Comprehensive treatment effect evaluation method and system for ecological space planning

Abstract

The invention provides a comprehensive treatment effect evaluation system for ecological space planning, and relates to the technical field of ecological space planning, and the system comprises a data collection module which is used for collecting multi-dimensional environment data in an ecological space in real time; the data processing module is used for preprocessing the collected ecological data; the multi-dimensional analysis module is used for carrying out comprehensive analysis on various environmental factors in the ecological space and the mutual relation of the environmental factors based on the collected environmental data, and the synergistic effect calculation module is used for evaluating the synergistic effect among different treatment measures. According to the comprehensive treatment effect evaluation method and system for ecological space planning, all aspects of the ecological space are comprehensively covered by utilizing environmental factors, biological factors and social economic factors through collection and processing of multi-dimensional data. By constructing the multi-dimensional ecological space evaluation index system and using the analytic hierarchy process to carry out weight distribution, different types of data can be effectively integrated, and the ecological space treatment effect can be comprehensively evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological space planning, and in particular to a method and system for evaluating the comprehensive management effect of ecological space planning. Background Art

[0002] Ecological space planning refers to the rational allocation and management of space within a specific area under certain natural conditions to achieve sustainable resource utilization and ecological protection. With increasingly severe global ecological and environmental challenges and the rapid development of urbanization, the rational planning and management of ecological space are crucial for improving environmental quality, protecting biodiversity, and addressing climate change. In this process, evaluating the effectiveness of ecological space management has become an indispensable component.

[0003] Existing assessment methods often fail to comprehensively and accurately reflect the impact of governance measures on the long-term development of ecological spaces, making it difficult to effectively guide planning decisions. For example, many traditional methods are limited to analyzing single indicators such as pollution control or land use change, while ignoring the complex interactions between various factors within the ecosystem. Not only are these assessment methods difficult to adapt to the complexities of ecological space governance, they may also fail to maximize governance effectiveness by ignoring the synergies and feedback mechanisms between different governance measures. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method and system for evaluating the comprehensive governance effect of ecological space planning. The technical problem to be solved by this invention is: how to evaluate and optimize the ecological space governance effect through multi-dimensional data collection and dynamic system model analysis, combined with causal inference and multi-objective optimization algorithms, to achieve a balance and continuous improvement of ecological and economic benefits.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a comprehensive management effect evaluation system for ecological space planning, comprising:

[0006] Data collection module, used to collect multi-dimensional environmental data in the ecological space in real time;

[0007] A data processing module, used for pre-processing the collected ecological data;

[0008] Multi-dimensional analysis module, used to conduct comprehensive analysis of various environmental factors and their interrelationships within the ecological space based on collected environmental data;

[0009] Synergy calculation module, used to evaluate the synergy between different governance measures;

[0010] Dynamic evaluation module, used to update the evaluation results of ecological space governance effects in real time;

[0011] Decision support module, used based on comprehensive governance effect evaluation results.

[0012] Preferably, the multidimensional environmental data includes air quality, water quality changes, land use, and species diversity, and the ecological data preprocessing includes data denoising, standardization, and normalization operations to ensure the accuracy and comparability of the data.

[0013] Preferably, the data acquisition module includes remote sensing technology, sensor network and data mining technology, which is used to comprehensively collect and monitor various environmental indicators in the ecological space in real time.

[0014] Preferably, the data processing module further includes:

[0015] Data cleaning unit, used to remove abnormal data to ensure that the collected data can reflect the true state of the ecological space;

[0016] The data fusion unit is used to fuse data from different sensors and generate a unified standard data set for subsequent analysis and processing.

[0017] A comprehensive management effect evaluation method for ecological space planning includes the following steps:

[0018] S1. Obtain multi-source data on the ecological space planning area, pre-process the multi-source data, and construct an indicator system based on the multi-source data as basic data for comprehensive governance effect evaluation;

[0019] S2. Construct a dynamic system model based on the basic data. The dynamic system model represents the interactions within the ecosystem. The ecological space includes biological factors, environmental factors, and socioeconomic factors. Within the same model framework, the evolution of the ecological space is simulated through the influence of governance measures.

