Rural space optimization method and system

Abstract

The invention relates to the technical field of big data processing and intelligent optimization, in particular to a rural space optimization method and system, and the method comprises the steps: collecting multi-dimensional data in a rural region in real time, carrying out the dynamic feedback analysis through an AI-driven feedback system, and combining a multi-agent system with a game theory model, thereby generating an optimal space layout path. And then, through association rule mining and a dynamic target re-optimization algorithm, continuously adjusting and optimizing the rural space layout, and realizing a long-acting optimization mechanism. According to the invention, changes can be responded in real time in a dynamic environment, sustainable development of a rural area is ensured, ecological protection, economic benefits and social acceptance are improved, and comprehensive optimization of a rural space is realized.

Description

Technical Field

[0001] The present invention relates to the field of big data processing and intelligent optimization technology, and in particular to a rural space optimization method and system. Background Art

[0002] With the development of modern agriculture, rural areas are facing the challenges of how to efficiently utilize resources, protect the ecological environment, and improve economic benefits. However, the existing rural space optimization technology (Chinese invention patent, publication number: CN117788227A, name: A spatial optimization method and system for agricultural rural settlements) has obvious shortcomings in dealing with these challenges. The existing technology mainly relies on agricultural modernization, industrialization construction and capital promotion to carry out rural mergers and the construction of "central towns and villages". These methods ignore the natural industry relationship and local development laws of rural settlements, resulting in the reconstruction of rural spatial layout often showing a leap-like change, which cannot fully demonstrate the characteristics of the agricultural economy. In addition, the existing methods of studying rural transformation models focus more on macro-functional zoning and the increase and decrease of rural construction land, lack of in-depth analysis of micro-functions and land changes, resulting in difficulty in achieving refined management and dynamic adjustment in practical applications. Summary of the invention

[0003] In view of the many problems existing in the above-mentioned prior art, the present invention provides a rural space optimization method and system. The present invention monitors and analyzes the dynamic changes of rural areas in real time by integrating IoT devices, AI-driven feedback systems, multi-agent systems and association rule mining algorithms, and dynamically optimizes the rural space layout through an adaptive path adjustment mechanism. It can continuously optimize the rural space layout in a changing environment, achieve coordinated development among various functional areas, and maintain a balance between ecological, economic and social benefits.

[0004] The rural space optimization method includes the following steps:

[0005] Collect multi-dimensional data in rural areas in real time through IoT devices, clean and format the data, integrate the processed multi-dimensional data into comprehensive rural data through data fusion algorithms, and use machine learning algorithms to conduct preliminary analysis of the comprehensive rural data to generate transformation dynamics analysis data;

[0006] Initialize the multi-agent system based on the transformation dynamics analysis data, generate the first agent optimization path data through the adaptive weight allocation algorithm and the multi-objective evolutionary algorithm, and generate the first space optimization path data through the smart contract execution and blockchain coordination mechanism;

[0007] Import the first space optimization path data into the virtual reality environment, apply the multi-dimensional conflict detection algorithm and the feedback integration algorithm based on game theory to perform simulation and feedback, perform multiple rounds of iterative optimization in the virtual reality environment, and finally generate the optimized iterative path data;

[0008] Based on the optimized iterative path data, the dynamic target re-optimization algorithm and the association rule mining algorithm are applied to dynamically adjust and optimize the optimized iterative path data, and finally generate the final optimized path data;

[0009] The final optimized path data is monitored in real time through IoT devices, dynamic feedback analysis is performed using an AI-driven feedback system, and dynamically adjusted path data is generated through an adaptive path adjustment mechanism to form a long-term optimization mechanism.

[0010] Preferably, the adaptive weight allocation algorithm is implemented by the following calculation expression:

[0011]

[0012] Among them, W i (t) represents the weight of the i-th agent in the t-th round of optimization; W i (t-1) represents the weight of the i-th agent in the t-1th round of optimization; F i (t) represents the fitness value of the i-th agent in the t-th round of optimization; represents the average fitness value of all agents in the tth round of optimization; α is the adjustment parameter used to adjust the weight update amplitude.

[0013] Preferably, the multi-objective evolutionary algorithm comprises the following steps:

[0014] Each path in the first agent's optimized path data is used as an individual in the evolutionary population;

[0015] Calculate the fitness of each individual for multiple optimization objectives;

[0016] Generate new populations through crossover, mutation and selection operations until a predetermined number of evolutionary generations is reached;

[0017] Output the individual with the highest fitness as the optimized path.

[0018] Preferably, the smart contract execution and blockchain coordination mechanism includes:

[0019] Recording the generation process of the first agent's optimization path data on the blockchain;

[0020] Leverage smart contracts to automate path verification and decision coordination.

[0021] Preferably, the multi-dimensional conflict detection algorithm generates conflict detection feedback data and provides adjustment suggestions by analyzing potential conflicts among ecological impacts, economic benefits and social acceptance among different paths.

[0022] Preferably, the feedback integration algorithm based on game theory includes:

[0023] Simulate the interaction and game between different stakeholders in a virtual reality environment;

[0024] Through the game theory model, we can find the balance point of the interests of all parties, optimize the path and generate game feedback optimization data.

[0025] Preferably, the dynamic target re-optimization algorithm is implemented by the following calculation expression:

[0026] L(t)=L(t-1)+λ·Δ(t)

[0027] Among them, L(t) represents the loss function value in the t-th round of optimization; L(t-1) represents the loss function value in the t-1th round of optimization; λ is the learning rate, which is used to control the speed of loss function adjustment; Δ(t) represents the error between the predicted value and the actual target value in the t-th round.

[0028] Preferably, the association rule mining algorithm comprises the following steps:

[0029] Extract key path features from optimization iteration path data;

[0030] Analyze the potential associations between paths using association rule mining algorithms;

[0031] The path is optimized and adjusted based on the association rules to generate association optimized path data.

[0032] Preferably, the continuous monitoring and dynamic adjustment include:

[0033] Use IoT devices to collect dynamic data in rural areas in real time and generate real-time monitoring data;

[0034] Use AI-driven feedback systems to analyze real-time monitoring data and generate feedback analysis data;

[0035] Based on feedback analysis data, dynamic adjustment path data is generated through an adaptive path adjustment mechanism and applied to the rural space optimization path.

[0036] A system for implementing the rural space optimization method, comprising:

[0037] The data collection and processing module is used to collect multi-dimensional data in rural areas in real time through IoT devices, and to clean and format the data. The processed multi-dimensional data is integrated into rural comprehensive data using data fusion algorithms, and the rural comprehensive data is preliminarily analyzed using machine learning algorithms to generate transformation dynamics analysis data.

