Rural ecological environment comprehensive planning system and method

Abstract

The invention discloses a rural ecological environment comprehensive planning system and method, and relates to the technical field of rural ecological environment planning, and the system comprises the following components: a data collection module, a data processing and analysis module, a dynamic planning model construction module, and a planning strategy generation and adjustment module. According to the method, historical data and real-time monitoring data are widely collected, an advanced climate trend prediction model and an advanced population dynamic prediction model are combined, a comprehensive space-time big data set can be formed, a scientific basis is provided for planning, and meanwhile, a self-defined data feature extraction algorithm and a space-time adaptive deep learning algorithm are applied, so that the planning efficiency is improved. The data value can be deeply mined, the change trend of the ecological environment and the association between different factors are found, and powerful support is provided for formulating a scientific and reasonable planning strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of rural ecological environment planning, and specifically provides a rural ecological environment comprehensive planning system and method. Background Art

[0002] With the acceleration of the urbanization process, rural areas are facing increasingly severe ecological environment problems, such as soil degradation, water resource shortage, and biodiversity reduction. These problems not only affect the sustainable development of rural areas but also pose threats to the quality of life and health of local residents. Therefore, it is particularly important to conduct comprehensive planning for the rural ecological environment to achieve the rational utilization of resources and the sustainable development of the environment.

[0003] Traditional technologies have some obvious disadvantages. On the one hand, these models are often limited to data from a single source in data collection and processing, lacking comprehensiveness and timeliness, resulting in inaccurate and incomplete basic data for planning. On the other hand, traditional dynamic planning models often ignore the complexity and dynamics within the ecological environment system when being constructed, making it difficult to accurately capture the changing trends of the ecological environment and the correlations between different factors.

[0004] In summary, traditional spatio-temporal big data-driven dynamic planning models have obvious deficiencies in the comprehensive planning of rural ecological environment. Therefore, it is particularly important to develop a rural ecological environment comprehensive planning system and method to overcome these disadvantages. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide a rural ecological environment comprehensive planning system and method. It can form a comprehensive spatio-temporal big data set by widely collecting historical data and real-time monitoring data, combining advanced climate trend prediction models and population dynamics prediction models, providing a scientific basis for planning. At the same time, by using custom data feature extraction algorithms and spatio-temporal adaptive deep learning algorithms, it deeply explores the data value and discovers the changing trends of the ecological environment and the correlations between different factors.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A rural ecological environment comprehensive planning system and method, the system includes the following components: a data collection module, a data processing and analysis module, a dynamic planning model construction module, and a planning strategy generation and adjustment module;

[0007] The data collection module: is used to widely collect historical data of the rural ecological environment, covering annual meteorological data, land use change data, water quality monitoring records, and use sensor networks, satellite remote sensing, and unmanned aerial vehicle monitoring means to collect current ecological environment data in real time, combined with a self-constructed climate trend prediction model, and a population dynamics prediction model, Predict future environmental changes, population movements, and resource demand data to form a comprehensive spatio-temporal big data set. Among them, in the climate trend prediction model CTPM, f i (t) is a characteristic function of historical meteorological data based on time t, and α i is a weight coefficient determined by multiple iterations and optimizations according to the correlation between historical meteorological data and actual climate changes. β is the weight of the influence of spatial factors, and g(s) is a function related to spatial location. In the population dynamics prediction model PDPM, h(p, e) is a correlation function between the current population p and the economic development level e, γ is the correlation weight between the two, and k j (t) is a population movement trend function based on time t, and δ j is a weight coefficient determined according to the population movement characteristics in different time periods;

[0008] The data processing and analysis module: performs preprocessing such as cleaning, denoising, and format unification on the collected massive data, and uses a custom data feature extraction algorithm to extract valuable information from the data. Among them, m k (d) is the k-th characteristic function of data d, and φ k is a weight coefficient determined by combining expert evaluation and machine learning according to the importance of ecological environment analysis for data characteristics, in order to discover the trends of ecological environment changes and the correlations between different factors;