[0020] S3. Analyze the dynamic system model using causal inference algorithms to quantify the causal impact and synergistic effects of governance measures on the long-term development of the ecological space, and to distinguish the interactive feedback mechanisms between different governance measures, thereby comprehensively evaluating the long-term effects of individual and combined governance measures;

[0021] S4. Based on the dynamic system model, obtain ecological and economic benefit evaluation indicators, and dynamically evaluate the governance effect to form simulation prediction results;

[0022] S5. Based on the simulation and prediction results, a multi-objective optimization algorithm is used to optimize and adjust the governance strategy, taking into account both ecological and economic benefits at the regional scale to achieve continuous improvement of governance measures.

[0023] 7. The comprehensive management effect evaluation method for ecological space planning according to claim 5 is characterized in that: S1 is specifically:

[0024] S1.1 Obtain multi-source data on the ecological space planning area, including environmental data, land use change data, biodiversity data, and socioeconomic data;

[0025] S1.2 preprocessing the data, including data cleaning, data standardization, and dimensionality reduction, wherein the dimensionality reduction is based on principal component analysis of the data;

[0026] S1.3 Construct a multi-dimensional ecological space evaluation index system, and assign weights to each index based on the data hierarchical analysis method to obtain a basic data set for comprehensive governance effect evaluation.

[0027] Preferably, S2 is specifically:

[0028] The environmental factors mentioned in S2.1 use differential equations to represent the impact of climate change and pollutant concentration on ecological space. The specific formula is:

[0029]

[0030] E(t) is the change of environmental factors, I(t) is the governance measures, and C(t) is the external environmental impact;

[0031] The biotic factors described in S2.2 use equations to describe the interactions between species and changes in species density. The formula is as follows:

[0032]

[0033] Where N is the number of species, K is the environmental capacity, α and β are the predation and competition coefficients respectively;

[0034] The socioeconomic factors described in S2.3 are modeled through a system dynamics model to establish the relationship between population density, land use change, and economic indices using the following equation:

[0035]

[0036] Where P is the population density, K P is the population capacity, γ and δ are the population growth and environmental pressure coefficients.

[0037] Preferably, S3 is specifically:

[0038] S3.1 Build a Bayesian network model based on data correlation and time series analysis to infer the causal relationship between the impact of different governance measures on various ecosystem elements;

[0039] S3.2 uses a structural equation model to quantify causal relationships and evaluate the long-term benefits of governance measures on ecological space. The formula is:

[0040] Y=β0+β1X1+β2X2+…+β n X n +∈,

[0041] Among them, Y is the governance effect, X1, X2,…, X n are influencing factors, β1,…,β n is the regression coefficient, ∈ is the error term;

[0042] S3.3 Evaluate the responses of governance measures to random sample simulation models through Monte Carlo simulation, and calculate the expected effects and uncertainties of governance measures.

[0043] Preferably, the Bayesian network model infers the long-term effects of different governance measures through maximum a posteriori estimation, and the specific steps include:

[0044] Build a time series model using historical data to analyze the effects of various governance measures at different time points and form a causal network;

[0045] The back-propagation algorithm is used to optimize the time series model parameters to ensure that the impact of governance measures can be accurately captured and inferred.

[0046] Preferably, the multi-objective optimization algorithm in S5 adopts a genetic algorithm, which includes a selection operation, a crossover operation, and a mutation operation, so as to improve the exploration capability of the solution space and ensure that the algorithm can better handle complex nonlinear optimization problems.

[0047] The present invention provides a comprehensive management effect evaluation method and system for ecological space planning. It has the following beneficial effects:

[0048] This comprehensive governance effectiveness assessment method and system for ecological space planning utilizes multidimensional data collection and processing, comprehensively covering all aspects of ecological space using environmental, biological, and socioeconomic factors. By constructing a multidimensional ecological space evaluation index system and assigning weights using the analytic hierarchy process, it effectively integrates different types of data to comprehensively evaluate the effectiveness of ecological space governance. This multidimensional, cross-disciplinary assessment approach provides a more comprehensive basis for decision-making and more accurately reflects the combined impact of different governance measures on ecological space.