[0038] Intelligent collaborative optimization module, used to initialize the multi-agent system based on transformation dynamics analysis data, execute adaptive weight allocation algorithm and multi-objective evolutionary algorithm to generate first agent optimization path data, and generate first space optimization path data through smart contract execution and blockchain coordination mechanism;

[0039] A virtual reality simulation and feedback module is used to import the first space optimization path data into the virtual reality environment, apply a multi-dimensional conflict detection algorithm and a feedback integration algorithm based on game theory to perform simulation and feedback, and perform multiple rounds of iterative optimization in the virtual reality environment to finally generate optimized iterative path data;

[0040] The adaptive optimization and path generation module is used to dynamically adjust and optimize the optimized iterative path data based on the optimized iterative path data by applying the dynamic target re-optimization algorithm and the association rule mining algorithm, and finally generate the final optimized path data;

[0041] The real-time monitoring and dynamic adjustment module is used to monitor the final optimized path data in real time through IoT devices, perform dynamic feedback analysis using the AI-driven feedback system, and generate dynamically adjusted path data through an adaptive path adjustment mechanism to form a long-term optimization mechanism.

[0042] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0043] The present invention overcomes the defects of the prior art through multi-dimensional technical means and achieves a more refined and dynamic rural space optimization effect;

[0044] The present invention realizes dynamic monitoring and real-time adjustment of rural spatial layout by using IoT devices to collect data in real time and AI-driven feedback system, effectively avoiding the problem of transitional spatial reconstruction.

[0045] The present invention utilizes a multi-agent system and a feedback integration algorithm based on game theory to ensure collaborative optimization between different functional areas and solve the coordination problem existing in macro-functional zoning.

[0046] The present invention realizes continuous optimization of rural spatial layout through association rule mining algorithm and dynamic target re-optimization algorithm, ensuring the sustainability of the plan in long-term development. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the process of the present invention;

[0048] Figure 2 A flow chart for generating an optimized path for the multi-agent system of the present invention;

[0049] Figure 3 It is a flow chart of the association rule mining algorithm in the present invention;

[0050] Figure 4 It is a schematic diagram of the dynamic feedback and adjustment mechanism in the present invention;

[0051] Figure 5 This is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0052] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0053] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0054] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0055] like Figure 1 As shown, the rural space optimization method includes the following steps:

[0056] Collect multi-dimensional data in rural areas in real time through IoT devices, clean and format the data, integrate the processed multi-dimensional data into comprehensive rural data through data fusion algorithms, and use machine learning algorithms to conduct preliminary analysis of the comprehensive rural data to generate transformation dynamics analysis data;

[0057] The deployment of IoT devices covers the entire rural area, and the types of data collected include but are not limited to real-time information on land use, ecological environment, economic activities, social culture, and infrastructure. The multi-dimensional characteristics of these data enable a comprehensive reflection of the current situation in the rural area, providing sufficient information support for subsequent analysis and optimization.

[0058] After collecting these multi-dimensional data, the system first cleans and formats the data. The purpose of data cleaning is to remove erroneous, incomplete or noisy data to ensure the accuracy and consistency of the data. This process is relatively mature in the field of data science. Its core is to automatically identify and correct outliers or missing values ​​in the data through algorithms to ensure the quality of the data. Formatting is to unify data from different sources into a standard format for subsequent integration and analysis. The effect of data cleaning and formatting is directly related to the accuracy and reliability of subsequent data analysis, so this link is crucial to achieving efficient rural space optimization.

[0059] The processed multi-dimensional data is integrated into rural comprehensive data through the data fusion algorithm. The data fusion algorithm integrates data from different sources and types, eliminates redundant information, and extracts the most critical features for rural space optimization. For example, the fusion of land use data from remote sensing images and ecological environment data from sensors can generate comprehensive indicators reflecting the health status of the land, thereby providing a more accurate basis for land use planning. The principle of data fusion is to generate more comprehensive and accurate rural comprehensive data than a single data source through the complementarity of multi-source data. Its effect is reflected in the ability to provide high-quality input data for subsequent machine learning algorithms, making the analysis results more reliable and accurate.

[0060] After generating comprehensive rural data, the system further uses machine learning algorithms to conduct preliminary analysis. The core of the machine learning algorithm is to automatically identify the transformation dynamics and potential problems of rural areas through learning from large-scale data. This process includes the joint analysis of historical data and real-time data, and generates transformation dynamics analysis data through pattern recognition and predictive analysis. Transformation dynamics analysis data is a quantitative description of rural spatial development trends, resource allocation, economic dynamics and other factors. For example, machine learning algorithms can identify that the agricultural transformation dynamics of a particular village comes from its unique geographical location and ecological advantages, and predict the best development path in the next few years. Through this analysis, the system can formulate more targeted optimization strategies for rural areas to ensure efficient use of spatial resources and sustainable development.

[0061] like Figure 2As shown, the multi-agent system is initialized based on the transformation dynamics analysis data, the first agent optimization path data is generated through the adaptive weight allocation algorithm and the multi-objective evolutionary algorithm, and the first space optimization path data is generated through the smart contract execution and blockchain coordination mechanism;

[0062] The core of the multi-agent system is to treat different functional areas in the rural area (such as agricultural areas, residential areas, ecological protection areas, etc.) as independent agents, and each agent makes autonomous decisions based on its specific goals and conditions. This method can fully consider the complexity and diversity of rural areas, allowing each agent to optimize for the area it is responsible for, thereby improving the decision-making efficiency and accuracy of the entire system.

[0063] The first step in initializing the multi-agent system is to use the transformation dynamics analysis data. The transformation dynamics analysis data is generated by machine learning algorithms in the early stage, which includes quantitative analysis of the power sources, development potential and potential problems of each functional area in the rural area. These data provide a basis for the agent to make decisions, enabling it to formulate optimization strategies that meet actual needs based on current regional characteristics and future development trends.

[0064] In the agent system, the first agent optimization path data is generated through the adaptive weight allocation algorithm and the multi-objective evolutionary algorithm. The principle of the adaptive weight allocation algorithm is to dynamically adjust the decision weights of each agent based on real-time feedback. Specifically, the agent will adjust its influence in the overall decision-making according to its current performance (for example, resource utilization efficiency, ecological impact, etc.) in each round of decision-making. This process is similar to adaptive evolution in nature, which enables the decision of each agent to be more in line with the actual situation, thereby optimizing the performance of the entire system. For example, in an agricultural agent, if its decision leads to higher ecological benefits and economic returns, then in the next round of decision-making, its weight will increase accordingly to ensure that its optimized path plays a greater role in the entire rural planning.

[0065] The multi-objective evolutionary algorithm optimizes the path decisions of each agent in multiple rounds of iterations by simulating the biological evolution process. The algorithm optimizes multiple objectives (such as ecological protection, economic benefits, social impact, etc.), generates new path plans through operations such as crossover, mutation, and selection, and retains the plan with the highest fitness. The effect of this process is to find the optimal balance between the agents, ensuring that the optimization paths of various functional areas in the rural area are not only independent and effective, but also coordinated and consistent as a whole. For example, on the same piece of land, agricultural agents and ecological protection agents may conflict. Through the multi-objective evolutionary algorithm, these agents can find a balance so that agricultural production and ecological protection can be optimized at the same time.

