Urban and rural planning resource allocation optimization method based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an urban and rural planning resource allocation optimization method based on artificial intelligence. According to the urban and rural planning resource allocation optimization method based on artificial intelligence, through automatic data acquisition and cleaning, the accuracy and consistency of the data are ensured, the information integration efficiency is improved, the future resource demand can be accurately predicted by using a trend analysis and spatial statistical method, a planner is helped to cope with the rapidly changing urban and rural environment in real time, and the planning efficiency is improved. According to the method, multi-objective optimization is carried out in combination with a genetic algorithm, cost minimization is achieved, the service coverage rate and comprehensive consideration of environmental influence are maximized, scientific and reasonable decision support is provided for urban and rural planning, resource distribution and analysis results are clearly displayed by utilizing a GIS and a data visualization tool, and the method is suitable for popularization and application. The planning scheme is more intuitive and easy to understand, and sustainable development of urban and rural integration is promoted.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an urban and rural planning resource allocation optimization method based on artificial intelligence. Background Art

[0002] Urban and rural planning is an important means to achieve sustainable development and has far-reaching practical significance. With the acceleration of urbanization, the problems of uneven resource allocation and insufficient infrastructure between urban and rural areas have become increasingly prominent. Reasonable urban and rural planning can not only improve the quality of life of residents, but also effectively promote economic development and social harmony. Through scientific and reasonable resource allocation, it can optimize land use, improve the transportation system, and increase the coverage of public services, thereby promoting the integrated development of urban and rural areas.

[0003] However, current urban and rural planning often faces technical problems such as data processing difficulties, inaccurate resource forecasting, and insufficient optimization model construction. The diversity of data sources makes information integration and analysis complicated, and traditional manual analysis methods are difficult to adapt to rapidly changing environmental needs. In addition, the planning process lacks effective multi-objective optimization technology, and often cannot achieve the best balance between cost, service coverage and environmental impact. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an artificial intelligence-based method for optimizing resource allocation in urban and rural planning. Through automated data acquisition and cleaning, the accuracy and consistency of data are ensured, and the efficiency of information integration is improved. By using trend analysis and spatial statistical methods, future resource demands can be accurately predicted, helping planners to respond to the rapidly changing urban and rural environment in real time. Combined with genetic algorithms for multi-objective optimization, not only costs are minimized, but also the comprehensive consideration of service coverage and environmental impact is maximized, thereby providing scientific and reasonable decision-making support for urban and rural planning. By using GIS and data visualization tools, resource distribution and analysis results are clearly displayed, making planning schemes more intuitive and easy to understand, and promoting the sustainable development of urban and rural integration.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: an artificial intelligence-based method for optimizing urban and rural planning resource allocation, comprising the following steps:

[0008] S1. Use artificial intelligence to obtain urban and rural population data, urban and rural land use data, urban and rural infrastructure data, urban and rural environmental data, and urban and rural economic data;

[0009] S2. Clean the data obtained in S1, remove duplicate and erroneous data, perform outlier detection, and merge data from different sources into a unified data set after data standardization;

[0010] S3. Forecast future resource demand through unified data sets and trend analysis, analyze the spatial distribution characteristics of population and resources using spatial statistical methods, and construct a multi-objective optimization model with objective functions such as minimizing costs, maximizing service coverage, and maximizing environmental impact;

[0011] S4. Combine the constructed model with the genetic algorithm, optimize the model through programming, run simulations, test the model's performance in optimizing resource allocation in urban and rural planning, and generate charts of the analysis results;

[0012] S5. Use GIS software to display the distribution map of urban and rural resources, and use data visualization tools to present the analysis results in charts;

[0013] S6. Artificial intelligence automatically generates planning schemes for optimizing urban and rural planning resource allocation based on charts of analysis results.

[0014] Preferably, the formula for removing duplicate data is as follows:

[0015] D′=D\{d∈D|d=d′and d′∈D}

[0016] In the formula, D ′ represents the dataset after deleting duplicate data, D represents the original dataset, d, d ′ Represents a data item, ensuring that d does not appear repeatedly.

