A reservoir ecological service value estimation method, system, equipment and medium

Abstract

The present invention relates to the field of ecological service technology, and in particular to a method for estimating the ecological service value of a reservoir, which constructs a reservoir model and evaluates its ecological service value by comprehensively analyzing the historical geographical, water quality and biological information data of the reservoir. The method first integrates historical data based on a common timeline, and then uses a water quality and biological value assessment model to evaluate the reservoir model and generate water quality and biological value assessment data. By integrating these data, the historical ecological value of the reservoir is estimated, and an ecological value data set is constructed. Using a machine self-learning algorithm, the method further analyzes the ecological value trend and predicts future ecological value. In addition, through real-time monitoring and prediction deviation analysis, a correction factor is generated to correct the predicted data, and finally a reservoir ecological service value estimation report is output. The present application has the effect of improving the accuracy and reliability of reservoir ecological service value assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological services, and in particular to a method, system, equipment and medium for estimating the value of reservoir ecological services. Background Art

[0002] As important water resource storage facilities, reservoirs not only play a key role in flood control, irrigation, power generation, etc., but also have an important impact on maintaining regional ecological balance and biodiversity. However, the ecological service value of reservoirs is often not fully recognized and rationally utilized due to the lack of systematic evaluation.

[0003] Existing technologies have some deficiencies in the assessment of reservoir ecological service value. Traditional assessment methods often rely on qualitative descriptions and empirical judgments, lacking quantitative and standardized means. In addition, existing assessment methods rarely take into account the complexity and dynamics of reservoir ecosystems, making it difficult to accurately predict and manage the long-term changes in the value of reservoir ecosystem services, thereby reducing the accuracy of reservoir ecological service value assessment, and therefore need to be improved. Summary of the invention

[0004] In order to improve the accuracy of reservoir ecological service value assessment, the present application provides a reservoir ecological service value estimation method, system, equipment and medium.

[0005] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0006] A method for estimating the value of reservoir ecological services, the method comprising the steps of:

[0007] Obtain historical regional information data and water quality information data of reservoirs;

[0008] Correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and constructing a corresponding reservoir model based on the associated regional information data and water quality information data;

[0009] Analyze and evaluate the water quality service value of the reservoir model based on a preset water quality value evaluation model to generate water quality value evaluation data corresponding to different reservoir models;

[0010] Acquiring historical biological information data of the reservoir, wherein the biological information data includes biological diversity data and biological quantity data;

[0011] Associating the biological information data with the reservoir model based on the common time axis, and analyzing and evaluating the biological service value of the reservoir model based on a preset biological value evaluation model to generate biological value evaluation data corresponding to different reservoir models;

[0012] Comprehensively evaluate the water quality value assessment data and the biological value assessment data corresponding to the reservoir model to generate historical reservoir ecological value data;

[0013] Recording the reservoir ecological value data based on the public timeline and constructing a reservoir ecological value data set;

[0014] The preset ecological value trend analysis model analyzes the reservoir ecological value data set based on a machine self-learning algorithm to generate an ecological value trend, wherein the ecological value trend includes predicted ecological value data for a future time period;

[0015] Acquire real-time regional information data and water quality information data of the reservoir and calculate and generate real-time reservoir ecological value data, compare the real-time reservoir ecological value data with the predicted ecological value data corresponding to the ecological value trend to calculate the predicted deviation data, and construct a predicted deviation data set;

[0016] The preset prediction deviation analysis model analyzes the prediction deviation data set based on a machine self-learning algorithm to generate a prediction correction factor, and the prediction correction factor corrects the predicted ecological value data;

[0017] A reservoir ecological service value estimation report is generated based on the predicted ecological value data and the predicted correction factor.

[0018] By adopting the above technical solutions, this solution not only comprehensively considers the water quality, biodiversity and other ecological factors of the reservoir, but also can monitor and respond to environmental changes in real time, and use machine self-learning algorithms to predict future trends, thereby providing forward-looking information for decision makers. In addition, this solution significantly improves the accuracy and reliability of the assessment through meticulous data analysis and model construction. Intelligent processing reduces human intervention, and adaptability ensures that the solution can automatically adjust the assessment model and parameters according to the characteristics and environmental conditions of different reservoirs. The final estimation report provides a scientific basis for policy makers and managers, helping them to formulate effective reservoir management and protection measures, and provides strong support for achieving sustainable development and ecological protection goals. Compared with traditional assessment methods, it can monitor and evaluate the ecological service value of the reservoir in real time, and can also dynamically adjust and optimize according to environmental factors and real-time data, significantly improving the scientific nature and foresight of reservoir management. In addition, the application of this method can also help extend the service life of the reservoir and reduce the ecological risks caused by environmental changes.

[0019] In a preferred example, the present application can be further configured as follows: the regional information includes the geographical location and terrain features of the reservoir, and in the step of correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and constructing a corresponding reservoir model based on the associated regional information data and water quality information data, the steps include the following:

[0020] Partitioning the terrain features based on preset terrain partitioning conditions to generate terrain partitioning data, wherein the terrain partitioning data is used for regional marking after constructing a reservoir model;

[0021] Associating the terrain partition data with the water quality information data to generate an association relationship between the terrain data and the water quality information data corresponding to different terrain partition data;

[0022] Constructing a zoning terrain model of the reservoir based on the association between the terrain zoning data and the water quality information data, wherein the zoning terrain model is marked with the water quality information data;

[0023] A corresponding reservoir model is constructed based on all the partitioned terrain models.

[0024] By adopting the above technical solutions, the complete reservoir model provides detailed spatial and attribute data for evaluating the overall ecological service value of the reservoir, which helps to fully understand the structure and function of the reservoir ecosystem. Through the construction of the terrain model, this solution can more accurately evaluate the ecological service value of the reservoir, especially the ecological contribution and environmental risks under different terrain conditions, which helps to formulate more effective reservoir management and protection strategies to ensure the sustainable use of water resources and the health of the ecosystem.

[0025] In a preferred example, the present application may be further configured as follows: after recording the reservoir ecological value data based on the public timeline and constructing a reservoir ecological value data set, the following steps are included:

[0026] Acquire historical environmental data of the reservoir, wherein the environmental data includes environmental temperature data, rainfall data, and humidity data;

[0027] Inputting the historical environmental data into a reservoir ecological value data set, and associating the environmental data with the reservoir ecological value data based on the common time axis to construct an environmental impact data set;

[0028] The preset environmental impact analysis model analyzes the environmental impact data set based on a machine self-learning algorithm to generate an environmental impact factor, and the environmental impact factor is used to correct the predicted ecological value data.

