → (+) annual development volume of resource industry → (-) resource industry profit and loss ratio;

[0063] Resource industry profit and loss ratio → (+) gross domestic product → (+) annual development of resource industry → (-) resource industry profit and loss ratio;

[0064] Resource industry profit and loss ratio → (-) resource industry development planning investment → (+) resource industry annual development volume → (+) resource industry profit and loss ratio;

[0065] The textually identical parts of all feedback loops are merged to obtain the resource industry balance feedback subsystem.

[0066] Preferably, step S3 includes the following sub-steps:

[0067] Step S31: merging the same parts of the water balance feedback subsystem, soil balance feedback subsystem, human balance feedback subsystem, ecological balance feedback subsystem and resource industry balance feedback subsystem to obtain a town development subsystem, and importing the town development subsystem into the town current data, wherein the current data includes all data required by the town development subsystem;

[0068] Step S32: Establishing a random forest model based on the town development subsystem includes:

[0069] The water resources supply-demand ratio, land resources supply-demand ratio, human resources supply-demand ratio, ecological resources increase-decrease ratio, and resource industry profit-loss ratio nodes in the town development subsystem are used as seed nodes in the random forest model, and as qualitative and quantitative evaluation indicators, and the qualitative and quantitative evaluation indicators are sequentially controlled to remain unchanged, and the values ​​of the nodes in the town development subsystem are adjusted according to the expected town development standard, and the future growth direction and development value of towns with different changes are predicted to obtain a first prediction set;

[0070] Step S33: replacing the resource industries in the town development subsystem with the resource industries in the service industry, heavy industry, light industry, agriculture and tourism industry for which the random forest model has not been established, and re-predicting the future growth direction and development numerical value of the town according to the step S32, and obtaining the second prediction set, the third prediction set, the fourth prediction set and the fifth prediction set respectively;

[0071] Step S34: All data that have been used as qualitative and quantitative evaluation indicators are established as an evaluation indicator system, and the random forest model whose predicted development value in all prediction sets is greater than the preset development data is selected to obtain a prediction of the urban development direction based on the evaluation indicator system.

[0072] Preferably, step S4 includes the following sub-steps:

[0073] Step S41: the prediction of the urban development direction according to the evaluation index system is divided into a development direction prediction and a development value prediction, wherein the development direction prediction includes the resource industry with the highest development value under the same evaluation index;

[0074] Step S42: normalizing the development direction prediction and the development value prediction to obtain development direction prediction features and development value prediction features of the same scale;

[0075] Step S43: taking the policies and terrain of the town as influencing factors, performing standardization operations on the policies and terrain respectively, taking the policies and terrain after the standardization operations as influencing factors of the AP genetic algorithm, and outputting the spatial distribution probability of suitability of each land use area in the town;

[0076] Step S44: Delineate boundaries by combining the suitability spatial distribution probability, the prediction of the urban development direction, and the delineation principles extracted from urban policies.

[0077] Preferably, the step S43 includes:

[0078] If the planning goal is to optimize resource allocation, the fitness function takes max{a};

[0079] If the planning goal is to maximize the benefit, the fitness function takes max{b};

[0080] If the planning goal is to optimize resource allocation and maximize benefits, the fitness function takes max{a*b};

[0081] Among them, max{} is the maximization representation, a is the value of the resource industry, b is the gross domestic product, and a*b is the convolution of a and b;

[0082] The gross domestic product includes the weighted sum of all evaluation index values;

[0083] A fitness function is defined according to planning objectives to evaluate the quality of each individual, wherein the planning objectives include optimizing resource allocation or maximizing benefits. The fitness function is used to evaluate the quality of each individual, wherein the individuals include the planning layout points of the town. Genetic operations are performed according to the evaluation results of the fitness function, and the score of the fitness function is continuously improved through multiple generations of evolution, and the suitability spatial distribution probability of each land use in the town is output.

[0084] Preferably, the step S44 includes the following sub-steps:

[0085] Step S441: inputting the suitability spatial distribution probability into the LSTM model to obtain the suitability spatial distribution probability of each land use area predicted for the future of the town according to the time year, and drawing the suitability spatial distribution probability according to the time year into a trend graph;

[0086] Step S442: obtaining a land use transfer matrix, wherein the land use transfer matrix includes land transfer relations, and the land transfer relations include mutual relations of land conversion classified according to land use, and standardizing the mutual relations to form a first land use dynamic;

[0087] Step S443: the demarcation principle, time, suitability spatial distribution probability, urban development direction, development numerical prediction, terrain and the preset thresholds of water resource supply and demand ratio, land resource supply and demand ratio, human resource supply and demand ratio, ecological resource increase and decrease ratio and resource industry profit and loss ratio in the urban development subsystem are used as land use driving factors, and the land use driving factors are also used as restriction data. The corresponding land use land is ranked according to the ranking of the thresholds of water resource supply and demand ratio, land resource supply and demand ratio, human resource supply and demand ratio, ecological resource increase and decrease ratio and resource industry profit and loss ratio in the urban development subsystem, and the weight value is between [0, 0.1];

[0088] Step S444: inputting the restriction data, the land use transfer matrix and the current land landscape of the entire town into the PLUS model to obtain a prediction of the land use spatial distribution state of the town in each future year under the simulation of the PLUS model;

[0089] Step S445: Calculate the weighted average sum of all land use driving factors, take the predicted result of the land use spatial distribution state of the town in each future year with the largest weighted average sum, and process the area and boundary details according to the terrain.