[0017] Preferably, the formula for removing erroneous data is as follows:

[0018] C′={s∈D′|d≥L and d≤U}

[0019] In the formula, C ′ represents the dataset after removing the erroneous data, D ′ Represents the dataset after deleting duplicate data. L represents the lower limit, filtering data below this value. U represents the upper limit, filtering data above this value.

[0020] Preferably, the formula for data normalization is as follows:

[0021]

[0022] In the formula, X ′ represents the standardized data, X represents the data to be annotated, μ represents the mean of the data to be annotated, and σ represents the annotation error of the data to be standardized.

[0023] Preferably, the formula used to merge data from different sources into a unified data set is as follows:

[0024]

[0025] In the formula, Z represents the value of the unified data set after merging, w j Indicates the weight of each standardized data value in the merging process, assigned by artificial intelligence, X ′ j represents the normalized data of the jth data source, m represents the number of different data sources being merged, and j represents the index subscript.

[0026] Preferably, the function for predicting future resource demand is as follows:

[0027] Y=β0+β1*X1+β2*X2+...+β n *X n

[0028] In the formula, Y represents the predicted resource demand, β0 represents the intercept, β1, β2, ..., β n Represents the weight coefficient, which is automatically assigned by artificial intelligence, X1, X2, ..., X n Indicates characteristics that influence demand.

[0029] Preferably, the formula for analyzing the spatial distribution characteristics of population and resources is as follows:

[0030]

[0031] In the formula, I represents the statistical index used to measure the autocorrelation of spatial data, N represents the total number of observation areas, W represents the sum of the spatial weight matrix, and w i,j represents the spatial weight between observation point i and observation point j, x i 、x j Represents the attribute values of observation point i and observation point j, Represents the mean of the attribute values of all observation points, and k represents the value used to traverse all observation points.

[0032] Preferably, the minimization cost function is as follows:

[0033] H=c1*a1+c2*a2+...+c n *a n

[0034] In the formula, H represents the total cost, c1, c2, ..., c n Represents the unit cost of resources, a1, a2, ..., a n Indicates the usage of resources.

[0035] Preferably, the function for maximizing service coverage is as follows:

[0036]

[0037] In the formula, R represents service coverage, s i represents the weight of service point i, which is dynamically assigned by artificial intelligence, h i Indicates the population covering the service point, i represents the index subscript, and n represents the total number of service points.

[0038] Preferably, the environmental impact function is as follows:

[0039]

[0040] In the formula, EBI represents the environmental burden index, f i Indicates the degree of environmental impact associated with the i-th activity, R i It represents the actual implementation degree of the i-th activity in a specific time, and m represents the number of items used to calculate the environmental burden.

[0041] Compared with the existing technology, the present invention provides an artificial intelligence-based method for optimizing urban and rural planning resource allocation, which has the following beneficial effects:

[0042] This invention ensures data accuracy and consistency through automated data acquisition and cleaning, improves the efficiency of information integration, and uses trend analysis and spatial statistical methods to accurately predict future resource demands, helping planners respond to the rapidly changing urban and rural environment in real time. Combined with genetic algorithms for multi-objective optimization, it not only minimizes costs but also maximizes the comprehensive consideration of service coverage and environmental impact, thereby providing scientific and reasonable decision-making support for urban and rural planning. By using GIS and data visualization tools, resource distribution and analysis results are clearly displayed, making planning schemes more intuitive and easy to understand, and promoting the sustainable development of urban and rural integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] In view of the technical problems that current urban and rural planning often faces, such as data processing difficulties, inaccurate resource prediction and insufficient optimization model construction, this paper proposes an urban and rural planning resource allocation optimization method based on artificial intelligence. Figure 1 , the method comprises the following steps:

[0046] S1. Use artificial intelligence to obtain urban and rural population data, urban and rural land use data, urban and rural infrastructure data, urban and rural environmental data, and urban and rural economic data;

[0047] Through artificial intelligence technology, combined with big data analysis and machine learning algorithms, we can acquire and process urban and rural population data, land use data, infrastructure data, environmental data, and economic data. First, we use natural language processing (NLP) technology to extract statistical information related to urban and rural populations from social media, government data, and other online resources, such as population density, age distribution, education level, and residents' income. At the same time, through remote sensing technology and geographic information systems (GIS), we can obtain detailed land use data covering various uses such as residential, commercial, industrial, and public facilities. In addition, by integrating various public and private databases, we use data mining technology to extract infrastructure data, including the distribution of transportation networks, public transportation facilities, medical institutions, and educational institutions.