[0049] This technical solution incorporates a dynamic assessment module and simulation prediction results into the evaluation and optimization process. Utilizing advanced algorithms such as differential equations, structural equation models, Bayesian network models, and Monte Carlo simulations, it is able to monitor and dynamically update the effectiveness of governance measures in real time. Through dynamic system models, the evaluation not only considers the current governance effects but also, through quantitative analysis of long-term benefits, considers the synergistic effects of different governance measures and possible interactive feedback mechanisms. Furthermore, through the combination of multi-objective optimization techniques such as genetic algorithms, it balances ecological and economic benefits at the regional scale and continuously improves governance measures. This continuous improvement mechanism ensures that governance solutions can be optimized as the environment changes, thereby achieving long-term, sustainable ecological governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart for realizing the invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] like Figure 1 As shown, an embodiment of the present invention provides a comprehensive management effect evaluation system for ecological space planning, including a data acquisition module for real-time collection of multi-dimensional environmental data in the ecological space, the multi-dimensional environmental data including air quality, water quality changes, land use, and species diversity. Ecological data preprocessing includes data denoising, standardization, and normalization operations to ensure the accuracy and comparability of the data. The data acquisition module includes remote sensing technology, sensor networks, and data mining technology, which are used to comprehensively collect and monitor various environmental indicators in the ecological space in real time.

[0053] The data processing module is used to pre-process the collected ecological data. The data processing module further includes:

[0054] The data cleaning unit is used to remove abnormal data and ensure that the collected data can reflect the real state of the ecological space.

[0055] The data fusion unit is used to fuse data from different sensors and generate a unified standard data set for subsequent analysis and processing.

[0056] The multi-dimensional analysis module is used to conduct a comprehensive analysis of various environmental factors and their interrelationships within the ecological space based on the collected environmental data.

[0057] The synergy calculation module is used to evaluate the synergy between different governance measures.

[0058] Dynamic evaluation module, used to update the evaluation results of ecological space governance effects in real time.

[0059] Decision support module, used based on comprehensive governance effect evaluation results.

[0060] A comprehensive management effect evaluation method for ecological space planning includes the following steps:

[0061] S1. Obtain multi-source data of the ecological space planning area, pre-process the multi-source data, and construct an indicator system based on the multi-source data as the basic data for comprehensive governance effect evaluation.

[0062] The details of S1 are:

[0063] S1.1 Obtain multi-source data on the ecological space planning area, including environmental data, land use change data, biodiversity data, and socioeconomic data.

[0064] Environmental data comes from weather stations, sensor networks and satellite remote sensing data: it includes air quality, precipitation, temperature and humidity data, and specific data include: PM2.5 concentration, carbon dioxide emission concentration, and daily average temperature.

[0065] Land use change data come from geographic information systems and satellite images. For example, satellite images have a resolution of 10 meters and reflect data on urban expansion and reduction of agricultural land.

[0066] Biodiversity data includes species distribution and species counts. This data is obtained through field surveys and ecological monitoring, recording species richness in different regions. Species richness is measured as the number of bird species and the plant species diversity index.

[0067] Socioeconomic data: This includes population density, economic index levels, and pollution source locations, obtained from statistical departments or social surveys. Specific data include regional population density and regional GDP levels.

[0068] S1.2 Preprocess the data. This includes data cleaning, data standardization, and dimensionality reduction. Dimensionality reduction is based on principal component analysis (PCA). This PCA reduces computational complexity while preserving key information. Assume the original data consists of 10 features, and PCA reduces them to 3 principal components. Assume the first principal component contributes 60% of the variance, the second 25%, and the third 10%.

