Environment bearing capacity calculation method and system based on cloud model improved state space method

Abstract

The invention provides an environment bearing capacity calculation method and system based on a cloud model improved state space method, and is applied to the technical field of data processing. According to the method, resource information, environment information and social economic information of a to-be-evaluated region are processed based on to-be-evaluated type information and historical statistical information of the to-be-evaluated region, and clouding state variable parameters and clouding parameters are generated; processing the clouding state variable parameter and the clouding parameter based on a state space model, and generating state variable dynamic change information, parameter uncertainty information and parameter constraint information of the to-be-evaluated region; processing state variable dynamic change information, parameter uncertainty information and parameter constraint information of the to-be-evaluated area based on the cloud model to generate demand driving parameter information; processing the demand driving parameter information to generate environmental bearing capacity probability distribution information; and processing the environmental bearing capacity probability distribution information to generate environmental bearing capacity information of the to-be-evaluated region.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a method and system for calculating environmental carrying capacity based on an improved state space method using a cloud model. Background Art

[0002] For a long time, there have been various traditional methods in the field of environmental carrying capacity assessment. These traditional methods have been widely used in past environmental assessment work, providing certain decision-making bases for regional environmental planning and management. In the development planning of some cities, the index system method is used to evaluate the environmental carrying capacity to determine the resource and environmental constraints for urban development, thereby guiding the layout of urban industries and the planning of population size. However, with the development of the times, their limitations have become increasingly prominent. The environmental system itself has complexity and uncertainty, and in actual assessments, data often has problems such as inaccuracy and incompleteness.

[0003] When facing such uncertain data, traditional methods lack effective processing means. Taking water quality assessment as an example, monitoring data may have certain errors due to the limitations of monitoring equipment accuracy, monitoring time, and space, but the traditional index system method often directly uses these data for calculation. It is unable to accurately quantify the impact of data uncertainty on the assessment results. In addition, environmental elements change continuously over time, while most traditional assessment methods focus on static assessment and have insufficient ability to simulate the dynamic changes of environmental elements.

[0004] Environmental carrying capacity involves multiple interrelated systems such as resources, environment, and social economy. Traditional methods often analyze each system in isolation and lack comprehensive consideration of the complex interaction relationships between multiple systems, resulting in the assessment results being unable to comprehensively and accurately reflect the true environmental carrying capacity of the scenic area.

[0005] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus includes information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for calculating environmental carrying capacity based on the improved state space method of cloud model, which can at least overcome the problems existing in the prior art to a certain extent. First, collect various information of the area to be evaluated, determine the target evaluation index, calculate the characteristics of expectation, entropy and hyper-entropy to generate cloudified state variable parameters, generate cloudified parameters according to the fluctuation characteristics of historical data, and quantify the data uncertainty. Then, use the state space model, combine the key variable information for initialization and iterative calculation to obtain the dynamic changes of state variables, parameter uncertainty and constraint information. Next, use the cloud model to process this information to obtain the demand-driven parameter information. After that, combine the probability statistical analysis with the cloud model uncertainty measurement information to generate the carrying capacity probability distribution information, further process it to obtain the basic estimated value, and finally determine the environmental carrying capacity information of the area to be evaluated after adjustment and verification, comprehensively and scientifically evaluating the environmental carrying capacity.

[0007] Other features and advantages of the present application will become apparent from the following detailed description, or be learned in part through the practice of the present invention.

[0008] According to one aspect of the present application, there is provided a method for calculating environmental carrying capacity based on the improved state space method of cloud model, including: obtaining resource information, environmental information, social and economic information of the area to be evaluated, as well as information on the type to be evaluated and historical statistical information of the area to be evaluated; processing the resource information, environmental information and social and economic information of the area to be evaluated based on the information on the type to be evaluated and the historical statistical information of the area to be evaluated to generate cloudified state variable parameters and cloudified parameters; processing the cloudified state variable parameters and cloudified parameters based on the state space model to generate dynamic change information of state variables, parameter uncertainty information and parameter constraint information of the area to be evaluated; processing the dynamic change information of state variables, parameter uncertainty information and parameter constraint information of the area to be evaluated based on the cloud model to generate demand-driven parameter information; processing the demand-driven parameter information to generate environmental carrying capacity probability distribution information; processing the environmental carrying capacity probability distribution information to generate environmental carrying capacity information of the area to be evaluated.

[0009] Another aspect of the present application is an environmental carrying capacity calculation device based on an improved state space method using a cloud model, which is characterized by including: an acquisition module for acquiring resource information, environmental information, and socioeconomic information of the area to be evaluated, as well as information on the type to be evaluated and historical statistical information of the area to be evaluated; a processing module for processing the resource information, environmental information, and socioeconomic information of the area to be evaluated based on the information on the type to be evaluated and the historical statistical information of the area to be evaluated to generate cloudified state variable parameters and cloudified parameters; processing the cloudified state variable parameters and cloudified parameters based on a state space model to generate information on the dynamic change of state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated; processing the information on the dynamic change of state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated based on a cloud model to generate demand-driven parameter information; processing the demand-driven parameter information to generate environmental carrying capacity probability distribution information; and processing the environmental carrying capacity probability distribution information to generate environmental carrying capacity information of the area to be evaluated.

[0010] According to yet another aspect of the present application, there is provided a computer-readable storage medium having stored thereon a computer program, which when executed by a second processor, implements the above-mentioned environmental carrying capacity calculation method based on an improved state space method using a cloud model.

[0011] For the environmental carrying capacity calculation method and system based on an improved state space method using a cloud model provided by the present application, the server acquires resource, environmental, and socioeconomic information of the area to be evaluated, as well as evaluation type and historical statistical information. Based on this information, cloudified state variable parameters and cloudified parameters are generated. By determining target evaluation indicators (such as water storage capacity, total phosphorus concentration, and number of tourist arrivals indicators), their expectation, entropy, and hyperentropy characteristics are calculated to construct the cloudified state variable parameters, and corresponding fluctuation characteristics are obtained based on historical data processing to generate the cloudified parameters, thereby quantifying the uncertainty of the data. Using the state space model, the model is initialized with the key variable information in the cloudified state variable parameters and cloudified parameters, and iterative calculations are performed to generate information on the dynamic change of state variables, showing the change trend of environmental elements over time. At the same time, the fluctuations of the cloudified parameters and error transmission are processed to obtain parameter uncertainty information, and parameter constraint information is obtained based on preset constraints. Then, a cloud model is used to fuse the above information to generate a comprehensive parameter set, and through forward and backward cloud operations and membership function screening, demand-driven parameter information is obtained. Probability statistical analysis is performed on the demand-driven parameter information, and combined with the uncertainty measurement information of the cloud model, environmental carrying capacity probability distribution information is generated. Further, the quantitative characteristic data of the carrying capacity is extracted from this information, the key indicators are integrated to obtain the core evaluation data, and the basic estimated value is obtained through statistical analysis. Then, the basic estimated value is adjusted in combination with the uncertainty measurement information of the cloud model to generate adjusted data considering uncertainty, and after reasonable verification, it is finally determined as the environmental carrying capacity information of the area to be evaluated.

