Ecological service evaluation method and device for marine ranching, medium and product

Abstract

The invention relates to the technical field of service evaluation, in particular to an ecological service evaluation method and device for a marine ranch, a medium and a product. The method comprises the following steps: acquiring historical production data and current production data of a historical production cycle of a marine ranch, wherein the historical production data and the current production data both comprise data of operation type indexes; generating an ecological service evaluation result of the current production cycle based on the historical production data and the current production data; formulating a global strategy for the next production cycle of the operation type index; and executing the next production cycle based on the global strategy, and dynamically adjusting the global strategy based on the production data of the next production cycle. The production strategy is dynamically adjusted based on the ecological service evaluation result, and balance of ecology and production is achieved.

Description

Technical Field

[0001] The present application relates to the field of service evaluation technology, and in particular to an ecological service evaluation method, equipment, medium and product for marine ranching. Background Art

[0002] With the continued development of marine resources and the increasing emphasis on marine ecological and environmental protection, marine ranching, as a sustainable marine fishery development model, is crucial for evaluating its ecological services while providing fishery products. This evaluation can help understand the role of marine ranching in maintaining biodiversity, improving the marine ecosystem, and regulating climate. This will help achieve the coordinated development of rational marine resource utilization and ecological and environmental protection, and provide a basis for the scientific management and sustainable development of marine ranching.

[0003] Currently, assessments of marine ranching ecosystem services are based on a single or limited set of ecological indicators, such as fish population size and biomass. Others use more complex models that consider multiple factors, including water quality, bottom sediments, and community structure.

[0004] However, relevant technologies used to evaluate marine ranch ecological services often focus on assessing ecological indicators themselves, while neglecting their close integration with marine ranch production operations. This makes it difficult to directly apply evaluation results to production practices, providing timely and effective guidance for daily marine ranch management and production decisions. Furthermore, it is difficult to adjust production strategies in real time based on the results of ecological service evaluations during marine ranching operations. Summary of the Invention

[0005] In order to solve the problem that existing technologies are difficult to dynamically adjust production strategies based on ecological service evaluation results, this application provides an ecological service evaluation method, equipment, medium and product for marine ranching.

[0006] First, this application provides a method for evaluating the ecological services of marine ranches, using the following technical solutions: A method for evaluating the ecological services of marine ranches, including: Obtaining historical production data and current production data of the marine ranch during its historical production period, wherein both the historical production data and the current production data include data of operational indicators; generating an ecological service evaluation result for the current production cycle based on the historical production data and the current production data; Formulate a global strategy for the next production cycle targeting the operational indicators; The next production cycle is executed based on the global strategy, and the global strategy is dynamically adjusted based on production data of the next production cycle.

[0007] By adopting the above technical solutions, historical and current production data covering operational indicators are obtained, providing a comprehensive and accurate data basis for subsequent analysis; based on these data, the ecological service evaluation results of the current production cycle are generated, which can intuitively reflect the current ecological service status of the marine ranch; a global strategy for operational indicators in the next production cycle is formulated to achieve scientific planning and guidance for future production activities; in the process of implementing the global strategy, the strategy is dynamically adjusted according to the production data of the next production cycle, which can respond to changes in the production process in a timely manner, ensure the effectiveness and adaptability of the strategy, and realize dynamic management of the entire cycle of marine ranch production activities.

[0008] In a preferred example, the present application may be further configured as follows: the historical production data and the current production data also include data of constraint-type indicators and response-type indicators; The generating of the ecological service evaluation result of the current production cycle based on the historical production data and the current production data includes: determining a standard range of the responsiveness indicator based on the historical production data; Comparing the data of the response indicator in the current production data with the corresponding standard range to obtain an indicator evaluation result of the response indicator; Obtaining the evaluation dimension to which each responsive indicator belongs, and calculating the dimension evaluation result of each evaluation dimension based on the indicator evaluation result; An ecological service evaluation result of the marine ranch in the current production cycle is generated based on the dimensional evaluation results.

[0009] By adopting the above technical solutions, the constraint indicators and response indicator data are incorporated, which enriches the evaluation data dimensions and makes the evaluation more comprehensive. The standard range of response indicators is determined based on historical data, providing a scientific reference for the evaluation. The current data is compared with them to obtain the indicator evaluation results, which can intuitively show the current status of each indicator. The dimension evaluation results are calculated according to the evaluation dimensions to which the response indicators belong, and the ecological service status is analyzed from multiple levels. Finally, the dimensional evaluation results are integrated to generate the ecological service evaluation results, realizing a systematic evaluation from local indicators to the overall ecological service.