[0009] The dynamic programming model construction module: constructs a dynamic programming model based on the spatio-temporal adaptive deep learning algorithm STADA. The core formula of this algorithm is where O t , s is the model output at time t and spatial position s, σ is the activation function, N u (t, s) is the input feature vector at time t and spatial position s, ω u is a weight determined by repeatedly training ecological environment data in different spatio-temporal contexts, aiming to minimize the prediction error, and is optimized by combining stochastic gradient descent and genetic algorithm. M v (t, s - 1) is the feature vector of the previous spatial position, θ v is a weight determined based on spatial correlation analysis, and b is the bias term. This model takes spatio-temporal data as input, and through continuous training and optimization, learns the evolution laws of the ecological environment at different time and spatial scales;

[0010] The planning strategy generation and adjustment module: automatically generates rural ecological environment planning strategies according to the output results of the dynamic programming model through the planning strategy decision algorithm where Z is the set of planning strategies, and P i (O t,s , c i)For the model output O t , s and the constraint condition c i , the evaluation function of the i-th planning strategy under the condition, λ i is the weight coefficient determined by the analytic hierarchy process according to the priorities of different planning goals. When the model detects environmental changes in terms of time or space, it can automatically adjust the planning strategy in a timely manner.

[0011] Furthermore, in the data collection module, the sensor network includes temperature and humidity sensors, light intensity sensors, and soil fertility sensors distributed in different regions of the countryside, and each sensor is deployed according to a specific grid layout. The grid spacing is dynamically adjusted according to the complexity of the rural terrain and the key areas for ecological environment monitoring, and the adjustment basis is the terrain complexity evaluation function where is the terrain gradient of region i, A i is the area of region i. When TCEF is greater than the set threshold τ, the sensor deployment is encrypted.

[0012] Even further, in the data cleaning process of the data processing and analysis module, the density-based noise data identification algorithm DBNDIA is adopted. This algorithm first calculates the density of each data point where N(p) is the set of neighborhood points of data point p, d(p, q) is the distance between point p and point q, and then, according to the density difference and the distribution of neighborhood points, through the noise judgment function the noise points are determined, where is the average density of all data points, σ D is the standard deviation of the density. When NDF is greater than the set threshold τ1, it is determined that the point is a noise point and is removed.

[0013] Even further, in the training process of the dynamic programming model construction module, the improved adaptive learning rate adjustment algorithm AALA is adopted. The initial learning rate is set to η0. In each iteration, according to the change ΔL = L t -L t-1 , L t is the loss value of the current iteration, L t-1 is the loss value of the previous iteration. The learning rate is adjusted through the learning rate adjustment formula where η t represents the learning rate at the t-th iteration, η t-1 is the learning rate at the η t-1 -th iteration, and α1 is the learning rate decay coefficient, which is determined by experimentally testing the effects of different values on the model convergence speed and stability.

[0014] Furthermore, when generating and adjusting the planning strategy, the planning strategy generation and adjustment module takes into account the needs of different stakeholders and adopts the multi-objective negotiation decision-making algorithm MODA. This algorithm first determines the demand vector R of each stakeholder s =[r s1 ,r s2 ,…,r sn , where R s represents the demand vector of each stakeholder. Then, through the benefit equilibrium function where μ si is the weight of stakeholder s for demand i, is the average expectation of demand i, and on the premise of meeting the basic needs of each stakeholder, it searches for the planning strategy combination that minimizes the BEF.

[0015] Furthermore, when collecting satellite remote sensing data, the data collection module adopts the multi-spectral image fusion algorithm MSIFA for different types of ecological environment monitoring targets. This algorithm represents the remote sensing images of different bands as I b (x, y), b represents the band, and x, y are the image coordinates. The fused image is generated through the fusion formula where ω b is the weight of each band image and is determined by experimentally comparing the effects of different band combinations on the extraction of ecological information according to the sensitivity of each band to different ecological elements.

[0016] Furthermore, when analyzing the changing trend of the ecological environment, the data processing and analysis module uses the time series decomposition and trend prediction algorithm TSDTPA. This algorithm first decomposes the time series data Y t into a trend term T t , a seasonal term S t , and a random term R t , that is, Y t =T t +S t +R t . The trend term is determined through the polynomial fitting algorithm T t =∑ n a i ×t i , where T t represents the trend term, a i is the fitting coefficient and is determined by least squares fitting. The seasonal term is calculated through the seasonal decomposition algorithm where s is the seasonal period and m is the number of data points within the period. The random term is calculated through the residual R t =Y t -T t -S t , and then the future trend is predicted based on the decomposition results.