[0012] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The flowchart shows a method for calculating environmental carrying capacity based on the improved state space method with cloud model provided by an embodiment of the present application;

[0014] Figure 2 The schematic structural diagram shows a device for calculating environmental carrying capacity based on the improved state space method with cloud model provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0015] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustration and explanation of the present invention, and are not intended to limit the present invention.

[0016] The following combines Figure 1 to describe the method for calculating environmental carrying capacity based on the improved state space method with cloud model according to the exemplary embodiments of the present application. As Figure 1 shown, this method is applied to a server and includes:

[0017] S101, obtaining resource information, environmental information, and socio-economic information of the area to be evaluated, as well as information on the type to be evaluated and historical statistical information of the area to be evaluated.

[0018] In one embodiment, the data sources of the area to be evaluated are rich and diverse, covering multiple fields such as historical hydrological data, tourism management statistical data, and environmental protection department monitoring reports. When evaluating the water resources carrying capacity of a certain lake basin, it is necessary to collect the hydrological data of this area for the past 10 years, including rainfall, evaporation, and inflow into the lake, etc. These data reflect the natural dynamic changes of water resources; the tourist volume statistical data of the tourism administration can reflect the impact of socio-economic activities on the environment; the monthly water quality monitoring reports of the environmental protection department record the changes in key indicators of environmental quality. These multi-source data comprehensively present the resource, environmental, and socio-economic conditions of the region from different angles, providing detailed original materials for subsequent analysis.

[0019] According to different evaluation objects and purposes, select corresponding key indicators to accurately represent resource, environmental, and socio-economic information. Taking the evaluation of water resources carrying capacity as an example: the resource information index is to select the water storage capacity index to measure the water resources situation, which is represented in the form of a cloud model, such as the expectation (Ex), entropy (En), and hyperentropy (He) of the water storage capacity. In the case of a certain lake, the water storage capacity is expressed as an average water storage capacity of 150 million m

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[0019] (Ex = 1.5), the normal fluctuation range is within ±0.2×10^8 m 3 (En = 0.2), in extreme cases, it may deviate by ±0.25×10^8 m 3 (He = 0.05). The expectation here represents the typical value of the water storage volume, the entropy reflects its fluctuation range, and the hyper-entropy reflects the uncertainty of the entropy. Combining these three parameters can more accurately describe the characteristics of the water storage volume. The total phosphorus concentration index is used to reflect the environmental quality and is also represented by the cloud model parameters. For example, the reference value of the total phosphorus concentration in a certain lake is 0.18 mg / L (Ex = 0.18), and occasional exceedances are allowed up to 0.24 mg / L (the fluctuation and uncertainty are reflected by En = 0.06 and He = 0.01). This means that the total phosphorus concentration fluctuates around 0.18 mg / L, and to a certain extent, exceeding this value is allowed. The cloud model parameters can better reflect the dynamic changes and uncertainties of the environmental quality. The number of tourist arrivals index is used to measure the intensity of social and economic activities. For example, the average annual number of tourist arrivals in a certain lake is 800,000 person-times (Ex = 80), it may reach 950,000 during the peak season and 650,000 during the off-season (the fluctuation range is reflected by En = 15 and He = 3). This clearly shows the average level of the number of tourist arrivals and the fluctuations between the peak and off-seasons, reflecting the uncertainty of social and economic activities.

[0020] The above information obtained is the cornerstone for a series of subsequent complex calculations and in-depth analyses. The information on the type to be evaluated clarifies the direction and focus of the evaluation. For example, determining whether to conduct an assessment of water resource carrying capacity or land resource carrying capacity, etc. Different evaluation types determine the core elements and key points of the subsequent analysis. The historical statistical information of the area to be evaluated is used to deeply analyze the changing trends, fluctuation characteristics, etc. of the data, providing strong support for generating cloud state variable parameters and cloud parameters. Based on this information, a series of key calculations can be smoothly carried out subsequently, providing accurate data input for the operation of the state space model and the cloud model, and then realizing a series of accurate analyses such as the dynamic changes of environmental carrying capacity, parameter uncertainties, and quantitative assessment of carrying capacity.

[0021] S102, process the resource information, environmental information, and social and economic information of the area to be evaluated based on the information on the type to be evaluated and the historical statistical information of the area to be evaluated, and generate cloud state variable parameters and cloud parameters.

[0022] In one embodiment, the resource information, environmental information and socio-economic information of the area to be assessed are processed based on the information on the type of assessment to be conducted, and target assessment indicators are generated, wherein the target assessment indicators include a water storage index, a total phosphorus concentration index and a tourist number index. Based on the information on the type of assessment to be conducted, representative key indicators are screened out from the resource, environmental and socio-economic information of the area to be assessed, namely, a water storage index, a total phosphorus concentration index and a tourist number index. Taking the water resource carrying capacity assessment of a certain lake basin as an example, if the assessment purpose is to determine the carrying capacity of water resources for tourism activities in the area under certain conditions, then the water storage capacity is related to the available amount of water resources, the total phosphorus concentration reflects the impact of environmental quality on water resources, and the number of tourists represents the intensity of socio-economic activities. These three indicators can directly reflect the core elements related to the water resource carrying capacity of the area.

[0023] The target evaluation indicators are processed to generate the expected characteristics of the target evaluation indicators, the entropy characteristics of the target evaluation indicators, and the super entropy characteristics of the target evaluation indicators. The target evaluation indicators are subjected to feature screening to obtain the expected characteristics. The expected characteristics represent the characteristic values of the target indicator characteristics that are greater than the preset threshold. In mathematical terms, it is the mean of the cloud droplet distribution and the corresponding indicator value when the membership is 1. It reflects the typical value or center point of the qualitative concept in the quantitative domain. For example, in the water storage capacity indicator of a certain lake, after analyzing and screening the water storage data for many years, the average water storage capacity is 150 million m 3 , which is the expected characteristic of the water storage index (Ex = 1.5), which represents the typical level of water storage in the lake. The entropy characteristic is calculated by combining the target evaluation index and the expected characteristic. The entropy characteristic is used to characterize the degree of dispersion of the target indicator characteristics around the expected characteristic. It reflects the fuzziness of the concept in qualitative terms, that is, the coverage; it characterizes the standard deviation of the random distribution in quantitative terms. For the water storage index, if its entropy value (En) is 0.2, it means that the normal fluctuation range of water storage is within the mean (expected characteristic) ± 0.2 billion m 3 , which reflects the fluctuation range of water storage and the degree of ambiguity of the concept. The super entropy feature is obtained by integrating the target evaluation index, expected characteristics and entropy characteristics. It is used to characterize the uncertainty of the entropy feature and reflect the degree of cloud droplet condensation. The larger the super entropy, the thicker the cloud layer and the more blurred the edge. For example, if the super entropy (He) of the water storage index is 0.05, it means that the fluctuation range of the water storage capacity of the lake (reflected by the entropy feature) itself has a certain uncertainty, and in extreme cases it may deviate from the normal fluctuation range of ±0.25 billion m 3 (determined by entropy and superentropy).