[0048] The acquisition of environmental data relies on Internet of Things (IoT) technology. Environmental sensors monitor climate conditions, air quality, and water resources in real time to ensure data timeliness and accuracy. At the same time, economic data analysis provides support for urban planning, using machine learning algorithms to analyze key economic indicators such as local economic activity, employment rates, and industry distribution. After collection, all this data will undergo data pre-processing methods such as data cleaning, standardization, and integration to form a high-quality unified data set, laying a solid foundation for subsequent resource allocation optimization, demand forecasting, and decision support. This comprehensive data acquisition and processing method based on artificial intelligence can effectively improve the scientific nature and accuracy of urban and rural planning and promote the achievement of sustainable development goals.

[0049] S2. Clean the data obtained in S1, remove duplicate and erroneous data, perform outlier detection, and merge data from different sources into a unified data set after data standardization;

[0050] When cleaning the data obtained from S1, we first use data cleaning technology to systematically remove duplicate and erroneous data to ensure data accuracy and reliability. Specifically, deduplication can be achieved through set operations, as follows:

[0051] D′=D\{d∈D|d=d′and d′∈D}

[0052] Among them, D is the original data set, and D ′ This is a dataset after duplicate values have been removed. Duplicate data not only increases the size of the dataset but can also lead to biased analysis results and misleading conclusions. By identifying and removing duplicate records, redundancy can be effectively reduced, data processing efficiency can be improved, and analytical models can be guaranteed to obtain independent and accurate samples. This is particularly important in resource allocation optimization, as inaccurate data can lead to wasted resources or incorrect strategic decisions.

[0053] Next, to remove erroneous data, we filter out unreasonable data points by setting a threshold. The formula is:

[0054] C′={s∈D′|d≥L and d≤U}

[0055] Here, L represents the lower limit and U represents the upper limit. This effectively filters out abnormal data below or above these limits. Incorrect data may arise from input errors, sensor failures, or data loss during transmission, all of which can lead to spurious analysis results. By implementing a rigorous error detection and removal mechanism, the risk in the decision-making process can be significantly reduced.

[0056] After ensuring the reliability of the data, we enter the stage of data standardization, which is achieved through the following formula:

[0057]

[0058] Normalized data X ′ It is crucial to make all variables comparable within the same dimension for data from different sources. Finally, after data standardization, we merge the unified standardized data and combine them in a linear weighted manner. The formula is:

[0059]

[0060] In this formula, Z is the value of the merged data set, w j is the weight of the jth data source to reflect the importance of different data sources in the final analysis, X ′ j is the value of the jth standardized data source, and m is the total number of data sources. Through this series of data cleaning, detection, standardization and merging steps, a high-quality unified data set is finally formed, providing a reliable basis for subsequent analysis and decision-making;

[0061] S3. Forecast future resource demand through unified data sets and trend analysis, analyze the spatial distribution characteristics of population and resources using spatial statistical methods, and construct a multi-objective optimization model with objective functions such as minimizing costs, maximizing service coverage, and maximizing environmental impact;

[0062] In AI-based urban and rural planning, future resource demand forecasts can be effectively achieved by building a unified data set and conducting trend analysis. First, the unified data set integrates information from different sources, including population, resource utilization, infrastructure, and economic indicators. This provides a solid foundation for data analysis. Using time series analysis or regression models, future trends in resource demand can be inferred based on historical data. The specific formula is:

[0063] Y=β0+β1*X1+β2*X2+...+β n *X n

[0064] Among them, Y represents resource demand, β0 is a constant term, and β1 to β n is the regression coefficient, X n The factors that affect resource demand (e.g., population growth, economic development) enable decision makers to plan resource allocation more effectively through accurate resource demand forecasts.