[0090] Preferably, step S5 includes the following sub-steps:

[0091] Step S51: The restriction data are multiplied by the Boolean superposition method to obtain comprehensive restriction data, and the comprehensive restriction data and the standardized land use driving factors are intersected by the weighted linear combination method to obtain the comprehensive dynamic degree of land use as the second land use dynamic degree. The comprehensive dynamic degree of land use is the degree of change of each land use type during the study period, and its mathematical expression is:

[0092]

[0093] Where S is the change rate of the land use type, n is the number of land use types, S i is the current total area of ​​land use type i, j is the state of land use type i when the prediction ends, ΔS i-j is the total area of ​​all land use types transferred out at the end of the forecast, and T is the total time range of the forecast;

[0094] Step S52: Use the Kappa coefficient to test the PLUS model, and its mathematical expression is:

[0095]

[0096] Wherein, K is the Kappa coefficient value, Pa is the correct result proportion of the land use type simulation, and Pb is the correct result proportion of the land use type simulation under random conditions;

[0097] The Kappa coefficient is used as the third land use dynamics;

[0098] Step S53: Using the Shannon diversity index to test the PLUS model, the Shannon diversity index is used as the fourth land use dynamics, and its mathematical expression is:

[0099]

[0100] Among them, SHDI is the heterogeneity and complexity of each land use type, P i is the area proportion of land use type i;

[0101] Step S54: using a multi-factor evaluation model to evaluate the spatial expansion suitability of land use with different factors, wherein the different factors include the land use driving factors, and the mathematical expression thereof is:

[0102]

[0103] Among them, M is the comprehensive evaluation index of suitability, D j is the weight of the jth evaluation factor, Y j is the score of the jth evaluation factor, and m is the total number of evaluation factors;

[0104] The suitability comprehensive evaluation index is used as the fifth land use dynamics;

[0105] Step S55: Utilize the law of large numbers and the covariance calculation formula to obtain the probability expectation value and the covariance value of the first land use dynamics, the second land use dynamics, the third land use dynamics, the fourth land use dynamics and the fifth land use dynamics, respectively, and adjust the boundary of the town according to the probability expectation value and the covariance value in combination with actual conditions.

[0106] Preferably, the step S55 comprises:

[0107] The realistic conditions include:

[0108] Whether resources meet demand, whether infrastructure adapts to current demand, whether environmental quality and social services meet residents' needs, changes in development demand for resources, future infrastructure demand and pressure, the impact of development on the environment and society, infrastructure risks, overdevelopment risks, environmental risks, social risks, and social needs;

[0109] Adjustments to town boundaries include:

[0110] The probability expectation value and covariance value are plotted as a two-dimensional linear graph, and the shape of the two-dimensional linear graph is adjusted as a prerequisite for using GIS tools for spatial analysis to simulate the impact of different boundary adjustments on risks, and the changes in the probability expectation value and covariance value are recorded. The changes in the optimized risk of the town boundary under various combinations of probability expectation value and covariance value are recorded to obtain the probability expectation value and covariance value combination with the lowest risk. According to the probability expectation value and covariance value combination with the lowest risk, all land use dynamic formulas are reversed to obtain the optimal internal and boundary planning of the town.

[0111] The beneficial effects of the present invention are as follows: it summarizes the complex interaction between towns and water environment systems, land environment systems, human systems, ecosystems and different resource industries, predicts the direction and extent of development of towns under different conditions at different times and years in the future, has a unified risk assessment for each development forecast of the town, and proposes optimization strategies to adjust boundaries. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 A basic flow chart of a method for optimizing urban development boundaries provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0113] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0114] Reference Figure 1 , as an embodiment of the present invention, provides a method for optimizing urban development boundaries, comprising the following steps:

[0115] Step S1: Collecting town information, including water resources information, land resources information, human resources information, ecological information and resource industry development information;

[0116] Step S2: constructing a water balance feedback subsystem, a soil balance feedback subsystem, a human balance feedback subsystem, an ecological balance feedback subsystem and a resource industry balance feedback subsystem according to the town information;

[0117] Step S3: combining the water balance feedback subsystem, soil balance feedback subsystem, human balance feedback subsystem, ecological balance feedback subsystem and resource industry balance feedback subsystem into a town development subsystem, performing qualitative and quantitative analysis and scenario analysis on the town development subsystem, and predicting the development direction of the town;

[0118] Step S4: taking the result of the town development direction prediction in step S3 as the dependent variable, inputting the dependent variable into the machine learning algorithm to output the optimal town planning, and performing detailed planning processing on the boundary according to the optimal town planning and the prediction of the town development direction;

[0119] Step S5: Conduct adaptability evaluation and risk identification on the current town and the town after the predicted town development direction, compare the risk of the current town with the risk of the town after the predicted town development direction, and adjust the town boundaries.

[0120] The present invention summarizes the complex interactions between towns and water environment systems, land environment systems, human systems, ecosystems and different resource industries, and predicts the development direction and degree of towns under different conditions at different times and years in the future. It has a unified risk assessment for each development forecast of the town and adjusts the boundaries.

[0121] The step S1 comprises the following sub-steps:

[0122] Step S11: Collecting urban information includes using remote sensing technology to scan the current land landscape of the entire town, and collecting the total water consumption, land crop output, total human production value, total ecological species quantity and resource industry output value of the town, and using the grid method to measure the land use area and divide the land topography, and the land topography includes plains, hills, basins, mountains, coastal areas and rivers;

[0123] Step S12: Classifying the overall land use into water resource utilization land, land resource utilization land, human resource utilization land, ecological utilization land and resource industry development utilization land according to land use;

[0124] Step S13: Calculate the ratio of the total water consumption of the town to the number of grids of water resource utilization land as water resource information, calculate the ratio of land crop output to the number of grids of land resource utilization land as land resource information, calculate the ratio of total human production value to the number of grids of human resource utilization land as human resource information, and calculate the ratio of ecological species prosperity to ecological utilization land as ecological information;

[0125] Step S14: Calculate the ratio of the output value of the resource industry that accounts for the largest proportion of the total resource industry output of the town to the resource industry development and utilization area of ​​the corresponding resource industry as resource industry development information;

[0126] The ecological species prosperity degree is the ratio of the total number of ecological species in the town to the total number of ecological species in the country;

[0127] The resource industries include services, heavy industry, light industry, agriculture and tourism.