[0024] Generate cloudified state variable parameters based on the expected characteristics, entropy characteristics, and hyper-entropy characteristics of the target evaluation index. Combining these three characteristics and representing the state variable in the form of a cloud model can more comprehensively reflect its uncertainty and ambiguity. For example, if the water storage is represented as a cloudified state variable parameter of (Ex = 1.5, En = 0.2, He = 0.05), it means that the water storage of the lake is approximately 150 million m 3 , and the normal fluctuation range is ±20 million m 3 , and the boundary is fuzzy. This representation method is more in line with the characteristics of actual monitoring data. Process the target evaluation index based on the historical statistical information of the area to be evaluated to generate the fluctuation characteristics of water storage, the fluctuation characteristics of total phosphorus concentration, and the fluctuation characteristics of the number of tourist arrivals. When analyzing the fluctuation characteristics of water storage, it is necessary to comprehensively collect the hydrological data of the lake basin over the years. Data such as rainfall, evaporation, and inflow into the lake are all important analysis bases. By means of statistical analysis, draw a line graph of the water storage changing with time or perform trend fitting, which can clearly present its fluctuation law. In some years, due to abundant rainfall and large inflow into the lake, the water storage increases significantly; while in dry years, the evaporation is large and the inflow into the lake decreases, and the water storage drops significantly. Through in-depth research on these historical data, not only can the annual change rate of the water storage be calculated, but also its fluctuation range in different seasons can be determined. For example, during the rainy season, the water storage may rise rapidly in a short period of time; while in the dry season, the water storage will gradually decrease. These fluctuation characteristics are crucial for understanding the dynamic changes of water resources and can accurately reflect the uncertainty of water resource supply when generating cloudified parameters later.

[0025] The acquisition of the fluctuation characteristics of the total phosphorus concentration depends on the long-term water quality monitoring data of the environmental protection department. These data record the values of the total phosphorus concentration in the lake at different time points. By statistically analyzing these data, the changing trends of the total phosphorus concentration in different seasons and years can be found. During the peak tourist season, due to the increase in the number of tourists and the increased impact of human activities on the lake, the total phosphorus concentration may increase; while in winter, due to the decrease in the number of tourists and the self-purification effect of the water body, the total phosphorus concentration may decrease. By calculating statistical quantities such as the mean and standard deviation of the total phosphorus concentration in different time periods, its fluctuation situation can be described more accurately. For example, through analysis, it is found that during the peak tourist season of a certain lake, the average value of the total phosphorus concentration is higher than usual by a certain proportion, and the fluctuation range is also larger. These fluctuation characteristics reflect the uncertainty of environmental quality affected by external factors and are of great significance for generating cloudified parameters later and then accurately evaluating the environmental carrying capacity of pollutants.

[0026] The fluctuation characteristics of the number of tourist arrivals are mainly reflected by the statistical data of the number of tourists collected by the tourism management department over the years. Tourism activities have obvious seasonality and periodicity, which are particularly prominent in the data of the number of tourist arrivals. By analyzing historical data, a curve of the number of tourist arrivals changing over time can be drawn, enabling a clear view of the fluctuations in the number of tourist arrivals in different seasons and years. During holidays and peak tourist seasons, the number of tourist arrivals will increase significantly; while in the off-season, the number of tourist arrivals will decrease significantly. In addition, with the development of the economy and the changes in the tourism market, the number of tourist arrivals may also show long-term growth or decline trends. For example, in recent years, due to tourism promotion and the improvement of transportation conditions, the number of tourist arrivals at a certain lake has shown an upward trend year by year, but the fluctuations in the number of tourist arrivals in different months of each year are still large. This fluctuation characteristic reflects the uncertainty of the intensity of social and economic activities, and when generating cloud parameters, the dynamic impact of tourism activities on the environmental carrying capacity can be fully considered.

[0027] Cloud parameters are generated based on the fluctuation characteristics of water storage, the fluctuation characteristics of total phosphorus concentration, and the fluctuation characteristics of the number of tourist arrivals. These cloud parameters are used to describe the uncertainty of the parameters in the model, enabling the model to better simulate the changes in the actual system. For example, if the resource consumption rate is affected by factors such as technological fluctuations, according to its fluctuation characteristics, it is expressed as a cloud parameter. For example, the cloud parameter corresponding to the resource consumption rate is (Ex = 0.2, En = 0.05, He = 0.01), indicating that its expected consumption rate is 0.2, the normal fluctuation range is ±0.05, and there is a certain degree of uncertainty in the fluctuation range itself (reflected by the hyperentropy 0.01). This representation method of cloud parameters can more accurately reflect the uncertainty factors in the actual situation in subsequent state equation calculations.

[0028] In another implementation, feature screening is performed on the target evaluation index to generate the expected features of the target evaluation index, where the expected features are used to represent the feature values in the target index features that are greater than the preset threshold. The expected feature is a parameter in the cloud model used to describe the typical value or central point of a qualitative concept in the quantitative domain. In this calculation method, it is used to represent the feature values in the target index features that are greater than the preset threshold. When evaluating the water resources carrying capacity of a certain lake basin, the water storage capacity index is selected as one of the target evaluation indexes. First, collect the historical water storage data of the lake over the years, such as the monthly water storage values in the past 10 years. Then, set a preset threshold according to the evaluation purpose and actual situation, assuming it is 100 million cubic meters (this threshold is determined comprehensively based on the actual water supply and demand situation and ecological needs of local water resources). Then, screen out the water storage data greater than the preset threshold from the collected data, conduct statistical analysis on these data, and calculate their average value. If after calculation, the average value of the water storage data greater than 100 million cubic meters is 150 million cubic meters, then this 150 million cubic meters is the expected feature of the water storage capacity index (Ex = 1.5). It represents the typical level of the lake's water storage under certain conditions, is the mean of the cloud droplet distribution, and is also the ideal central value corresponding to a membership degree of 1.

[0029] Process the target evaluation index and the expected features to generate the entropy features of the target evaluation index, where the entropy features are used to represent the degree of dispersion of the target index features around the expected features. The entropy feature has a dual meaning in the cloud model. It not only reflects the fuzziness (coverage range) of the concept but also represents the standard deviation of the random distribution. In this solution, it is used to represent the degree of dispersion of the target index features around the expected features. After obtaining the expected feature of the water storage capacity index (Ex = 150 million cubic meters), calculate in combination with all the water storage data (including data less than the preset threshold). Use the standard deviation calculation method in statistics to measure the degree of dispersion of the data. Suppose after calculation, the entropy value (En) of the water storage capacity index is 20 million cubic meters, which means that the normal fluctuation range of the lake's water storage is between the mean (expected feature) ± 20 million cubic meters. The larger the entropy value, the wider the fluctuation range of the water storage, the higher the fuzziness of the concept, that is, the more extensive the actual situation covered by the concept of "water storage capacity"; at the same time, it also indicates that the degree of dispersion of its random distribution is larger and the stability of the data is relatively poor.