[0029] By adopting the above technical solutions, we collect the ambient temperature, rainfall and humidity of the reservoir and its surroundings. These historical environmental data provide us with background information on the reservoir ecosystem under different seasons and climatic conditions, which helps us understand the long-term impact of environmental changes on the ecological service value of the reservoir. Environmental impact factors can help us predict the potential impact of future environmental changes on the ecological service value of the reservoir, thereby providing support for the sustainable management of the reservoir and further improving the accuracy of the assessment.

[0030] In a preferred example, the present application may be further configured as follows: after the step of analyzing the environmental impact data set based on the machine self-learning algorithm by the preset environmental impact analysis model to generate the environmental impact factor, the following steps are included:

[0031] Acquiring environmental prediction data in a weather forecast, wherein the environmental impact analysis model generates an environmental impact trend based on the environmental impact factors and the environmental prediction data;

[0032] The ecological value trend amends the predicted ecological value data based on the environmental impact trend to generate environmentally amended predicted ecological value data.

[0033] By adopting the above technical solutions, the prediction correction factor enables the prediction model to adapt to new environmental conditions and ecological changes, improves the accuracy and reliability of the prediction, and further improves the accuracy of the assessment.

[0034] In a preferred example, the present application may be further configured as follows: after the step of associating the terrain partition data with the water quality information data to generate an association relationship between the terrain data and the water quality information data corresponding to different terrain partition data, the following steps are included:

[0035] Based on the association relationship between the terrain data and the water quality information data corresponding to the different terrain partition data, and recording the historical terrain data and water quality information data according to the common time axis to construct a terrain-water quality data set;

[0036] The preset terrain analysis model analyzes the terrain-water quality data set based on a machine self-learning algorithm to generate a terrain influence factor, wherein the terrain influence factor is used to represent the influence of terrain changes on water quality information data;

[0037] The terrain analysis model generates terrain change trends based on historical terrain data corresponding to different terrain partition data;

[0038] The preset water quality information data prediction model generates a water quality change trend based on the terrain change trend and the terrain influencing factor, and the water quality change trend is used to provide a basic reference value for predicting ecological value data.

[0039] By adopting the above technical scheme, the present invention dynamically evaluates and monitors the ecological service value of the reservoir, especially in terms of the impact of terrain changes on water quality and ecosystem service value. The implementation of this scheme helps to further improve the scientific nature and foresight of reservoir management and ensure the sustainable use of water resources and the health of the ecosystem.

[0040] In a preferred example, the present application may be further configured as follows: after the step of generating a terrain change trend based on the terrain analysis model corresponding to the historical terrain data of different terrain partition data, the following steps are included:

[0041] Obtaining reservoir-related operating strategies, including plans for maintenance and modification of reservoir terrain;

[0042] The terrain change trend is modified based on the operation strategy to generate a modified terrain change trend.

[0043] By adopting the above technical solutions, the corrected terrain change trends generated provide more accurate predictions for the assessment of the ecological service value of the reservoir, which helps to formulate more effective reservoir management measures and ensure the health and sustainability of the reservoir ecosystem.

[0044] In a preferred example, the present application can be further configured as follows: the reservoir ecological service value report includes the spatial distribution, temporal changes and management adjustment recommended strategies of the reservoir ecological service value.

[0045] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0046] A reservoir ecological service value estimation system, the system comprising: a reservoir information acquisition unit, used to acquire historical regional information data and water quality information data of the reservoir;

[0047] A reservoir model building unit, used for correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and building a corresponding reservoir model based on the associated regional information data and water quality information data;

[0048] A water quality value assessment data generating unit, used to analyze and assess the water quality service value of the reservoir model based on a preset water quality value assessment model, so as to generate water quality value assessment data corresponding to different reservoir models;

[0049] A biological information data acquisition unit, used to acquire historical biological information data of the reservoir, wherein the biological information data includes biological diversity data and biological quantity data;

[0050] A biological value assessment data generating unit, used to associate the biological information data with the reservoir model based on the common time axis, and analyze and assess the biological service value of the reservoir model based on a preset biological value assessment model to generate biological value assessment data corresponding to different reservoir models;

[0051] A reservoir ecological value data generating unit, used for generating historical reservoir ecological value data based on a comprehensive evaluation of the water quality value evaluation data and the biological value evaluation data corresponding to the reservoir model;

[0052] A reservoir ecological value data set construction unit, used to record the reservoir ecological value data based on the public time axis and construct a reservoir ecological value data set;

[0053] An ecological value trend generating unit, configured to be pre-set with an ecological value trend analysis model to analyze the reservoir ecological value data set based on a machine self-learning algorithm to generate an ecological value trend, wherein the ecological value trend includes predicted ecological value data for a future time period;

[0054] A prediction deviation data set construction unit, used to obtain real-time regional information data and water quality information data of the reservoir and calculate and generate real-time reservoir ecological value data, compare the real-time reservoir ecological value data with the predicted ecological value data corresponding to the ecological value trend to calculate the prediction deviation data, and construct the prediction deviation data set;

[0055] A prediction correction factor generating unit, which is pre-set with a prediction deviation analysis model to analyze the prediction deviation data set based on a machine self-learning algorithm to generate a prediction correction factor, wherein the prediction correction factor corrects the predicted ecological value data;

[0056] A service value estimation report generating unit is used to generate a reservoir ecological service value estimation report based on the predicted ecological value data and the predicted correction factor.

[0057] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0058] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for estimating the ecological service value of a reservoir are implemented.

[0059] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0060] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for estimating the ecological service value of a reservoir are implemented.