[0017] Furthermore, when constructing the model, the dynamic programming model construction module introduces a spatial attention mechanism and determines the attention weights of different spatial positions through a spatial attention weight calculation function where WA s represents the attention weight at spatial position s, s represents the index of the spatial position, exp represents the natural exponential function, n represents the number of features, X i (s) is the i-th feature at spatial position s, β i is the feature weight, s′ represents that in the summation in the denominator, it traverses all spatial positions from 1 to s, s represents the total number of spatial positions divided by the entire studied rural ecological environment area, and is obtained through model training optimization to make the model pay more attention to the spatial regions that have a greater impact on ecological environment changes.

[0018] Furthermore, when adjusting the planning strategy, the planning strategy generation and adjustment module takes into account the recovery ability and ecological threshold of the ecosystem and adopts the ecological resilience constraint algorithm ERCA. This algorithm first determines the key index threshold T k of the ecosystem. When the model detects that environmental changes may cause the key index to exceed the threshold, by adjusting the planning strategy, the ecological system resilience function where I i is the current ecological index value, T i is the corresponding threshold, and γ i is the index weight.

[0019] On the other hand, a comprehensive rural ecological environment planning method is characterized in that the specific steps of this method are as follows:

[0020] S1. Data collection step: used to widely collect historical data of the rural ecological environment, covering historical meteorological data, land use change data, water quality monitoring records over the years, and use sensor networks, satellite remote sensing, and unmanned aerial vehicle monitoring means to collect current ecological environment data in real time. Combining with the self-constructed climate trend prediction model and the population dynamics prediction model, predict future environmental changes, population flows, and resource demand data to form a comprehensive spatio-temporal big data set. Among them, in the climate trend prediction model CTPM, f i (t) is the characteristic function of historical meteorological data based on time t, α i is the weight coefficient determined by multiple iterations and optimizations according to the correlation between historical meteorological data and actual climate changes, β is the weight of the influence of spatial factors, g(s) is the function related to spatial positions. In the population dynamics prediction model PDPM, h(p, e) is the correlation function between the current population p and the economic development level e, γ is the correlation weight between the two, and is determined by the regression analysis of historical data on local population growth and economic development, kj (t) is the population flow trend function based on time t, and δ j is the weight coefficient determined according to the population flow characteristics in different time periods;

[0021] S2. Data processing and analysis steps: Clean, denoise, and preprocess the collected massive data to unify the format, and use a custom data feature extraction algorithm to extract valuable information from the data, where m k (d) is the k-th feature function of data d, and φ k is the weight coefficient determined by combining expert evaluation and machine learning according to the importance of ecological environment analysis based on data features, so as to discover the changing trend of the ecological environment and the correlation between different factors;

[0022] S3. Dynamic programming model construction steps: Construct a dynamic programming model based on the spatio-temporal adaptive deep learning algorithm STADA. The core formula of this algorithm is where O t , s is the model output at time t and space s, σ is the activation function, N u (t, s) is the input feature vector at time t and space s, ω u is the weight determined by repeatedly training the ecological environment data under different spatio-temporal conditions, aiming to minimize the prediction error, and is optimized by combining stochastic gradient descent and genetic algorithm. M v (t, s - 1) is the feature vector of the previous space position, θ v is the weight determined based on spatial correlation analysis, b is the bias term. This model takes spatio-temporal data as input, and through continuous training and optimization, learns the evolution law of the ecological environment at different time and space scales;

[0023] S4. Planning strategy generation and adjustment steps: According to the output result of the dynamic programming model, use the planning strategy decision algorithm to automatically generate rural ecological environment planning strategies, where Z is the set of planning strategies, and P i (O t，s , c i ) is the i-th planning strategy evaluation function under the model output O t , s and the constraint condition c i , λ i is the weight coefficient determined by the analytic hierarchy process according to the priority of different planning objectives. When the model detects environmental changes in time or space, it can automatically adjust the planning strategy in a timely manner.

[0024] Compared with the prior art, this rural ecological environment comprehensive planning system and method has the following

[0025] beneficial effects:

[0026] 1. The invention can form a comprehensive spatio-temporal big dataset by widely collecting historical data and real-time monitoring data, combining advanced climate trend prediction models and population dynamics prediction models, providing a scientific basis for planning. At the same time, by using custom data feature extraction algorithms and spatio-temporal adaptive deep learning algorithms, it can deeply explore the data value, discover the trends of ecological environment changes and the correlations between different factors, providing strong support for formulating scientific and reasonable planning strategies.