[0030] Process the target evaluation index, expected characteristics, and entropy characteristics to generate the hyper-entropy characteristics of the target evaluation index, where the hyper-entropy characteristics are used to characterize the uncertainty of the entropy characteristics. The hyper-entropy characteristics are used to characterize the uncertainty of the entropy characteristics and reflect the degree of cloud droplet condensation. On the basis of having obtained the expected characteristics and entropy characteristics of the water storage index, further calculate the hyper-entropy characteristics. It is a measure of the uncertainty of the entropy characteristics themselves. Still taking the water storage index as an example, by statistically analyzing the multiple calculation results of the entropy value and considering its fluctuation situation to determine the hyper-entropy value. If the calculated hyper-entropy (He) of the water storage index is 0.05, this indicates that there is a certain degree of uncertainty in the fluctuation range (reflected by the entropy characteristics) of the lake's water storage. The greater the hyper-entropy, the thicker the cloud layer and the more blurred the edge, meaning that the fluctuation range of the water storage is more unstable, and the probability of extreme situations occurring is relatively high. For example, in some special years, due to factors such as abnormal climate, the water storage may deviate more from the normal fluctuation range (±0.2 billion cubic meters) and exceed ±0.25 billion cubic meters, and this uncertainty is reflected by the hyper-entropy characteristics.

[0031] S103. Process the cloudification state variable parameters and cloudification parameters based on the state space model to generate the dynamic change information of the state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated.

[0032] In one implementation, initialize the state space model based on the key variable information in the cloudification state variable parameters and cloudification parameters to generate the initial condition information for model operation. The key variable information in the cloudification state variable parameters and cloudification parameters is the basis for initializing the state space model. The key variable information is included in the cloudification state variable parameters and cloudification parameters. For example, in the evaluation of water resources carrying capacity, the expected water storage (Ex), entropy (En), hyper-entropy (He) in the cloudification state variable parameters, and cloud parameters such as the resource consumption rate and regeneration rate in the cloudification parameters all belong to the key variable information. Taking a certain lake basin as an example, the cloudification state variable parameters of the water storage are (Ex = 1.5, En = 0.2, He = 0.05), where 1.5 (expectation) represents the average water storage, 0.2 (entropy) represents the normal fluctuation range, and 0.05 (hyper-entropy) reflects the uncertainty of the fluctuation; the cloud parameter of the resource consumption rate is assumed to be (Ex = 0.2, En = 0.05, He = 0.01). Using this key variable information, set the initial state vector, input vector, etc. conditions for the state space model, thereby generating the initial condition information for model operation and determining the starting state of model calculation.

[0033] Based on the initial condition information of model operation, the state space model performs iterative calculations on the cloudification state variable parameters and cloudification parameters to generate the dynamic change information of the state variables. Among them, the dynamic change information of the state variables is used to characterize the dynamic change trend data of the state variables over time. The state space model is based on the initial condition information of model operation and performs iterative calculations on the cloudification state variable parameters and cloudification parameters. The state space model continuously updates the values of the state variables by establishing state equations and observation equations and combining the initial conditions. At each time step, according to the material balance equation, the Logistic growth model, etc. (which constitute the function f in the state equation), and the cloudification parameters (such as the cloudified parameters of the resource consumption coefficient, pollution emission coefficient, etc.), the change of the state variables is calculated. Taking the water storage state variable in the water resource system as an example, from time step t to (t + 1), the new water storage is calculated according to the cloudification parameters corresponding to factors such as rainfall recharge, evaporation loss, and tourism water use and the current water storage state. After multiple iterations, the values of the state variables at different time points are obtained. These values constitute the dynamic change information of the state variables, which is used to characterize the dynamic change trend data of the state variables over time and show the changes of the various elements of the environmental system over time.

[0034] Process the fluctuation characteristics of the cloudification parameters and the error transmission situation in the calculation process to generate parameter uncertainty information for characterizing the degree of parameter uncertainty. The cloudification parameters themselves have uncertainty, and their fluctuation characteristics and the error transmission situation in the calculation process need to be processed to generate parameter uncertainty information. The fluctuation characteristics of the cloudification parameters are determined by their expectation (Ex), entropy (En), and hyperentropy (He). Entropy (En) reflects the fluctuation range of the parameters, and hyperentropy (He) represents the uncertainty of the entropy. In the calculation process, error transmission will occur due to data measurement errors. By statistically analyzing the changes of the cloudification parameters in multiple iterative calculations, such as calculating statistical quantities such as the value range and variance of the parameters at different time steps, the degree of parameter uncertainty is quantified, thereby generating parameter uncertainty information for characterizing the degree of parameter uncertainty. This information enables the evaluator to understand the reliability of the model parameters and their potential impact on the calculation results.

[0035] The state space model processes the cloudification state variable parameters and cloudification parameters based on the preset constraint information of the model to generate parameter constraint information. The preset constraint information is defined according to the sustainable development goals and covers aspects such as resource constraints, environmental constraints, and social constraints. Resource constraints stipulate the minimum guarantee amount of resources. For example, the amount of water resources shall not be lower than a certain value. Environmental constraints set the threshold of pollutant concentration. For instance, the total phosphorus concentration shall not exceed a specific value. Social constraints limit social and economic indicators such as the population size from exceeding the carrying capacity limit. During the model calculation process, the cloudification state variable parameters and cloudification parameters are compared and judged with these preset constraints. If some parameters or state variables violate the constraint conditions, the model will be adjusted or recorded according to the corresponding rules, and finally generate parameter constraint information, which is used to clarify whether the system operation is within a reasonable range, and which parameters or state variables may be close to or exceed the limits, providing a basis for subsequent decision-making.

[0036] S104, process the dynamic change information of state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated based on the cloud model to generate demand-driven parameter information.

[0037] In one implementation, the dynamic change information of state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated are fused and processed based on the cloud model to generate a comprehensive parameter set. The dynamic change information of state variables in the area to be evaluated describes the change trend of state variables over time. For example, in the assessment of water resource carrying capacity, the increase or decrease of water storage over time; parameter uncertainty information characterizes the degree of uncertainty of model parameters, such as the fluctuation range of parameters such as resource consumption rate and regeneration rate; parameter constraint information stipulates the boundary conditions of system operation, such as the minimum guarantee amount of resources, the threshold of pollutant concentration, etc. Integrate the current value and change rate of state variables with the uncertainty range of parameters, relevant indicators of constraint conditions, etc. to form a comprehensive parameter set containing multi-faceted information.