[0010] In a preferred example, the present application may be further configured as follows: formulating a global strategy for the next production cycle of the operational indicator includes: Construct a response model for each response indicator; Predicting based on the data of the constraint indicators in the historical production data to obtain constraint indicator prediction data for the next production cycle; The global strategy is generated based on the constraint indicator prediction data and the response model.

[0011] By adopting the above technical solutions, a response model of responsive indicators is constructed, which can accurately depict the complex relationship between operational indicators, constraint indicators and response indicators; based on historical data, constraint indicators are predicted, and constraint indicator prediction data for the next production cycle are obtained, so as to grasp the changing trend of the marine ranch production environment in advance; the constraint indicator prediction data and the response model are combined to generate a global strategy, and the environmental prediction and indicator relationship model are deeply integrated, so that the formulated strategy fully considers the impact of environmental changes on production.

[0012] In a preferred example, the present application may be further configured as follows: the target response indicator is any response indicator, and a response model of the target response indicator is constructed, including: constructing a generalized additive model of the target response indicator as an initial response model; Calculating the importance scores of each operational indicator and each constraint indicator in the initial response model, and adjusting the initial response model based on the importance scores to obtain an intermediate response model; Analyzing the direct effect index of each operational indicator in the intermediate response model; Constructing cross-action items of the operational indicators and adding them into the intermediate response model, and calculating the synergistic effect index of the cross-action items; Constructing an interaction term between the operational indicator and the constraint indicator, and adding the interaction term of the constraint indicator to the intermediate response model, and calculating the moderation effect index of the interaction term; The direct effect index, the synergistic effect index and the regulatory effect index are substituted into the intermediate response model to obtain the response model of the target response indicator.

[0013] By adopting the above technical solution, a generalized additive model is constructed as the initial response model, which can preliminarily capture the complex nonlinear relationship between the target response indicators and the operational and constraint indicators; the indicator importance scores are calculated and the initial model is adjusted to obtain the intermediate response model, which can remove indicators with less impact on the target response indicators, making the model more concise and efficient; the direct effect index of the operational indicators is analyzed to clarify the degree of direct impact of each operational indicator on the target response indicator; cross-action terms are constructed and the synergistic effect index is calculated to incorporate the impact of the interaction between operational and constraint indicators, and between constraint indicators; each effect index is substituted into the intermediate response model to obtain the final response model, which greatly improves the model's explanatory power and prediction accuracy for the target response indicators.

[0014] In a preferred example, the present application may be further configured as follows: generating the global strategy based on the constraint indicator prediction data and the response model includes: Dividing the next production cycle into a plurality of sub-production cycles based on the constraint indicator prediction data; generating an optimal strategy set for each of the plurality of sub-production cycles; Based on the constraint indicator prediction data and optimal strategy set of each of the multiple sub-production cycles, the state space, action space and reward function are defined, and the optimal strategy sequence is generated using the value iteration algorithm, and the obtained optimal decision sequence is used as the global strategy.

[0015] By adopting the above technical solution, the next production cycle is divided into multiple sub-production cycles based on the constraint indicator prediction data. The production process can be refined according to the law of environmental changes, so that the strategy formulation is more in line with the actual production rhythm; the optimal strategy set for each sub-production cycle is generated, and the local optimal operational indicator strategy combination is screened out according to the environmental conditions and production goals of different stages, thereby improving the accuracy of the strategy; the value iteration algorithm is used to generate the optimal strategy sequence, and the strategies of each sub-production cycle are coordinated from a global perspective. Through continuous optimization and iteration, the global optimal decision sequence that comprehensively considers ecological and production benefits is obtained. As a global strategy, it effectively ensures the scientificity, consistency and efficiency of marine ranch production activities in complex environments, and maximizes ecological services and production benefits.

[0016] In a preferred example, the present application may be further configured as follows: the target sub-production cycle is any sub-production cycle, and an optimal strategy set for the target sub-production cycle is generated, including: Calculating the average forecast data of the constraint indicator forecast data within the target sub-production cycle; Substituting the average forecast data into the response model, and generating an optimal strategy set for the target sub-production cycle using a multi-objective optimization algorithm based on the feasible domain of the operational indicator; Each strategy in the optimal strategy set includes: an operational indicator parameter combination, a response indicator prediction value, and a multi-objective comprehensive evaluation score based on the response indicator prediction value.