[0027] 2. The invention can automatically adjust the planning strategy in a timely manner according to environmental changes. Through the planning strategy decision algorithm and ecological resilience constraint algorithm, it can comprehensively consider the needs of different stakeholders and the restoration ability of the ecosystem, generating planning strategies that meet the actual needs. At the same time, the system can also monitor environmental changes in real time. When detecting changes that may cause key indicators of the ecosystem to exceed the threshold, it can adjust the planning strategy in a timely manner to ensure the stability and sustainability of the ecosystem. This highly automated and intelligent planning method greatly improves the planning efficiency and accuracy, providing strong guarantee for the sustainable development of the rural ecological environment.

[0028] Other advantages, objectives and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 It is a flow operation diagram of a comprehensive rural ecological environment planning system;

[0031] Figure 2 It is a flow operation diagram of a comprehensive rural ecological environment planning method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following will, in conjunction with the drawings and preferred embodiments, describe in detail the specific embodiments, structures, features and their effects of the present invention as follows.

[0033] Embodiment 1

[0034] This embodiment describes a certain rural area mainly engaged in agricultural production. To improve the ecological sustainability of agricultural production and reasonably plan farmland resources, this comprehensive planning system is applied.

[0035] Collect the annual tourist flow data, combine with meteorological data to judge the impact of weather on tourists' travel, use satellite remote sensing and unmanned aerial vehicle monitoring to obtain the current situation of landscape resources, and adopt the multi-spectral image fusion algorithm MSIFA to fuse remote sensing images to clearly present the overall view of the landscape, use the climate trend prediction model to predict the impact of future climate change on the landscape, and the population dynamics prediction model to predict the change of tourist numbers.

[0036] Use the time series decomposition and trend prediction algorithm TSDTPA to analyze the tourist flow data, and decompose the time series data Y t = T t + S t + R t , the trend term seasonal term random term R t = Y t - T t - S t , and it is found that the tourist flow shows seasonal fluctuations and an overall upward trend.

[0037] Construct a dynamic programming model, introduce a spatial attention mechanism, and make the model pay more attention to the core area of the landscape through the spatial attention weight calculation function . After training, the model can predict the tourist distribution and resource demand according to spatio-temporal changes.

[0038] Adopt the multi-objective negotiation decision algorithm MODA to generate a planning strategy, determine the demand vectors R s = [r s1 , r s2 , …, r sn of tourists and villagers' stakeholders. Through the benefit balance function , on the premise of meeting the needs of all parties, plan the layout of tourism facilities and activity arrangements. When the model detects that the tourist flow exceeds the carrying capacity of the scenic area, adjust the strategy to control the number of tourists and protect the ecological environment.

[0039] Embodiment 2

[0040] This embodiment describes a certain rural area with unique natural scenery, surrounded by green mountains and clear waters, having rich natural landscape resources and the potential for developing rural tourism. However, the development of tourism activities may bring pressure to the local ecological environment. For example, the increase in tourists leads to more garbage, the destruction of natural landscapes, and water resource pollution. To achieve the sustainable development of rural tourism and balance the relationship between tourism development and ecological environment protection, a rural ecological environment comprehensive planning system is introduced.

[0041] Using satellite remote sensing technology, obtain the ecological environment information of large rural areas, including vegetation cover and land type distribution. At the same time, use drones equipped with high-definition cameras and various sensors to conduct low-altitude monitoring of key scenic spots and ecologically fragile areas in the countryside, and collect high-precision topographic and water quality data.

[0042] Collect historical tourist flow data, including the changes in the number of tourists in different seasons and holidays, analyze the time distribution law of tourists, and sort out the data on land use changes to understand the conversion of land for tourism facility construction and agricultural land use with the development of tourism.

[0043] Adopt a multi-spectral image fusion algorithm to process satellite remote sensing data, fuse images of different bands to highlight the information valuable for ecological environment monitoring. Combine the self-built climate trend prediction model and population dynamics prediction model, consider the impact of climate change on tourists' travel willingness, as well as the changes in the tourist source areas and numbers caused by population flow, and predict the environmental carrying capacity of the countryside during the tourist peak season, such as the tourist capacity of scenic spots, the carrying capacity of infrastructure, and the changes in tourists' demands for accommodation, catering, and entertainment.