[0038] Based on forward cloud operation and backward cloud operation, the comprehensive parameter set is transformed and calculated to generate an initial cloudification result. Forward cloud operation is an operation method in the cloud model, which is used to generate a set of random cloud droplets. The cloud model is a mathematical model that combines fuzzy set theory and probability statistics ideas to describe the uncertainty transformation between qualitative concepts and quantitative values, and characterizes uncertainty through three numerical features: expectation (Ex), entropy (En), and hyperentropy (He). For each parameter in the comprehensive parameter set, according to its cloud parameters (Ex, En, He), random cloud droplets are generated according to the forward cloud operation rules. For example, for a certain parameter with cloud parameters (Ex = 0.5, En = 0.1, He = 0.05), first generate a normal random number En' that follows N(En, He 2 )), then generate a cloud droplet x with Ex as the center and En' as the standard deviation, and finally calculate the membership degree This process is repeated to generate a large number of cloud droplets, which constitute the output of the forward cloud operation.

[0039] Backward cloud operation processes the cloud droplet set generated by forward cloud operation to obtain the synthetic cloud droplet parameters. Input the cloud droplet set generated by forward cloud operation The synthetic cloud droplet parameters are calculated by the algorithm. entropy Super Entropy here Through backward cloud computing, the information of numerous cloud droplets is integrated to obtain a more representative synthetic cloud droplet parameter, namely the initial cloudification result.

[0040] The initial cloudification results are filtered based on the membership function of the cloud model to generate the target cloudification results. The membership function of the cloud model is used to measure the degree of certainty that a quantitative value belongs to a qualitative concept. The value range is \([0,1]\) and it is random. For the mth constraint condition, its cloudification form membership function is in is the cloud parameter with constraint limit. Substitute each cloud droplet in the initial cloudification result into the corresponding membership function for calculation. If the membership of a cloud droplet satisfies μ m (x i )≥a m (a m is the set threshold, such as a m = 0.5 corresponds to a confidence level of 68.3%), the cloud droplet is retained; otherwise, the cloud droplet is retained with a probability of 1-μ m (x i ) to remove the cloud droplet. The remaining cloud droplets after screening constitute the target cloudification result, which is more in line with the system constraints and actual conditions.

[0041] The target cloudification results are processed based on the cloud model to generate demand-driven parameter information. Cloud droplet distribution reflects the uncertainty of qualitative concepts in the quantitative domain. By observing the density and distribution range of cloud droplets, we can understand the probability of different state variable values. In water resource carrying capacity assessment, if cloud droplets are densely distributed near a certain water storage value, it indicates a high probability of that water storage value occurring; if the distribution range is wide, it means that the water storage value has a large uncertainty. Using statistical methods such as kernel density estimation, the probability density of cloud droplet distribution is quantified. The probability density of cloud droplets is calculated within different water storage ranges. If the probability density in a certain range is high, the water supply in that range is relatively stable; otherwise, the stability is poor. This helps to determine the reliability of water resources under different conditions and provides a basis for evaluating the carrying capacity of water resources for socioeconomic activities such as tourism.

[0042] The expectation (Ex) represents the typical value of a qualitative concept in a quantitative domain and is a key reference value. When evaluating the tourism carrying capacity of a lake, the expectation of the number of tourist arrivals can be used as an indicator to measure the normal number of tourists received by the lake. If the actual number of tourist arrivals deviates from the expectation for a long time, it means that the environmental carrying capacity is under pressure. If the expected number of tourist arrivals is 800,000 and the actual number has far exceeded this value for several consecutive years, it is necessary to pay attention to whether the environment can withstand this intensity of tourism activities beyond the normal level and the possible resource and environmental problems brought about.

[0043] Entropy (En) reflects the fuzziness of a concept and the standard deviation of a random distribution. In terms of resource management, taking water resources as an example, the magnitude of the entropy value reflects the fluctuation range of the water resource volume. A large entropy value indicates a large fluctuation in the water resource volume. For example, the change in the water resource volume caused by seasonal precipitation is large, which poses challenges to the planning and utilization of water resources. When formulating a tourism activity plan, it is necessary to consider the unstable factors of the water resource volume and reasonably arrange the activity scale during the peak and off-peak tourist seasons to avoid water shortages or waste.

[0044] Hyperentropy (He) characterizes the uncertainty of entropy and reflects the degree of cloud droplet condensation. In environmental quality assessment, such as the hyperentropy of the total phosphorus concentration, if the hyperentropy value is large, it means that the fluctuation range of the total phosphorus concentration itself is unstable and extreme situations may occur. This reminds that when evaluating the environmental carrying capacity, it cannot be judged only based on the conventional fluctuation range. It is necessary to consider the impact of extreme situations such as sudden pollution incidents on the environmental carrying capacity and strengthen the monitoring and early warning of environmental quality.

[0045] In the actual evaluation of environmental carrying capacity, various factors are interrelated. In a certain lake basin, an increase in the number of tourists will lead to an increase in water consumption, affecting the water storage capacity. At the same time, tourist activities may cause the total phosphorus concentration to rise. When processing the target cloudification results based on the cloud model, the interaction of these factors needs to be considered comprehensively. A multi-variable cloud operation equation is constructed, incorporating cloudified variables such as the number of tourists, water storage capacity, and total phosphorus concentration. Through cloud operations, their dynamic relationships are reflected, so as to more accurately evaluate the environmental carrying capacity. Different evaluation purposes have different requirements for the processing results. If the evaluation purpose is to determine the sustainable tourism scale, the upper limit of the number of tourists under the condition of meeting resource and environmental constraints needs to be focused on. Using the operation rules of the cloud model, calculate the maximum bearable value of the number of tourists and its probability distribution on the premise of ensuring the sustainable use of water resources and meeting water quality standards. If the evaluation purpose is to analyze the impact of environmental changes on the carrying capacity, then focus on studying the change trends of cloudified state variables under different environmental conditions (such as different precipitation patterns, pollution emission scenarios), providing data support for formulating strategies to cope with environmental changes. Through the above processing, parameter information that can reflect the actual needs and dynamic changes of the system is obtained. In the evaluation of environmental carrying capacity, these demand-driven parameter information can be parameters related to resource utilization, environmental capacity, social and economic activity intensity, etc., which will serve as an important basis for subsequent calculation of the probability distribution information of environmental carrying capacity.

[0046] S105, process the demand-driven parameter information to generate environmental carrying capacity probability distribution information.