[0017] By adopting the above technical solution, the average predicted data is substituted into the response model, and the multi-objective optimization algorithm is used in combination with the feasible domain of the operational indicators to generate the optimal strategy set. Under the premise of considering the actual production constraints, it is possible to give full play to the response model's ability to accurately characterize the relationship between indicators, balance multiple goals such as ecological protection and production efficiency, and screen out operational indicator parameter combinations that take into account multiple needs; each strategy in the optimal strategy set is accompanied by a response indicator prediction value and a multi-objective comprehensive evaluation score, which not only intuitively shows the expected ecological and production effects after the implementation of the strategy, but also provides a clear judgment standard for the pros and cons of the strategy through quantitative scoring.

[0018] In a preferred example, the present application may be further configured as follows: dynamically adjusting the global strategy based on the production data of the next production cycle includes: During the production process of the next production cycle, obtaining sub-production data of each sub-production cycle, and determining whether there is a deterioration response indicator in the sub-production data of the current sub-production cycle; If so, determining the operational indicator to be adjusted and the parameter adjustment method based on the response model of the deterioration response indicator; In the global strategy, the sub-strategy for the next sub-production cycle of the current sub-production cycle is updated based on the parameter adjustment method of the operational indicator to be adjusted.

[0019] By adopting the above technical solution, deterioration-responsive indicators can be identified, abnormal changes in the marine ranch ecosystem can be captured in a timely manner, and dynamic monitoring of the production process can be achieved; when deterioration-responsive indicators are found, the operational indicators to be adjusted and the parameter adjustment methods are determined based on their response models, and the relationship between indicators is accurately analyzed with the help of the model to ensure that the adjustment plan has a scientific basis and is targeted; the sub-strategy of the next sub-production cycle is updated in the global strategy, so that the production strategy can quickly adapt to changes in the ecological environment and avoid production losses caused by ecological deterioration.

[0020] In a second aspect, the present application provides an electronic device, which adopts the following technical solution: one or more processors; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the ecological service evaluation method of the marine ranch as described in any one of the first aspects.

[0021] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the marine ranch ecological service evaluation method as described in any one of the first aspects.

[0022] In a fourth aspect, the present application provides a computer program product that adopts the following technical solution: A computer program product includes a computer program. When the computer program is executed by a processor, it implements the ecological service evaluation method of the marine ranch as described in any one of the first aspects.

[0023] In summary, this application has the following beneficial technical effects: This application obtains historical and current production data covering operational indicators to provide a comprehensive and accurate data basis for subsequent analysis; generates ecological service evaluation results for the current production cycle based on these data, which can intuitively reflect the current ecological service status of the marine ranch; formulates an overall strategy for operational indicators in the next production cycle to achieve scientific planning and guidance for future production activities; in the process of implementing the overall strategy, dynamically adjusts the strategy based on the production data of the next production cycle, can respond to changes in the production process in a timely manner, ensure the effectiveness and adaptability of the strategy, and realize dynamic management of the entire cycle of marine ranch production activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a method for evaluating the ecological services of a marine ranch provided in an embodiment of the present application; Figure 2 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1 -Attached Figure 2 This application is described in further detail.

[0026] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the claims of the present application, they are protected by the patent law.

[0027] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0029] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0030] This application embodiment provides a method for evaluating the ecological services of a marine ranch, such as Figure 1 As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S104, wherein: S101. Obtain historical production data and current production data of the marine ranch's historical production cycle. Both the historical production data and the current production data include data on operational indicators.

[0031] Specifically, both historical and current production data include operational, response, and constraint indicators. Operational indicators represent those that can be directly manipulated and controlled during the marine ranch production process, such as stocking density, feed intake, and the type and number of fry released. These indicators directly impact marine ranch production activities and output. Constraint indicators are those that cannot be directly manipulated and controlled, and include environmental indicators such as water temperature, salinity, tides, current velocity, and light intensity. Response indicators reflect the state and service functions of the marine ranch ecosystem, providing a direct reflection of the ranch's performance in terms of ecological health, resource output, and environmental regulation. These indicators include biodiversity indices, water quality indicators such as dissolved oxygen content, pH, and chemical oxygen demand, fishery resource yield and biomass, and ecotourism income.