[0044] Apply the time series decomposition and trend prediction algorithm to decompose the historical tourist flow data into trend terms, seasonal terms, and random terms. Determine the trend terms through polynomial fitting algorithm to analyze the long-term growth or change trend of tourist flow. Use the seasonal decomposition algorithm to calculate the seasonal terms to master the fluctuation law of tourist flow in different seasons. Obtain the random terms through residual calculation to exclude the influence of accidental factors.

[0045] Combine the custom data feature extraction algorithm to extract valuable information from the collected ecological environment data and tourist flow data, and determine the correlation between tourism activities and ecological environment indicators. For example, the relationship between the number of tourists and the amount of garbage generated in scenic spots and the amount of water resources used, and the impact of tourism facility construction on vegetation coverage and soil quality.

[0046] Build a dynamic programming model based on the spatio-temporal adaptive deep learning algorithm, using the collected spatio-temporal data as input, including ecological environment data and tourist flow data at different times and spatial positions.

[0047] During the training process, adopt an improved adaptive learning rate adjustment algorithm. Set the initial learning rate to a specific value. In each iteration, according to the change of the loss function of the model on the validation set, dynamically adjust the learning rate through the learning rate adjustment formula. Through continuous training and optimization, the model learns the evolution law of tourism activities and the ecological environment at different time and spatial scales. For example, the change patterns of the ecological environment in different scenic spots during the tourist peak season and off-season, and the spatio-temporal impact of tourist activities on the surrounding ecological environment.

[0048] According to the output results of the dynamic programming model, rural ecological environment planning strategies are generated through the planning strategy decision algorithm. The multi-objective negotiation decision algorithm is used to determine the demand vectors of multiple stakeholders including tourists, villagers, and ecological protection. Through the interest balance function, on the premise of meeting the basic needs of all parties, a planning strategy combination that minimizes the value of the interest balance function is found, such as determining a reasonable tourism ticket price and the construction scale of accommodation and catering facilities.

[0049] When the model detects that the tourist flow during the peak tourist season may exceed the environmental carrying capacity, the ecological elasticity constraint algorithm is used to adjust the strategy. For example, the number of tourists is restricted, and the number of tourists entering the scenic area is controlled through ticket reservations and timed visits. New tour routes are opened to guide tourists to disperse and reduce the pressure on the local ecological environment. At the same time, environmental protection publicity and education for tourists are strengthened to improve tourists' environmental awareness, achieve the balance between tourism and ecological protection, and ensure the sustainable development of rural tourism. Regarding the relationship between protection, the rural ecological environment comprehensive planning system is introduced.

[0050] The above is only a preferred embodiment of the present invention and does not impose any formal limitations on the present invention. Although the present invention has been disclosed as above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to obtain equivalent embodiments with equivalent changes. However, as long as it does not depart from the technical content of the present invention, any brief modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A rural ecological environment comprehensive planning system, characterized in that: The system includes a data collection module, a data processing and analysis module, a dynamic programming model building module, and a planning strategy generation and adjustment module: The data collection module is used to widely collect historical data on the rural ecological environment, including meteorological data over the years, land use change data, and water quality monitoring records. It uses sensor networks, satellite remote sensing, and drone monitoring to collect current ecological environment data in real time, combined with a self-built climate trend prediction model. and population dynamics prediction models, Predict future environmental changes, population mobility and resource demand data to form a comprehensive spatiotemporal data set. In the climate trend prediction model CTPM, f i (t) is the characteristic function of historical meteorological data based on time t, a i It is a weight coefficient determined by multiple iterations of optimization based on the correlation between historical meteorological data and actual climate change. β is the weight of spatial factors, g(s) is the spatial location correlation function, and in the population dynamics prediction model PDPM, h(p, e) is the correlation function between the current population status p and the economic development level e, γ is the correlation weight between the two, and k j (t) is the population mobility trend function based on time t, δ j It is a weight coefficient determined according to the population mobility characteristics in different time periods; The data processing and analysis module: cleans, removes noise, and pre-processes the collected massive data in a unified format, using a custom data feature extraction algorithm Extract valuable information from the data, where m k (d) is the kth characteristic function of data d, φ k It is a weight coefficient determined by combining expert evaluation with machine learning based on the importance of data features to ecological environment analysis, in order to discover the trend of ecological environment changes and the relationship between different factors; The dynamic programming model construction module: constructs a dynamic programming model based on the spatiotemporal adaptive deep learning algorithm STADA. The core formula of the algorithm is: Among them, t , s is the model output at time t and space s, σ is the activation function, N u (t, s) is the input feature vector in time t and space s, ω u The weights determined by repeated training of ecological environment data in different time and space are determined by using stochastic gradient descent combined with genetic algorithm to optimize the prediction error. v (t,s-1) is the eigenvector of the previous spatial position, θ v is the weight determined based on spatial correlation analysis, and b is the bias term. The model takes spatiotemporal data as input and learns the evolution of the ecological environment at different time and space scales through continuous training and optimization; The planning strategy generation and adjustment module: according to the output results of the dynamic programming model, through the planning strategy decision algorithm Automatically generate rural ecological environment planning strategies, where Z is the planning strategy set, P i (O t,s ,c i ) is the output of the model O t ,s and constraint c i The evaluation function of the i-th planning strategy under i It is a weight coefficient determined by the hierarchical analysis method according to the priority of different planning goals. When the model detects environmental changes in time or space, it can automatically adjust the planning strategy in time.