[0128] The step S14 includes the following sub-steps:

[0129] Step S141: Collecting the service industry income and land area, heavy industry income and land area, light industry income and land area, agricultural income and land area, and tourism income and land area of ​​the town respectively;

[0130] Step S142: Divide the service industry income by the land area to obtain the service industry income ratio, divide the heavy industry income by the land area to obtain the heavy industry income ratio, divide the light industry income by the land area to obtain the light industry income ratio, divide the agricultural income by the land area to obtain the agricultural income ratio, and divide the tourism industry income by the land area to obtain the tourism industry income ratio;

[0131] Step S143: Draw a pie chart model of the service industry income ratio, heavy industry income ratio, light industry income ratio, agricultural income ratio and tourism industry income ratio, so as to intuitively see the most prosperous resource industry in the current urban development, select the resource industry with the largest proportion in the pie chart model, and take the output value of the resource industry with the largest proportion as the quotient of the resource industry development and utilization area to obtain the resource industry development information.

[0132] The step S2 comprises the following sub-steps:

[0133] Step S21: construct a water balance feedback subsystem, which takes the water resource supply-demand ratio variable as the core element and forms multiple feedback loops including:

[0134] When the water resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, optimize the development and utilization of water resources. The optimization of water resource development and utilization includes strengthening the development and utilization of recycled water, seawater desalination and rainwater collection and utilization of water-saving technologies, and increasing the water supply of other water sources;

[0135] The causal feedback loop of the water balance feedback subsystem of the town includes:

[0136] Water resource supply-demand ratio → (+) total population → (+) annual water demand → (-) water resource supply-demand ratio;

[0137] Water resource supply-demand ratio → (+) urbanization rate → (+) annual water demand → (-) water resource supply-demand ratio;

[0138] Water resources supply-demand ratio → (+) GDP → (+) annual water demand → (-) water resources supply-demand ratio;

[0139] Water resource supply-demand ratio → (-) water supply from other water sources → (+) annual water supply of water resources → (+) water resource supply-demand ratio;

[0140] Among them, (-) is negative feedback, (+) is positive feedback, and → is the cause-effect relationship.

[0141] The other water sources include water source supply, rainwater recycling supply and seawater desalination supply;

[0142] Water shortages in urban development have affected the growth rates of total population, GDP and urbanization rate variables, and the water shortages have led to the need to optimize the way water resources are developed and utilized;

[0143] Merge the textually identical parts of all feedback loops to obtain the water balance feedback subsystem;

[0144] Step S22: constructing a soil balance feedback subsystem, wherein the soil balance feedback subsystem takes the land resource supply-demand ratio variable as a core element and forms multiple feedback loops including:

[0145] When the land resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, optimize the development and utilization of land resources. The optimization of land resource development and utilization includes strengthening land recycling, artificial land fertilization and land seasonal rotation technology to increase the efficiency of land resource utilization;

[0146] The town's soil balance feedback subsystem causal feedback loop includes:

[0147] Land resource supply-demand ratio → (+) total population → (+) annual land resource occupation → (-) land resource supply-demand ratio;

[0148] Land resource supply-demand ratio → (+) urbanization rate → (+) annual land resource occupation → (-) land resource supply-demand ratio;

[0149] Land resource supply-demand ratio → (+) gross domestic product → (+) annual land resource occupation → (-) land resource supply-demand ratio;

[0150] Land resource supply-demand ratio → (-) land optimization input → (+) annual land resource occupation → (+) land resource supply-demand ratio;

[0151] The land optimization inputs include technical inputs for land recycling, increasing the accuracy of land measurement, artificially increasing land fertilizer, and seasonal rotation of land utilization;

[0152] The shortage of land resources in urban development has affected the growth rates of total population, GDP and urbanization rate, and the shortage of land resources has led to the need to optimize the way of land resource development and utilization;

[0153] Merge the textually identical parts of all feedback loops to obtain the soil balance feedback subsystem;

[0154] Step S23: construct a human resource balance feedback subsystem, which takes the human resource supply-demand ratio variable as a core element and forms multiple feedback loops including:

[0155] When the human resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, optimize the development and utilization of human resources. The methods of optimizing the development and utilization of human resources include career development planning, talent recruitment and introduction plans, and talent retention methods that strengthen personalized training, so as to achieve the effect of improving the quality of human resources;

[0156] The causal feedback loop of the manpower balance feedback subsystem of the town includes:

[0157] Human resource supply-demand ratio → (+) total population → (+) annual demand for human resources → (-) human resource supply-demand ratio;

[0158] Human resource supply-demand ratio → (+) urbanization rate → (+) annual demand for human resources → (-) human resource supply-demand ratio;

[0159] Human resource supply-demand ratio → (+) gross domestic product → (+) annual demand for human resources → (-) human resource supply-demand ratio;

[0160] Human resource supply-demand ratio → (-) Talent retention method investment → (+) Annual supply and demand of human resources → (+) Human resource supply-demand ratio;

[0161] Urban development has a shortage of human resources, which affects the growth rate of total population, GDP and urbanization rate variables. The shortage of human resources has also led to the need to optimize the development and utilization of human resources;

[0162] Merge the textually identical parts of all feedback loops to obtain the manpower balance feedback subsystem;

[0163] Step S24: construct an ecological balance feedback subsystem, wherein the ecological balance feedback subsystem takes the ecological resource increase / decrease ratio variable as a core element and forms multiple feedback loops including:

[0164] When the increase / decrease ratio of ecological resources is greater than or equal to the upper carrying capacity threshold, the ecological resource protection is optimized. The optimized ecological resource protection methods include the establishment of protected areas, the introduction and breeding of animals and plants, research and protection, the recording of ecological increase / decrease species, and the detection and control of environmental deterioration data technology to achieve an ecological balance method, so as to achieve a thriving ecological environment;

[0165] The causal feedback loop of the ecological balance feedback subsystem of the town includes:

[0166] Ecological resource increase / decrease ratio → (+) environmental ecological status → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio;

[0167] Ecological resource increase / decrease ratio → (+) urbanization rate → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio;