[0066] After the first agent optimization path data is generated, the first space optimization path data is generated through the smart contract execution and blockchain coordination mechanism. Smart contract is an automated program embedded in the blockchain. Its principle is to automatically perform coordination and path verification between agents according to predefined rules. The characteristics of blockchain are decentralization and data immutability, which enables the decision-making process and results of each agent to be openly and transparently recorded on the blockchain, ensuring the safe, reliable and transparent operation of the entire system. For example, the smart contract can automatically detect that the optimization path of a certain agent conflicts with other agents, and execute the coordination mechanism on the blockchain to automatically adjust the relevant paths, thereby generating the coordinated first space optimization path data.

[0067] The effect of this process is to ensure that the execution of rural space optimization plans is not only efficient, but also highly reliable and transparent. Through the combination of smart contracts and blockchain, the decision-making process of the entire system becomes traceable and tamper-proof, solving the inefficiency and injustice caused by information asymmetry and human intervention in traditional rural planning.

[0068] In summary, the multi-agent system is initialized based on the transformation dynamics analysis data, the first agent optimization path data is generated through the adaptive weight allocation algorithm and the multi-objective evolutionary algorithm, and the first space optimization path data is generated through the smart contract execution and blockchain coordination mechanism. This not only improves the decision-making efficiency and accuracy of rural space optimization, but also ensures the transparency and reliability of the decision-making process through blockchain technology. This process provides a solid foundation for the subsequent virtual reality simulation and feedback links, so that the entire rural space optimization plan can better meet the actual application needs and ensure the sustainable development of the countryside and the efficient use of resources.

[0069] Preferably, the adaptive weight allocation algorithm is implemented by the following calculation expression:

[0070]

[0071] Among them, W i (t) represents the weight of the i-th agent in the t-th round of optimization; W i (t-1) represents the weight of the i-th agent in the t-1th round of optimization; F i (t) represents the fitness value of the i-th agent in the t-th round of optimization; represents the average fitness value of all agents in the tth round of optimization; α is the adjustment parameter used to adjust the weight update amplitude.

[0072] The adaptive weight allocation algorithm is a key algorithm for dynamically adjusting the decision weights of agents in a multi-agent system. The core principle of the algorithm is to update the weight of each agent in real time through each round of feedback, so that the system can adaptively optimize various decisions in rural spatial planning. This process is crucial in rural spatial optimization because it ensures that various functional areas (such as agricultural areas, residential areas, ecological protection areas, etc.) can be dynamically balanced in global decision-making, thereby achieving the best overall planning effect.

[0073] The core idea of ​​the adaptive weight allocation algorithm is to dynamically adjust the weight of the agent in the overall decision-making based on its performance in each round of optimization. i (t) is a key indicator to measure the performance of the agent in the current round, which is usually calculated based on multi-dimensional factors such as resource utilization efficiency, environmental protection effect, and economic benefits in the area where the agent is responsible. Then it will get a higher weight in the next round of decision-making, thus playing a greater role in global optimization. Conversely, if the fitness value is lower than the average level, the weight of the agent will be reduced accordingly to reduce its adverse impact on the overall decision-making.

[0074] Effect: Through this dynamic adjustment mechanism, the entire system can adaptively optimize the spatial layout of each functional area, so that those agents with better performance can have a greater impact on the final spatial optimization path, while those agents with poor performance gradually reduce their influence on the decision. This mechanism ensures that rural spatial optimization can be continuously adjusted as the actual situation changes, maintaining the adaptability and dynamic balance of planning. For example, in an agricultural agent, if the agent significantly improves agricultural output and ecological benefits through reasonable resource allocation and environmental protection measures in the current round, then its weight will increase in the next round, thereby affecting land use and agricultural layout to a greater extent in the entire rural planning.

[0075] Example: Assume that in a certain rural area, agent A is responsible for the agricultural area and agent B is responsible for the ecological area. In the initial stage, agent A has a significantly higher fitness value than other agents because it has chosen an efficient planting method and a reasonable irrigation system. Under the action of the algorithm, the weight of agent A increases in the next round of decision-making, making its decision more dominant in the entire rural planning. At the same time, the fitness value of agent B is lower than the average due to the lack of effective ecological restoration measures, and its weight is reduced in the next round, forcing it to make adjustments in the next decision to improve its fitness value. Finally, through multiple rounds of adaptive adjustments, the system can find the optimal balance between agricultural production and ecological protection, and achieve the optimization and sustainable development of rural space.

[0076] Preferably, the multi-objective evolutionary algorithm comprises the following steps:

[0077] Each path in the first agent's optimized path data is used as an individual in the evolutionary population;

[0078] Calculate the fitness of each individual for multiple optimization objectives;

[0079] Generate new populations through crossover, mutation and selection operations until a predetermined number of evolutionary generations is reached;

[0080] Output the individual with the highest fitness as the optimized path.

[0081] The goal evolution algorithm is a key algorithm for optimizing rural spatial layout. Its core is to gradually optimize the path decisions of each intelligent agent in multiple rounds of iterations by simulating the biological evolution process. The design of this algorithm enables the system to find the optimal balance between multiple goals and ensure the coordinated development of different functional areas (such as agricultural areas, residential areas, ecological protection areas, etc.) in rural areas.

[0082] The working mechanism of the multi-objective evolutionary algorithm can be divided into four main steps:

[0083] Treat each path in the first agent's optimized path data as an individual in the evolutionary population: In this stage, the system treats the path data of each agent generated in the previous round of optimization as an individual in the population. Each individual represents a possible rural spatial layout path. These path data cover the specific arrangements of each functional area, including land use methods, infrastructure layout, ecological protection measures, etc.

[0084] Calculate the fitness of each individual for multiple optimization goals: In this stage, the system calculates the fitness value for each path individual. The fitness calculation is based on multiple optimization goals, such as ecological balance, economic benefits, social impact, etc. Each goal may have different weights, which determine their influence on the overall fitness. For example, a path that can protect the ecological environment while also bringing significant economic returns will have a higher fitness value. The higher the fitness value of an individual, the closer its path plan is to the ideal state, and the more likely it is to be retained or optimized in the next round.

[0085] Generate new populations through crossover, mutation and selection operations until the predetermined evolutionary generation is reached: This stage is the core of the multi-objective evolutionary algorithm. Through crossover, mutation and selection operations, the system generates new path individuals in each round. The crossover operation is to merge the two better path solutions to generate a new combined path. The mutation operation randomly introduces some changes in the existing path to increase the diversity of the population. The selection operation is to retain individuals with higher fitness to enter the next generation based on the fitness value of the individual, while eliminating individuals with lower fitness. This process continues until the predetermined evolutionary generation is reached, that is, the system has performed enough rounds of optimization iterations to make the path solution stable and optimal.