2. A rural ecological environment comprehensive planning system according to claim 1, characterized in that: In the data collection module, the sensor network includes temperature and humidity sensors, light intensity sensors, and soil fertility sensors distributed in different areas of the village, and each sensor is deployed according to a specific grid layout. The grid spacing is dynamically adjusted according to the complexity of the rural terrain and the key areas of ecological environment monitoring. The adjustment is based on the terrain complexity evaluation function. in is the terrain gradient of region i, A i is the area of ​​region i. When TCEF is greater than the set threshold τ, the sensor deployment is encrypted.

3. A rural ecological environment comprehensive planning system according to claim 1, characterized in that: The data processing and analysis module uses a density-based noise data identification algorithm DBNDIA during the data cleaning process. The algorithm first calculates the density of each data point Where N(p) is the neighborhood point set of data point p, d(p, q) is the distance between point p and point q, and then according to the density difference and the distribution of neighborhood points, the noise judgment function is used Determine the noise point, where is the average density of all data points, σ D is the standard deviation of the density. When NDF is greater than the set threshold τ1, the point is judged as a noise point and removed.

4. A rural ecological environment comprehensive planning system according to claim 1, characterized in that: The dynamic programming model building module adopts the improved adaptive learning rate adjustment algorithm AALA during the training process, and the initial learning rate is set to η0. In each iteration, according to the change of the loss function of the model on the validation set, ΔL=L t -L t-1 , L t is the loss value of the current iteration, L t-1 is the loss value of the previous iteration, and the learning rate is adjusted by the formula Adjust the learning rate, where η t represents the learning rate at the tth iteration, η t-1 It is the ηth t-1 The learning rate at the iteration, α1 is the learning rate attenuation coefficient, which is determined by experimentally testing the effects of different values ​​on the convergence speed and stability of the model.

5. A rural ecological environment comprehensive planning system according to claim 1, characterized in that: The planning strategy generation and adjustment module takes into account the needs of different stakeholders when generating planning strategies and adopts the multi-objective negotiation decision algorithm MODA, which first determines the demand vector R of each stakeholder. s =[r s1 ,r s2 ,…,r sn ], where R s Represents the demand vector of each stakeholder, and then uses the interest equilibrium function where μ si is the weight of stakeholder s on demand i, is the average expectation of demand i. Under the premise of meeting the basic needs of all stakeholders, we seek the planning strategy combination that minimizes BEF.

6. A rural ecological environment comprehensive planning system according to claim 1, characterized in that: When collecting satellite remote sensing data, the data collection module uses a multispectral image fusion algorithm MSIFA for different types of ecological environment monitoring targets. The algorithm represents remote sensing images of different bands as I b (x, y), b represents the band, x, y are the image coordinates, through the fusion formula Generate a fused image, where ω b is the weight of each band image, which is determined according to the sensitivity of each band to different ecological elements and by experimentally comparing the effects of different band combinations on ecological information extraction.