[0069] S1.3 Construct a multi-dimensional ecological space evaluation indicator system and assign weights to each indicator using the data analytic hierarchy process (AHP) to obtain a basic data set for comprehensive governance effectiveness evaluation. AHP was used to assign weights to each indicator. The weights are as follows: Environmental dimension weight: 0.4, Land use change dimension weight: 0.3, Biodiversity dimension weight: 0.2, Socioeconomic dimension weight: 0.1.

[0070] S2. Construct a dynamic system model based on basic data. The dynamic system model represents the interaction relationship within the ecosystem. The ecological space includes biological factors, environmental factors, and socioeconomic factors. Within the same model framework, simulate the evolution of the ecological space through the influence of governance measures.

[0071] The details of S2 are:

[0072] S2.1 Environmental factors use differential equations to represent the impact of climate change and pollutant concentrations on ecological space. The specific formula is:

[0073]

[0074] in:

[0075] E is the change value of environmental factors (such as PM2.5 concentration, temperature change).

[0076] G is the impact of governance measures (such as greening, pollution source control).

[0077] I represents external environmental impacts (such as natural disasters and climate fluctuations).

[0078] α, β, and γ are the coefficients in the model, representing the self-attenuation rate of environmental factors, the adjustment coefficients of the effects of governance measures and the external environmental impact, respectively.

[0079] Example data:

[0080] The temperature change ΔT is 0.1℃ per year, the control measures can reduce the PM2.5 concentration by 5% per year, and the annual range of external climate fluctuation is ±10%.

[0081] The coefficients set are α=0.02, β=0.05 and γ=0.1.

[0082] By using these formulas and data, the dynamic impacts of different governance measures on climate change and air quality can be simulated.

[0083] S2.2 Biological factors use equations to describe the interactions between species and changes in species density. The formula is as follows:

[0084]

[0085] in:

[0086] N is the number of species.

[0087] K is the environmental capacity, which represents the maximum number of species that the environment can support.

[0088] r is the intrinsic growth rate of the species.

[0089] P is the number of predators and C is the number of competing species.

[0090] α and β represent the predation and competition coefficients, respectively.

[0091] Example data:

[0092] Assume that the initial number of a certain plant species in a certain area is N0 = 1000 plants, the intrinsic growth rate r = 0.05, and the environmental capacity K = 10,000 plants.

[0093] The number of predators (eg, insects) is P = 150, and the number of competing species is C = 500.

[0094] Predation coefficient α = 0.01, competition coefficient β = 0.02.

[0095] This model can simulate the dynamic changes of biological populations under different environmental changes and species interactions. The number of species will change over time, affected by environmental capacity and the interactions between species.

[0096] S2.3 Socioeconomic factors The relationship between population density, land use change, and economic index was established through a system dynamics model using the following equation:

[0097]

[0098] in:

[0099] P is the population density (unit: person / km 2 ).

[0100] L is the population capacity, which indicates the maximum number of people that the area can accommodate.

[0101] γ is the population growth coefficient.

[0102] E is the economic index level, which is related to resource consumption and land use.

[0103] δ is the economic pressure coefficient, which indicates the inhibitory effect of the economic index on population growth.

[0104] Example data:

[0105] Assume that the initial population density of a region is P0 = 500 people / km 2 , population capacity L = 2000 people / km2 , population growth coefficient γ = 0.02.

[0106] The economic index level E is expressed as the annual GDP growth rate of the region, for example, E=5% (calculated annually), and the economic pressure coefficient δ=0.1.

[0107] Analyze the dynamic process of population density and land use changes under different economic index levels and governance measures.

[0108] S3. Use causal inference algorithms to analyze dynamic system models, quantify the causal impact and synergistic effects of governance measures on the long-term development of ecological spaces, and distinguish the interactive feedback mechanisms between different governance measures, so as to comprehensively evaluate the long-term effects of individual governance measures and combined governance measures.

[0109] S3 specifically:

[0110] S3.1 Construct a Bayesian network model based on the correlation between data and time series analysis to infer the causal relationship between the impact of different governance measures on various elements of the ecosystem.

[0111] The specific steps are as follows:

[0112] Collect historical data: PM2.5 concentration, species count, GDP level data, once a year, with a cycle of 10 years.