[0061] In summary, the present application includes at least one of the following beneficial technical effects:

[0062] 1. This scheme not only comprehensively considers the water quality, biodiversity and other ecological factors of the reservoir, but also monitors and responds to environmental changes in real time, and uses machine self-learning algorithms to predict future trends, thereby providing forward-looking information for decision makers. In addition, this scheme significantly improves the accuracy and reliability of the assessment through meticulous data analysis and model construction. Intelligent processing reduces human intervention, and adaptability ensures that the scheme can automatically adjust the assessment model and parameters according to the characteristics and environmental conditions of different reservoirs. The final estimation report provides a scientific basis for policy makers and managers to help them formulate effective reservoir management and protection measures, and provides strong support for achieving sustainable development and ecological protection goals;

[0063] 2. The complete reservoir model provides detailed spatial and attribute data for evaluating the overall ecological service value of the reservoir, which helps to fully understand the structure and function of the reservoir ecosystem. Through the construction of the terrain model, this program can more accurately evaluate the ecological service value of the reservoir, especially the ecological contribution and environmental risks under different terrain conditions, which helps to formulate more effective reservoir management and protection strategies to ensure the sustainable use of water resources and the health of the ecosystem;

[0064] 3. The present invention dynamically evaluates and monitors the ecological service value of the reservoir, especially in terms of the impact of topographic changes on water quality and ecosystem service value. The implementation of this solution will help to further improve the scientificity and foresight of reservoir management and ensure the sustainable use of water resources and the health of the ecosystem;

[0065] 4. The generated modified topographic change trend provides a more accurate prediction for the assessment of the ecological service value of the reservoir, which helps to formulate more effective reservoir management measures and ensure the health and sustainability of the reservoir ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flowchart of the steps in step S020 in a method for estimating the value of a reservoir ecological service in one embodiment of the present application;

[0067] Figure 2 This is a principle block diagram of a reservoir ecological service value estimation system in one embodiment of the present application;

[0068] Figure 3 It is a schematic diagram of an electronic device in an embodiment of the present application.

[0069] Figure Number:

[0070] 1. Reservoir information acquisition unit; 2. Reservoir model construction unit; 3. Water quality value assessment data generation unit; 4. Biological information data acquisition unit; 5. Biological value assessment data generation unit; 6. Reservoir ecological value data generation unit; 7. Reservoir ecological value data set construction unit; 8. Ecological value trend generation unit; 9. Prediction deviation data set construction unit; 10. Prediction correction factor generation unit; 11. Service value estimation report generation unit. DETAILED DESCRIPTION

[0071] The present application is further described in detail below in conjunction with the accompanying drawings.

[0072] In one embodiment, if Figure 1 As shown, the present application discloses a method for estimating the value of reservoir ecological services, which specifically includes the following steps:

[0073] S010: Obtain historical regional information data and water quality information data of the reservoir;

[0074] Specifically, the regional information includes the geographical location and topographical features of the reservoir; the water quality information data includes the volume of the reservoir, water quality parameters, hydrodynamic characteristics, pH value, dissolved oxygen, chemical oxygen demand (COD), ammonia nitrogen content, etc.

[0075] S020: Correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and constructing a corresponding reservoir model based on the associated regional information data and water quality information data;

[0076] Specifically, in step S020, the steps include: S021: partitioning the terrain features based on preset terrain partitioning conditions to generate terrain partitioning data, wherein the terrain partitioning data is used for regional marking after constructing a reservoir model;

[0077] S022: Associating the terrain partition data with the water quality information data to generate association relationships between terrain data and water quality information data corresponding to different terrain partition data;

[0078] S023: constructing a zoning terrain model of the reservoir based on the association between the terrain zoning data and the water quality information data, wherein the zoning terrain model is marked with the water quality information data;

[0079] S024: Constructing a corresponding reservoir model according to all the partitioned terrain models.

[0080] In the embodiment of the present application, historical geographical and water quality data are used to construct a three-dimensional model of the reservoir on the GIS platform to reflect the geographical characteristics and water quality conditions of the reservoir.

[0081] In the embodiment of the present application, GIS technology is used to divide the reservoir basin into different terrain areas, such as plains, hilly areas and mountainous areas, according to conditions such as terrain undulation, altitude and slope. In other embodiments, the reservoir basin can also be divided according to the size of the water flow, the distance from the reservoir gate, etc. The preset terrain zoning conditions can be diverse. The terrain zoning helps to identify the specific impacts of different terrain areas on reservoir water quality and ecosystem services, and provides a spatial basis for subsequent water quality information analysis and ecological service value assessment; the water quality monitoring data (such as dissolved oxygen, pH value, nutrient content) of each terrain zone is compared with the corresponding terrain Regional association establishes the corresponding relationship between terrain and water quality. Through association, the water quality characteristics and potential environmental problems in different terrain areas can be analyzed, providing a basis for formulating targeted water quality management and protection measures. In GIS, a zoning terrain model is created for each terrain zone, and water quality monitoring points and corresponding water quality indicators are marked on the model. The zoning terrain model provides an intuitive tool for displaying and analyzing the terrain and water quality conditions of different areas of the reservoir, which helps to identify water quality hotspots and ecologically sensitive areas. All zoning terrain models are integrated to construct a complete reservoir model that integrates the terrain and water quality information of all regions.

[0082] The complete reservoir model provides detailed spatial and attribute data for evaluating the overall ecological service value of the reservoir, which helps to fully understand the structure and function of the reservoir ecosystem. Through the construction of the terrain model, this scheme can more accurately evaluate the ecological service value of the reservoir, especially the ecological contribution and environmental risks under different terrain conditions. The implementation of this method will help to formulate more effective reservoir management and protection strategies to ensure the sustainable use of water resources and the health of the ecosystem.

[0083] S030: Analyze and evaluate the water quality service value of the reservoir model based on a preset water quality value evaluation model to generate water quality value evaluation data corresponding to different reservoir models;

[0084] Specifically, the water quality service value of the reservoir is evaluated based on water quality indicators and preset water quality value assessment models (such as the ecological impact value of each unit of pollutant). The assessment results help identify the impact of water quality improvement or degradation on the ecological service value.