[0086] Output the individual with the highest fitness as the optimized path: In the final stage, the system will select the path individual with the highest fitness as the final optimized path. This path represents the best balance point found between multiple optimization objectives after multiple rounds of evolution. It not only meets the needs of ecological protection, economic development and social benefits, but also realizes the coordinated optimization among various functional areas in the global perspective.

[0087] Through the multi-objective evolutionary algorithm, the system can find a balance between multiple optimization objectives in the complex rural spatial layout, ensuring that the final spatial optimization path is not only locally optimal, but also globally optimal. The advantage of this algorithm lies in its dynamic adaptability and global optimization capabilities, which enable rural spatial planning to not only achieve the optimal layout at the moment, but also cope with potential changes in the future. For example, through continuous iterative optimization, the system can find a land use method that maximizes economic returns while protecting the natural environment, or find the best balance between infrastructure construction and ecological restoration.

[0088] Assume that in a certain rural area, agent A is responsible for the agricultural area, agent B is responsible for the residential area, and agent C is responsible for the ecological protection area. In the initial path plan, agent A's path focuses on efficient use of land for agricultural production, but ignores ecological protection, while agent C's path emphasizes ecological restoration, but has lower requirements for land use efficiency. Through the multi-objective evolutionary algorithm, the system can generate a new path by partially crossing the paths of agent A and agent C, and continuously optimize the balance between the two through multiple rounds of mutation and selection operations, and finally generate an optimal path that can protect the ecological environment and maintain agricultural production efficiency. This path plan not only meets the current ecological and economic needs, but also can adapt to possible environmental changes and social needs in the future, ensuring the sustainable development of rural areas.

[0089] Preferably, the smart contract execution and blockchain coordination mechanism includes:

[0090] Recording the generation process of the first agent's optimization path data on the blockchain;

[0091] Leverage smart contracts to automate path verification and decision coordination.

[0092] The core of blockchain technology is a decentralized distributed ledger, which is characterized by the fact that once the data is written to the blockchain, it cannot be tampered with. Each block contains the encrypted hash value of the previous block, forming an unchangeable chain. In this scheme, the generation process of the first agent's optimization path data is recorded on the blockchain, which includes the generation, update and verification of each agent's path data. By writing this data to the blockchain, the system ensures that the path data generated by each agent is transparent and traceable throughout the rural space optimization process. Collaboration or conflict between agents, as well as the handling decisions of these events, are recorded on the blockchain, thereby ensuring the fairness and transparency of the entire optimization process.

[0093] After the first agent's optimized path data is recorded on the blockchain, the smart contract begins to execute. A smart contract is a self-executing code that automatically operates according to pre-set rules. In this scheme, the main role of the smart contract is to perform path verification and decision coordination. Path verification refers to the smart contract automatically checking whether the optimized path generated by each agent meets the goal of global optimization, for example, whether multiple goals such as ecological protection, economic development and social benefits are taken into account. If a certain path data does not meet the requirements, the smart contract will automatically trigger the adjustment mechanism and require the relevant agent to regenerate or adjust its path data. Decision coordination is to automatically coordinate the path conflicts between multiple agents through smart contracts. For example, when the path in the agricultural area conflicts with the path in the ecological protection area, the smart contract will automatically coordinate the priorities between the two agents and decide how to adjust the path according to the preset rules to avoid conflicts and ensure the optimality of the global path.

[0094] Through the execution of smart contracts and the coordination mechanism of blockchain, this scheme has achieved several important effects in the process of rural space optimization. First, the immutability of blockchain ensures the authenticity and reliability of all path data, making it impossible for any party to tamper with the optimization process, thereby ensuring the fairness and transparency of decision-making. Secondly, the automatic execution of smart contracts greatly improves the efficiency of the decision-making process, reduces the possibility of human intervention, and ensures real-time adjustment and dynamic coordination of the optimization path. Most importantly, this mechanism ensures that the various paths in the multi-agent system can effectively coordinate and avoid conflicts between paths, thereby achieving the global optimal rural space layout.

[0095] In the spatial optimization process of a rural area, assume that agent A is responsible for the agricultural area and agent B is responsible for the ecological protection area. Agent A generates an optimization path to increase agricultural production on a piece of land, while agent B generates another path to restore the ecology on the same piece of land. There is an obvious conflict between the two paths. At this point, the generation process of the two paths has been recorded on the blockchain. After detecting the conflict, the smart contract automatically triggers the coordination mechanism and decides to adjust the path of agent A according to the preset priority rules (such as ecological protection first). The smart contract then notifies agent A to regenerate the path, ultimately ensuring that the two can achieve their respective optimization goals without conflict. This process does not require human intervention, and the entire adjustment process is fast and efficient.

[0096] Import the first space optimization path data into the virtual reality environment, apply the multi-dimensional conflict detection algorithm and the feedback integration algorithm based on game theory to perform simulation and feedback, perform multiple rounds of iterative optimization in the virtual reality environment, and finally generate the optimized iterative path data;

[0097] Importing the first spatial optimization path data into the virtual reality environment and applying the multi-dimensional conflict detection algorithm and the feedback integration algorithm based on game theory for simulation and feedback is one of the key steps to achieve rural space optimization. The core of this process is to use virtual reality (VR) technology to create a highly simulated rural space model, and through multi-dimensional conflict detection and game interaction between stakeholders, multiple rounds of iterative optimization are carried out to generate the optimal spatial layout plan.

[0098] The first spatial optimization path data is a preliminary optimization plan generated by the intelligent system and algorithm in the early stage, covering the spatial layout of various functional areas in the rural area. After these data are imported into the virtual reality environment, the system will generate a three-dimensional model in the virtual space that is highly similar to the real world. This virtual model not only contains geographic information, but also includes multi-dimensional information such as ecological environment, infrastructure, and economic activities, allowing users to observe and analyze the rural spatial layout in an all-round way in the virtual environment.

[0099] In the virtual reality environment, the system first applies a multi-dimensional conflict detection algorithm to analyze the imported path data. The principle of the multi-dimensional conflict detection algorithm is to identify possible conflict points by analyzing the performance of each path plan in multiple dimensions such as ecological protection, land use, social benefits, and economic development. For example, a certain area may be covered by both agricultural and ecological protection paths, resulting in land use conflicts; or the planning of a certain road may affect the integrity of the ecological corridor and cause ecological conflicts. The effect of conflict detection is to be able to identify these potential problems in advance and provide a basis for subsequent optimization.