[0168] Ecological resource increase / decrease ratio → (+) gross domestic product → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio;

[0169] Ecological resource increase / decrease ratio → (-) ecological balance method input → (+) annual value of ecological resources to be maintained → (+) ecological resource increase / decrease ratio;

[0170] Urban development has the problem of upper or lower limits of ecological resource development, which affects the growth rate of environmental ecological status, gross domestic product and urbanization rate variables. The upper or lower limit of ecological resource development also leads to the need to optimize the way of ecological resource protection;

[0171] Merge the textually identical parts of all feedback loops to obtain the ecological balance feedback subsystem;

[0172] Step S25: construct a resource industry balance feedback subsystem, wherein the resource industry balance feedback subsystem takes the resource industry profit and loss ratio variable as a core element and forms multiple feedback loops including:

[0173] When the profit-loss ratio of resource industries is greater than or equal to the carrying upper limit threshold, the development and utilization of resource industries is optimized. The method of optimizing the development and utilization of resource industries includes implementing talent introduction plans, technology upgrades, sustainable development and regional interactive cooperation resource industry development plans for the resource industries, so that the resource industries that the town focuses on can be catalyzed and evolved, thereby promoting the overall economic development of the town more quickly;

[0174] The causal relationship feedback loop of the resource industry balance feedback subsystem of the town includes:

[0175] Resource industry profit and loss ratio → (+) total population → (+) annual development of resource industry → (-) resource industry profit and loss ratio;

[0176] Resource industry profit and loss ratio → (+) urbanization rate → (+) annual development volume of resource industry → (-) resource industry profit and loss ratio;

[0177] Resource industry profit and loss ratio → (+) gross domestic product → (+) annual development of resource industry → (-) resource industry profit and loss ratio;

[0178] Resource industry profit and loss ratio → (-) resource industry development planning investment → (+) resource industry annual development volume → (+) resource industry profit and loss ratio;

[0179] Urban development has encountered the problem of resource industries not making enough money to cover expenditures, which has affected the growth rates of total population, GDP and urbanization rate variables. The problem of resource industries not making enough money has also led to the need to optimize the way of resource industry development and utilization;

[0180] The textually identical parts of all feedback loops are merged to obtain the resource industry balance feedback subsystem.

[0181] The step S3 comprises the following sub-steps:

[0182] Step S31: merging the same parts of the water balance feedback subsystem, soil balance feedback subsystem, human balance feedback subsystem, ecological balance feedback subsystem and resource industry balance feedback subsystem to obtain a town development subsystem, and importing the town development subsystem into the town current data, wherein the current data includes all data required by the town development subsystem;

[0183] Step S32: Establishing a random forest model based on the town development subsystem includes:

[0184] The water resources supply-demand ratio, land resources supply-demand ratio, human resources supply-demand ratio, ecological resources increase-decrease ratio, and resource industry profit-loss ratio nodes in the town development subsystem are used as seed nodes in the random forest model, and as qualitative and quantitative evaluation indicators, and the qualitative and quantitative evaluation indicators are sequentially controlled to remain unchanged, and the values ​​of the nodes in the town development subsystem are adjusted according to the expected town development standard, and the future growth direction and development value of towns with different changes are predicted to obtain a first prediction set;

[0185] Step S33: replacing the resource industries in the town development subsystem with the resource industries in the service industry, heavy industry, light industry, agriculture and tourism industry for which the random forest model has not been established, and re-predicting the future growth direction and development numerical value of the town according to the step S32, and obtaining the second prediction set, the third prediction set, the fourth prediction set and the fifth prediction set respectively;

[0186] Step S34: All data that have been used as qualitative and quantitative evaluation indicators are established as an evaluation indicator system, and the random forest model whose predicted development value in all prediction sets is greater than the preset development data is selected to obtain a prediction of the urban development direction based on the evaluation indicator system.

[0187] The random seed mechanism generated by random forest for planned towns fully takes into account the impact of natural environmental factors and human factors in planned towns on the development and changes of the spatial pattern of land use in the study area, and increases the guiding and driving role of human factors on the future evolution of land use in the study area.

[0188] The step S4 comprises the following sub-steps:

[0189] Step S41: the prediction of the urban development direction according to the evaluation index system is divided into a development direction prediction and a development value prediction, wherein the development direction prediction includes the resource industry with the highest development value under the same evaluation index;

[0190] Step S42: normalizing the development direction prediction and the development value prediction to obtain development direction prediction features and development value prediction features of the same scale;

[0191] Step S43: taking the policies and terrain of the town as influencing factors, performing standardization operations on the policies and terrain respectively, taking the policies and terrain after the standardization operations as influencing factors of the AP genetic algorithm, and outputting the spatial distribution probability of suitability of each land use area in the town;

[0192] Step S44: Delineate boundaries by combining the suitability spatial distribution probability, the prediction of the urban development direction, and the delineation principles extracted from urban policies.

[0193] The step S43 comprises:

[0194] If the planning goal is to optimize resource allocation, the fitness function takes max{a};

[0195] If the planning goal is to maximize the benefit, the fitness function takes max{b};

[0196] If the planning goal is to optimize resource allocation and maximize benefits, the fitness function takes max{a*b};

[0197] Among them, max{} is the maximization representation, a is the value of the resource industry, b is the gross domestic product, and a*b is the convolution of a and b;

[0198] The gross domestic product includes the weighted sum of all evaluation index values;

[0199] A fitness function is defined according to planning objectives to evaluate the quality of each individual, wherein the planning objectives include optimizing resource allocation or maximizing benefits. The fitness function is used to evaluate the quality of each individual, wherein the individuals include the planning layout points of the town. Genetic operations are performed according to the evaluation results of the fitness function, and the score of the fitness function is continuously improved through multiple generations of evolution, and the suitability spatial distribution probability of each land use in the town is output.

