Regional sustainable development mode display system based on virtual simulation

Abstract

The invention relates to the technical field of regional sustainability, and discloses a regional sustainable development mode display system based on virtual simulation, and the system comprises a data collection module which is used for collecting related resource data of regional sustainability in real time; the multi-dimensional modeling module is used for constructing an agricultural production model, a social economic system model and an ecological environment model based on the resource data; the optimization decision-making module is used for generating a multi-objective and multi-constraint optimization problem based on the agricultural production model, the social economic system model and the ecological environment model and performing resource allocation optimization, and the optimization problem comprises multiple objectives of economic benefit, ecological protection and social benefit; the invention also provides a regional sustainable development mode display method based on virtual simulation. The method comprises the following steps: collecting regional sustainable resource data in real time; by adopting a real-time data acquisition and feedback control scheme based on the Internet of Things technology, precise monitoring and dynamic adjustment of regional sustainable resources are achieved.

Description

Technical Field

[0001] The invention relates to the field of regional sustainable technology, in particular to a regional sustainable development model display system based on virtual simulation. Background Art

[0002] At present, regional sustainability is an important development strategy in my country, involving agricultural, ecological and social factors. Traditional resource allocation methods usually rely on experience and static models, which are difficult to cope with rapidly changing environmental conditions (such as climate change, soil quality fluctuations, etc.). These methods lack flexibility, easily lead to waste or shortage of resources, and are difficult to respond to changes in the dynamic environment in a timely manner.

[0003] Most existing regional sustainable development models are single-objective optimization models, which usually focus on economic benefits and ignore the balance between ecological benefits and social benefits. This bias leads to an imbalance in resource allocation and fails to fully consider the issues of ecological protection and social welfare.

[0004] In addition, traditional decision support systems mostly present solutions through data reports or static charts, lacking sufficient interactivity and visualization. It is difficult for decision makers to intuitively understand and adjust the effects of different resource allocation solutions, resulting in inefficient decision-making.

[0005] Although the Internet of Things and big data analysis technologies are gradually being applied to regional sustainability, the existing system still lacks an effective closed-loop feedback mechanism and cannot adjust resource allocation based on real-time data. Without timely feedback control, there may be a disconnect between the optimization plan and actual needs, affecting the implementation effect.

[0006] In summary, the existing technology has obvious deficiencies in multi-objective optimization, dynamic resource adjustment and feedback control.

[0007] Therefore, the present invention proposes a regional sustainable development model display system based on virtual simulation to solve the deficiencies of the prior art. Summary of the invention

[0008] In view of the deficiencies of the prior art, the present invention provides a regional sustainable development model display system based on virtual simulation, which solves the obvious deficiencies of the prior art in multi-objective optimization, dynamic resource adjustment and feedback control.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A regional sustainable development model display system based on virtual simulation, comprising:

[0010] Data collection module, used to collect relevant regional sustainable resource data in real time;

[0011] A multidimensional modeling module, used to construct an agricultural production model, a social economic system model and an ecological environment model based on the resource data;

[0012] An optimization decision-making module is used to generate multi-objective and multi-constraint optimization problems based on the agricultural production model, the socio-economic system model and the ecological environment model, and optimize resource allocation. The optimization problem includes multiple objectives such as economic benefits, ecological protection and social benefits;

[0013] A virtual simulation display module, used to generate a sustainable display scene of a virtual area according to the optimization decision result, and support real-time interaction and decision adjustment;

[0014] The real-time feedback control module is used to receive and analyze data during the regional sustainable implementation process in real time, and dynamically adjust the resource allocation plan based on the analysis results for optimal use and continuous balance of resources.

[0015] Preferably, the data acquisition module includes a plurality of sensors, drones and remote sensing equipment for acquiring environmental data, crop growth data and resource usage data in different areas within the region.

[0016] Preferably, the resource data includes water resources, land quality, climate data, crop growth status, socio-economic data and environmental monitoring data.

[0017] Preferably, the multidimensional modeling module uses a diffusion equation to describe the flow of water resources in the soil, and establishes the interaction relationship between the ecosystem and agricultural production based on the ecological Lotka-Volterra model;

[0018] The diffusion equation is as follows:

[0019]

[0020] Among them, θ(x,t) is the moisture content in the soil, t is the time variable, x is the spatial coordinate, D(x,t) is the moisture diffusion coefficient, is the spatial gradient operator, S(x,t) is the water consumption rate;

[0021] The Lotka-Volterra equation is as follows:

[0022]

[0023] Among them, N(t) is the number of crop populations in the ecosystem, r is the growth rate, K is the environmental carrying capacity, C(N) is the competition coefficient, is the rate of change of species population over time.

[0024] Preferably, the optimization decision module includes:

[0025] A model building module, used to build a mathematical model of agricultural production, social economy and ecological environment based on the multidimensional modeling module;

[0026] A target setting module is used to set an optimization target function, which includes economic benefits, ecological protection and social benefits, and assign weights to each target;

[0027] The constraint definition module is used to define resource usage limits, environmental protection requirements, and social benefit requirements.

[0028] The optimization solution module is used to solve the problem based on the objective function and constraints using a multi-objective optimization algorithm to obtain the optimal resource allocation solution.

[0029] Preferably, the optimization decision module performs a global search for resource configuration using a genetic algorithm and a particle swarm optimization algorithm to handle high-dimensional and complex resource configuration problems.

[0030] Preferably, the virtual simulation display module generates virtual scenes under different resource allocation schemes in the regional sustainable process through three-dimensional modeling technology, and supports user interaction to adjust the resource allocation scheme.

[0031] Preferably, the real-time feedback control module collects real-time data of the regional sustainable process through the Internet of Things technology, and dynamically adjusts resource allocation in combination with the optimal control algorithm to ensure optimal resource utilization.

[0032] The present invention also provides a method for displaying a regional sustainable development model based on virtual simulation, comprising the following steps:

[0033] Collect relevant resource data on regional sustainability in real time;

[0034] Establishing agricultural production models, social economic system models and ecological environment models based on the collected resource data;

[0035] Generate optimization problems through multi-objective optimization methods, optimize resource allocation, and generate sustainable display scenarios for virtual areas based on the optimization results;

[0036] The optimization decision results are displayed through three-dimensional visualization, so that users can generate sustainable display scenes of virtual areas according to the optimization decision results, and support interactive decision adjustments;

[0037] Receive and analyze data from the regional sustainable implementation process in real time, and dynamically adjust resource allocation plans based on the analysis results.