[0100] After identifying the conflict, the system will simulate and feedback through a feedback integration algorithm based on game theory. The core of the game theory algorithm is to simulate the interaction process between different stakeholders and find the balance point of the interests of all parties through this process. In this solution, stakeholders may include local governments, farmers, ecological protection organizations, and community residents. Each stakeholder makes decisions and adjustments based on his or her interests in the virtual environment. For example, the government may favor economic development, while ecological organizations pay more attention to environmental protection. The game theory algorithm simulates the game process between these different entities to find an optimization solution that can meet the needs of multiple parties and integrate these feedbacks into the path adjustment.

[0101] This simulation and feedback process is not just a single round, but is achieved through multiple rounds of iterative optimization. In each round of optimization, the system will adjust the path plan based on the conflict detection results and game feedback from the previous round, and re-simulate and analyze until the interests of all parties are optimally balanced and conflicts are minimized. Each round of iteration can further optimize the path data, so that the final spatial layout plan reaches the best state in multiple dimensions such as ecology, economy, and society.

[0102] By importing the first spatial optimization path data into the virtual reality environment, the system can comprehensively analyze and optimize the spatial layout of rural areas in the virtual space. The multi-dimensional conflict detection algorithm ensures that the path plan does not conflict in multiple dimensions, while the feedback integration algorithm of game theory ensures the balance of interests of all parties. The effect of this process is to generate a comprehensive and balanced rural spatial layout plan after multiple rounds of optimization, which can reduce potential conflicts and meet the needs of multiple parties in practical applications. For example, through the feedback of game theory, the government and ecological organizations may reach a consensus to limit agricultural expansion in a specific area and retain more ecological land, thereby protecting the environment while developing the economy.

[0103] Assume that in a rural area, the first spatial optimization path data initially planned a road that runs through the ecological zone in order to improve traffic efficiency. However, the ecological organization found through simulation in a virtual reality environment that this road would have a significant impact on the migration of wild animals and cause ecological damage. Through multi-dimensional conflict detection, the system identified this conflict, and in the subsequent game theory feedback, the ecological organization interacted with the local government and proposed to establish an ecological corridor on both sides of the road. After multiple rounds of iterative optimization, an adjusted road path was finally generated, which not only met the traffic needs but also protected the ecological environment, becoming an optimal solution that takes into account the interests of multiple parties.

[0104] In summary, importing the first spatial optimization path data into the virtual reality environment and applying the multi-dimensional conflict detection algorithm and the feedback integration algorithm based on game theory for simulation and feedback can effectively identify and resolve potential conflicts in rural spatial layout and achieve a balance of interests among multiple parties. Through multiple rounds of iterative optimization, the optimized iterative path data finally generated not only displays the optimal rural spatial layout plan in the virtual environment, but also provides a scientific basis for practical application, ensuring that the plan achieves the best results in all dimensions of ecology, economy and society.

[0105] Preferably, the multi-dimensional conflict detection algorithm generates conflict detection feedback data and provides adjustment suggestions by analyzing potential conflicts among ecological impacts, economic benefits and social acceptance among different paths.

[0106] The multi-dimensional conflict detection algorithm first conducts a multi-dimensional analysis of the spatial layout of each functional area in the rural area based on the imported optimized path data. In the ecological dimension, the algorithm evaluates the impact of each path on the ecological environment, such as whether land use changes will destroy natural habitats, whether water resource development will cause river ecological imbalance, etc. In the economic dimension, the algorithm analyzes whether the path plan can bring the expected economic benefits, evaluates the efficiency of resource allocation and the sustainability of economic activities. In the dimension of social acceptance, the algorithm focuses on the acceptance of the path plan by community residents and other stakeholders, and analyzes whether the path plan may cause social conflicts or have a negative impact on community life.

[0107] The process of multi-dimensional conflict detection is not limited to simple single-dimensional analysis, but identifies possible conflict points by comprehensively evaluating the interrelationships between multiple dimensions. For example, a path plan may be very effective economically, but will cause serious damage to the ecological environment, or although it has good ecological and economic benefits, it is difficult to gain social acceptance because it involves community demolition. The algorithm quantifies the impact between these dimensions and generates conflict detection feedback data, which records the performance of each path in each dimension in detail and points out potential conflicts and contradictions.

[0108] The application of multi-dimensional conflict detection algorithms can significantly improve the decision-making quality of rural space optimization. First, through multi-dimensional evaluation of different path options, the system can fully understand the advantages and disadvantages of each path, so as to identify potential risks and conflicts in advance during the planning stage. This can not only avoid unforeseen problems in actual implementation, but also provide a scientific basis for planning adjustments. Secondly, the conflict detection feedback data generated by the algorithm can provide decision makers with clear adjustment directions. For example, if the ecological impact score of a path is low, the system will recommend considering modifying the land use method of the path or adding ecological protection measures to reduce the negative impact on the environment.

[0109] In a spatial optimization project in a rural area, the planning team designed an irrigation channel that runs through an agricultural area to improve the irrigation efficiency of farmland. However, after evaluation, the multi-dimensional conflict detection algorithm found that although the construction of this channel can bring significant economic benefits, it poses a major threat to the wetland ecosystem along the way, and community residents have expressed strong opposition to possible environmental damage. The algorithm generates conflict detection feedback data by analyzing data from three dimensions: ecological impact, economic benefits, and social acceptance, pointing out that the path plan has serious conflicts in the ecological and social dimensions. To this end, the system provides adjustment suggestions, including optimizing the route of the channel to avoid wetlands, or adopting a more environmentally friendly construction method, and negotiating the adjusted plan with community residents. This adjustment not only retains the original economic benefits, but also avoids ecological damage and social conflicts, and finally forms an optimization plan that satisfies all parties.

[0110] Preferably, the feedback integration algorithm based on game theory includes:

[0111] Simulate the interaction and game between different stakeholders in a virtual reality environment;

[0112] Through the game theory model, we can find the balance point of the interests of all parties, optimize the path and generate game feedback optimization data.

[0113] Game theory is a mathematical model used to analyze the decision-making behavior and results of multiple decision makers (i.e. stakeholders) under certain rules. In the context of rural space optimization, stakeholders may include local governments, farmers, ecological protection organizations, community residents, etc., and their respective goals and interests may conflict. For example, local governments may be more inclined to economic development and promote the commercial use of land, while ecological organizations are more concerned about environmental protection and hope to restrict development behavior. These different interests need to be analyzed through game theory models to find a balance point so that all parties can accept and jointly promote the implementation of the optimization plan.

[0114] In a virtual reality environment, the system simulates the interaction and game of these stakeholders. First, stakeholders can "see" different spatial layout plans for rural areas in a virtual environment, and evaluate and provide feedback on each plan. Each subject makes adjustment suggestions or objections based on its own interests, and the system analyzes these interactive behaviors through a game theory model. The game theory model considers the strategy choices of each subject and its possible results. By solving Nash equilibrium or other game solutions, the system can find a balance point for the interests of all parties. At this balance point, no party can obtain a better result by unilaterally changing its strategy, which means that all participants have achieved the optimal distribution of benefits in the current optimization plan.