[0047] In one implementation, analyze and process the demand-driven parameter information based on probability statistics methods to generate initial environmental carrying capacity probability distribution data. Probability statistics methods include but are not limited to kernel density estimation, Monte Carlo simulation, etc. In actual applications, appropriate methods will be selected according to the characteristics of the demand-driven parameter information and the data distribution. Taking the evaluation of water resources carrying capacity in a certain lake basin as an example, if the demand-driven parameters show a relatively complex distribution, Monte Carlo simulation is more applicable. It simulates the system behavior through a large number of random samplings and can effectively handle multi-parameter uncertainty problems. Assume that the demand-driven parameter information contains multiple variables related to environmental carrying capacity, such as the numerical values converted from cloudified parameters such as water storage capacity, total phosphorus concentration, and the number of tourists. Based on these variables, analyze using the selected probability statistics method. For example, in Monte Carlo simulation, set a large number of simulation times (such as 10,000 times). Each simulation generates random samples according to the expectation (Ex), entropy (En), and hyperentropy (He) of the cloudified parameters. For the water storage capacity cloud parameter (Ex = 1.5, En = 0.2, He = 0.05), generate a large number of random water storage values that conform to this distribution characteristic according to the generation rules of the cloud model.

[0048] Through multiple simulations or calculations, the values of different environmental carrying capacity indicators under various possible scenarios are obtained. These values form a dataset, and statistical analysis is performed on it, such as calculating the frequencies of different carrying capacity values, and then an initial probability distribution data of the environmental carrying capacity is constructed. For example, through simulation, a series of possible values of the tourism population carrying capacity are obtained, and the number of occurrences of these values in different intervals is counted. Using frequency to approximate probability, an initial probability distribution histogram or cumulative distribution function curve is plotted to preliminarily show the possible distribution of the environmental carrying capacity at different values.

[0049] Based on the uncertainty measurement information of the cloud model, the initial probability distribution data of the environmental carrying capacity is processed to generate the probability distribution information of the environmental carrying capacity. Among them, the uncertainty measurement information of the cloud model is used to characterize the uncertainty in the conversion process between qualitative concepts and quantitative values. The cloud model characterizes uncertainty through expectation (Ex), entropy (En), and hyperentropy (He). The expectation represents the typical value of a qualitative concept in the quantitative domain; the entropy measures the fuzzy degree of the concept and the standard deviation of the random distribution, reflecting the dispersion degree of the data; the hyperentropy characterizes the uncertainty of the entropy, reflecting the degree of cloud droplet aggregation. In the evaluation of the environmental carrying capacity, these parameters are used to describe the uncertainty characteristics of various factors. For example, the cloud parameters of the total phosphorus concentration (Ex = 0.18, En = 0.06, He = 0.01) indicate that the typical value of the total phosphorus concentration is 0.18 mg / L, the normal fluctuation range is affected by the entropy of 0.06, and the hyperentropy of 0.01 reflects the uncertainty of this fluctuation range.

[0050] Integrate the uncertainty measurement information of the cloud model into the initial probability distribution data of the environmental carrying capacity. Utilize the characteristics of the cloud model to adjust and optimize the initial probability distribution data. For example, according to the membership function of the cloud model, for each possible value of the environmental carrying capacity, calculate its membership degree to a certain qualitative concept (such as "high carrying capacity", "medium carrying capacity", "low carrying capacity"). If the value of a certain tourism population carrying capacity is 900,000 person-times, calculate its membership degree to the qualitative concept of "high carrying capacity" through the cloud model. This membership degree takes into account the uncertainty represented by the cloud parameters, that is, it not only considers the distance between the current value and the expectation, but also considers the fuzziness and uncertainty reflected by the entropy and hyperentropy.

[0051] Comprehensively consider the membership degree calculated by the cloud model and the initial probability distribution data to correct the initial probability distribution. Use the membership degree as a weight to adjust the probability values in the initial probability distribution to obtain the probability distribution information of the environmental carrying capacity that more accurately reflects the actual situation. The final probability distribution information not only includes the possible distribution obtained by probability statistical methods, but also incorporates the uncertainty in the conversion process between the qualitative concept and quantitative value expressed by the cloud model, so as to more comprehensively and accurately describe the uncertainty characteristics of the environmental carrying capacity and provide a more reliable basis for subsequent environmental carrying capacity evaluation and decision-making.

[0052] S106. Process the probability distribution information of the environmental carrying capacity to generate the environmental carrying capacity information of the area to be evaluated.

[0053] In one implementation, analyze and process the probability distribution information of the environmental carrying capacity to generate quantitative characteristic data of the carrying capacity. The probability distribution information of the environmental carrying capacity includes various possible distribution situations of the environmental carrying capacity under different conditions. Analyze and process this information to extract quantitative data that can reflect the characteristics of the carrying capacity, such as calculating the probabilities and frequencies of different carrying capacities, determining statistical quantities such as the peak value, median, and mode of the probability distribution, and calculating the cumulative probabilities of different carrying capacity intervals. In the evaluation of the water resources carrying capacity of a certain lake basin, according to the probability distribution information of the tourist population carrying capacity calculated in the early stage, count the occurrence probabilities of different tourist population quantity intervals, such as the probability of the tourist population in the interval of 700,000 - 800,000 person-times, the probability in the interval of 800,000 - 900,000 person-times, etc. These probability values and the corresponding population quantity intervals are part of the quantitative characteristic data of the carrying capacity; at the same time, calculate the median of the probability distribution. If the median is 850,000 person-times, this also becomes a key indicator of the quantitative characteristic data. It represents the value in the middle position among all possible carrying capacities and can reflect the intermediate level of the carrying capacity to a certain extent.

[0054] Integrate and process based on the key indicator information in the quantitative characteristic data of the carrying capacity to generate the core evaluation data of the environmental carrying capacity. The quantitative characteristic data of the carrying capacity contains a lot of information. Select key indicator information from it, such as the expected value in the probability distribution, the boundary values of a specific confidence interval (such as the upper and lower limits of the 95% confidence interval), the overloading risk probability, etc. In the above-mentioned lake basin case, the expected value of the probability distribution of the tourist population carrying capacity represents the average number of tourist populations that the lake can carry; the lower limit value of the 95% confidence interval means that there is a 95% possibility that the actual carrying capacity will be higher than this value, and the upper limit value means that there is a 95% possibility that the actual carrying capacity will be lower than this value. Integrate these key indicator information to generate data that can comprehensively reflect the core situation of the environmental carrying capacity. For example, construct a comprehensive indicator, weight and calculate the expected value, the upper and lower limits of the confidence interval, etc. according to a certain weight. The result obtained is the core evaluation data of the environmental carrying capacity, which can more concentratedly reflect the key characteristics of the environmental carrying capacity.

[0055] Statistically analyze and process the core assessment data of environmental carrying capacity to generate a basic estimated value of the environmental carrying capacity of the area to be evaluated. Statistically analyze and process the core assessment data of environmental carrying capacity. Through statistical methods such as calculating the mean value and weighted average, a basic estimated value representing the environmental carrying capacity of the area to be evaluated is obtained. If the core assessment data includes multiple key indicators, such as expected values, boundary values of different confidence intervals, etc., corresponding weights can be assigned according to the importance of these indicators for the carrying capacity assessment. Assume that the weight of the expected value is 0.5, the weight of the lower limit of the 95% confidence interval is 0.2, and the weight of the upper limit is 0.3. Through weighted average calculation: Basic estimated value = Expected value × 0.5 + Lower limit value × 0.2 + Upper limit value × 0.3. The obtained value is the basic estimated value of the environmental carrying capacity of the area to be evaluated, which provides a basic reference value for subsequent precise assessment.