[0113] The Pearson correlation coefficient was used to analyze the relationships between greening, pollution control, and ecological factors PM2.5 and species number.

[0114] Assume that the relationship between the pollutant concentration P and the number of species S in a certain area is as follows:

[0115] P=0.6S+∈,

[0116] Where P is the pollutant concentration, S is the number of species, and ∈ is the error term.

[0117] S3.2 uses a structural equation model to quantify causal relationships and evaluate the long-term benefits of governance measures on ecological space. The formula is:

[0118] Y=βX+γZ+∈,

[0119] in:

[0120] Y is the governance effect (such as the effect of improving ecological space).

[0121] X is the influencing factor (such as the intensity and type of governance measures).

[0122] Z is an external factor (such as climate change, population growth).

[0123] β and γ are regression coefficients, representing the impact of governance measures and external factors on governance effectiveness, respectively.

[0124] ∈ is the error term, which represents the part that the model cannot explain.

[0125] Assuming that the impact of greening measures (X) on ecological space improvement is to be evaluated and the greening effect can be quantified by the increase in species number (Y), a structural equation model is used for regression analysis to obtain the regression coefficients β and γ.

[0126] Example data:

[0127] The intensity of greening measures (X) is to increase the green coverage rate by 10% each year.

[0128] The external factor (Z) is the average annual temperature change (+0.5°C per year).

[0129] The species population increase (Y) is 5% per year.

[0130] The SEM model was used to quantify the long-term impact of greening measures on species abundance changes.

[0131] S3.3 Monte Carlo simulation is used to evaluate the responses of governance measures and random sample simulation models, and the expected effects and uncertainties of governance measures are calculated. The Bayesian network model uses maximum a posteriori estimation to infer the long-term effects of different governance measures. The specific steps include:

[0132] By building a time series model through historical data, we can analyze the effects of various governance measures at different time points and form a causal network.

[0133] The back-propagation algorithm is used to optimize the time series model parameters to ensure that the impact of governance measures can be accurately captured and inferred.

[0134] Historical data and time series models: Based on the changes in PM2.5 concentrations over the past 10 years and the implementation of greening measures, a time series model is constructed to predict the effects of different governance measures.

[0135] Assume the following governance measures time series data (yearly):

[0136] Year 1: Green coverage rate is 20%, PM2.5 concentration is 50μg / m 3 .

[0137] Year 2: Green coverage rate was 22%, PM2.5 concentration was 48 μg / m 3 .

[0138] Year 3: Green coverage rate is 25%, PM2.5 concentration is 45μg / m 3 .

[0139] Maximum a posteriori estimation: Using the maximum a posteriori estimation method in the Bayesian network model, we infer the long-term effects of different governance measures. Assuming that the annual changes in green coverage and PM2.5 concentration fluctuate randomly according to a certain distribution, we can use Bayesian inference to estimate their long-term effects.

[0140] Monte Carlo simulation: Run 10,000 simulations, randomly select different parameter values ​​in each simulation, calculate the control effect in each simulation, and finally obtain the expected effect of the control measures and their uncertainty. Suppose we simulate the PM2.5 concentration under different control measures, and the results show:

[0141] The expected effect of increasing green coverage by 10% per year is to reduce PM2.5 concentration to 40μg / m 3 , with a standard deviation of 5 μg / m 3 .

[0142] The expected effect of comprehensive treatment measures (greening + pollution control) is to reduce PM2.5 concentration to 30μg / m 3 , with a standard deviation of 3 μg / m 3 .

[0143] S4. Based on the dynamic system model, obtain ecological and economic benefit evaluation indicators, and dynamically evaluate the governance effects to form simulation prediction results.

[0144] Dynamic system model, simulating the impact of different governance measures on ecological and economic benefits. The model is as follows:

[0145]

[0146] E eco = Species Diversity Index Weight + Air Quality Improvement Index Weight + Water Quality Improvement Index Weight, where α1, α2, α3, and α4 are the weights of each indicator. The weights are calculated using the Analytic Hierarchy Process (AHP).