[0032] During the marine ranch production process, sensors, such as water quality sensors and biomass sensors, are installed in key areas of the marine ranch (e.g., breeding areas and monitoring areas) to collect relevant data from the production process in real time, which serves as current production data. Historical production data from past production cycles is retrieved from a database. The duration of the production cycle can be set by management personnel based on the characteristics of the marine ranch and is not limited in this embodiment.

[0033] S102. Generate an ecological service evaluation result of the current production cycle based on historical production data and current production data.

[0034] Specifically, historical production data is statistically analyzed to determine the standard range for each response indicator under normal production conditions. The response indicator data in the current production data is then compared one by one with the corresponding standard range to determine whether each response indicator data is within the standard range. This results in an indicator evaluation result for each response indicator, such as whether it meets the standard, fails to meet the standard, and an evaluation score.

[0035] Classify the response indicators in advance, determine the evaluation dimension (such as ecological dimension, economic dimension, cultural dimension, etc.) to which each response indicator belongs, calculate the dimension evaluation results of each evaluation dimension based on the indicator evaluation results, and generate the ecological service evaluation results of the marine ranch in the current production cycle based on the dimension evaluation results.

[0036] S103. Develop a global strategy for the next production cycle targeting operational indicators.

[0037] Specifically, a response model is constructed for each responsive indicator. The model characterizes the relationship between operational indicators, constraint indicators, and responsive indicators. Based on the constraint indicator data in historical production data, a forecast is performed to obtain the constraint indicator forecast data for the next production cycle.

[0038] Based on the constraint indicator prediction data, the next production cycle is divided into multiple sub-production cycles, and the optimal strategy sets for each of the multiple sub-production cycles are generated. Based on the constraint indicator prediction data and the optimal strategy sets for each of the multiple sub-production cycles, the value iteration algorithm is used to generate the optimal strategy sequence, and the obtained optimal decision sequence is used as the global strategy.

[0039] S104: Execute the next production cycle based on the global strategy, and dynamically adjust the global strategy based on the production data of the next production cycle.

[0040] During the production process of the next production cycle, the sub-production data for each sub-production cycle is obtained, and a determination is made as to whether a deterioration-responsive indicator exists in the sub-production data for the current sub-production cycle. If so, the operational indicator to be adjusted and the parameter adjustment method are determined based on the response model of the deterioration-responsive indicator. The strategy for each sub-production cycle in the global strategy is treated as a sub-strategy. Within the global strategy, the sub-strategy for the next sub-production cycle of the current sub-production cycle is updated based on the parameter adjustment method for the operational indicator to be adjusted.

[0041] This embodiment obtains historical and current production data covering operational indicators, providing a comprehensive and accurate data basis for subsequent analysis; generates ecological service evaluation results for the current production cycle based on these data, which can intuitively reflect the current ecological service status of the marine ranch; formulates a global strategy for operational indicators in the next production cycle, and realizes scientific planning and guidance of future production activities; in the process of executing the global strategy, dynamically adjusts the strategy according to the production data of the next production cycle, can respond to changes in the production process in a timely manner, ensure the effectiveness and adaptability of the strategy, and realize dynamic management of the entire cycle of marine ranch production activities.

[0042] In a possible implementation of the embodiment of the present application, the historical production data and the current production data further include data of constraint indicators and response indicators; Based on historical production data and current production data, generate the ecological service evaluation results of the current production cycle, including: Determine the standard range of responsive indicators based on historical production data; Compare the data of the response-type indicators in the current production data with the corresponding standard range to obtain the indicator evaluation results of the response-type indicators; Obtain the evaluation dimension to which each responsive indicator belongs, and calculate the dimension evaluation result of each evaluation dimension based on the indicator evaluation result; Based on the dimensional evaluation results, the ecological service evaluation results of the marine ranch in the current production cycle are generated.

[0043] In this embodiment, any responsive indicator is used as a target responsive indicator, the data of the target responsive indicator in each historical period in the historical production data is determined, and the quantiles (5th percentile and 95th percentile) of the data are calculated as the standard range.

[0044] Compare the target response indicator data in the current production data with the corresponding standard range. If the data is within the standard range, mark the indicator as meeting the standard and set the evaluation score to 1. If the data is not within the standard range, mark the indicator as failing the standard and calculate the evaluation score. Determine the first difference between the maximum and minimum values ​​in the standard range. If the data is greater than the maximum value of the standard range, calculate the second difference between the maximum value and the data. The second difference is a negative number. The ratio of the second difference to the first difference is used as the evaluation score. If the data is less than the minimum value of the standard range, calculate the third difference between the data and the minimum value. The third difference is a negative number. The ratio of the third difference to the first difference is used as the evaluation score.