7. A rural ecological environment comprehensive planning system according to claim 1, characterized in that: The data processing and analysis module uses the time series decomposition and trend prediction algorithm TSDTPA when analyzing the trend of ecological environment changes. The algorithm first converts the time series data Y t Decomposed into trend term T t , Seasonal ItemS t and the random term R t , that is, Y t =T t +S t +R t , the trend term is obtained by polynomial fitting algorithm T t =Σ n a i ×t i Determine, where T t represents the trend term, a i is the fitting coefficient, which is determined by the least squares fitting method, and the seasonal term is calculated by the seasonal decomposition algorithm Where s is the seasonal period, m is the number of data points in the period, and the random term is calculated by the residual R t =Y t -T t -S t , and then predict future trends based on the decomposition results.

8. A rural ecological environment comprehensive planning system according to claim 1, characterized in that: The dynamic programming model construction module introduces the spatial attention mechanism when constructing the model, and calculates the function through the spatial attention weight. Determine the attention weights of different spatial positions, where WA s represents the attention weight at the spatial position s, s represents the index of the spatial position, exp represents the natural exponential function, n represents the number of features, X i (s) is the i-th feature of spatial position s, β i is the feature weight, s′ represents the sum of the denominator, it traverses all spatial positions, from 1 to s, s represents the total number of spatial positions divided by the entire studied rural ecological environment area, which is obtained through model training optimization, so that the model pays more attention to the spatial areas that have a greater impact on ecological environment changes.

9. A rural ecological environment comprehensive planning system according to claim 1, characterized in that: The planning strategy generation and adjustment module takes into account the resilience and ecological threshold of the ecosystem when adjusting the planning strategy. The ecological resilience constraint algorithm ERCA is adopted. The algorithm first determines the threshold value of the key indicator T of the ecosystem. k When the model detects that environmental changes may cause key indicators to exceed thresholds, the ecosystem resilience function is adjusted by adjusting the planning strategy. Among them I i is the current ecological index value, T i is the corresponding threshold, γ i is the indicator weight.

10. A method for comprehensive planning of rural ecological environment, characterized in that: The specific steps of this method are: S1. Data collection steps: It is used to widely collect historical data on rural ecological environment, including meteorological data over the years, land use change data, and water quality monitoring records. It uses sensor networks, satellite remote sensing, and drone monitoring methods to collect current ecological environment data in real time, combined with a self-built climate trend prediction model. and population dynamics prediction models, Predict future environmental changes, population mobility and resource demand data to form a comprehensive spatiotemporal data set. In the climate trend prediction model CTPM, f i (t) is the characteristic function of historical meteorological data based on time t, α i It is a weight coefficient determined through multiple iterations of optimization based on the correlation between historical meteorological data and actual climate change. β is the weight of spatial factors, g(s) is the spatial location correlation function. In the population dynamics prediction model PDPM, h(p, e) is the correlation function between the current population status p and the economic development level e, and γ is the correlation weight between the two, which is determined by regression analysis of historical data on local population growth and economic development. k j (t) is the population mobility trend function based on time t, δ j It is a weight coefficient determined according to the population mobility characteristics in different time periods; S2. Data processing and analysis steps: Clean, denoise, and pre-process the massive amount of data collected, and use a custom data feature extraction algorithm Extract valuable information from the data, where m k (d) is the kth characteristic function of data d, φ k It is a weight coefficient determined by combining expert evaluation with machine learning based on the importance of data features to ecological environment analysis, in order to discover the trend of ecological environment changes and the relationship between different factors; S3. Dynamic programming model construction steps: A dynamic programming model is constructed based on the spatiotemporal adaptive deep learning algorithm STADA. The core formula of the algorithm is: Among them, t , s is the model output at time t and space s, σ is the activation function, N u (t, s) is the input feature vector in time t and space s, ω u The weights determined by repeated training of ecological environment data in different time and space are determined by using stochastic gradient descent combined with genetic algorithm to optimize the prediction error. v (t, s-1) is the eigenvector of the previous spatial position, θ v is the weight determined based on spatial correlation analysis, and b is the bias term. The model takes spatiotemporal data as input and learns the evolution of the ecological environment at different time and space scales through continuous training and optimization; S4, planning strategy generation and adjustment step: According to the output results of the dynamic programming model, through the planning strategy decision algorithm Automatically generate rural ecological environment planning strategies, where Z is the planning strategy set, P i (O t，s , c i ) is the output of the model O t , s and constraint c i The evaluation function of the i-th planning strategy under i It is a weight coefficient determined by the hierarchical analysis method according to the priority of different planning goals. When the model detects environmental changes in time or space, it can automatically adjust the planning strategy in time.

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