[0147] Assume that after one year of governance in an area, the green coverage rate increases from 20% to 25%, and the PM2.5 concentration decreases from 60μg / m 3 Reduced to 50 μg / m 3 , the water quality improvement index increased by 5%.

[0148] Ecological benefit calculation: Calculate the ecological benefit based on the simulation results.

[0149] The species diversity index (SDI) increased by 0.2.

[0150] Air quality index (AQI) improved from 60μg / m 3 Improved to 50 μg / m 3, corresponding to an AQI improvement index of 0.3.

[0151] The water quality improvement index (WC) increased by 5%.

[0152] Calculate the ecological benefit index E eco .

[0153] Economic benefit indicators can be evaluated by regional GDP growth rate, land use efficiency, etc. For example, assuming that after greening and pollution control measures, the regional GDP growth rate is 2% and the land use efficiency increases by 10%. The economic benefit index E is calculated. eco .

[0154] S5. Based on the simulation prediction results, a multi-objective optimization algorithm is used to optimize and adjust the governance strategy, taking into account both ecological and economic benefits at the regional scale to achieve continuous improvement of governance measures. The multi-objective optimization algorithm in S5 adopts a genetic algorithm, which includes selection operations, crossover operations, and mutation operations to improve the exploration ability of the solution space and ensure that the algorithm can better handle complex nonlinear optimization problems.

[0155] Selection operation: select individuals with higher fitness in the current population for reproduction. Assume that there are 100 individuals in the initial population, each of which represents a governance strategy (such as the rate of change of green coverage, the intensity of pollutant control, etc.).

[0156] Crossover operation: Select some individuals to cross and generate new individuals. For example, cross some genes of two parent individuals to generate new offspring.

[0157] Assuming that the genes of individuals contain the intensity of greening measures and the intensity of pollution control, new governance plans are generated through cross-operation.

[0158] Mutation operation: Perform small-scale mutations on some individuals to increase the exploratory power of the solution space. For example, randomly changing the intensity of a certain individual's governance measures (such as changing the green coverage rate from 25% to 30%).

[0159] Run the genetic algorithm

[0160] Assume the following parameters are set:

[0161] Initial population size: 100 individuals.

[0162] Select the action: Roulette wheel selection.

[0163] Crossover probability: 80%.

[0164] Mutation probability: 10%.

[0165] Number of iterations: 1000 generations.

[0166] Run the genetic algorithm, and the optimization process is as follows:

[0167] Each generation generates a new generation of population through selection, crossover and mutation.

[0168] Evaluate the fitness of each newly generated individual.

[0169] Continue iterating until the preset number of iterations is reached.

[0170] Get optimization results

[0171] After multiple generations of iteration, the genetic algorithm will give the optimal governance strategy. For example, suppose the optimal solution is:

[0172] Green coverage rate: increase by 5% each year.

[0173] Pollutant control: Reduce PM2.5 concentration by 5% annually.

[0174] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive management effect evaluation system for ecological space planning, characterized by: include: Data collection module, used to collect multi-dimensional environmental data in the ecological space in real time; A data processing module, used for pre-processing the collected ecological data; Multi-dimensional analysis module, used to conduct comprehensive analysis of various environmental factors and their interrelationships within the ecological space based on collected environmental data; Synergy calculation module, used to evaluate the synergy between different governance measures; Dynamic evaluation module, used to update the evaluation results of ecological space governance effects in real time; Decision support module, used based on comprehensive governance effect evaluation results.

2. The comprehensive management effect evaluation system for ecological space planning according to claim 1 is characterized by: The multi-dimensional environmental data includes air quality, water quality changes, land use, and species diversity, and the ecological data preprocessing includes data denoising, standardization, and normalization operations.

3. The comprehensive management effect evaluation system for ecological space planning according to claim 1 is characterized by: The data acquisition module includes remote sensing technology, sensor network and data mining technology, which is used to comprehensively collect and monitor various environmental indicators in the ecological space in real time.