[0045] Refer to the above process to determine the evaluation score of each responsive indicator.

[0046] Take any evaluation dimension as the target evaluation dimension, determine multiple responsive indicators under the target evaluation dimension according to the pre-set evaluation dimension to which each responsive indicator belongs, and take the result of weighted summation of the evaluation scores of the determined multiple responsive indicators as the dimension evaluation result. The weight of each responsive indicator can be flexibly set in advance by the management personnel, and this embodiment does not limit it.

[0047] After determining the dimension evaluation results of each evaluation dimension, the dimension evaluation results of each evaluation dimension are weighted and summed, and the result obtained is used as the ecological service evaluation result of the current production cycle.

[0048] This embodiment incorporates constraint-type indicator and response-type indicator data, enriching the evaluation data dimension and making the evaluation more comprehensive. It determines the standard range of response-type indicators based on historical data, providing a scientific reference for the evaluation; compares the current data with them to obtain the indicator evaluation results, which can intuitively show the current compliance status of each indicator; calculates the dimension evaluation results based on the evaluation dimensions to which the response-type indicators belong, and analyzes the ecological service status from multiple levels; finally, the dimensional evaluation results are integrated to generate the ecological service evaluation results, realizing a systematic evaluation from local indicators to the overall ecological service.

[0049] A possible implementation of the embodiment of the present application is to formulate a global strategy for the next production cycle of the operational indicators, including: Construct a response model for each response indicator; Based on the data of the constraint indicators in the historical production data, the constraint indicator forecast data for the next production cycle is obtained; Generate a global strategy based on constraint indicator prediction data and response models.

[0050] Specifically, based on the data of constraint indicators in historical production data, time series analysis and machine learning prediction algorithms (such as ARIMA model, LSTM neural network, etc.) are used to make predictions to obtain the constraint indicator prediction data for the next production cycle.

[0051] This embodiment constructs a response model for responsive indicators, which can accurately depict the complex relationship between operational indicators, constraint indicators and response indicators; predicts constraint indicators based on historical data, obtains constraint indicator prediction data for the next production cycle, and can grasp the changing trend of the marine ranch production environment in advance; combines the constraint indicator prediction data and the response model to generate a global strategy, deeply integrates the environmental prediction with the indicator relationship model, and ensures that the formulated strategy fully considers the impact of environmental changes on production.

[0052] In a possible implementation of the embodiment of the present application, the target response indicator is any response indicator, and a response model of the target response indicator is constructed, including: A generalized additive model of the target response indicator was constructed as the initial response model; Calculate the importance score of each operational indicator and each constraint indicator in the initial response model, and adjust the initial response model based on the importance score to obtain an intermediate response model; Analyze the direct effect index of each operational indicator in the intermediate response model; Construct cross-action items of operational indicators and add them into the intermediate response model to calculate the synergistic effect index of cross-action items; Construct the interaction terms of operational indicators and constraint indicators, and add the interaction terms of constraint indicators into the intermediate response model, and calculate the moderation effect index of the interaction terms; Substituting the direct effect index, synergistic effect index and regulatory effect index into the intermediate response model, the response model of the target response indicator is obtained.

[0053] In this embodiment, operational indicator data, constraint indicator data, and the target response indicator data itself, related to the target response indicator, are extracted from the acquired historical and current production data. This data is normalized, mapping data of different dimensions to the same interval (e.g., [0, 1]) to prevent dimensional differences from affecting model construction.

[0054] A generalized additive model is constructed using the target response indicator as the dependent variable and the operational and constraint indicators as the independent variables. During the construction process, an appropriate smoothing function (such as a spline function) is selected based on the data characteristics. The nonlinear relationship between the independent and dependent variables is fitted to initially establish a relationship model between the indicators and obtain the initial response model.

[0055] Using the variable importance assessment method of the random forest algorithm, the operational and constraint indicators in the initial response model are used as input variables, and the target response indicator is used as the output variable to train the random forest model. Each indicator's importance score is calculated by calculating its contribution to reducing node impurity in the model. Alternatively, a method based on model parameter estimation is used to calculate the significance level of each indicator's parameter estimate, which is used as the basis for measuring the importance score. An importance score threshold is set (e.g., by calculating the mean or median of all indicator importance scores). Indicators with importance scores below the threshold are removed from the initial response model. The weights of the retained indicators are adjusted based on their importance scores to obtain an intermediate response model.