[0085] Further explanation, in the embodiment of the present application, the water quality information data also includes historical water value data, and the water value data includes the cost of treating each ton of sewage, the amount of pollution treated, the water supply price, the amount of drinking water, the agricultural water price and the agricultural water amount, etc. It should be noted that the water value data is used for the data calculation of the water quality value assessment model;

[0086] Among them, the water quality value assessment model is preset with the following calculation formula to calculate the water quality value assessment data:

[0087] Water supply value assessment formula: ,in, is the water purification value (yuan), C is the cost of treating one ton of sewage (yuan / t), is the pollution treatment volume (t) corresponding to the jth sewage area. It should be noted that in the embodiment of the present application, the sewage treatment volume and the corresponding equipment requirements are divided into j sewage treatment areas. The removal efficiency of COD, NH3-N and TP of the water body is calculated by this formula to reflect the water purification function and value of the water ecosystem, and this part of the value is evaluated based on the replacement cost method;

[0088] Drinking water value assessment formula: ,in, is the water supply price (yuan / ton), is the amount of drinking water (tons), is the drinking water value (yuan);

[0089] Irrigation water value assessment formula: in, is the price of agricultural water (yuan / ton), is the amount of water used for agriculture (tons), is the irrigation water value (yuan);

[0090] Hydraulic energy storage value assessment formula: Based on the market value method, the calculation formula is:

[0091] ,

[0092] in, is the value of hydropower generation (yuan), is the annual power generation of the pth power station in the reservoir (kW h), is the unit electricity price corresponding to the pth power station in the reservoir (yuan / kW·h). It should be noted that, in the embodiment of the present application, the reservoir has n available power stations, and the unit electricity price corresponding to the power station is input by the operator according to actual data;

[0093] The formula for calculating the value of leisure and entertainment is: in, is the tourism income ratio of the ith tourist area (reservoir sub-area), is the total income of the ith tourist area (reservoir area). It is a preset value and can be modified or changed according to the actual proportion;

[0094] Water quality and air purification value assessment: ,in, is the water quality and air purification value (yuan), is the unit sewage treatment cost (yuan / ton), is the amount of sewage treated (tons);

[0095] Carbon fixation and oxygen release value assessment: ; in, is the unit carbon price (yuan / ton), is the amount of carbon sequestered (tons), is the unit oxygen price (yuan / ton), is the oxygen released (tons), It is the carbon fixation and oxygen release value;

[0096] In the embodiment of the present application, the calculation formula for evaluating the overall water quality value of the reservoir is: ;

[0097] in, is the value of drinking water, is the irrigation water value, is the carbon fixation and oxygen release value, is the hydropower storage value, It is the leisure and entertainment value. It is the water supply value. arrive are weights of various service values, which can be determined based on the importance and urgency of the service value. In the embodiment of the present application, .

[0098] S040: Acquire historical biological information data of the reservoir, wherein the biological information data includes biological diversity data and biological quantity data;

[0099] Specifically, biodiversity data of reservoirs are collected, including species richness, number of endemic species, etc., as well as biological quantity data, such as population size of fish and aquatic plants. Biological information is a key indicator for assessing the ecological health of reservoirs.

[0100] S050: Associating the biological information data with the reservoir model based on the common time axis, and analyzing and evaluating the biological service value of the reservoir model based on a preset biological value evaluation model to generate biological value evaluation data corresponding to different reservoir models;

[0101] Specifically, biodiversity data and biological quantity data of reservoirs, such as the presence, population size and distribution of specific species, are collected, and biological information data are associated with reservoir models recorded according to a public timeline to ensure that biological data corresponds to specific reservoir areas and time points. The biological service value of the reservoir model is analyzed and evaluated using a preset biological value assessment model, such as an ecosystem service value (ESV) assessment system. This may include direct calculation methods such as market pricing, productivity, and travel cost methods, or indirect analysis and calculation methods based on land use / cover data.

[0102] Through the above steps, by associating biological information with reservoir models, the contribution of biodiversity to the value of ecological services can be more accurately assessed. The generated biological value assessment data provides a scientific basis for reservoir management and protection, and the assessment results help identify key ecological areas and biological resources that need priority protection.

[0103] It should be noted that in the embodiments of the present application, the biodiversity data includes the proportion of the number of individuals of a species to the total number of individuals, and the biological quantity data includes the total number of species in a certain area. It should be further noted that in the embodiments of the present application, the proportion of the number of individuals of a species to the total number of individuals and the total number of species are determined by regional sampling, and the average is calculated through multiple sampling to improve the accuracy of the sampled data;

[0104] The biological value assessment model is preset with the following calculation formula to calculate the biological value assessment data:

[0105] Biodiversity value assessment: Biodiversity value can be assessed by species richness, species evenness and species rarity. In the examples of this application, the Shannon-Wiener index is used to quantify biodiversity:

[0106] where H is the Shannon-Wiener index of biodiversity, is the proportion of individuals of the i-th species to the total number of individuals, and n is the total number of species in the biomass data;

[0107] Biological quantity value assessment: Biological quantity value can be assessed by calculating the economic value of a specific biological population. In the embodiment of this application, the value of fishery resources is calculated by the market value method: in, is the value of biological population, is the price of aquatic products per unit weight, It is the catch volume. Specifically, the price of aquatic products per unit weight is input into the biological value assessment model by reservoir managers based on actual conditions. The catch volume data is obtained based on actual statistics. In the embodiment of the present application, the biological quantity data includes the catch volume data.

[0108] Comprehensive biological value assessment: Taking into account the value of biodiversity and biological quantity, the following formula is used to calculate the overall biological service value score of the reservoir: , in, is the comprehensive biological service value score, Pre-set weights for biodiversity values, The weight of the value of biological population.

[0109] S060: Comprehensively evaluate the water quality value assessment data and the biological value assessment data corresponding to the reservoir model to generate historical reservoir ecological value data;

[0110] Specifically, in the embodiment of the present application, the calculation formula of the reservoir ecological value data is: ,in, is the overall weight of the water quality service value, It is the overall weight of the biological service value, which can be determined according to the importance and urgency of the service value.

[0111] S070: Recording the reservoir ecological value data based on the public timeline, and constructing a reservoir ecological value data set;

[0112] S080: The preset ecological value trend analysis model analyzes the reservoir ecological value data set based on a machine self-learning algorithm to generate an ecological value trend, wherein the ecological value trend includes predicted ecological value data for a future time period;

[0113] S090: Acquire real-time regional information data and water quality information data of the reservoir and calculate and generate real-time reservoir ecological value data, compare the real-time reservoir ecological value data with the predicted ecological value data corresponding to the ecological value trend to calculate predicted deviation data, and construct a predicted deviation data set;

[0114] S100: The preset prediction deviation analysis model analyzes the prediction deviation data set based on a machine self-learning algorithm to generate a prediction correction factor, and the prediction correction factor corrects the predicted ecological value data;

[0115] Specifically, the prediction deviation data set is analyzed in depth using machine self-learning algorithms, which include random forests and gradient boosted trees (GBM) in the embodiments of the present application. These algorithms can identify key factors that affect prediction accuracy, such as seasonal climate change, human activity interference, etc.