[0038] Preferably, the feedback data is based on real-time environmental data, agricultural production data and socio-economic data during the regional sustainable implementation process, and resource allocation is dynamically adjusted through a real-time feedback control algorithm.

[0039] The present invention provides a regional sustainable development model display system based on virtual simulation. It has the following beneficial effects:

[0040] 1. The present invention achieves accurate monitoring and dynamic adjustment of regional sustainable resources by adopting a real-time data collection and feedback control solution based on the Internet of Things technology. Compared with the system in the prior art that is difficult to respond to changes in a timely manner, the present invention can dynamically adjust resource allocation according to real-time data, avoiding the problem of resource waste and excessive consumption, and improving the efficiency and sustainability of resource use.

[0041] 2. The present invention can take into account economic, ecological and social benefits at the same time through a multi-objective and multi-constrained optimization decision-making module to ensure optimal resource allocation for regional sustainable projects. Unlike traditional single-objective optimization methods, the present invention achieves a balance of multiple objectives and effectively solves the problems of one-sided resource allocation and emphasis on short-term benefits in traditional solutions.

[0042] 3. The present invention introduces virtual simulation display technology and combines the optimization decision results to generate interactive regional sustainable scenarios, allowing users to view and adjust plans in real time. Different from the traditional static display method, the present invention provides a more intuitive decision-making basis through interactive virtual scenes. Users can see the effects of adjustments in real time, thereby optimizing resource allocation more accurately.

[0043] 4. The present invention introduces the ecological Lotka-Volterra model in multidimensional modeling, deeply integrates the mutual influence of agricultural production and ecological environment, and considers social benefit factors at the same time. Compared with the existing technology that ignores or separates ecological protection and social benefits, the present invention solves the shortcomings of previous solutions that ignore environmental and social impacts by comprehensively considering economic, ecological and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a system architecture diagram of the present invention;

[0045] Figure 2 It is a schematic diagram of the structure of the optimization decision module of the present invention;

[0046] Figure 3 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0048] See also Figure 1-Figure 2 The embodiment of the present invention provides a regional sustainable development model display system based on virtual simulation, including:

[0049] Data collection module, used to collect relevant regional sustainable resource data in real time;

[0050] The data acquisition module collects the resource data needed for regional sustainability in real time through sensors, drones, remote sensing equipment and other hardware means. The system can comprehensively collect water resources, land quality, climate data, crop growth status, socio-economic data and environmental monitoring data, providing a basis for subsequent modeling, optimization decision-making and feedback control.

[0051] The design of the data collection module aims to ensure the accuracy, comprehensiveness and timeliness of the collected data. The timely collection and transmission of data is the basis for the successful optimization and real-time adjustment of resource allocation in the system.

[0052] In some embodiments, the data collection module realizes real-time data collection by arranging multiple sensors in the regional sustainable area. These sensors include soil moisture sensors, temperature sensors, pH sensors, meteorological sensors, etc. The specific functions are as follows:

[0053] Soil moisture sensor: used to monitor the moisture content in the soil in real time. Its working principle is based on the resistance change method or capacitance change method, and the moisture content of the soil is calculated by measuring the conductivity of the soil. The output signal of the sensor is sent to the central control system via wireless transmission.

[0054] Meteorological sensors: used to obtain meteorological parameters such as temperature, humidity, wind speed, and air pressure. These data are used to analyze and predict the impact of climate change on agricultural production and help decision makers optimize planting plans.

[0055] Light sensor: used to monitor light intensity, help determine the growth environment of crops, and ensure that crops receive enough sunlight. Light data can be combined with crop growth models to optimize agricultural production decisions.

[0056] The data acquisition module is combined with the Internet of Things technology to achieve communication and data synchronization between sensors, so that the entire monitoring system forms an intelligent network that automatically collects, analyzes and transmits real-time data.

[0057] In this embodiment, the drone is used to obtain image data of the agricultural production area and monitor the growth of crops in real time. The drone is equipped with high-definition cameras, infrared sensors and other environmental monitoring equipment, and can quickly cover a large area and collect key data in the agricultural production process.

[0058] For example, drones can obtain high-definition images of farmland through flight, and use image recognition technology to analyze crop health, pest and disease conditions, and changes in soil quality. Based on the analysis results, the system can provide timely feedback on the growth status of crops, thereby providing improved planting or irrigation suggestions.

[0059] Image analysis: Image data captured by drones is processed by computer vision technology to identify crop diseases, weeds, and regional differences in crop growth. Using deep learning algorithms, crop health can be automatically assessed, reducing the burden of manual intervention.

[0060] Remote sensing equipment obtains data on a large area through satellites, aircraft or ground sensors. It can monitor important factors such as climate change, land quality, soil pollution, etc. by acquiring remote sensing images and geographic information.

[0061] For example, remote sensing technology can help monitor land degradation, vegetation cover, forest cover, etc. over a large area. Through these data, the system can provide more extensive support for regional sustainable decision-making and avoid the misuse of land that is not suitable for development.

[0062] Remote sensing image analysis: Remote sensing images usually use spectral reflectance values ​​to obtain soil information and vegetation changes. By using different spectral bands, the system can analyze vegetation growth and soil quality, thereby predicting the impact of environmental changes on agricultural production.

[0063] All collected data is transmitted to the central data processing system via wireless networks (such as Wi-Fi, LTE, LoRa, etc.). The system uses the Internet of Things (IoT) technology to achieve real-time data synchronization, ensuring that the collected data can quickly enter the next step of processing and analysis. In order to ensure the accuracy and stability of the data, the collection system can also use data redundancy and verification technology to ensure that the data collected by different sensors are consistent.

[0064] In some embodiments, the data acquisition module is also provided with a data preprocessing function, which performs format conversion, noise removal and outlier removal on the transmission data to ensure that the system receives only reliable data.

[0065] In this embodiment, the data collection module can be combined with the cloud computing platform to perform big data analysis. By storing and processing data in the cloud, the system can perform trend analysis, anomaly detection and prediction of historical data, further improving the scientificity and timeliness of regional sustainable decision-making. Through data analysis, machine learning and other methods, the system can perform real-time prediction and adjustment while collecting data to ensure that various resources are reasonably allocated.