[0056] Based on the intermediate response model, the values ​​of other operational and constraint indicators are fixed (the average or common values ​​of each indicator can be selected), and the value of only one operational indicator is changed at a time. Multiple different value points (e.g., evenly spaced values) are selected within the feasible region of that operational indicator. Each value point is substituted into the intermediate response model to calculate the predicted value of the target response indicator. By analyzing the slope or rate of change of the predicted value of the target response indicator with the value of the operational indicator, the direct impact of the operational indicator on the target response indicator is measured and quantified as a direct effect index. For example, the ratio of the difference between the predicted values ​​of the target response indicator and the difference between the operational indicator values ​​at adjacent value points can be used as the direct effect index.

[0057] For all operational indicators, cross-talk terms were constructed for pairwise combinations (e.g., cross-talk terms A×B for operational indicator A and operational indicator B). These cross-talk terms were then added to the intermediate response model to generate an expanded model. Using a stepwise regression approach, the intermediate response model containing only a single operational indicator and a constraint indicator was used as the base model. Cross-talk terms were then added sequentially for regression analysis. By comparing the goodness-of-fit (e.g., R² value) and AIC information criterion before and after the addition of cross-talk terms, the degree to which the cross-talk terms enhance the model's explanatory power was quantified. This improvement was used as a synergistic effect index to assess the impact of interactions between operational indicators on the target response indicator.

[0058] Interaction terms between operational and constraint indicators (e.g., operational indicator A and constraint indicator C, interaction term A×C) and between constraint indicators (e.g., constraint indicator C and constraint indicator D, interaction term C×D) were constructed and added to the model containing the cross-term. Using structural equation modeling or hierarchical regression analysis, the effects of operational indicators on target-response indicators and the effects of interactions between constraint indicators on target-response indicators were analyzed under varying constraint indicator values. The degree of moderation of the effects of interaction terms on target-response indicators was quantified by comparing the differences in model parameter estimates across different conditions. This moderation degree was used as a moderating effect index.

[0059] Substitute the calculated direct effect index, synergistic effect index, and moderating effect index as weights or correction parameters into the intermediate response model containing cross-effect and interaction terms to further optimize and adjust the model. Validate the adjusted model using data not included in model construction (e.g., a test set generated using cross-validation). Evaluate model performance by calculating the model's prediction error (e.g., mean squared error, mean absolute error), goodness-of-fit, and other metrics. If model performance does not meet expectations, return to the previous steps and adjust the model until a response model with good performance for the target response indicator is obtained.

[0060] This embodiment constructs a generalized additive model as the initial response model, which can preliminarily capture the complex nonlinear relationship between the target response indicator and the operational and constraint indicators; calculates the indicator importance score and adjusts the initial model to obtain an intermediate response model, which can remove indicators with less impact on the target response indicator, making the model more concise and efficient; analyzes the direct effect index of the operational indicator, which can clarify the degree of direct impact of each operational indicator on the target response indicator; constructs cross-action terms and calculates the synergistic effect index, incorporating the impact of the interaction between the operational and constraint indicators, and the constraint indicators; substitutes each effect index into the intermediate response model to obtain the final response model, which greatly improves the model's explanatory power and prediction accuracy for the target response indicator.

[0061] A possible implementation of the embodiment of the present application generates a global strategy based on constraint indicator prediction data and a response model, including: Divide the next production cycle into multiple sub-production cycles based on the constraint indicator forecast data; Generate optimal strategy sets for multiple sub-production cycles; Based on the constraint indicator prediction data and optimal strategy set of multiple sub-production cycles, the state space, action space and reward function are defined, and the value iteration algorithm is used to generate the optimal strategy sequence, and the obtained optimal decision sequence is used as the global strategy.

[0062] In this embodiment, the basis for dividing and length of sub-production cycles is determined based on the changing characteristics of the constraint indicators and the production and management needs of the marine ranch. If the water temperature varies significantly seasonally, the next production cycle can be divided into four sub-production cycles according to the season: spring, summer, autumn, and winter. If the tide has a significant impact on production, several shorter sub-production cycles can be divided according to the tidal cycle. The cycle length can be fixed (such as one sub-production cycle per month) or dynamically adjusted according to actual conditions (such as setting sub-production cycles of different lengths based on the magnitude of water temperature changes). According to the determined division basis and cycle length, the next production cycle is divided into multiple sub-production cycles, and each sub-production cycle is marked with a corresponding time interval and number to ensure the accuracy and completeness of the division.