[0115] The game feedback optimization data generated in this process records in detail the process of balancing the interests of all parties and the spatial layout optimization plan finally reached. These data not only provide a solid basis for path optimization, but also provide a reference for subsequent decision-making and adjustments.

[0116] Through the feedback integration algorithm based on game theory, rural space optimization can achieve a balance and coordination of interests among multiple stakeholders, avoiding the one-sidedness or conflict that may be caused by a single decision-making subject. First, the algorithm can effectively capture and reflect the needs of different stakeholders, so that the optimization plan not only takes into account ecological, economic and social goals, but also ensures the coordination and balance between these goals. Second, by simulating in a virtual reality environment, the system can identify and resolve potential conflicts of interest in advance, thereby reducing the risks and costs in actual implementation.

[0117] In a rural space optimization project, the planning team designed a new industrial zone expansion plan. The local government supports this plan because it can bring significant economic benefits, but ecological protection organizations and local residents are worried that the expansion will cause air and water pollution. Through the feedback integration algorithm based on game theory, the system simulates the interaction process of all parties in a virtual reality environment. The local government proposed some incentives to promote industrial development, while the ecological organization required the increase of green space and pollution control measures. Through multiple rounds of games, the system found a balance point: the industrial zone can be moderately expanded, while adding environmental protection facilities, and retaining a certain amount of green space as a buffer zone. The game feedback optimization data finally generated records the formation process of this balance point and is reflected in the final optimization path, so that the expansion plan can obtain economic benefits while also being recognized by environmental protection organizations and residents.

[0118] Based on the optimized iterative path data, the dynamic target re-optimization algorithm and the association rule mining algorithm are applied to dynamically adjust and optimize the optimized iterative path data, and finally generate the final optimized path data;

[0119] Preferably, the dynamic target re-optimization algorithm is implemented by the following calculation expression:

[0120] L(t)=L(t-1)+λ·Δ(t)

[0121] Among them, L(t) represents the loss function value in the t-th round of optimization; L(t-1) represents the loss function value in the t-1th round of optimization; λ is the learning rate, which is used to control the speed of loss function adjustment; Δ(t) represents the error between the predicted value and the actual target value in the t-th round.

[0122] In the specific application of rural space optimization, the loss function L(t) is usually used to quantify the gap between the current spatial layout and the ideal state. This gap may be reflected in many aspects, such as land use efficiency, ecological environment protection, and residents' satisfaction. As the optimization proceeds, the algorithm will calculate the loss function value of the current path plan in real time based on the optimization results of each round, and compare it with the previous round. If the effect of the current round of the plan is not as expected, the optimization target is adjusted by the error Δ(t), and the optimization path is corrected according to the learning rate λ. Through multiple rounds of iterations, the algorithm gradually reduces the loss function value, and finally makes the optimization plan as close to the global optimal solution as possible.

[0123] The application of the dynamic target re-optimization algorithm ensures the gradual improvement and adaptability of the rural space optimization plan. In practical applications, rural space optimization faces a variety of dynamically changing constraints and multi-objective balance requirements, such as real-time changes in the ecological environment, adjustments in policy orientations, and phased goals of economic development. By introducing the dynamic target re-optimization algorithm, the system can continuously adjust the optimization path based on real-time feedback to ensure that the final spatial layout plan can effectively adapt to these changes and maintain the optimality of the plan.

[0124] In addition, the setting of the learning rate λ directly affects the convergence speed and stability of the algorithm. A higher learning rate will accelerate convergence, but may lead to larger fluctuations in the optimization process; a lower learning rate will converge more slowly, but the optimization process will be more stable. Therefore, according to the specific rural space optimization needs, the balance between optimization efficiency and stability can be achieved by adjusting the learning rate. For example, in the case of urgent need for adjustment, a higher learning rate can be selected to respond to external changes more quickly; in optimization scenarios that require high precision, a lower learning rate can be selected to ensure the stability and accuracy of the optimization.

[0125] Example: In an actual rural area development project, the initial optimization plan planned large tracts of farmland for commercial agricultural planting, but in the actual implementation process, due to water shortages caused by climate change, the farmland output was far lower than expected. At this time, the dynamic target re-optimization algorithm discovered this problem through real-time monitoring and feedback, and adjusted the optimization goal from high-yield agriculture to water-saving agriculture by calculating the current loss function value L(t). Through rounds of iterations, the algorithm gradually optimized the irrigation path and planting strategy of the farmland, and finally generated an agricultural layout plan that adapts to the current water resource situation, which not only maintains economic benefits but also avoids excessive consumption of water resources. This optimization process reflects the flexibility and adaptability of the algorithm. When faced with complex real-world conditions, it can quickly adjust strategies to achieve sustainable development of rural space.

[0126] like Figure 3 As shown, preferably, the association rule mining algorithm includes the following steps:

[0127] Extract key path features from optimization iteration path data;

[0128] Analyze the potential associations between paths using association rule mining algorithms;

[0129] The path is optimized and adjusted based on the association rules to generate association optimized path data.

[0130] The association rule mining algorithm was originally used in the field of data mining, mainly to discover association rules between different items in a data set. In this scheme, the algorithm is applied to rural space optimization, by extracting key path features (such as land use patterns, resource distribution, infrastructure layout, etc.) from the optimization iterative path data, and analyzing the potential associations between paths. These associations can be direct, such as sharing the same resources or functional areas; or indirect, such as mutually influential ecosystems or chains of economic activities.

[0131] Specifically, the algorithm first extracts the key features of each optimization path, which are usually the main factors that can affect the overall optimization effect. For example, in land use planning, this may include land type, slope, available water resources, etc. After extracting these key features, the algorithm uses association rule mining technology to analyze the potential correlation between paths. The association rules here are usually expressed as an "if-then" pattern, such as "If path A chooses ecological restoration as the main goal, then path B should consider reducing development intensity in the adjacent area." Such rules can help the system understand the interdependence between different paths.

[0132] After identifying these potential associations, the system will optimize and adjust the paths based on these association rules. For example, if a path is found to conflict with another path in terms of resource utilization, the system can adjust the planning of one of the paths based on the association rules so that the two can achieve a synergistic effect in terms of resource utilization. Through this adjustment, the associated optimized path data generated by the system can not only reduce conflicts between paths, but also maximize the use of available resources and improve the overall optimization effect.

[0133] The association rule mining algorithm can significantly improve the coordination of path planning and resource utilization efficiency in rural space optimization. First, the algorithm can effectively discover and resolve potential conflicts between paths, avoiding resource waste or environmental damage caused by path incoordination. Second, by identifying potential synergies between paths, the algorithm can help the system achieve higher overall benefits in the planning process. For example, through optimization and adjustment, an agricultural path that originally required a lot of irrigation may reduce irrigation needs due to water resource allocation in neighboring areas, thereby saving water resources and freeing up more available resources for other paths.