4. The comprehensive management effect evaluation system for ecological space planning according to claim 1 is characterized by: The data processing module further includes: Data cleaning unit, used to remove abnormal data; The data fusion unit is used to fuse data from different sensors and generate a unified standard data set.

5. A comprehensive management effect evaluation method for ecological space planning, characterized in that: The following steps are involved: S1. Obtain multi-source data on the ecological space planning area, pre-process the multi-source data, and construct an indicator system based on the multi-source data as basic data for comprehensive governance effect evaluation; S2. constructing a dynamic system model based on the basic data, wherein the dynamic system model represents the interaction relationship within the ecosystem, and the ecological space includes biological factors, environmental factors, and socioeconomic factors; S3. Analyze the dynamic system model using causal inference algorithms to quantify the causal impact and synergistic effects of governance measures on the long-term development of ecological space, and to distinguish the interactive feedback mechanisms between different governance measures; S4. Based on the dynamic system model, obtain ecological and economic benefit evaluation indicators, and dynamically evaluate the governance effect to form simulation prediction results; S5. Based on the simulation and prediction results, a multi-objective optimization algorithm is used to optimize and adjust the governance strategy, taking into account both ecological and economic benefits at the regional scale to achieve continuous improvement of governance measures.

6. The method for comprehensive management effect evaluation of ecological space planning according to claim 5 is characterized in that: The details of S1 are: S1.1 Obtain multi-source data on the ecological space planning area, including environmental data, land use change data, biodiversity data, and socioeconomic data; S1.2 preprocessing the data, including data cleaning, data standardization, and dimensionality reduction, wherein the dimensionality reduction is based on principal component analysis of the data; S1.3 Construct a multi-dimensional ecological space evaluation index system, and assign weights to each index based on the data hierarchical analysis method to obtain a basic data set for comprehensive governance effect evaluation.

7. The method for comprehensive management effect evaluation of ecological space planning according to claim 5 is characterized by: The details of S2 are: The environmental factors mentioned in S2.1 use differential equations to represent the impact of climate change and pollutant concentration on ecological space. The specific formula is: E(t) is the change of environmental factors, I(t) is the governance measures, and C(t) is the external environmental impact; The biotic factors described in S2.2 use equations to describe the interactions between species and changes in species density. The formula is as follows: Where N is the number of species, K is the environmental capacity, α and β are the predation and competition coefficients respectively; The socioeconomic factors described in S2.3 are modeled through a system dynamics model to establish the relationship between population density, land use change, and economic indices using the following equation: Where P is the population density, K P is the population capacity, γ and δ are the population growth and environmental pressure coefficients.

8. The method for comprehensive management effect evaluation of ecological space planning according to claim 5 is characterized by: S3 specifically: S3.1 Build a Bayesian network model based on data correlation and time series analysis to infer the causal relationship between the impact of different governance measures on various ecosystem elements; S3.2 uses a structural equation model to quantify causal relationships and evaluate the long-term benefits of governance measures on ecological space. The formula is: Y=β0+β1X1+β2X2+…+β n X n +∈ Among them, Y is the governance effect, X1, X2,…, X n are influencing factors, β1,…,β n is the regression coefficient, ∈ is the error term; S3.3 Evaluate the responses of governance measures and random sample simulation models through Monte Carlo simulation, and calculate the expected effects and uncertainties of governance measures.

9. The method for comprehensive management effect evaluation of ecological space planning according to claim 6, characterized in that: The Bayesian network model infers the long-term effects of different governance measures through maximum a posteriori estimation. The specific steps include: Build a time series model using historical data to analyze the effects of various governance measures at different time points and form a causal network; The back-propagation algorithm is used to optimize the time series model parameters to ensure that the impact of governance measures can be accurately captured and inferred.

10. The method for comprehensive management effect evaluation of ecological space planning according to claim 5, characterized in that: The multi-objective optimization algorithm in S5 adopts a genetic algorithm, which includes a selection operation, a crossover operation, and a mutation operation.

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