[0116] For example, in the assessment of the ecological service value of a reservoir, if it is found that the abnormally high winter temperature causes the growth rate of aquatic plants to accelerate, which may affect the water purification service value of the reservoir, the algorithm will include this phenomenon as a key factor in the calculation of the prediction correction factor.

[0117] Further in the embodiment of the present application, the calculation formula for the revised reservoir ecological service value is as follows: , in, Represents the regulating service value of the reservoir, such as climate regulation, hydrological regulation, etc.; Represents the expected benefits and values ​​mentioned in local policies related to the reservoir, such as expected benefits related to leisure, tourism, education, etc. and are the weights of regulatory services and cultural services respectively.

[0118] The generation of prediction correction factors enables the prediction model to adapt to new environmental conditions and ecological changes, improving the accuracy and reliability of predictions.

[0119] S110: generating a reservoir ecological service value estimation report based on the predicted ecological value data and the predicted correction factor;

[0120] Among them, the reservoir ecological service value report includes the spatial distribution, temporal changes and management adjustment recommended strategies of the reservoir ecological service value.

[0121] Specifically, the predicted ecological value data and the predicted correction factor are combined to generate a reservoir ecological service value estimation report. The report lists the predicted ecological value data in different time periods in detail and compares and analyzes them with the actual observed data.

[0122] For example, the report might indicate that the value of a reservoir's water purification services has increased in a particular season due to climate change and recommend that managers take steps to adapt to this change, such as adjusting water quality management strategies.

[0123] In summary, through these steps, the present invention can not only monitor and evaluate the ecological service value of the reservoir in real time, but also dynamically adjust and optimize according to environmental factors and real-time data, which significantly improves the scientific nature and foresight of reservoir management. In addition, the application of this method can also help extend the service life of the reservoir and reduce the ecological risks caused by environmental changes, which has important economic and social value.

[0124] The beneficial effects of this scheme are mainly reflected in its comprehensiveness, real-time and dynamic nature, predictability, accuracy, intelligence, adaptability and decision support. Compared with traditional technologies, this scheme not only comprehensively considers the water quality, biodiversity and other ecological factors of the reservoir, but also can monitor and respond to environmental changes in real time, and use machine self-learning algorithms to predict future trends, thereby providing forward-looking information for decision makers. In addition, this scheme significantly improves the accuracy and reliability of the assessment through meticulous data analysis and model construction, intelligent processing reduces human intervention, and adaptability ensures that the scheme can automatically adjust the assessment model and parameters according to the characteristics and environmental conditions of different reservoirs. The final estimation report provides a scientific basis for policy makers and managers, helping them to formulate effective reservoir management and protection measures, and providing strong support for achieving sustainable development and ecological protection goals.

[0125] After the step of S070: recording the reservoir ecological value data based on the public time axis and constructing a reservoir ecological value data set, the following steps are included:

[0126] S071: Acquire historical environmental data of the reservoir, wherein the environmental data includes environmental temperature data, rainfall data, and humidity data;

[0127] Specifically, the environmental temperature, rainfall and humidity of the reservoir and its surroundings are collected. In this application, these data are automatically recorded by meteorological stations installed around the reservoir. These historical environmental data provide us with background information on the reservoir ecosystem under different seasons and climatic conditions, which helps us understand the long-term impact of environmental changes on the ecological service value of the reservoir.

[0128] S072: inputting the historical environmental data into the reservoir ecological value data set, and associating the environmental data with the reservoir ecological value data based on the common time axis to construct an environmental impact data set;

[0129] Specifically, the collected environmental data is combined with the reservoir ecological value dataset. For example, we found that the water purification service value of the reservoir increases during the rainy season because the additional water brought by rainfall helps dilute pollutants. At the same time, increased rainfall may also lead to flood risks, affecting the flood control service value of the reservoir.

[0130] By associating environmental data with ecological value data, we can more accurately assess the service value of reservoirs under different environmental conditions and provide a scientific basis for reservoir management.

[0131] S073: The preset environmental impact analysis model analyzes the environmental impact data set based on a machine self-learning algorithm to generate an environmental impact factor, and the environmental impact factor is used to correct the predicted ecological value data;

[0132] Specifically, we use machine learning algorithms to analyze environmental impact data sets. For example, we train the model to identify the relationship between rainfall and the value of reservoir water purification services, and generate an environmental impact factor that indicates that for every 10% increase in rainfall, the value of water purification services increases by 5%.

[0133] Environmental impact factors can help us predict the potential impact of future environmental changes on the ecological service value of reservoirs, thereby providing support for the sustainable management of reservoirs.

[0134] In practical applications, these steps can help reservoir managers develop more effective water management strategies, such as increasing reservoir storage capacity when a drought season is predicted, or taking advance measures to reduce flood risks when a flood season is predicted.

[0135] In addition, these steps can also provide decision support for policy makers, such as considering the impact of environmental factors on the ecological service value of reservoirs when formulating water resource protection policies or ecological compensation mechanisms.

[0136] After the step S0731: the preset environmental impact analysis model analyzes the environmental impact data set based on the machine self-learning algorithm to generate an environmental impact factor, the following steps are included:

[0137] Acquiring environmental prediction data in a weather forecast, wherein the environmental impact analysis model generates an environmental impact trend based on the environmental impact factors and the environmental prediction data;

[0138] The ecological value trend amends the predicted ecological value data based on the environmental impact trend to generate environmentally amended predicted ecological value data.

[0139] Specifically, machine self-learning algorithms, such as random forests and gradient boosted trees (GBM), are used to analyze prediction deviation data sets. For example, the change in water temperature of a reservoir in a specific season has a significant impact on fish populations. The algorithm will generate a correction factor that reflects this impact. It should be noted that the prediction correction factor can be for various aspects affected by environmental factors, such as biological diversity and the number of organisms. The prediction correction factor enables the prediction model to adapt to new environmental conditions and ecological changes, improving the accuracy and reliability of the prediction.

[0140] Combining the predicted ecological value data and the predicted correction factor, a reservoir ecological service value estimation report is generated. The report lists the predicted ecological value data for different time periods in detail and compares and analyzes them with the actual observed data. The report provides users with a comprehensive perspective to help decision makers understand the dynamic changes in the reservoir ecological service value and formulate corresponding management strategies.