[0134] Suppose that in a rural area, the planning team initially designed two paths: Path A is used for the construction of agricultural irrigation facilities, and Path B is used for the expansion of residential areas. After mining association rules, the system found that there is a potential conflict in water resource utilization between the two paths. The large-scale irrigation demand of Path A may cause the residential areas of Path B to face water shortages during the dry season. Based on this association rule, the system recommends adjustments to Path A, such as adopting more water-saving irrigation technology or adjusting the irrigation time period, so that water resources can be more reasonably distributed in the region. The resulting association optimization path data ensures the synergistic effect of the two paths in resource utilization, which not only meets the agricultural irrigation needs, but also ensures the water safety of residential areas.

[0135] The final optimized path data is monitored in real time through IoT devices, dynamic feedback analysis is performed using an AI-driven feedback system, and dynamically adjusted path data is generated through an adaptive path adjustment mechanism to form a long-term optimization mechanism.

[0136] like Figure 4 As shown, preferably, the continuous monitoring and dynamic adjustment include:

[0137] Use IoT devices to collect dynamic data in rural areas in real time and generate real-time monitoring data;

[0138] Use AI-driven feedback systems to analyze real-time monitoring data and generate feedback analysis data;

[0139] Based on feedback analysis data, dynamic adjustment path data is generated through an adaptive path adjustment mechanism and applied to the rural space optimization path.

[0140] IoT devices are widely deployed in rural areas, collecting multi-dimensional dynamic data in real time. These data include but are not limited to climate change, land use, resource consumption, ecological environment changes, population mobility, etc. These dynamic data reflect the changes in rural areas at different time points, providing a basis for continuous monitoring of the system. After the data collected by IoT devices is processed, real-time monitoring data is generated to ensure that the system can always grasp the latest status of rural areas.

[0141] Based on real-time monitoring data, the AI-driven feedback system plays a key role. Through deep learning and data analysis technology, the system conducts a comprehensive analysis of the collected real-time monitoring data to identify potential problems or optimization opportunities in the spatial layout. The feedback system can not only identify explicit problems such as land degradation and water shortage, but also discover potential trends and risks through pattern recognition and predictive models. The generated feedback analysis data provides the system with a basis for further adjusting the optimization path.

[0142] Based on the feedback analysis data, the system starts the adaptive path adjustment mechanism. This mechanism dynamically adjusts the current optimized path data by analyzing the anomalies and trends in the feedback data to generate dynamically adjusted path data. The core of the adaptive path adjustment mechanism lies in its flexibility and adaptability. It can quickly adjust the path planning according to the changes in the actual situation to ensure that the rural spatial layout is always in the optimal state. For example, when it is detected that the land use efficiency in a certain area has decreased, the adaptive path adjustment mechanism may recommend reallocating land use or introducing new agricultural technologies to restore the productivity of the land.

[0143] Through continuous monitoring and dynamic adjustment, this solution can form a closed-loop feedback system in rural space optimization and achieve long-term optimization. First, real-time monitoring ensures the system's sensitivity and response speed to changes in rural areas, so that the optimization path can adapt to changes in a timely manner and avoid the accumulation of potential risks. Secondly, the AI-driven feedback system can predict possible problems in advance through intelligent analysis and provide data support for decision-making, thereby improving the foresight and accuracy of the optimization path. Finally, the adaptive path adjustment mechanism ensures that the rural spatial layout can maintain the optimal state in dynamic changes, further enhancing the sustainable development capacity of rural areas.

[0144] In an actual rural area, the initial spatial optimization plan planned a large agricultural area for food production. However, as climate change leads to frequent droughts, data collected by IoT devices show that soil moisture in the area has dropped significantly, and agricultural output has decreased accordingly. The AI-driven feedback system identified this trend by analyzing real-time monitoring data and predicted that if no adjustments are made, the agricultural area may face serious production reduction problems. Based on this feedback, the adaptive path adjustment mechanism recommends converting some agricultural areas to dryland agriculture and introducing drip irrigation technology to improve water resource utilization efficiency. Finally, the dynamically adjusted path data was applied to the rural spatial optimization path, which not only solved the challenges brought by drought, but also maintained the economic benefits of the agricultural area. This adjustment process demonstrates the flexibility and adaptability of this solution in responding to environmental changes.

[0145] like Figure 5 As shown, a system for implementing the rural space optimization method comprises:

[0146] The data collection and processing module is used to collect multi-dimensional data in rural areas in real time through IoT devices, and to clean and format the data. The processed multi-dimensional data is integrated into rural comprehensive data using data fusion algorithms, and the rural comprehensive data is preliminarily analyzed using machine learning algorithms to generate transformation dynamics analysis data. The principle of the data collection and processing module is based on IoT technology, and multi-dimensional data in rural areas, such as climate, land use, and ecological environment, are collected in real time through sensor networks. After data collection, multi-source data is integrated into rural comprehensive data through data cleaning and formatting, and data fusion algorithms are used. Then, the comprehensive data is preliminarily analyzed using machine learning algorithms to generate analysis data reflecting the rural transformation dynamics. The core effect of this module is to provide high-precision, real-time updated basic data, which provides a scientific basis for subsequent spatial optimization.

[0147] The intelligent collaborative optimization module is used to initialize the multi-agent system based on the transformation dynamics analysis data, execute the adaptive weight allocation algorithm and the multi-objective evolutionary algorithm to generate the first agent optimization path data, and generate the first space optimization path data through the smart contract execution and blockchain coordination mechanism; the intelligent collaborative optimization module uses these transformation dynamics analysis data to initialize the multi-agent system, execute the adaptive weight allocation algorithm and the multi-objective evolutionary algorithm, and generate the first agent optimization path data. Through smart contracts and blockchain technology, the execution of the optimization path and the transparent coordination of data are ensured. The core effect of this module is to ensure that the spatial optimization path achieves a balance between different goals, and the credibility and immutability of the path decision are guaranteed through blockchain technology.

[0148] The virtual reality simulation and feedback module is used to import the first space optimization path data into the virtual reality environment, apply the multi-dimensional conflict detection algorithm and the feedback integration algorithm based on game theory for simulation and feedback, and perform multiple rounds of iterative optimization in the virtual reality environment to finally generate the optimized iterative path data; The principle of the virtual reality simulation and feedback module is to import the first space optimization path data into the virtual reality environment, use the multi-dimensional conflict detection algorithm and the feedback integration algorithm based on game theory for simulation and feedback optimization. After multiple rounds of iterations, the optimized iterative path data is finally generated. The core effect of this module is to achieve forward-looking verification of the space optimization plan and balance of interests among multiple parties through virtual reality technology, and reduce the risk of conflict in practical applications.