[0200] The step S44 includes the following sub-steps:

[0201] The delineation principles include:

[0202] Reverse thinking based on the bottom line: starting from the bottom line safety of ecological environment protection, safeguarding the bottom line of the city;

[0203] Positive thinking based on urban development: starting from urban strategic development to meet the needs of future urban development;

[0204] Food development: ensuring food production and maintaining basic farmland;

[0205] The principle of strong operability: ensure that the data source is reliable, the data is accurate, and the technical operation is simple and adaptable, which can provide a reference for the delineation of development boundaries of most small and medium-sized towns in northern Anhui in the future.

[0206] Step S441: inputting the suitability spatial distribution probability into the LSTM model to obtain the suitability spatial distribution probability of each land use area predicted for the future of the town according to the time year, and drawing the suitability spatial distribution probability according to the time year into a trend graph;

[0207] Step S442: according to the obtained land use transfer matrix, the land use transfer matrix includes land transfer relations, and the land transfer relations include the mutual relations of land conversion utilization classified according to land use, such as land resource utilization land converted into water resource utilization land, and after the mutual relations are standardized, they constitute the first land use dynamics;

[0208] Step S443: the demarcation principle, time, suitability spatial distribution probability, urban development direction, development numerical prediction, terrain and the preset thresholds of water resource supply and demand ratio, land resource supply and demand ratio, human resource supply and demand ratio, ecological resource increase and decrease ratio and resource industry profit and loss ratio in the urban development subsystem are used as land use driving factors, and the land use driving factors are also used as restriction data. The corresponding land use land is ranked according to the ranking of the thresholds of water resource supply and demand ratio, land resource supply and demand ratio, human resource supply and demand ratio, ecological resource increase and decrease ratio and resource industry profit and loss ratio in the urban development subsystem, and the weight value is between [0, 0.1];

[0209] Step S444: inputting the restriction data, the land use transfer matrix and the current land landscape of the entire town into the PLUS model to obtain a prediction of the land use spatial distribution state of the town in each future year under the simulation of the PLUS model;

[0210] Step S445: Calculate the weighted average sum of all land use driving factors, where the weight values ​​of positive influencing factors are positive and the weight values ​​of negative influencing factors are negative. Take the predicted results of the spatial distribution status of land use in the future years of the town with the largest weighted average sum, and process the area and boundary details according to the terrain.

[0211] For example, when water resource utilization land encounters a mountain range, the boundary is divided along the foot of the mountain, and the excess area is allocated to the side of the mountain range; when human resource utilization land encounters the lateral interception of water resource utilization land, the population density distribution of human resources is calculated to build a bridge or the human resource utilization land is translated, or the human resource utilization land is divided into small pieces and spliced ​​along the water's edge.

[0212] The step S5 comprises the following sub-steps:

[0213] Step S51: The restriction data are multiplied by the Boolean superposition method to obtain comprehensive restriction data, and the comprehensive restriction data and the standardized land use driving factors are intersected by the weighted linear combination method to obtain the comprehensive dynamic degree of land use as the second land use dynamic degree. The comprehensive dynamic degree of land use is the degree of change of each land use type during the study period, and its mathematical expression is:

[0214]

[0215] Where S is the change rate of the land use type, n is the number of land use types, S i is the current total area of ​​land use type i, j is the state of land use type i when the prediction ends, ΔS i-j is the total area of ​​all land use types transferred out at the end of the forecast, and T is the total time range of the forecast;

[0216] Step S52: Use the Kappa coefficient to test the PLUS model, and its mathematical expression is:

[0217]

[0218] Wherein, K is the Kappa coefficient value, Pa is the correct result proportion of the land use type simulation, and Pb is the correct result proportion of the land use type simulation under random conditions;

[0219] The Kappa coefficient is used as the third land use dynamics; the larger the K value, the larger the Kappa coefficient, indicating that the model simulation result is more accurate, and the smaller the K value, the smaller the Kappa coefficient, indicating that the model simulation result is less credible.

[0220] Step S53: Use the Shannon diversity index to test the PLUS model, and use the Shannon diversity index as the fourth land use dynamics, and its mathematical expression is:

[0221]

[0222] Among them, SHDI is the heterogeneity and complexity of each land use type, P i is the area proportion of land use type i;

[0223] Step S54: using a multi-factor evaluation model to evaluate the spatial expansion suitability of land use with different factors, wherein the different factors include the land use driving factors, and the mathematical expression thereof is:

[0224]

[0225] Among them, M is the comprehensive evaluation index of suitability, D j is the weight of the jth evaluation factor, Y j is the score of the jth evaluation factor, and m is the total number of evaluation factors;

[0226] The suitability comprehensive evaluation index is used as the fifth land use dynamics;

[0227] Step S55: Utilize the law of large numbers and the covariance calculation formula to obtain the probability expectation value and the covariance value of the first land use dynamics, the second land use dynamics, the third land use dynamics, the fourth land use dynamics and the fifth land use dynamics, respectively, and adjust the boundary of the town according to the probability expectation value and the covariance value in combination with actual conditions.

[0228] The step S55 comprises:

[0229] The realistic conditions include:

[0230] Whether resources meet demand, whether infrastructure is adapted to current needs, whether environmental quality and social services meet residents' needs, changes in development demand for resources, future infrastructure needs and pressures, the impact of development on the environment and society, infrastructure risks, overdevelopment risks, environmental risks, social risks, social needs, adjust boundaries to meet the stated real conditions, solicit opinions from communities and stakeholders, and ensure that adjustment plans meet actual needs;

[0231] Adjustments to town boundaries include:

[0232] The probability expectation value and covariance value are plotted as a two-dimensional linear graph, and the shape of the two-dimensional linear graph is adjusted as a prerequisite for using GIS tools for spatial analysis to simulate the impact of different boundary adjustments on risks, and the changes in the probability expectation value and covariance value are recorded. The changes in the optimized risk of the town boundary under various combinations of probability expectation value and covariance value are recorded to obtain the probability expectation value and covariance value combination with the lowest risk. According to the probability expectation value and covariance value combination with the lowest risk, all land use dynamic formulas are reversed to obtain the optimal internal and boundary planning of the town.