[0065] In order to gain a deeper understanding of the spatial distribution characteristics of resources, it is particularly important to use spatial statistical methods for analysis. The Moran's I index is used to measure the spatial concentration and distribution characteristics of population and resources. The formula is:

[0066]

[0067] In this formula, N is the total number of sample points, W represents the sum of spatial weights, and w i,j is the weight between corresponding points, x i and x j is the attribute value of the sample point, and It is the mean of the attribute values. Through spatial statistical analysis, decision makers can identify the areas with advantages and disadvantages in the distribution of service facilities and resources, and thus optimize planning in a targeted manner.

[0068] After resource demand forecasting and spatial distribution analysis, a multi-objective optimization model will be constructed. The optimization objective functions include minimizing cost, maximizing service coverage, and optimizing environmental impact. The objective function of minimizing cost can be expressed as:

[0069] H=c1*a1+c2*a2+...+c n *a n

[0070] Here, H is the total cost, c i is the unit cost of the i-th resource, a i is the corresponding resource allocation amount. Through this objective function, we can identify the costly links in resource allocation and optimize expenditure.

[0071] The objective function of maximizing service coverage can be expressed as:

[0072]

[0073] Among them, R represents service coverage, s i is the demand weight of the ith region, h i The coverage of services within the area helps ensure that urban residents have equal access to basic services, improving their quality of life and social well-being;

[0074] At the same time, the objective function of optimizing environmental impact can be expressed as:

[0075]

[0076] Among them, EBI represents environmental impact, f i is the weight of the i-th environmental indicator, R i The amount of resources or activities involved will ensure that the allocation of resources is consistent with environmental protection, reducing the ecological footprint and promoting sustainable development.

[0077] By constructing a multi-objective optimization model with the above three objective functions, decision makers can comprehensively consider cost-effectiveness, service fairness, and environmental sustainability, thereby forming a comprehensive, scientific, and reasonable urban and rural planning strategy. This multi-dimensional optimization strategy not only improves the efficiency of resource allocation, but also enhances the overall welfare of society and promotes the coordinated development of urban and rural areas.

[0078] S4. Combine the constructed model with the genetic algorithm, optimize the model through programming, run simulations, test the model's performance in optimizing resource allocation in urban and rural planning, and generate charts of the analysis results;

[0079] In the optimization of resource allocation in urban and rural planning, combining the constructed multi-objective optimization model with the genetic algorithm can effectively improve the efficiency and accuracy of the model solution. The genetic algorithm is an optimization technology based on the principles of natural selection and genetics. It is suitable for solving multi-objective optimization problems, especially showing strong capabilities in combinatorial optimization and complex search spaces. The basic framework of the genetic algorithm needs to be implemented through a programming language (such as Python, R or MATLAB). The encoding process of the model will represent the configuration plan of each resource as a genome (i.e., chromosome), where each gene represents the configuration amount of a certain resource in a specific area. These individuals (plans) are then initialized to create an initial population. These individuals will continue to undergo selection, crossover and mutation operations in subsequent iterations to find the optimal solution.

[0080] During the optimization process, a fitness function is designed to evaluate the performance of each individual. The fitness function comprehensively considers multiple objectives, such as minimizing costs, maximizing service coverage, and optimizing environmental impact. Through this fitness function, the genetic algorithm can select based on the performance of each individual, retaining excellent individuals and introducing new mutations, and continue iterating until convergence conditions are reached.

[0081] The key to running a simulation is to set appropriate genetic algorithm parameters, such as population size, crossover rate, mutation rate, and number of iterations. These parameters have a significant impact on the final optimization results, so they usually need to be tuned through experiments. After the simulation is completed, resource allocation solutions at different iteration stages can be collected and analyzed for their performance in optimizing urban and rural planning resource allocation.