[0066] The data acquisition module can comprehensively and in real time acquire a variety of resource data required for regional sustainability. Through the comprehensive application of sensors, drones, and remote sensing equipment, the system can cover resource data from all aspects of the region, ensuring that subsequent modeling and optimization decisions are based on accurate and dynamically changing data sources. Through the combination of IoT technology and cloud platforms, the system has stronger data processing capabilities and real-time feedback adjustment capabilities, providing solid technical support for the refined management of regional sustainability.

[0067] A multidimensional modeling module, used to construct an agricultural production model, a social economic system model and an ecological environment model based on the resource data;

[0068] The multidimensional modeling module is responsible for constructing mathematical models of agricultural production, ecological environment and social and economic systems through various regional resource data provided by the data acquisition module. Through these models, the system can scientifically predict and optimize various resources in the regional sustainable process, providing strong support for the subsequent optimization decision module and virtual simulation display module.

[0069] In general, the multidimensional modeling module is one of the cores of the entire system. It mainly helps decision makers understand the complex relationship between agriculture, environment and economy by simulating the relationship between various resources in the regional sustainable process, and provides a basis for the optimization and adjustment of the system. The multidimensional modeling module uses multiple mathematical models to describe the flow of resources, interactions and optimization of resource allocation in different fields.

[0070] Specifically, the multidimensional modeling module of this embodiment includes the following three parts: agricultural production model, ecological environment model and social economic system model. These models are tightly coupled and jointly determine the resource allocation in the regional sustainable process through interaction and feedback mechanism.

[0071] The core goal of agricultural production models is to describe the flow of water and soil resources during crop growth, as well as the impact of environmental changes on crop growth. In the agricultural production process, factors such as the distribution of water resources, soil quality, and climate change will have an important impact on crop growth. Therefore, when constructing agricultural production models, the flow and consumption of water resources are important factors.

[0072] As an important part of the model, the water diffusion equation is used to describe the flow and changes of water resources in the soil. The diffusion equation simulates the propagation process of water in the soil by calculating the diffusion coefficient of water. The diffusion equation is as follows:

[0073]

[0074] Where θ(x,t) is the moisture content in the soil, in m 3 Water / m 3 Soil, represents the volumetric water content in the soil at time t and spatial position x; t is the time variable, which represents the time scale of the water flow process in the soil; x is the spatial coordinate, which represents the location of the water distribution in the soil; D(x,t) is the water diffusion coefficient, the unit is m 2 / s, represents the diffusion capacity of water in the soil, which depends on factors such as soil type and humidity; is the spatial gradient operator, which indicates the change of spatial distribution and reflects the flow direction of water in the soil; S(x,t) is the water consumption rate, in m 3 / s, represents the rate at which crops absorb water, taking into account evaporation and the water demand of the plant.

[0075] By solving the distribution of water in the soil, this equation can predict the water demand of crops under different irrigation strategies and assist decision makers in formulating reasonable irrigation plans.

[0076] Eco-environmental models are used to describe the interactions between crops and other organisms in an ecosystem. Through the Lotka-Volterra equation, the eco-environmental model can simulate the competition and cooperation between crops and other ecological factors (such as pests, weeds, microorganisms, etc.).

[0077] The Lotka-Volterra equation is used to describe the dynamic relationship between species in an ecosystem and is often used in ecology to describe the interaction between predators and prey, and between competing species. The competitive and cooperative relationship between crops, microorganisms in the soil, pests and other species has a significant impact on agricultural production, especially when resources are scarce, where competitive relationships often determine the growth and survival of species.

[0078] The general form of the Lotka-Volterra equation is as follows:

[0079]

[0080] Among them, N(t) is the number of crop populations in the ecosystem, in units of crop individuals or crop density; r is the growth rate, in units of 1 / year, which represents the natural growth rate of crops under ideal conditions; K is the environmental carrying capacity, in units of individuals, which represents the maximum number of crop populations that the ecosystem can support, subject to resource constraints; C(N) is the competition coefficient, which reflects the mutual competition between crops and other ecological factors. This function usually increases with the increase of crop population density, indicating the consumption of resources by crops; It is the rate of change of species population over time, indicating the changes in crop populations under environmental factors and competitive pressures.

[0081] Specifically, the equation can describe the growth and competition of crop populations under different ecological conditions. In an ecosystem, crops need to compete with other species (such as microorganisms and weeds) for limited resources. By solving this equation, the system can predict the growth trend of crops based on the interaction between crops and other species and reasonably adjust the resource allocation plan.

[0082] The socio-economic system model is used to describe the flow and allocation of various resources (such as labor, land, capital, etc.) in regional economic activities. In the process of regional sustainability, labor flow, agricultural output value and the allocation of social resources are important factors affecting the benefits of regional development. This model can simulate the allocation and flow of various resources in the regional economic system based on economic theory.

[0083] An important component of the socio-economic system model is the population mobility model, which describes the inflow and outflow of regional labor. In the process of regional sustainability, the allocation of labor resources is crucial to agricultural production and socio-economic activities. Through this model, the system can predict the demand and supply of labor and provide decision support for resource allocation in agricultural production, infrastructure construction and other aspects.

[0084] There are complex relationships between agricultural production models, ecological environment models and social economic system models. In this embodiment, multiple models are integrated through a coupling algorithm so that these models can interact with each other and provide more accurate prediction and decision support.

[0085] For example, the results of the agricultural production model (such as crop yield, soil moisture, etc.) will be passed as input to the ecological environment model to calculate the ecological impact; and the output of the socio-economic model (such as labor demand, market demand, etc.) will also affect the input of the agricultural production model and the ecological environment model. Through this coupling method, the system can fully consider the interaction between various fields and optimize resource allocation.

[0086] In some embodiments, the coupling algorithm uses an iterative optimization approach. During each calculation process, the output of the model is updated and passed to the next model until the outputs of all models are consistent, ultimately resulting in an optimal resource allocation solution.

[0087] In order to ensure the accuracy and stability of the model, the multidimensional modeling module in this embodiment adopts a numerical solution method and an optimization algorithm. For example, the numerical solution of the diffusion equation may use a finite difference method or a finite element method to gradually calculate the change of water in the soil by discretizing space and time. In the Lotka-Volterra model, the dynamic process of population change is solved by a numerical integration method (such as the Euler method, the Runge-Kutta method, etc.).