[0233] The adaptability and risks of current and predicted towns can be effectively assessed, and town boundaries can be adjusted based on the assessment results to achieve safer and more sustainable town development.

[0234] The present invention summarizes the complex interactions between towns and water environment systems, land environment systems, human systems, ecosystems and different resource industries, and predicts the development direction and degree of towns under different conditions in different years and years in the future through a machine learning model. It also uses different assessment angles to conduct a unified risk assessment on the prediction of each development of the town in detail, and proposes an optimization strategy to adjust the boundaries, which jointly promotes the methods and practices of optimizing town development boundaries to achieve sustainable urban development, rational use of resources and coordinated development of social economy.

[0235] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0236] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing urban development boundaries, characterized in that: The following steps are involved: Step S1: Collecting town information, including water resources information, land resources information, human resources information, ecological information and resource industry development information; Step S2: constructing a water balance feedback subsystem, a soil balance feedback subsystem, a human balance feedback subsystem, an ecological balance feedback subsystem and a resource industry balance feedback subsystem according to the town information; Step S3: combining the water balance feedback subsystem, soil balance feedback subsystem, human balance feedback subsystem, ecological balance feedback subsystem and resource industry balance feedback subsystem into a town development subsystem, performing qualitative and quantitative analysis and scenario analysis on the town development subsystem, and predicting the development direction of the town; Step S4: taking the result of the town development direction prediction in step S3 as the dependent variable, inputting the dependent variable into the machine learning algorithm to output the optimal town planning, and performing detailed planning processing on the boundary according to the optimal town planning and the prediction of the town development direction; Step S5: Conduct adaptability evaluation and risk identification on the current town and the town after the predicted town development direction, compare the risk of the current town with the risk of the town after the predicted town development direction, and adjust the town boundaries.

2. A method for optimizing urban development boundaries as claimed in claim 1, characterized in that: The step S1 comprises the following sub-steps: Step S11: Collecting urban information includes using remote sensing technology to scan the current land landscape of the entire town, and collecting the total water consumption, land crop output, total human production value, total ecological species quantity and resource industry output value of the town, and using the grid method to measure the land use area and divide the land topography, and the land topography includes plains, hills, basins, mountains, coastal areas and rivers; Step S12: Classifying the overall land use into water resource utilization land, land resource utilization land, human resource utilization land, ecological utilization land and resource industry development utilization land according to land use; Step S13: Calculate the ratio of the total water consumption of the town to the number of grids of water resource utilization land as water resource information, calculate the ratio of land crop output to the number of grids of land resource utilization land as land resource information, calculate the ratio of total human production value to the number of grids of human resource utilization land as human resource information, and calculate the ratio of ecological species prosperity to ecological utilization land as ecological information; Step S14: Calculate the ratio of the output value of the resource industry that accounts for the largest proportion of the total resource industry output of the town to the resource industry development and utilization area of ​​the corresponding resource industry as resource industry development information; The ecological species prosperity degree is the ratio of the total number of ecological species in the town to the total number of ecological species in the country; The resource industries include services, heavy industry, light industry, agriculture and tourism.

3. A method for optimizing urban development boundaries as claimed in claim 2, characterized in that: The step S14 includes the following sub-steps: Step S141: Collecting the service industry income and land area, heavy industry income and land area, light industry income and land area, agricultural income and land area, and tourism income and land area of ​​the town respectively; Step S142: Divide the service industry income by the land area to obtain the service industry income ratio, divide the heavy industry income by the land area to obtain the heavy industry income ratio, divide the light industry income by the land area to obtain the light industry income ratio, divide the agricultural income by the land area to obtain the agricultural income ratio, and divide the tourism industry income by the land area to obtain the tourism industry income ratio; Step S143: Draw a pie chart model of the service industry income ratio, heavy industry income ratio, light industry income ratio, agricultural income ratio and tourism industry income ratio, select the resource industry with the largest proportion in the pie chart model, and take the output value of the resource industry with the largest proportion as the quotient of the resource industry development and utilization area to obtain the resource industry development information.