[0141] After the step of associating the terrain partition data with the water quality information data to generate an association relationship between the terrain data and the water quality information data corresponding to different terrain partition data, the following steps are included:

[0142] Based on the association relationship between the terrain data and the water quality information data corresponding to the different terrain partition data, and recording the historical terrain data and water quality information data according to the common time axis to construct a terrain-water quality data set;

[0143] The preset terrain analysis model analyzes the terrain-water quality data set based on a machine self-learning algorithm to generate a terrain influence factor, wherein the terrain influence factor is used to represent the influence of terrain changes on water quality information data;

[0144] The terrain analysis model generates terrain change trends based on historical terrain data corresponding to different terrain partition data;

[0145] The preset water quality information data prediction model generates a water quality change trend based on the terrain change trend and the terrain influencing factor, and the water quality change trend is used to provide a basic reference value for predicting ecological value data.

[0146] Further explanation of the above steps: Specifically, using GIS technology, the collected terrain data and water quality information data are spatially associated to construct a comprehensive terrain-water quality dataset. For example, mountainous areas may have higher erosion rates, affecting the concentration of suspended particulate matter in water quality. The terrain-water quality dataset provides detailed spatial and temporal information for evaluating the impact of terrain on water quality, which helps to understand the spatial heterogeneity of water quality changes.

[0147] Apply machine self-learning algorithms, such as random forests or gradient boosting trees, to analyze terrain-water quality data sets and identify the relationship between terrain features and water quality indicators. For example, the algorithm may find that the slope of a mountain area is positively correlated with the phosphorus concentration in a reservoir. The topographic analysis model analyzes historical terrain data to identify terrain change trends, such as changes in erosion rates in mountain areas or changes in reservoir shorelines. Based on terrain change trends and terrain influencing factors, the water quality information data prediction model predicts water quality change trends. If the erosion rate in the mountain area is predicted to increase, the model will predict an increasing trend in the concentration of suspended particulate matter in the reservoir. The water quality change trend provides dynamic prediction information for water quality management and ecological service value assessment of the reservoir, which helps to formulate adaptive management strategies.

[0148] Combining water quality change trends and terrain influencing factors, the prediction model assesses potential changes in the ecological service value of the reservoir. If the water quality deteriorates, the ecological service value of the reservoir, such as biodiversity and water purification functions, may be reduced. The predicted ecological value data helps decision makers understand the long-term impact of management measures on the ecosystem service value of the reservoir and promote sustainable development.

[0149] Through these steps, the present invention dynamically evaluates and monitors the ecological service value of the reservoir, especially in terms of the impact of terrain changes on water quality and ecosystem service value. The implementation of this solution helps to further improve the scientificity and foresight of reservoir management and ensure the sustainable use of water resources and the health of the ecosystem.

[0150] After the step of generating a terrain change trend based on the terrain partition data corresponding to the historical terrain data, the terrain analysis model includes the following steps:

[0151] Obtaining reservoir-related operating strategies, including plans for maintenance and modification of reservoir terrain;

[0152] The terrain change trend is modified based on the operation strategy to generate a modified terrain change trend.

[0153] To further illustrate the above steps, consider a reservoir located in an area prone to seasonal floods. The reservoir management department has developed a set of operation strategies, including regular sediment removal to maintain reservoir capacity, as well as strengthening the dam and improving the spillway design to cope with extreme weather events. By obtaining the reservoir operation strategy, we can better understand the current needs and future plans of reservoir management, and provide a practical basis for the analysis of terrain change trends.

[0154] In the step of correcting the topographic change trend, the terrain analysis model was used to analyze the reservoir's topographic change data over the past decade, identifying the trend of the reservoir capacity gradually decreasing due to sedimentation. Combined with the reservoir operation strategy, especially the sediment cleaning plan, the topographic change trend was corrected. For example, if the sediment cleaning plan can be effectively implemented, the reservoir capacity is expected to remain stable or increase slightly. The corrected topographic change trend more accurately reflects the actual state of the reservoir after considering the operation strategy, which helps to predict the future shape and capacity of the reservoir and provide more reliable data for the long-term ecological service value assessment of the reservoir.

[0155] Based on the corrected terrain change trend, a reservoir terrain change prediction model is generated which comprehensively considers natural terrain changes and human operation activities.

[0156] For example, if the operational strategy includes implementing soil and water conservation measures upstream of the reservoir, this will reduce the amount of sediment entering the reservoir and thus slow the sedimentation rate, and the revised model will reflect this change.

[0157] The generated modified topographic change trends provide more accurate predictions for the assessment of the ecological service value of reservoirs, which helps to formulate more effective reservoir management measures and ensure the health and sustainability of reservoir ecosystems.

[0158] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0159] In one embodiment, a reservoir ecological service value estimation system is provided, and the reservoir ecological service value estimation system corresponds one-to-one to the reservoir ecological service value estimation method in the above embodiment. Figure 2 As shown, the reservoir ecological service value estimation system includes a reservoir information acquisition unit, which is used to obtain historical regional information data and water quality information data of the reservoir;

[0160] A reservoir model building unit 2, used for correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and building a corresponding reservoir model based on the associated regional information data and water quality information data;

[0161] A water quality value assessment data generating unit 3 is used to analyze and assess the water quality service value of the reservoir model based on a preset water quality value assessment model to generate water quality value assessment data corresponding to different reservoir models;

[0162] A biological information data acquisition unit 4 is used to acquire biological information data of the reservoir history, wherein the biological information data includes biological diversity data and biological quantity data;

[0163] A biological value assessment data generating unit 5, used to associate the biological information data with the reservoir model based on the common time axis, and analyze and assess the biological service value of the reservoir model based on a preset biological value assessment model to generate biological value assessment data corresponding to different reservoir models;

[0164] A reservoir ecological value data generating unit 6, configured to generate historical reservoir ecological value data based on a comprehensive evaluation of the water quality value evaluation data and the biological value evaluation data corresponding to the reservoir model;

[0165] A reservoir ecological value data set construction unit 7, used to record the reservoir ecological value data based on the public time axis and construct a reservoir ecological value data set;

[0166] An ecological value trend generating unit 8, which is pre-set with an ecological value trend analysis model to analyze the reservoir ecological value data set based on a machine self-learning algorithm to generate an ecological value trend, wherein the ecological value trend includes predicted ecological value data for a future time period;

[0167] A prediction deviation data set construction unit 9 is used to obtain real-time regional information data and water quality information data of the reservoir and calculate and generate real-time reservoir ecological value data, compare the real-time reservoir ecological value data with the predicted ecological value data corresponding to the ecological value trend to calculate the prediction deviation data, and construct the prediction deviation data set;

[0168] A prediction correction factor generating unit 10 is used to pre-set a prediction deviation analysis model to analyze the prediction deviation data set based on a machine self-learning algorithm to generate a prediction correction factor, and the prediction correction factor corrects the predicted ecological value data;

[0169] The service value estimation report generating unit 11 is used to generate a reservoir ecological service value estimation report based on the predicted ecological value data and the predicted correction factor.