[0088] By adopting these numerical solutions and optimization algorithms, the system can accurately calculate the results of different resource configurations under various constraints, thus providing a scientific basis for the optimization decision-making module.

[0089] In this embodiment, a comprehensive multi-dimensional modeling system is constructed by coupling the agricultural production model, the ecological environment model and the socio-economic system model. This system can comprehensively consider the interaction of various resources in the regional sustainable process and provide scientific support for optimization decision-making and virtual simulation display. Through numerical solution and iterative optimization algorithm, the system can accurately simulate the dynamic changes of resource allocation in the regional sustainable process, provide a reliable basis for regional sustainable decision-making, and ensure the rational use of resources and sustainable development.

[0090] An optimization decision module is used to generate multi-objective and multi-constraint optimization problems based on the model to optimize resource allocation. The optimization problems include multiple objectives such as economic benefits, ecological protection, and social benefits.

[0091] The optimization decision module is one of the core components of the present invention, which is responsible for converting the data and model results provided by the multidimensional modeling module into specific decision plans. The optimization decision module generates the optimal resource allocation plan under multi-objective and multi-constraint conditions by processing the output of the agricultural production, ecological environment and socio-economic system models. Based on the multi-objective optimization algorithm, the module can calculate the best resource allocation plan under the premise of considering the relationship between different objectives, and continuously adjust the optimization strategy according to the real-time feedback data.

[0092] In general, the optimization decision module not only considers multiple goals such as economic benefits, ecological protection and social benefits, but also needs to ensure a reasonable balance between the goals under limited resources. As an option, the optimization decision module uses genetic algorithms and particle swarm optimization algorithms to perform global searches to obtain the global optimal solution and can handle complex constraints.

[0093] Specifically, the optimization decision module includes the following submodules: model building module, goal setting module, constraint definition module, optimization solution module and feedback adjustment module. Each submodule undertakes specific tasks in the optimization process and works together to ensure the optimal allocation of various resources in the regional sustainable process.

[0094] In this embodiment, the model building module receives the mathematical models of agricultural production, ecological environment and social economic system output by the multidimensional modeling module, integrates the data of various fields, and forms a comprehensive optimization model. The task of this module is to model the relationship between various fields (agriculture, environment, economy, etc.) through mathematical expressions and convert them into objective functions and constraints.

[0095] By analyzing the results provided by the multidimensional modeling module, the model building module integrates the crop growth and irrigation water demand in the agricultural production model, the soil quality and environmental pollution in the ecological environment model, and the labor distribution and market demand in the socio-economic model into the optimization problem. These factors not only occupy an important position in the objective function, but also directly affect the setting of constraints.

[0096] Specifically, factors such as water demand and crop growth in the agricultural production model, as well as labor demand and agricultural product market demand in the socio-economic model, are all incorporated into the overall optimization framework in the form of mathematical formulas to ensure that each objective and resource limitation is accurately reflected in the optimization process.

[0097] The function of the goal setting module is to define the objective function of the optimization problem. The optimization objectives include but are not limited to economic benefits, ecological protection and social benefits. Each objective function has a different weight coefficient. This module sets the objective function and assigns weights to each objective based on the specific regional sustainable needs.

[0098] The construction of the objective function takes into account multiple factors and can be dynamically adjusted according to actual needs. For example:

[0099] Economic benefit: It can be measured by the market output value of agricultural products, expressed as the product of total crop output and market price. The economic benefit formula is:

[0100]

[0101] Where: P i is the market price of crop i; Y i is the yield of crop i; n is the total number of crops planted.

[0102] Ecological benefit: usually measured by reducing pollution emissions, improving land quality and other indicators. It can be evaluated by optimizing soil quality and reducing water consumption. The ecological benefit formula is:

[0103]

[0104] Among them, Q j Water consumption of the jth crop or ecological region; S j is the pollution emission of the jth crop or ecological region; α j is the environmental impact factor of the jth crop or ecological region.

[0105] Social benefits: usually measured by socio-economic indicators such as job creation and increased income of villagers. For example, after optimizing decision-making, the number of jobs created in regional sustainable projects, the social benefit formula is:

[0106]

[0107] Among them, E k is the influence coefficient of the kth social benefit indicator; J k is the contribution of the kth social benefit (such as the number of jobs); p is the number of indicators of social benefits, indicating the number of indicators involved in calculating social benefits.

[0108] The objective function obtains the overall optimization goal by weighting different objectives. In some embodiments, the objective function is:

[0109] Objective Function = w 1 ·Economic Benefit+w 2 ·Ecological Benefit+w 3 Social Benefit;

[0110] Among them, w 1 ,w 2 ,w 3 They are the weight coefficients of economic benefit, ecological benefit and social benefit, respectively, which are usually adjusted through user input or historical data; Economic Benefit is the economic benefit value calculated according to the above formula; Ecological Benefit is the social benefit value calculated according to the above formula.

[0111] The constraint definition module is used to determine the constraints of the optimization problem to ensure that factors such as the rational use of resources and environmental protection are taken into account during the optimization process. Usually, the constraints include but are not limited to resource use restrictions, environmental protection requirements and social benefit requirements.

[0112] Common constraints include:

[0113] Resource usage restrictions: such as the maximum usage of water resources, land, labor, etc. The resource usage restriction formula is:

[0114] R water ≤R water max ;

[0115] Among them, R water is the current water resource usage; R water max It can be the maximum value of water resources.

[0116] Environmental protection requirements: such as the upper limit of factors such as soil quality and climate pollution. The formula for environmental protection requirements is:

[0117] Q pollution ≤Q pollution max ;

[0118] Among them, Q pollution is the current pollution emission; Q pollution max is the maximum acceptable pollution emission.

[0119] Social benefit requirements: such as the number of jobs and income levels that must be met. Social benefit requirements are:

[0120] J min ≤J actual ≤J max ;

[0121] Among them, J actual is the number of jobs actually created; min is the minimum employment requirement; max The maximum number of jobs.

[0122] These constraints ensure the sustainable use of resources during the optimization process and avoid negative impacts on the environment or society due to overexploitation.