4. A method for optimizing urban development boundaries as claimed in claim 3, characterized in that: The step S2 comprises the following sub-steps: Step S21: construct a water balance feedback subsystem, which takes the water resource supply-demand ratio variable as the core element and forms multiple feedback loops including: When the water resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, optimize the development and utilization of water resources. The optimization of water resource development and utilization includes strengthening the development and utilization of recycled water, seawater desalination and rainwater collection and utilization of water-saving technologies, and increasing the water supply of other water sources; The causal feedback loop of the water balance feedback subsystem of the town includes: Water resource supply-demand ratio → (+) total population → (+) annual water demand → (-) water resource supply-demand ratio; Water resource supply-demand ratio → (+) urbanization rate → (+) annual water demand → (-) water resource supply-demand ratio; Water resources supply-demand ratio → (+) GDP → (+) annual water demand → (-) water resources supply-demand ratio; Water resource supply-demand ratio → (-) water supply from other water sources → (+) annual water supply of water resources → (+) water resource supply-demand ratio; Among them, (-) is negative feedback, (+) is positive feedback, and → is the cause-effect relationship. The other water sources include water source supply, rainwater recycling supply and seawater desalination supply; Merge the textually identical parts of all feedback loops to obtain the water balance feedback subsystem; Step S22: constructing a soil balance feedback subsystem, wherein the soil balance feedback subsystem takes the land resource supply-demand ratio variable as a core element and forms multiple feedback loops including: When the land resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, the land resource development and utilization is optimized, and the methods of optimizing the development and utilization of land resources include strengthening land recycling, artificial land fertilization and land seasonal rotation land recuperation technology; The town's soil balance feedback subsystem causal feedback loop includes: Land resource supply-demand ratio → (+) total population → (+) annual land resource occupation → (-) land resource supply-demand ratio; Land resource supply-demand ratio → (+) urbanization rate → (+) annual land resource occupation → (-) land resource supply-demand ratio; Land resource supply-demand ratio → (+) gross domestic product → (+) annual land resource occupation → (-) land resource supply-demand ratio; Land resource supply-demand ratio → (-) land optimization input → (+) annual land resource occupation → (+) land resource supply-demand ratio; The land optimization inputs include technical inputs for land recycling, increasing the accuracy of land measurement, artificially increasing land fertilizer, and seasonal rotation of land utilization; Merge the textually identical parts of all feedback loops to obtain the soil balance feedback subsystem; Step S23: construct a human resource balance feedback subsystem, which takes the human resource supply-demand ratio variable as a core element and forms multiple feedback loops including: When the human resource supply-demand ratio is greater than or equal to the carrying upper limit threshold, optimize the development and utilization of human resources, and the methods of optimizing the development and utilization of human resources include career development planning, talent recruitment and introduction plans, and talent retention methods that strengthen personalized training; The causal feedback loop of the manpower balance feedback subsystem of the town includes: Human resource supply-demand ratio → (+) total population → (+) annual demand for human resources → (-) human resource supply-demand ratio; Human resource supply-demand ratio → (+) urbanization rate → (+) annual demand for human resources → (-) human resource supply-demand ratio; Human resource supply-demand ratio → (+) gross domestic product → (+) annual demand for human resources → (-) human resource supply-demand ratio; Human resource supply-demand ratio → (-) Talent retention method investment → (+) Annual supply and demand of human resources → (+) Human resource supply-demand ratio; Merge the textually identical parts of all feedback loops to obtain the manpower balance feedback subsystem; Step S24: construct an ecological balance feedback subsystem, wherein the ecological balance feedback subsystem takes the ecological resource increase / decrease ratio variable as a core element and forms multiple feedback loops including: When the increase / decrease ratio of ecological resources is greater than or equal to the upper carrying limit threshold, the ecological resource protection is optimized, and the optimized ecological resource protection methods include the establishment of protected areas, the introduction and breeding and research and protection of animals and plants, the recording of ecological increase / decrease species, and the ecological balance method of detecting and controlling environmental deterioration data technology; The causal feedback loop of the ecological balance feedback subsystem of the town includes: Ecological resource increase / decrease ratio → (+) environmental ecological status → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio; Ecological resource increase / decrease ratio → (+) urbanization rate → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio; Ecological resource increase / decrease ratio → (+) gross domestic product → (+) annual value of ecological resources to be maintained → (-) ecological resource increase / decrease ratio; Ecological resource increase / decrease ratio → (-) ecological balance method input → (+) annual value of ecological resources to be maintained → (+) ecological resource increase / decrease ratio; Merge the textually identical parts of all feedback loops to obtain the ecological balance feedback subsystem; Step S25: construct a resource industry balance feedback subsystem, wherein the resource industry balance feedback subsystem takes the resource industry profit and loss ratio variable as a core element and forms multiple feedback loops including: When the profit-loss ratio of a resource industry is greater than or equal to the carrying upper limit threshold, the resource industry development and utilization is optimized, and the method of optimizing the resource industry development and utilization includes implementing a talent introduction plan, technology upgrade, sustainable development and regional interactive cooperation resource industry development planning for the resource industry; The causal relationship feedback loop of the resource industry balance feedback subsystem of the town includes: Resource industry profit and loss ratio → (+) total population → (+) annual development of resource industry → (-) resource industry profit and loss ratio; Resource industry profit and loss ratio → (+) urbanization rate → (+) annual development volume of resource industry → (-) resource industry profit and loss ratio; Resource industry profit and loss ratio → (+) gross domestic product → (+) annual development of resource industry → (-) resource industry profit and loss ratio; Resource industry profit and loss ratio → (-) resource industry development planning investment → (+) resource industry annual development volume → (+) resource industry profit and loss ratio; The textually identical parts of all feedback loops are merged to obtain the resource industry balance feedback subsystem.

5. A method for optimizing urban development boundaries as claimed in claim 4, characterized in that: The step S3 comprises the following sub-steps: Step S31: merging the same parts of the water balance feedback subsystem, soil balance feedback subsystem, human balance feedback subsystem, ecological balance feedback subsystem and resource industry balance feedback subsystem to obtain a town development subsystem, and importing the town development subsystem into the town current data, wherein the current data includes all data required by the town development subsystem; Step S32: Establishing a random forest model based on the town development subsystem includes: The water resources supply-demand ratio, land resources supply-demand ratio, human resources supply-demand ratio, ecological resources increase-decrease ratio, and resource industry profit-loss ratio nodes in the town development subsystem are used as seed nodes in the random forest model, and as qualitative and quantitative evaluation indicators, and the qualitative and quantitative evaluation indicators are sequentially controlled to remain unchanged, and the values ​​of the nodes in the town development subsystem are adjusted according to the expected town development standard, and the future growth direction and development value of towns with different changes are predicted to obtain a first prediction set; Step S33: replacing the resource industries in the town development subsystem with the resource industries in the service industry, heavy industry, light industry, agriculture and tourism industry for which the random forest model has not been established, and re-predicting the future growth direction and development numerical value of the town according to the step S32, and obtaining the second prediction set, the third prediction set, the fourth prediction set and the fifth prediction set respectively; Step S34: All data that have been used as qualitative and quantitative evaluation indicators are established as an evaluation indicator system, and the random forest model whose predicted development value in all prediction sets is greater than the preset development data is selected to obtain a prediction of the urban development direction based on the evaluation indicator system.