[0056] Integrate the uncertainty measurement information of the cloud model and the basic estimated value of environmental carrying capacity for comprehensive processing to generate adjusted data of environmental carrying capacity considering uncertainty. The uncertainty measurement information of the cloud model consists of expectation (Ex), entropy (En), and hyperentropy (He). Expectation represents the typical value of a qualitative concept in the quantitative domain; entropy reflects the fuzziness of the concept and the standard deviation of the random distribution, reflecting the degree of data dispersion; hyperentropy characterizes the uncertainty of entropy, reflecting the degree of cloud droplet aggregation. Integrate this uncertainty measurement information with the basic estimated value of environmental carrying capacity. Using the membership function of the cloud model and combining with the basic estimated value, calculate the adjustment coefficient under different degrees of uncertainty. Assume that the basic estimated value is a certain carrying capacity value. Calculate the membership degrees of this value belonging to different qualitative concepts (such as "high carrying capacity", "medium carrying capacity", "low carrying capacity") according to the cloud model parameters, and adjust the basic estimated value according to the membership degrees. If the membership degree shows that the current carrying capacity is closer to the "high carrying capacity" concept, and the cloud model parameters reflect a certain uncertainty range, a certain proportion (determined according to the cloud model parameters) can be added to the basic estimated value to obtain adjusted data of environmental carrying capacity considering uncertainty, making it more in line with the uncertainty characteristics in the actual situation.

[0057] Conduct a rationality verification process on the adjusted data of environmental carrying capacity considering uncertainty to generate the environmental carrying capacity information of the area to be evaluated. Compare the adjusted data with historical data to check whether it conforms to the historical change trend of the environmental carrying capacity of this area. For example, although the tourist population carrying capacity of a certain lake has fluctuated in the past few years, it has generally shown a slow upward trend. If the adjusted data shows a significant decline or abnormal fluctuation contrary to this trend, it is necessary to re-examine. At the same time, compare the carrying capacity data of similar areas. If the average tourist population carrying capacity of other similar lakes is between 800,000 and 1 million person-times, and the adjusted data of this lake deviates too much from this range, such as being lower than 600,000 person-times or higher than 1.2 million person-times, it is necessary to analyze the reasons for the difference and judge the rationality of the adjusted data.

[0058] By changing the model input parameters, observe the changes in the adjusted data. For example, in the improved state space model of the cloud model, appropriately adjust the key cloud parameters such as the resource consumption rate and pollutant emission coefficient, and check whether the response of the adjusted data of the environmental carrying capacity is reasonable. If a slight increase in the resource consumption rate leads to a substantial decrease in the adjusted data far beyond expectations, it indicates that the adjusted data may be too sensitive to parameter changes and does not conform to the actual situation, and needs to be readjusted. Make a judgment based on relevant theoretical knowledge such as environmental science and ecology. For example, in the assessment of water resource carrying capacity, according to factors such as the total amount of water resources and the water self-purification ability, and combined with the pollutant accommodation ability of the ecosystem, analyze whether the carrying capacity in the adjusted data will lead to excessive consumption of water resources or ecological imbalance. If the adjusted data shows that at a certain carrying capacity, the water resources will be exhausted in a short time or the ecosystem will be severely damaged, this obviously does not conform to the theory of ecological balance, indicating that the data is unreasonable and needs to be re-evaluated and adjusted. Only when the adjusted data passes the above-mentioned multi-faceted rationality verification can it be finally determined as the environmental carrying capacity information of the area to be evaluated. This information comprehensively considers various factors and uncertainties, can more scientifically and accurately reflect the environmental carrying capacity of the area, and provides a reliable basis for subsequent decision-making such as resource management and environmental planning.

[0059] The server first obtains the resource, environment, and socioeconomic information of the area to be evaluated, as well as the evaluation type and historical statistical information. Based on this information, generate cloudified state variable parameters and cloudified parameters. By determining the target evaluation indicators (such as water storage, total phosphorus concentration, and tourist arrival indicators), calculate their expectation, entropy, and hyper-entropy characteristics to construct cloudified state variable parameters, and generate cloudified parameters based on the corresponding fluctuation characteristics obtained from historical data processing, so as to quantify the uncertainty of the data.

[0060] Using the state space model, initialize the model with the key variable information in the cloudified state variable parameters and cloudified parameters, and iteratively calculate to generate the dynamic change information of the state variables, showing the changing trend of environmental elements over time. At the same time, process the fluctuations of the cloudified parameters and error transmission, obtain the parameter uncertainty information, and obtain the parameter constraint information according to the preset constraints. Then, use the cloud model to fuse the above information to generate a comprehensive parameter set, and through forward and backward cloud operations and membership function screening, obtain the demand-driven parameter information.

[0061] Conduct probability statistical analysis on the demand-driven parameter information, and combine the uncertainty measurement information of the cloud model to generate the probability distribution information of the environmental carrying capacity. Further extract the carrying capacity quantification characteristic data from this information, integrate the key indicators to obtain the core evaluation data, and obtain the basic estimated value through statistical analysis. Then, adjust the basic estimated value in combination with the cloud model uncertainty measurement information to generate the adjusted data considering uncertainty, and after passing the rationality verification, it is finally determined as the environmental carrying capacity information of the area to be evaluated.

[0062] In one implementation, as Figure 2 shown, the present application further provides an environmental carrying capacity calculation device based on the improved state space method of the cloud model, including:

[0063] An acquisition module 201, configured to acquire resource information, environmental information, and socioeconomic information of the area to be evaluated, as well as information on the type to be evaluated and historical statistical information of the area to be evaluated;

[0064] A processing module 202, configured to process the resource information, environmental information, and socioeconomic information of the area to be evaluated based on the information on the type to be evaluated and the historical statistical information of the area to be evaluated, to generate cloudified state variable parameters and cloudified parameters; process the cloudified state variable parameters and cloudified parameters based on the state space model to generate dynamic change information of the state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated; process the dynamic change information of the state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated based on the cloud model to generate demand-driven parameter information; process the demand-driven parameter information to generate environmental carrying capacity probability distribution information; and process the environmental carrying capacity probability distribution information to generate environmental carrying capacity information of the area to be evaluated.

[0065] Each embodiment in the present application is described in a related manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the environmental carrying capacity calculation method, electronic device, electronic equipment, and readable storage medium based on the improved state space method of the cloud model, since they are basically similar to the embodiment of the environmental carrying capacity calculation method based on the improved state space method of the cloud model described above, the description is relatively simple, and reference can be made to the partial description of the embodiment of the environmental carrying capacity calculation method based on the improved state space method of the cloud model described above for the related parts.