[0123] The optimization solution module performs global optimization through genetic algorithm (GA) and particle swarm optimization algorithm (PSO) to solve multi-objective and multi-constraint optimization problems.

[0124] Genetic Algorithm: Genetic Algorithm simulates the process of natural selection and conducts a global search in the solution space. Each generation generates new individuals through operations such as selection, crossover, and mutation, and finally selects the most suitable solution. This algorithm is particularly suitable for dealing with complex, multi-dimensional optimization problems.

[0125] Particle Swarm Optimization Algorithm: Particle Swarm Optimization Algorithm simulates the behavior of bird flocks foraging, and continuously searches for the optimal solution by adjusting the position of each particle in the solution space. Particle Swarm Optimization Algorithm has good global search capabilities and can quickly find the optimal solution that meets multiple objective constraints.

[0126] The feedback adjustment module continuously adjusts and optimizes the decision-making plan by acquiring and analyzing real-time data. In some embodiments, the system will make dynamic adjustments based on actual data (such as soil quality, crop growth conditions, resource consumption, etc.). For example, when real-time monitoring shows that there is a shortage of water resources in a certain area, the system will automatically adjust the water resource allocation plan for that area.

[0127] In order to improve the efficiency of the optimization solution module, the genetic algorithm and particle swarm optimization algorithm adopt optimization technology during the execution process. Specifically, the genetic algorithm improves the search efficiency by adaptively adjusting the crossover rate and mutation rate, and retaining the best solution through the elite retention strategy. The particle swarm optimization algorithm combines local search technology to improve the accuracy of the solution by refining the search range of particles.

[0128] Through the design of the optimization decision module, the system can automatically generate the best resource allocation plan according to the requirements of multiple objectives and multiple constraints. The optimization decision module can efficiently solve the resource allocation problem in the regional sustainable process by combining genetic algorithm and particle swarm optimization algorithm. The dynamic adjustment mechanism can optimize the decision in time according to the real-time feedback to ensure the flexibility and adaptability of resource allocation.

[0129] The optimization decision module of this embodiment realizes the efficient optimization of multi-dimensional resources in regional sustainability through the collaborative work of sub-modules such as model construction, goal setting, constraint definition, optimization solution and feedback adjustment. Through the combination of genetic algorithm and particle swarm optimization algorithm, the system can quickly find the global optimal solution under complex constraints and provide strong decision support for regional sustainability.

[0130] A virtual simulation display module, used to generate a sustainable display scene of a virtual area according to the optimization decision result, and support real-time interaction and decision adjustment;

[0131] The virtual simulation display module is responsible for generating virtual regional sustainable display scenes based on the output of the optimization decision module and providing interactive functions. This module combines the optimal resource allocation scheme generated by the multidimensional modeling module and the optimization decision module, and uses three-dimensional modeling technology to visualize the results of resource allocation so that users (such as decision makers, farmers, researchers, etc.) can intuitively understand the regional sustainable effects under different resource allocation schemes.

[0132] In general, the purpose of the virtual simulation display module is to enable users to view the optimized regional resource allocation in real time in a virtual environment through dynamic and interactive three-dimensional scene display, and then make decision adjustments. As an option, the virtual simulation display module not only supports result display, but also allows users to flexibly adjust optimization goals or resource allocation strategies based on feedback from the virtual scene, forming a closed-loop decision optimization system.

[0133] Specifically, the virtual simulation display module in this embodiment consists of four main parts: scene modeling, data visualization, interactive operation and real-time feedback. These parts work closely together in the overall system to improve the flexibility and adaptability of decision-making through the real-time feedback mechanism.

[0134] The scenario modeling part is mainly responsible for constructing a virtual three-dimensional scene in the regional sustainable process based on the results of the multi-dimensional modeling module and the optimization decision module. This part uses three-dimensional modeling technology to transform data such as agricultural production, ecological environment, and social and economic systems into a visual three-dimensional scene.

[0135] The scenario modeling is done in the following way:

[0136] Land and resource allocation modeling: Based on the results of the optimization decision module, information such as land use, distribution of agricultural crops, and use of water resources are converted into a three-dimensional topographic map. Through the division of virtual land, users can see the types of crops, water resource allocation, and ecological protection measures on each piece of land.

[0137] Visualization of ecological environment and social impact: In terms of ecological environment, the scene can display information such as water pollution and soil quality; in terms of social economy, it can display regional economic development, employment opportunities, etc. Different colors, graphics and annotations are used to help users clearly understand the status of various resources.

[0138] For example, crop planting areas can be marked by different color blocks or layers, water consumption areas can be displayed through dynamic water level models, and social benefits can be displayed in the form of numbers or icons.

[0139] The data visualization part displays the resource allocation results in the optimization decision module in a graphical way, so that users can more intuitively understand the impact of various resource allocations on regional sustainability. The visualization content includes but is not limited to:

[0140] Crop distribution and growth status: Display the distribution of crops and crop growth status (such as health, impact of pests and diseases, etc.) in each area through charts, bar graphs or dynamic effects.

[0141] Water resource use: Shows the distribution of water resources in different regions, which may include changes in reservoir water levels, soil moisture distribution, etc.

[0142] Pollution emissions and ecological restoration effects: Use heat maps, trend charts, etc. to display pollutant emission levels in different areas and the effects of restoration measures taken.

[0143] Economic and social benefits: Use dynamic charts and digital models to display the market value of agricultural products, employment of villagers, infrastructure construction, etc.

[0144] Specifically, when a user selects an indicator, the system will dynamically update charts and data based on the optimization decision results, providing real-time feedback on the effects of different resource configurations. All data will be presented in a clear and intuitive graphical manner, making it easier for users to conduct multi-dimensional decision analysis.

[0145] The interactive operation part is mainly responsible for providing an interactive interface between users and virtual scenes, supporting users to make real-time adjustments to regional sustainable resource allocation. The interactive functions include:

[0146] Resource allocation adjustment: Users can adjust resource allocation plans (such as water, land, labor, etc.) in real time through sliders, buttons or input boxes, and immediately see the effects of the adjustments. This function enables decision makers to simulate different resource allocation situations in virtual scenes, thereby optimizing the decision-making process.

[0147] Dynamic scene updates: As resource allocation is adjusted, the virtual scene will be dynamically updated. For example, increasing water resources in a certain area may lead to faster growth of crops in that area, and the corresponding water pollution situation will also change.