6. A method for optimizing urban development boundaries as claimed in claim 5, characterized in that: The step S4 comprises the following sub-steps: Step S41: the prediction of the urban development direction according to the evaluation index system is divided into a development direction prediction and a development value prediction, wherein the development direction prediction includes the resource industry with the highest development value under the same evaluation index; Step S42: normalizing the development direction prediction and the development value prediction to obtain development direction prediction features and development value prediction features of the same scale; Step S43: taking the policies and terrain of the town as influencing factors, performing standardization operations on the policies and terrain respectively, taking the policies and terrain after the standardization operations as influencing factors of the AP genetic algorithm, defining a fitness function according to the planning objectives to evaluate the quality of each individual, wherein the planning objectives include optimizing resource allocation or maximizing benefits, using the fitness function to evaluate the quality of each individual, and outputting the spatial distribution probability of suitability of each land use in the town; Step S44: Delineate boundaries by combining the suitability spatial distribution probability, the prediction of the urban development direction, and the delineation principles extracted from urban policies.

7. A method for optimizing urban development boundaries as claimed in claim 6, characterized in that: The step S43 comprises: If the planning goal is to optimize resource allocation, the fitness function takes max{a}; If the planning goal is to maximize the benefit, the fitness function takes max{b}; If the planning goal is to optimize resource allocation and maximize benefits, the fitness function takes max{a*b}; Among them, max{} is the maximization representation, a is the value of the resource industry, b is the gross domestic product, and a*b is the convolution of a and b; The gross domestic product includes the weighted sum of all evaluation index values; The individuals include the planning layout points of the town, and genetic operations are performed according to the fitness function evaluation results. The fitness function score is continuously improved through multiple generations of evolution, and the suitability spatial distribution probability of each land use in the town is output.

8. A method for optimizing urban development boundaries as claimed in claim 7, characterized in that: The step S44 includes the following sub-steps: Step S441: inputting the suitability spatial distribution probability into the LSTM model to obtain the suitability spatial distribution probability of each land use area predicted for the future of the town according to the time year, and drawing the suitability spatial distribution probability according to the time year into a trend graph; Step S442: obtaining a land use transfer matrix, wherein the land use transfer matrix includes land transfer relations, and the land transfer relations include mutual relations of land conversion classified according to land use, and standardizing the mutual relations to form a first land use dynamic; Step S443: the demarcation principle, time, suitability spatial distribution probability, urban development direction, development numerical prediction, terrain and the preset thresholds of water resource supply and demand ratio, land resource supply and demand ratio, human resource supply and demand ratio, ecological resource increase and decrease ratio and resource industry profit and loss ratio in the urban development subsystem are used as land use driving factors, and the land use driving factors are also used as restriction data. The corresponding land use land is ranked according to the ranking of the thresholds of water resource supply and demand ratio, land resource supply and demand ratio, human resource supply and demand ratio, ecological resource increase and decrease ratio and resource industry profit and loss ratio in the urban development subsystem, and the weight value is between [0, 0.1]; Step S444: inputting the restriction data, the land use transfer matrix and the current land landscape of the entire town into the PLUS model to obtain a prediction of the land use spatial distribution state of the town in each future year under the simulation of the PLUS model; Step S445: Calculate the weighted average sum of all land use driving factors, take the predicted result of the land use spatial distribution state of the town in each future year with the largest weighted average sum, and process the area and boundary details according to the terrain.

9. A method for optimizing urban development boundaries as claimed in claim 8, characterized in that: The step S5 comprises the following sub-steps: Step S51: The restriction data are multiplied by the Boolean superposition method to obtain comprehensive restriction data, and the comprehensive restriction data and the standardized land use driving factors are intersected by the weighted linear combination method to obtain the comprehensive dynamic degree of land use as the second land use dynamic degree. The comprehensive dynamic degree of land use is the degree of change of each land use type during the study period, and its mathematical expression is: Where S is the change rate of the land use type, n is the number of land use types, S i is the current total area of ​​land use type i, j is the state of land use type i when the prediction ends, ΔS i-j is the total area of ​​all land use types transferred out at the end of the forecast, and T is the total time range of the forecast; Step S52: Use the Kappa coefficient to test the PLUS model, and its mathematical expression is: Wherein, K is the Kappa coefficient value, Pa is the correct result proportion of the land use type simulation, and Pb is the correct result proportion of the land use type simulation under random conditions; The Kappa coefficient is used as the third land use dynamics; Step S53: Use the Shannon diversity index to test the PLUS model, and use the Shannon diversity index as the fourth land use dynamics, and its mathematical expression is: Among them, SHDI is the heterogeneity and complexity of each land use type, P i is the area proportion of land use type i; Step S54: using a multi-factor evaluation model to evaluate the spatial expansion suitability of land use with different factors, wherein the different factors include the land use driving factors, and the mathematical expression thereof is: Among them, M is the comprehensive evaluation index of suitability, D j is the weight of the jth evaluation factor, Y j is the score of the jth evaluation factor, and m is the total number of evaluation factors; The suitability comprehensive evaluation index is used as the fifth land use dynamics; Step S55: Utilize the law of large numbers and the covariance calculation formula to obtain the probability expectation value and the covariance value of the first land use dynamics, the second land use dynamics, the third land use dynamics, the fourth land use dynamics and the fifth land use dynamics, respectively, and adjust the boundary of the town according to the probability expectation value and the covariance value in combination with actual conditions.

10. A method for optimizing urban development boundaries as claimed in claim 9, characterized in that: The step S55 comprises: The realistic conditions include: Whether resources meet demand, whether infrastructure adapts to current demand, whether environmental quality and social services meet residents' needs, changes in development demand for resources, future infrastructure demand and pressure, the impact of development on the environment and society, infrastructure risks, overdevelopment risks, environmental risks, social risks, and social needs; Adjustments to town boundaries include: The probability expectation value and covariance value are plotted as a two-dimensional linear graph, and the shape of the two-dimensional linear graph is adjusted as a prerequisite for using GIS tools for spatial analysis to simulate the impact of different boundary adjustments on risks, and the changes in the probability expectation value and covariance value are recorded. The changes in the optimized risk of the town boundary under various combinations of probability expectation value and covariance value are recorded to obtain the probability expectation value and covariance value combination with the lowest risk. According to the probability expectation value and covariance value combination with the lowest risk, all land use dynamic formulas are reversed to obtain the optimal internal and boundary planning of the town.

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