[0082] Finally, to present the analysis results more intuitively, it is necessary to generate charts using data visualization tools (such as Matplotlib, Seaborn, or Tableau). These charts include fitness change curves during the optimization process, heat maps of resource allocation distribution in various regions, relationship diagrams between cost and service coverage, and even trend diagrams of environmental impact indicators. The visualization of these analysis results not only clearly demonstrates the optimization process and effects of the model, but also provides an intuitive basis for decision makers, facilitating the implementation and evaluation of subsequent decisions.

[0083] By using such technical means, combined with the advantages of genetic algorithms and effective programming implementation, the performance of the model in optimizing resource allocation in urban and rural planning can be greatly improved, making the allocation of resources more scientific and reasonable;

[0084] S5. Use GIS software to display the distribution map of urban and rural resources, and use data visualization tools to present the analysis results in charts;

[0085] In the process of optimizing resource allocation in urban and rural planning, using geographic information system (GIS) software to display urban and rural resource distribution maps is a key step. GIS software, such as QGIS or ArcGIS, enables planners to combine spatial data with attribute data to generate detailed and intuitive resource distribution maps. This process usually involves importing multiple data sources, such as population density, infrastructure distribution, land use types, and environmental characteristics, to build a comprehensive spatial database.

[0086] In GIS software, users can perform spatial analysis on data, including buffer analysis, overlay analysis, and hotspot analysis. These analyses can reveal the concentration and dispersion of resources across regions. Users can intuitively express resource distribution characteristics through color gradients, symbolization, and vector layer combinations. For example, users can use different colors to display the use of different types of land, or use dot maps to illustrate the location of infrastructure. These visualizations help planners and decision makers quickly identify resource-rich and resource-scarce areas, providing important evidence for policy and resource allocation.

[0087] In addition to GIS presentation, data visualization tools (such as Tableau, Power BI, or Matplotlib) can be used to further present analysis results in charts and graphs. These tools can transform complex data results into easy-to-understand charts, such as bar charts, pie charts, line charts, and heat maps. Through visualization, we can effectively display key indicators such as resource utilization, service coverage, environmental impact, and cost-benefit analysis, facilitating multi-dimensional comparison and analysis.

[0088] For example, a heat map of service coverage can be generated to visually demonstrate which areas have unmet service needs. Time series data can also be displayed in line charts to analyze trends in resource demand. This visual approach makes data analysis less obscure and more clearly presented, facilitating communication between planners and stakeholders and making urban and rural planning strategies more persuasive and scientifically based.

[0089] S6. Artificial intelligence automatically generates planning schemes for optimizing urban and rural planning resource allocation based on the charts of analysis results;

[0090] By integrating charts and graphs of analytical results, AI models can quickly analyze patterns and trends in the data and automatically formulate resource allocation plans based on established optimization goals (such as minimizing costs, maximizing service coverage, and optimizing environmental impact);

[0091] This process first involves data integration and preprocessing. Artificial intelligence algorithms, particularly machine learning and deep learning models, can process large amounts of information from diverse data sources (e.g., GIS analysis results, historical resource usage data, and demographic and economic indicators). These models can be trained to learn the relationship between data characteristics and optimal resource allocation. For example, models such as random forests, support vector machines, or neural networks can be used for regression analysis or classification tasks to predict the impact of different resource allocations on urban and rural development goals.

[0092] Once the model is fully trained, it can generate optimized planning solutions based on new input data (such as updated resource demand forecasts and spatial distribution analysis). Specifically, the AI system can use optimization algorithms such as genetic algorithms to explore possible resource allocation options and evaluate the effectiveness of each solution using an objective function.

[0093] The resulting planning scheme will not only be static text or drawings, but AI can also be integrated with GIS software to automatically create resource distribution maps and service coverage maps. This visual planning scheme can help decision makers intuitively understand the specific impact of different configurations on urban and rural development, making it easier to evaluate and implement.

[0094] In addition, AI can also take into account various constraints (such as budget restrictions, environmental regulations, and social needs) and make intelligent adjustments when generating plans to ensure the feasibility and effectiveness of the plans. Through this series of automated processing, artificial intelligence not only improves the efficiency and scientific nature of urban and rural planning resource allocation, but also makes the planning process more flexible and responsive, ultimately promoting better urban and rural development.