[0148] Decision feedback and analysis: The system provides instant feedback and displays data on economic, ecological and social benefits after resource allocation adjustments. It helps users evaluate the effects of different plans through charts and data visualization.

[0149] Simulation testing in a virtual environment: In some embodiments, users can also use the simulation function to conduct long-term simulation testing to view the sustained effects of resource allocation over a long period of time. For example, simulate the long-term impact of certain resource allocation plans on regional sustainability after three or five years.

[0150] The real-time feedback part in this embodiment closely connects the optimization decision module with the virtual simulation display module to ensure that each decision adjustment can be reflected in the virtual scene. Specifically, when the optimization decision module generates a new optimal resource allocation plan, the system will automatically update the data in the virtual scene and provide instant feedback to the user.

[0151] Data update: Collect and transmit data from the data acquisition module (such as soil quality, water resource usage, climate change, etc.) in real time and apply it to the virtual scene display.

[0152] Resource adjustment feedback: When a user adjusts a resource configuration, the system will immediately provide feedback on the adjusted results, such as economic benefits, environmental impact, social benefits, etc. Feedback information helps users evaluate the feasibility of new solutions.

[0153] For example, the system will automatically update soil moisture maps, crop growth maps, etc. based on real-time resource feedback to ensure that the situation displayed in the scene is always consistent with the actual data.

[0154] The realization of the virtual simulation display module relies on modern 3D modeling technology and computer graphics technology, and uses efficient graphics rendering engines (such as Unity3D, UnrealEngine, etc.) to realize the dynamic display of the scene. In addition, the module also combines the Internet of Things technology to obtain and update environmental data (such as sensor data, meteorological data, etc.) in real time to ensure that the data in the scene matches the actual situation.

[0155] 3D modeling technology: Using highly customized 3D models, users can see the changes in different resource configurations in the regional sustainable process in real time.

[0156] Rendering and animation technology: In order to ensure the smoothness and interactivity of virtual scenes, efficient rendering technology and physical engines are used to simulate the flow and impact of resources in the real world.

[0157] Real-time data transmission: Real-time connection with the data acquisition module ensures that all changes are synchronized with the actual data and provides dynamic feedback.

[0158] Beneficial effects: Through the virtual simulation display module, users can evaluate and adjust the resource allocation plan in the regional sustainable process in an intuitive and interactive way. Through three-dimensional visualization, interactive functions and real-time feedback, the module significantly improves the transparency and flexibility of the decision-making process, helps to make more scientific and reasonable decisions, and promotes the smooth progress of regional sustainability.

[0159] The virtual simulation display module in this embodiment combines 3D modeling, data visualization and real-time feedback technology to provide visual and dynamic support for the regional sustainable decision-making process. Through close connection with the optimization decision-making module, this module not only displays the optimization effect of resource allocation, but also provides real-time adjustment and decision analysis functions, providing decision makers with a powerful tool to help them make the best decision in a changing environment.

[0160] It is used to receive and analyze data from the regional sustainable implementation process in real time, and dynamically adjust the resource allocation plan according to the analysis results, so as to optimize the use and continuous balance of resources;

[0161] The real-time feedback control module is responsible for making dynamic adjustments based on the real-time collected data during the implementation of regional sustainable development to ensure the optimization of resource allocation. This module is closely connected with the optimization decision module and the virtual simulation display module to form a closed-loop system to achieve continuous optimization and dynamic control of regional sustainable resource allocation.

[0162] In general, the real-time feedback control module obtains data on various resources and environments in real time through the Internet of Things technology, such as soil moisture, crop growth status, water resource consumption, climate change, etc. Based on this feedback information, the module can dynamically adjust the previous resource allocation plan to respond to sudden environmental changes or adjust the optimization target. As an option, the real-time feedback control module combines the optimal control algorithm for data analysis, quickly responds to environmental changes, adjusts resource allocation, and ensures the continuous and reasonable use of various resources in the regional sustainable process.

[0163] Specifically, the real-time feedback control module includes four sub-modules: data collection, data processing, control decision-making, and adjustment execution. Through the collaborative work of these modules, real-time feedback control can provide flexible and efficient resource optimization management for regional sustainability.

[0164] The data collection part relies on the Internet of Things technology and various sensing devices (such as sensors, drones, remote sensing equipment, etc.) to monitor various resource data in the regional sustainable process in real time. The content of data collection mainly includes the following categories:

[0165] Agricultural production data: such as crop growth status, soil moisture, meteorological data, and farmland water resource usage.

[0166] Ecological and environmental data: including environmental factors such as water pollution, soil quality, and air quality.

[0167] Socio-economic data: including employment situation, infrastructure construction progress, income level and other socio-economic indicators.

[0168] These data are transmitted to the real-time feedback control module through the real-time transmission system, providing necessary information support for subsequent feedback control.

[0169] For example, during crop growth, soil moisture sensors can provide real-time information about soil moisture. These data will be transmitted to the data processing module in real time, thereby affecting the allocation of water resources.

[0170] The main task of the data processing part is to analyze and process the data collected in real time so as to make appropriate adjustment decisions based on these data. This part uses technologies such as data fusion, statistical analysis and prediction algorithms to extract useful information from massive amounts of raw data to provide a basis for control decisions.

[0171] Specifically, the data processing part uses the following technical means:

[0172] Data preprocessing: Perform preprocessing operations such as denoising, missing value filling and normalization on the raw data to improve data quality and the accuracy of subsequent analysis.

[0173] State estimation and trend prediction: Predict resource demand or environmental change trends in the future through machine learning, regression analysis and other methods. For example, based on historical climate data and real-time soil moisture data, predict water demand in the next few days.

[0174] In one possible implementation, a time series model (such as an ARIMA model) is used to predict future resource demand. The time series model formula is:

[0175]

[0176] in, is the predicted value, indicating the resource demand at time t; Y t-1 ,Y t-2 , is the historical data; φ 1 ,φ 2 , is the parameter of ARIMA model; ∈ t is the error term.

[0177] Real-time anomaly detection: By analyzing real-time data, when anomalies (such as excessive resource consumption, excessive pollution emissions, etc.) are detected, a real-time feedback mechanism is triggered. For example, by comparing the standard deviation of current resource usage with historical usage, a warning and adjustment mechanism is initiated when a preset threshold is exceeded.