Claims

1. A method for calculating environmental carrying capacity based on an improved state space method using the cloud model, characterized in that, Including: Obtain the resource information, environmental information, and socioeconomic information of the area to be evaluated, as well as the information of the type to be evaluated and the historical statistical information of the area to be evaluated; Process the resource information, environmental information, and socioeconomic information of the area to be evaluated based on the information of the type to be evaluated and the historical statistical information of the area to be evaluated, and generate cloudified state variable parameters and cloudification parameters; Process the cloudified state variable parameters and cloudification parameters based on the state space model to generate the dynamic change information of the state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated; Process the dynamic change information of the state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated based on the cloud model to generate demand-driven parameter information; Process the demand-driven parameter information to generate environmental carrying capacity probability distribution information; Process the environmental carrying capacity probability distribution information to generate the environmental carrying capacity information of the area to be evaluated.

2. The method according to claim 1, characterized in that Process the resource information, environmental information, and socioeconomic information of the area to be evaluated based on the information of the type to be evaluated and the historical statistical information of the area to be evaluated, and generate cloudified state variable parameters and cloudification parameters, including: Process the resource information, environmental information, and socioeconomic information of the area to be evaluated based on the information of the type to be evaluated to generate target evaluation indicators, where the target evaluation indicators include water storage volume indicators, total phosphorus concentration indicators, and tourist volume indicators; Process the target evaluation indicators to generate the expected characteristics of the target evaluation indicators, the entropy characteristics of the target evaluation indicators, and the hyperentropy characteristics of the target evaluation indicators; Generate cloudified state variable parameters based on the expected characteristics of the target evaluation indicators, the entropy characteristics of the target evaluation indicators, and the hyperentropy characteristics of the target evaluation indicators; Process the target evaluation indicators based on the historical statistical information of the area to be evaluated to generate water storage volume fluctuation characteristics, total phosphorus concentration fluctuation characteristics, and tourist volume fluctuation characteristics; Generate cloudification parameters based on the water storage volume fluctuation characteristics, total phosphorus concentration fluctuation characteristics, and tourist volume fluctuation characteristics.

3. The method according to claim 2, wherein Process the target evaluation indicators to generate the expected characteristics of the target evaluation indicators, the entropy characteristics of the target evaluation indicators, and the hyperentropy characteristics of the target evaluation indicators, including: Perform feature screening processing on the target evaluation indicators to generate the expected characteristics of the target evaluation indicators, where the expected characteristics are used to represent the characteristic values in the target indicator characteristics that are greater than the preset threshold; Process the target evaluation indicators and the expected characteristics to generate the entropy characteristics of the target evaluation indicators, where the entropy characteristics are used to represent the degree of dispersion of the target indicator characteristics around the expected characteristics; Process the target evaluation indicators, the expected characteristics, and the entropy characteristics to generate the hyperentropy characteristics of the target evaluation indicators, where the hyperentropy characteristics are used to represent the uncertainty of the entropy characteristics.

4. The method according to claim 1, wherein Process the cloudified state variable parameters and cloudification parameters based on the state space model to generate the dynamic change information of the state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated, including: Perform initialization settings on the state space model based on the key variable information in the cloudified state variable parameters and cloudification parameters to generate the initial condition information for model operation; Based on the initial condition information of the model operation, the state space model iteratively calculates the cloudification state variable parameters and cloudification parameters to generate the dynamic change information of the state variables, where the dynamic change information of the state variables is used to characterize the dynamic change trend data of the state variables over time; Process the fluctuation characteristics of the cloudification parameters and the error transmission situation during the calculation process to generate parameter uncertainty information for characterizing the degree of parameter uncertainty; The state space model processes the cloudification state variable parameters and cloudification parameters based on the preset constraint information of the model to generate parameter constraint information.

5. The method according to claim 4, wherein Based on the cloud model, process the dynamic change information of the state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated to generate demand-driven parameter information, including: Based on the cloud model, fuse and process the dynamic change information of the state variables, parameter uncertainty information, and parameter constraint information of the area to be evaluated to generate a comprehensive parameter set; Based on the forward cloud operation and backward cloud operation, perform transformation and calculation processing on the comprehensive parameter set to generate an initial cloudification result; Based on the membership function of the cloud model, perform screening processing on the initial cloudification result to generate a target cloudification result; Based on the cloud model, process the target cloudification result to generate demand-driven parameter information.

6. The method according to claim 1, wherein Process the demand-driven parameter information to generate environmental carrying capacity probability distribution information, including: Based on probability statistical methods, analyze and process the demand-driven parameter information to generate initial environmental carrying capacity probability distribution data; Based on the uncertainty measurement information of the cloud model, process the initial environmental carrying capacity probability distribution data to generate environmental carrying capacity probability distribution information, where the uncertainty measurement information of the cloud model is used to characterize the uncertainty during the conversion process between qualitative concepts and quantitative values.

7. The method according to claim 6, characterized in that Process the environmental carrying capacity probability distribution information to generate the environmental carrying capacity information of the area to be evaluated, including: Analyze and process the environmental carrying capacity probability distribution information to generate carrying capacity quantification characteristic data; Based on the key index information in the carrying capacity quantification characteristic data, perform integration processing to generate the core evaluation data of the environmental carrying capacity; Perform statistical analysis processing on the core evaluation data of the environmental carrying capacity to generate the basic estimated value of the environmental carrying capacity of the area to be evaluated; Combine the uncertainty measurement information of the cloud model and the basic estimated value of the environmental carrying capacity for comprehensive processing to generate environmental carrying capacity adjustment data considering uncertainty; Perform rationality verification processing on the environmental carrying capacity adjustment data considering uncertainty to generate the environmental carrying capacity information of the area to be evaluated.

8. An environmental carrying capacity calculation device based on an improved state space method using the cloud model, characterized in that The device includes: An acquisition module for acquiring the resource information, environmental information, and socioeconomic information of the area to be evaluated, as well as the information of the type to be evaluated and the historical statistical information of the area to be evaluated; A processing module is configured to process the resource information, environmental information, and socioeconomic information of the area to be evaluated based on the information of the type to be evaluated and the historical statistical information of the area to be evaluated, and generate cloudification state variable parameters and cloudification parameters; process the cloudification state variable parameters and cloudification parameters based on a state space model to generate state variable dynamic change information, parameter uncertainty information, and parameter constraint information of the area to be evaluated; process the state variable dynamic change information, parameter uncertainty information, and parameter constraint information of the area to be evaluated based on a cloud model to generate demand-driven parameter information; process the demand-driven parameter information to generate environmental carrying capacity probability distribution information; and process the environmental carrying capacity probability distribution information to generate the environmental carrying capacity information of the area to be evaluated.

9. An electronic device, characterized in that, It includes: A first processor; And a memory for storing executable instructions of the first processor; Wherein, the first processor is configured to execute the environmental carrying capacity calculation method based on the improved state space method of cloud model according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a second processor, it implements the environmental carrying capacity calculation method based on the improved state space method of cloud model according to any one of claims 1 to 7.

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