[0095] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based method for optimizing resource allocation in urban and rural planning, characterized in that: The following steps are involved: S1. Use artificial intelligence to obtain urban and rural population data, urban and rural land use data, urban and rural infrastructure data, urban and rural environmental data, and urban and rural economic data; S2. Clean the data obtained in S1, remove duplicate and erroneous data, perform outlier detection, and merge data from different sources into a unified data set after data standardization; S3. Forecast future resource demand through unified data sets and trend analysis, analyze the spatial distribution characteristics of population and resources using spatial statistical methods, and construct a multi-objective optimization model with objective functions such as minimizing costs, maximizing service coverage, and maximizing environmental impact; S4. Combine the constructed model with the genetic algorithm, optimize the model through programming, run simulations, test the model's performance in optimizing resource allocation in urban and rural planning, and generate charts of the analysis results; S5. Use GIS software to display the distribution map of urban and rural resources, and use data visualization tools to present the analysis results in charts; S6. Artificial intelligence automatically generates planning schemes for optimizing urban and rural planning resource allocation based on charts of analysis results.

2. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 1, characterized in that: The formula for removing duplicate data is as follows: D′=D\{d∈D|d=d′and d′∈D} In the formula, D′ represents the dataset after deduplication, D represents the original dataset, and d and d′ represent data items, ensuring that d does not appear repeatedly.

3. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 2, characterized in that: The formula for removing erroneous data is as follows: C′={s∈D′|d≥L and d≤U} In the formula, C' represents the data set after removing erroneous data, D' represents the data set after removing duplicate data, L represents the lower limit, and data below this value is filtered out; U represents the upper limit, and data above this value is filtered out.

4. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 3, characterized in that: The formula for data normalization is as follows: In the formula, X′ represents the standardized data, X represents the data to be annotated, μ represents the mean of the data to be annotated, and σ represents the annotation error of the data to be standardized.

5. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 4, characterized in that: The formula used to combine data from different sources into a unified dataset is as follows: In the formula, Z represents the value of the unified data set after merging, w j Represents the weight of each standardized data value in the merging process, assigned by artificial intelligence, X′ j represents the normalized data of the jth data source, m represents the number of different data sources being merged, and j represents the index subscript.

6. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 5, characterized in that: The function for predicting future resource requirements is as follows: Y=β0+β1*X1+β2*X2+...+β n *X n In the formula, Y represents the predicted resource demand, β0 represents the intercept, β1, β2, ..., β n Represents the weight coefficient, which is automatically assigned by artificial intelligence, X1, X2, ..., X n Indicates characteristics that influence demand.

7. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 6, characterized in that: The formula for analyzing the spatial distribution characteristics of population and resources is as follows: In the formula, I represents the statistical index used to measure the autocorrelation of spatial data, N represents the total number of observation areas, W represents the sum of the spatial weight matrix, and w i,j represents the spatial weight between observation point i and observation point j, x i 、x j Represents the attribute values of observation point i and observation point j, Represents the mean of the attribute values of all observation points, and k represents the value used to traverse all observation points.

8. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 7, characterized in that: The minimization cost function is as follows: H=c1*a1+c2*a2+...+c n *a n In the formula, H represents the total cost, c1, c2, ..., c n Represents the unit cost of resources, a1, a2, ..., a n Indicates the usage of resources.

9. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 8, characterized in that: The function for maximizing service coverage is as follows: In the formula, R represents service coverage, s i represents the weight of service point i, which is dynamically assigned by artificial intelligence, h i Indicates the population covering the service point, i represents the index subscript, and n represents the total number of service points.

10. The method for optimizing resource allocation for urban and rural planning based on artificial intelligence according to claim 9, characterized in that: The environmental impact function is as follows: In the formula, EBI represents the environmental burden index, f i Indicates the degree of environmental impact associated with the i-th activity, R i It represents the actual implementation degree of the i-th activity in a specific time, and m represents the number of items used to calculate the environmental burden.

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