[0178] The control decision part generates resource allocation adjustment plans based on the results of data processing using the optimal control algorithm. This part is the core of the real-time feedback control module, responsible for converting the processed data into specific operation strategies and sending adjustment instructions to other modules (such as the virtual simulation display module or the optimization decision module).

[0179] Common control decision-making methods include:

[0180] Model-based control decision: According to the models provided by the optimization decision module and the multidimensional modeling module, the optimal resource allocation scheme is calculated through the optimal control algorithm (such as dynamic programming, PID control, etc.). For example, the water resource allocation in a certain area is adjusted so that crops can get appropriate moisture.

[0181] Real-time adjustment strategy: Through real-time data feedback, the control decision-making part can flexibly adjust the optimization target. For example, when the system detects that water resources in a certain area are insufficient, the water resource allocation plan for that area is immediately adjusted, while reducing the water supply to other non-urgent areas.

[0182] Collaborative optimization strategy: In some embodiments, the control decision part works in collaboration with the optimization decision module. When the latest real-time data is received, the system can adjust the optimization decision plan and display the new resource allocation plan in real time.

[0183] The adjustment execution part puts the adjustment plan of the control decision part into practice to ensure that the adjusted resource allocation can take effect immediately and be reflected in the actual operation. This part mainly dynamically updates the resource allocation and displays the results through the connection with the data acquisition module and the virtual simulation display module.

[0184] The adjustments include:

[0185] Resource allocation adjustment: According to the resource allocation plan generated by the decision-making part, adjust the specific resource allocation. For example, adjust the water resource allocation, labor allocation, crop planting area, etc.

[0186] Real-time scene update: By connecting to the virtual simulation display module, the resource configuration in the virtual scene is updated immediately. Users can see the effect of the adjustment in real time in the virtual simulation display module to verify the rationality of the resource configuration.

[0187] For example, when the water resource allocation plan is adjusted, the real-time feedback control module will update the actual distribution of water resources and synchronously update the water level changes in the virtual simulation display to ensure the consistency of the entire system.

[0188] In this embodiment, the control decision part adopts an optimal control algorithm to ensure that each adjustment can effectively achieve resource optimization. A common form of the optimal control algorithm is the linear quadratic regulator (LQR), whose optimization goal is to minimize the difference between resource configuration and expected goals. The formula form is as follows:

[0189]

[0190] Among them, J is the objective function, which represents the total cost or benefit function; x(t) is the state variable, which represents the current resource allocation state of the system (such as water resource usage, soil moisture, etc.); u(t) is the control variable, which represents the resource adjustment amount (such as the change in water resource allocation); Q and R are weight matrices, which represent the weighting coefficients of state variables and control variables, respectively, and control the trade-offs in the optimization process; T is the optimization duration, which is usually a certain time window.

[0191] Through the optimal control algorithm, the system can calculate the best resource adjustment strategy based on the constraints, thereby achieving the optimal use of resources.

[0192] In order to improve the efficiency and response speed of the feedback control module, a multi-level control strategy is adopted in this embodiment. Specifically, the low-level control is mainly responsible for real-time data monitoring and simple adjustment, while the high-level control combines the results of the multidimensional modeling and optimization decision-making modules for global adjustment. In addition, the system adopts adaptive control technology, which can adjust the control parameters in real time according to environmental changes, improving the robustness and adaptability of the system.

[0193] Adaptive control: Dynamically adjust the parameters of the control algorithm based on the actual operating environment and feedback data to cope with the impact of unexpected events or long-term changes.

[0194] The real-time feedback control module in this embodiment ensures that resources in the regional sustainable project can be continuously and reasonably allocated through precise real-time data collection, processing and control decision-making mechanisms. Through close connection with the optimization decision-making module and the virtual simulation display module, the real-time feedback control module provides strong support for the dynamic optimization of resource allocation, ensures that resources in the regional sustainable process are always in the optimal state, and realizes closed-loop decision-making optimization.

[0195] See also Figure 3 The present invention also provides a method for displaying a regional sustainable development model based on virtual simulation, comprising the following steps:

[0196] S1. Real-time collection of regional sustainable resource data;

[0197] S2. Establishing an agricultural production model, a social economic system model and an ecological environment model based on the resource data;

[0198] S3. Generate optimization problems through multi-objective optimization methods, optimize resource allocation, and generate sustainable display scenes of virtual areas based on the optimization results;

[0199] S4, displaying the optimization decision results through three-dimensional visualization, so that users can generate sustainable display scenes of virtual areas according to the optimization decision results, and support interactive decision adjustment;

[0200] S5. Real-time monitoring of resource data during the regional sustainable implementation process, and adjustment of resource allocation plans based on feedback data for optimal use of resources in the regional sustainable process.

[0201] For S1, this step collects multi-dimensional data related to regional sustainability through multiple sensors, drones, remote sensing equipment, etc. These data include resource data in agricultural production, environmental monitoring, social economy, etc. The collected data will provide a basis for subsequent modeling and decision-making. By deploying sensors, agricultural production-related data such as soil moisture, meteorological conditions, and crop growth status are collected in real time. Combine drones and remote sensing equipment to obtain a wide range of environmental monitoring data, including water resource consumption, pollution emissions, air quality, etc. Collect social and economic data, covering information such as villagers' income, employment, and infrastructure construction. These collected data will be transmitted to the data processing center in real time through the Internet of Things technology to provide support for subsequent analysis and optimization.

[0202] For S2, based on data collection, this step constructs a multidimensional mathematical model to comprehensively describe the relationship between agricultural production, ecological environment and socio-economic system.

[0203] Agricultural production model: simulates resource demand, farmland management and production efficiency during crop planting, reflecting the dynamic relationship between crops and resources.

[0204] Ecological environment model: describes the changes in environmental factors such as water resources, soil quality, and pollution emissions to ensure the realization of ecological protection goals.

[0205] Socio-economic model: Analyze the impact of resource allocation on villagers’ income, employment opportunities, social welfare, etc., and help evaluate social benefits.

[0206] These models are combined to construct a comprehensive resource allocation model that can fully reflect the various factors and their interactions in the regional sustainable process.

[0207] For S3, according to the model generated by the multi-dimensional modeling module, this step calculates the optimal resource allocation plan through a multi-objective and multi-constraint optimization method.