[0170] For the specific limitations of the reservoir ecological service value estimation system, please refer to the limitations of the reservoir ecological service value estimation method mentioned above, which will not be repeated here. Each module in the above-mentioned reservoir ecological service value estimation system can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0171] In one embodiment, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The electronic device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store a database. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for estimating the ecological service value of a reservoir is implemented.

[0172] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0173] Obtain historical regional information data and water quality information data of reservoirs;

[0174] Correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and constructing a corresponding reservoir model based on the associated regional information data and water quality information data;

[0175] Analyze and evaluate the water quality service value of the reservoir model based on a preset water quality value evaluation model to generate water quality value evaluation data corresponding to different reservoir models;

[0176] Acquiring historical biological information data of the reservoir, wherein the biological information data includes biological diversity data and biological quantity data;

[0177] Associating the biological information data with the reservoir model based on the common time axis, and analyzing and evaluating the biological service value of the reservoir model based on a preset biological value evaluation model to generate biological value evaluation data corresponding to different reservoir models;

[0178] Comprehensively evaluate the water quality value assessment data and the biological value assessment data corresponding to the reservoir model to generate historical reservoir ecological value data;

[0179] Recording the reservoir ecological value data based on the public timeline and constructing a reservoir ecological value data set;

[0180] The preset ecological value trend analysis model analyzes the reservoir ecological value data set based on a machine self-learning algorithm to generate an ecological value trend, wherein the ecological value trend includes predicted ecological value data for a future time period;

[0181] Acquire real-time regional information data and water quality information data of the reservoir and calculate and generate real-time reservoir ecological value data, compare the real-time reservoir ecological value data with the predicted ecological value data corresponding to the ecological value trend to calculate the predicted deviation data, and construct a predicted deviation data set;

[0182] The preset prediction deviation analysis model analyzes the prediction deviation data set based on a machine self-learning algorithm to generate a prediction correction factor, and the prediction correction factor corrects the predicted ecological value data;

[0183] A reservoir ecological service value estimation report is generated based on the predicted ecological value data and the predicted correction factor.

[0184] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0185] Obtain historical regional information data and water quality information data of reservoirs;

[0186] Correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and constructing a corresponding reservoir model based on the associated regional information data and water quality information data;

[0187] Analyze and evaluate the water quality service value of the reservoir model based on a preset water quality value evaluation model to generate water quality value evaluation data corresponding to different reservoir models;

[0188] Acquiring historical biological information data of the reservoir, wherein the biological information data includes biological diversity data and biological quantity data;

[0189] Associating the biological information data with the reservoir model based on the common time axis, and analyzing and evaluating the biological service value of the reservoir model based on a preset biological value evaluation model to generate biological value evaluation data corresponding to different reservoir models;

[0190] Comprehensively evaluate the water quality value assessment data and the biological value assessment data corresponding to the reservoir model to generate historical reservoir ecological value data;

[0191] Recording the reservoir ecological value data based on the public timeline and constructing a reservoir ecological value data set;

[0192] The preset ecological value trend analysis model analyzes the reservoir ecological value data set based on a machine self-learning algorithm to generate an ecological value trend, wherein the ecological value trend includes predicted ecological value data for a future time period;

[0193] Acquire real-time regional information data and water quality information data of the reservoir and calculate and generate real-time reservoir ecological value data, compare the real-time reservoir ecological value data with the predicted ecological value data corresponding to the ecological value trend to calculate the predicted deviation data, and construct a predicted deviation data set;

[0194] The preset prediction deviation analysis model analyzes the prediction deviation data set based on a machine self-learning algorithm to generate a prediction correction factor, and the prediction correction factor corrects the predicted ecological value data;

[0195] A reservoir ecological service value estimation report is generated based on the predicted ecological value data and the predicted correction factor.

[0196] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0197] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0198] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for estimating the value of reservoir ecological services, characterized in that: The method comprises the steps of: obtaining historical regional information data and water quality information data of the reservoir; Correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and constructing a corresponding reservoir model based on the associated regional information data and water quality information data; Analyze and evaluate the water quality service value of the reservoir model based on a preset water quality value evaluation model to generate water quality value evaluation data corresponding to different reservoir models; Acquiring historical biological information data of the reservoir, wherein the biological information data includes biological diversity data and biological quantity data; Associating the biological information data with the reservoir model based on the common time axis, and analyzing and evaluating the biological service value of the reservoir model based on a preset biological value evaluation model to generate biological value evaluation data corresponding to different reservoir models; Comprehensively evaluate the water quality value assessment data and the biological value assessment data corresponding to the reservoir model to generate historical reservoir ecological value data; Recording the reservoir ecological value data based on the public timeline and constructing a reservoir ecological value data set; The preset ecological value trend analysis model analyzes the reservoir ecological value data set based on a machine self-learning algorithm to generate an ecological value trend, wherein the ecological value trend includes predicted ecological value data for a future time period; Acquire real-time regional information data and water quality information data of the reservoir and calculate and generate real-time reservoir ecological value data, compare the real-time reservoir ecological value data with the predicted ecological value data corresponding to the ecological value trend to calculate the predicted deviation data, and construct a predicted deviation data set; The preset prediction deviation analysis model analyzes the prediction deviation data set based on a machine self-learning algorithm to generate a prediction correction factor, and the prediction correction factor corrects the predicted ecological value data; Generating a reservoir ecological service value estimation report based on the predicted ecological value data and the predicted correction factor; The regional information includes the geographical location and topographical features of the reservoir. In the step of correspondingly associating the historical regional information data and water quality information data based on a preset common time axis, and constructing a corresponding reservoir model based on the associated regional information data and water quality information data, the following steps are included: Partitioning the terrain features based on preset terrain partitioning conditions to generate terrain partitioning data, wherein the terrain partitioning data is used for regional marking after constructing a reservoir model; Associating the terrain partition data with the water quality information data to generate an association relationship between the terrain data and the water quality information data corresponding to different terrain partition data; Constructing a zoning terrain model of the reservoir based on the association between the terrain zoning data and the water quality information data, wherein the zoning terrain model is marked with the water quality information data; Constructing a corresponding reservoir model according to all the partitioned terrain models; Specifically, GIS technology is used to divide the reservoir basin into different terrain areas according to terrain undulation, altitude and slope, or to divide the area according to the size of the water flow and the distance from the reservoir gate; water quality monitoring data of each terrain area is obtained, and the water quality monitoring data includes dissolved oxygen, pH value and nutrient content, and the water quality monitoring data is associated with the corresponding terrain area to establish a corresponding relationship between terrain and water quality. Through the association, the water quality characteristics and potential environmental problems of different terrain areas are analyzed to provide a basis for formulating targeted water quality management and protection measures; in GIS, a zoning terrain model is created for each terrain area, and water quality monitoring points and corresponding water quality indicators are marked on the model. The zoning terrain model provides an intuitive tool for displaying and analyzing the terrain and water quality conditions of different areas of the reservoir, which helps to identify water quality hot spots and ecologically sensitive areas, and integrates all zoning terrain models to construct a complete reservoir model, which integrates the terrain and water quality information of all areas.