[0149] The adaptive optimization and path generation module is used to dynamically adjust and optimize the optimized iterative path data based on the optimized iterative path data by applying the dynamic target re-optimization algorithm and the association rule mining algorithm, and finally generate the final optimized path data; the adaptive optimization and path generation module further applies the dynamic target re-optimization algorithm and the association rule mining algorithm based on the optimized iterative path data generated by the virtual reality module, and dynamically adjusts and optimizes to generate the final optimized path data. The core effect of this module is to ensure that the optimized path maintains adaptability and optimality in a changing environment, and can dynamically respond to new challenges and requirements.

[0150] The real-time monitoring and dynamic adjustment module is used to monitor the final optimized path data in real time through IoT devices, use the AI-driven feedback system to perform dynamic feedback analysis, and generate dynamic adjustment path data through an adaptive path adjustment mechanism to form a long-term optimization mechanism. The real-time monitoring and dynamic adjustment module monitors the final optimized path in real time through IoT devices, uses the AI-driven feedback system to perform dynamic feedback analysis, and generates dynamic adjustment path data through an adaptive path adjustment mechanism to form a long-term optimization mechanism. The effect is to ensure that the rural spatial layout can respond to environmental changes in real time and maintain the long-term adaptability of the optimized path.

[0151] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0152] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A rural space optimization method, characterized in that: The following steps are involved: Collect multi-dimensional data in rural areas in real time through IoT devices, clean and format the data, integrate the processed multi-dimensional data into comprehensive rural data through data fusion algorithms, and use machine learning algorithms to conduct preliminary analysis of the comprehensive rural data to generate transformation dynamics analysis data; Initialize the multi-agent system based on the transformation dynamics analysis data, generate the first agent optimization path data through the adaptive weight allocation algorithm and the multi-objective evolutionary algorithm, and generate the first space optimization path data through the smart contract execution and blockchain coordination mechanism; Import the first space optimization path data into the virtual reality environment, apply the multi-dimensional conflict detection algorithm and the feedback integration algorithm based on game theory to perform simulation and feedback, perform multiple rounds of iterative optimization in the virtual reality environment, and finally generate the optimized iterative path data; Based on the optimized iterative path data, the dynamic target re-optimization algorithm and the association rule mining algorithm are applied to dynamically adjust and optimize the optimized iterative path data, and finally generate the final optimized path data; The final optimized path data is monitored in real time through IoT devices, dynamic feedback analysis is performed using an AI-driven feedback system, and dynamically adjusted path data is generated through an adaptive path adjustment mechanism to form a long-term optimization mechanism.

2. The rural space optimization method according to claim 1, characterized in that: The adaptive weight allocation algorithm is implemented by the following calculation expression: Among them, W i (t) represents the weight of the i-th agent in the t-th round of optimization; W i (t-1) represents the weight of the i-th agent in the t-1th round of optimization; F i (t) represents the fitness value of the i-th agent in the t-th round of optimization; represents the average fitness value of all agents in the tth round of optimization; α is the adjustment parameter used to adjust the weight update amplitude.

3. The rural space optimization method according to claim 1, characterized in that: The multi-objective evolutionary algorithm comprises the following steps: Each path in the first agent's optimized path data is used as an individual in the evolutionary population; Calculate the fitness of each individual for multiple optimization objectives; Generate new populations through crossover, mutation and selection operations until a predetermined number of evolutionary generations is reached; Output the individual with the highest fitness as the optimized path.

4. The rural space optimization method according to claim 1, characterized in that: The smart contract execution and blockchain coordination mechanism includes: Recording the generation process of the first agent's optimization path data on the blockchain; Leverage smart contracts to automate path verification and decision coordination.

5. The rural space optimization method according to claim 1, characterized in that: The multi-dimensional conflict detection algorithm generates conflict detection feedback data and provides adjustment suggestions by analyzing the potential conflicts between ecological impacts, economic benefits and social acceptance among different paths.

6. The rural space optimization method according to claim 5, characterized in that: The feedback integration algorithm based on game theory includes: Simulate the interaction and game between different stakeholders in a virtual reality environment; Through the game theory model, we can find the balance point of the interests of all parties, optimize the path and generate game feedback optimization data.

7. The rural space optimization method according to claim 1, characterized in that: The dynamic target re-optimization algorithm is implemented by the following calculation expression: L(t)=L(t-1)+λ·Δ(t) Among them, L(t) represents the loss function value in the t-th round of optimization; L(t-1) represents the loss function value in the t-1th round of optimization; λ is the learning rate, which is used to control the speed of loss function adjustment; Δ(t) represents the error between the predicted value and the actual target value in the t-th round.

8. The rural space optimization method according to claim 1, characterized in that: The association rule mining algorithm comprises the following steps: Extract key path features from optimization iteration path data; Analyze the potential associations between paths using association rule mining algorithms; The path is optimized and adjusted based on the association rules to generate association optimized path data.

9. The rural space optimization method according to claim 1, characterized in that: The continuous monitoring and dynamic adjustment include: Use IoT devices to collect dynamic data in rural areas in real time and generate real-time monitoring data; Use AI-driven feedback systems to analyze real-time monitoring data and generate feedback analysis data; Based on feedback analysis data, dynamic adjustment path data is generated through an adaptive path adjustment mechanism and applied to the rural space optimization path.

10. A system for implementing the rural space optimization method according to any one of claims 1 to 9, characterized in that: include: The data collection and processing module is used to collect multi-dimensional data in rural areas in real time through IoT devices, and to clean and format the data. The processed multi-dimensional data is integrated into rural comprehensive data using data fusion algorithms, and the rural comprehensive data is preliminarily analyzed using machine learning algorithms to generate transformation dynamics analysis data. Intelligent collaborative optimization module, used to initialize the multi-agent system based on transformation dynamics analysis data, execute adaptive weight allocation algorithm and multi-objective evolutionary algorithm to generate first agent optimization path data, and generate first space optimization path data through smart contract execution and blockchain coordination mechanism; A virtual reality simulation and feedback module is used to import the first space optimization path data into the virtual reality environment, apply a multi-dimensional conflict detection algorithm and a feedback integration algorithm based on game theory to perform simulation and feedback, and perform multiple rounds of iterative optimization in the virtual reality environment to finally generate optimized iterative path data; The adaptive optimization and path generation module is used to dynamically adjust and optimize the optimized iterative path data based on the optimized iterative path data by applying the dynamic target re-optimization algorithm and the association rule mining algorithm, and finally generate the final optimized path data; The real-time monitoring and dynamic adjustment module is used to monitor the final optimized path data in real time through IoT devices, perform dynamic feedback analysis using the AI-driven feedback system, and generate dynamically adjusted path data through an adaptive path adjustment mechanism to form a long-term optimization mechanism.

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