[0208] Goal setting: Set multiple optimization goals such as economic benefits, ecological protection and social benefits, and assign different weight coefficients to each goal.

[0209] Constraints: Define the maximum limits on resource use (such as water resources, land, labor, etc.), and set constraints on pollution emissions and social benefits.

[0210] Optimization solution: Through multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.), the optimal resource allocation plan is solved to meet various objectives and constraints.

[0211] Finally, the optimization decision module outputs an optimal resource allocation plan, providing a basis for subsequent display and adjustment.

[0212] For S4, this step converts the results of the optimization decision module into a virtual scene, and generates a regional sustainable virtual display environment through 3D modeling technology to help users intuitively understand the impact of different resource configurations.

[0213] 3D modeling: Based on the results of the optimization decision, create virtual regional scenes including farmland, ecological environment, infrastructure, etc.

[0214] Data visualization: Display the impact of resource allocation on economic, ecological and social benefits through charts and dynamic effects.

[0215] Interactive decision support: Users can interact with the system through virtual scenes, adjust resource allocation plans in real time, and view the effects of different adjustment plans.

[0216] This step aims to enable users to experience the impact of different resource configuration options through virtual scenarios and provide decision support.

[0217] For S5, this step receives and analyzes the data of regional sustainable implementation in real time, and dynamically adjusts the resource allocation plan according to the analysis results.

[0218] Data reception and collection: Receive agricultural production, environmental and socio-economic data in real time through IoT technology to monitor resource usage.

[0219] Dynamic Adjustment Scheme: Use optimal control algorithms to adjust resource allocation based on real-time data, such as automatically reducing water allocation when water is scarce or repairing soil when soil quality deteriorates.

[0220] Feedback and optimization: The adjusted plan is fed back to the optimization decision module and virtual simulation display module in real time to update the resource allocation results and continuously optimize system performance.

[0221] This step ensures that regional sustainable resource allocation can be flexibly adjusted according to real-time data during implementation, thereby achieving optimal resource utilization.

[0222] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A regional sustainable development model display system based on virtual simulation, characterized by: include: Data collection module, used to collect relevant regional sustainable resource data in real time; A multidimensional modeling module, used to construct an agricultural production model, a social economic system model and an ecological environment model based on the resource data; An optimization decision-making module is used to generate multi-objective and multi-constraint optimization problems based on the agricultural production model, the socio-economic system model and the ecological environment model, and optimize resource allocation. The optimization problem includes multiple objectives such as economic benefits, ecological protection and social benefits; A virtual simulation display module, used to generate a sustainable display scene of a virtual area according to the optimization decision result, and support real-time interaction and decision adjustment; The real-time feedback control module is used to receive and analyze data during the regional sustainable implementation process in real time, and dynamically adjust the resource allocation plan based on the analysis results for optimal use and continuous balance of resources.

2. The regional sustainable development model display system based on virtual simulation according to claim 1 is characterized in that: The data acquisition module includes multiple sensors, drones and remote sensing equipment, which are used to obtain environmental data, crop growth data and resource usage data from different areas in the region.

3. The regional sustainable development model display system based on virtual simulation according to claim 1 is characterized in that: The resource data include water resources, land quality, climate data, crop growth status, socio-economic data and environmental monitoring data.

4. The regional sustainable development model display system based on virtual simulation according to claim 1 is characterized in that: The multidimensional modeling module uses diffusion equations to describe the flow of water resources in the soil, and establishes the interaction relationship between the ecosystem and agricultural production based on the ecological Lotka-Volterra model; The diffusion equation is as follows: Among them, θ(x,t) is the moisture content in the soil, t is the time variable, x is the spatial coordinate, D(x,t) is the moisture diffusion coefficient, is the spatial gradient operator, S(x,t) is the water consumption rate; The Lotka-Volterra equation is as follows: Among them, N(t) is the number of crop populations in the ecosystem, r is the growth rate, K is the environmental carrying capacity, C(N) is the competition coefficient, is the rate of change of species population over time.

5. The regional sustainable development model display system based on virtual simulation according to claim 1 is characterized in that: The optimization decision module includes: A model building module, used to build a mathematical model of agricultural production, social economy and ecological environment based on the multidimensional modeling module; A target setting module is used to set an optimization target function, which includes economic benefits, ecological protection and social benefits, and assign weights to each target; The constraint definition module is used to define resource usage limits, environmental protection requirements, and social benefit requirements. The optimization solution module is used to solve the problem based on the objective function and constraints using a multi-objective optimization algorithm to obtain the optimal resource allocation solution.

6. The regional sustainable development model display system based on virtual simulation according to claim 1 is characterized in that: The optimization decision module processes high-dimensional and complex resource allocation problems by using genetic algorithms and particle swarm optimization algorithms to perform global search for resource allocation.

7. The regional sustainable development model display system based on virtual simulation according to claim 1 is characterized in that: The virtual simulation display module generates virtual scenes under different resource allocation schemes in the regional sustainable process through three-dimensional modeling technology, and supports user interaction to adjust the resource allocation scheme.

8. The regional sustainable development model display system based on virtual simulation according to claim 1 is characterized in that: The real-time feedback control module collects real-time data of regional sustainable processes through Internet of Things technology, and dynamically adjusts resource allocation in combination with the optimal control algorithm to ensure optimal resource utilization.

9. A method for displaying a regional sustainable development model based on virtual simulation, applied to a system for displaying a regional sustainable development model based on virtual simulation as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Collect relevant resource data on regional sustainability in real time; Establishing agricultural production models, social economic system models and ecological environment models based on the collected resource data; Generate optimization problems through multi-objective optimization methods, optimize resource allocation, and generate sustainable display scenarios for virtual areas based on the optimization results; The optimization decision results are displayed through three-dimensional visualization, so that users can generate sustainable display scenes of virtual areas according to the optimization decision results, and support interactive decision adjustments; Receive and analyze data from the regional sustainable implementation process in real time, and dynamically adjust resource allocation plans based on the analysis results.

10. The method for displaying regional sustainable development model based on virtual simulation according to claim 9 is characterized in that: The feedback data is based on real-time environmental data, agricultural production data and socio-economic data during the regional sustainable implementation process, and dynamically adjusts resource allocation through a real-time feedback control algorithm.

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