2. A reservoir ecological service value estimation method according to claim 1, characterized in that: After recording the reservoir ecological value data based on the public timeline and constructing a reservoir ecological value data set, the following steps are included: Acquire historical environmental data of the reservoir, wherein the environmental data includes environmental temperature data, rainfall data, and humidity data; Inputting the historical environmental data into a reservoir ecological value data set, and associating the environmental data with the reservoir ecological value data based on the common time axis to construct an environmental impact data set; The preset environmental impact analysis model analyzes the environmental impact data set based on a machine self-learning algorithm to generate an environmental impact factor, and the environmental impact factor is used to correct the predicted ecological value data.

3. A reservoir ecological service value estimation method according to claim 2, characterized in that: After the step of analyzing the environmental impact data set by the preset environmental impact analysis model based on the machine self-learning algorithm to generate the environmental impact factor, the following steps are included: Acquiring environmental prediction data in a weather forecast, wherein the environmental impact analysis model generates an environmental impact trend based on the environmental impact factors and the environmental prediction data; The ecological value trend amends the predicted ecological value data based on the environmental impact trend to generate environmentally amended predicted ecological value data.

4. A reservoir ecological service value estimation method according to claim 1, characterized in that: After the step of associating the terrain partition data with the water quality information data to generate an association relationship between the terrain data and the water quality information data corresponding to different terrain partition data, the following steps are included: Based on the association relationship between the terrain data and the water quality information data corresponding to the different terrain partition data, and recording the historical terrain data and water quality information data according to the common time axis to construct a terrain-water quality data set; The preset terrain analysis model analyzes the terrain-water quality data set based on a machine self-learning algorithm to generate a terrain influence factor, wherein the terrain influence factor is used to represent the influence of terrain changes on water quality information data; The terrain analysis model generates terrain change trends based on historical terrain data corresponding to different terrain partition data; The preset water quality information data prediction model generates a water quality change trend based on the terrain change trend and the terrain influencing factor, and the water quality change trend is used to provide a basic reference value for predicting ecological value data.

5. A reservoir ecological service value estimation method according to claim 4, characterized in that: After the step of generating a terrain change trend based on the terrain partition data corresponding to the historical terrain data, the terrain analysis model includes the following steps: Obtaining reservoir-related operating strategies, including plans for maintenance and modification of reservoir terrain; The terrain change trend is modified based on the operation strategy to generate a modified terrain change trend.

6. A reservoir ecological service value estimation method according to claim 1, characterized in that: The reservoir ecological service value report includes the spatial distribution, temporal changes and management adjustment recommended strategies of the reservoir ecological service value.

7. A reservoir ecological service value estimation system, applied to a reservoir ecological service value estimation method according to any one of claims 1 to 6, characterized in that: The system comprises: a reservoir information acquisition unit (1) for acquiring historical regional information data and water quality information data of the reservoir; A reservoir model building unit (2) is used to associate the historical regional information data and water quality information data based on a preset common time axis, and to build a corresponding reservoir model based on the associated regional information data and water quality information data; A water quality value assessment data generating unit (3) is used to analyze and assess the water quality service value of the reservoir model based on a preset water quality value assessment model, so as to generate water quality value assessment data corresponding to different reservoir models; A biological information data acquisition unit (4), used to acquire historical biological information data of the reservoir, wherein the biological information data includes biological diversity data and biological quantity data; A biological value assessment data generating unit (5) is used to associate the biological information data with the reservoir model based on the common time axis, and analyze and assess the biological service value of the reservoir model based on a preset biological value assessment model to generate biological value assessment data corresponding to different reservoir models; A reservoir ecological value data generating unit (6), configured to generate historical reservoir ecological value data based on a comprehensive evaluation of the water quality value evaluation data and the biological value evaluation data corresponding to the reservoir model; A reservoir ecological value data set construction unit (7), used to record the reservoir ecological value data based on the public time axis and construct a reservoir ecological value data set; An ecological value trend generating unit (8) is used to pre-set an ecological value trend analysis model to analyze the reservoir ecological value data set based on a machine self-learning algorithm to generate an ecological value trend, wherein the ecological value trend includes predicted ecological value data for a future time period; A prediction deviation data set construction unit (9) is used to obtain real-time regional information data and water quality information data of the reservoir and calculate and generate real-time reservoir ecological value data, compare the real-time reservoir ecological value data with the predicted ecological value data corresponding to the ecological value trend to calculate prediction deviation data, and construct a prediction deviation data set; A prediction correction factor generating unit (10) is used to pre-set a prediction deviation analysis model to analyze the prediction deviation data set based on a machine self-learning algorithm to generate a prediction correction factor, wherein the prediction correction factor corrects the predicted ecological value data; A service value estimation report generating unit (11) is used to generate a reservoir ecological service value estimation report based on the predicted ecological value data and the predicted correction factor.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a reservoir ecological service value estimation method as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a reservoir ecological service value estimation method as described in any one of claims 1 to 6 are implemented.

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