[0063] The state space describes the system state within each sub-production cycle. It includes information such as forecast data for constraint indicators within the sub-production cycle (such as real-time water temperature, salinity, and tides), the results of the previous phase's ecological service evaluation (such as biodiversity indices and water quality scores), and the implemented operational indicator parameter combinations and their effects (such as changes in fishery yields after adjusting stocking density in the previous phase). This information is integrated and quantified to form a state vector in the state space. The different states of each sub-production cycle can be represented by a corresponding state vector.

[0064] The action space is the set of operational indicator adjustment strategies that can be implemented within each sub-production cycle. Examples include adjusting stocking density, changing feed dosage, or switching the type of fry released. By clearly defining the adjustment method and possible value range for each operational indicator, a complete action space is formed. Each action corresponds to a specific set of operational indicator parameter adjustment plans.

[0065] The reward function evaluates the benefits or effects of different actions taken during each sub-production cycle. Based on the marine ranch's production goals and ecological protection requirements, the reward function is designed by comprehensively considering factors such as fishery output, ecological environment quality, and economic benefits. For example, positive rewards may be given when fishery output increases or water quality improves, while negative rewards may be given when it does not. Additional rewards may be given for achieving pre-set ecological protection or production efficiency goals. The reward function can take the form of a linear or nonlinear function, with different weight coefficients used to reflect the importance of each factor.

[0066] A value function is initialized for each state in each sub-production cycle, typically initializing all states to 0. The value function measures the long-term cumulative reward obtained by adopting the optimal strategy starting from a given state. In each iteration, for each state in the state space, all actions in the action space are traversed. For each action, the probability of transitioning to the next state after executing that action is calculated, as well as the immediate reward, based on the predicted data from the response model and the constraint indicators. The value function for each state is then updated according to the Bellman equation until the value function converges, meaning that the change in the value function between two consecutive iterations is less than a preset threshold.

[0067] After the value function converges, the optimal action for each state is determined through backtracking. Starting from the initial state, the action that maximizes the value is selected based on the value function. This action is then executed to enter the next state. The optimal action is then selected in the new state, and so on. This results in an optimal action sequence from the first to the last sub-production cycle, known as the optimal strategy sequence. This optimal strategy sequence serves as the global strategy for the next production cycle, guiding actual marine ranching operations.

[0068] This embodiment divides the next production cycle into multiple sub-production cycles based on the constraint indicator prediction data, which can refine the production process according to the law of environmental changes, so that strategy formulation is more in line with the actual production rhythm; generates the optimal strategy set for each sub-production cycle, and screens out the locally optimal operational indicator strategy combination based on the environmental conditions and production goals of different stages, thereby improving the accuracy of the strategy; uses the value iteration algorithm to generate the optimal strategy sequence, coordinates the strategies of each sub-production cycle from a global perspective, and obtains the global optimal decision sequence that comprehensively considers ecological and production benefits through continuous optimization and iteration. As a global strategy, it effectively ensures the scientificity, consistency and efficiency of marine ranch production activities in a complex environment, and maximizes ecological services and production benefits.

[0069] In a possible implementation of the embodiment of the present application, the target sub-production cycle is any sub-production cycle, and generating an optimal strategy set for the target sub-production cycle includes: Calculate the average forecast data of the constraint indicator forecast data within the target sub-production cycle; Substitute the average forecast data into the response model, and use the multi-objective optimization algorithm based on the feasible domain of the operational indicators to generate the optimal strategy set for the target sub-production cycle; Among them, each strategy in the optimal strategy set includes: an operational indicator parameter combination, a response indicator prediction value, and a multi-objective comprehensive evaluation score based on the response indicator prediction value.

[0070] In this embodiment, all constraint indicator prediction data belonging to the target sub-production cycle are extracted from the completed constraint indicator prediction data of the next production cycle, and are classified and sorted according to different constraint indicators (such as water temperature, salinity, tide, etc.) to ensure the integrity and accuracy of the data.

[0071] For each constraint indicator, use the arithmetic mean method to calculate its average forecast data within the target sub-production cycle. This means adding up all the forecast data for that indicator within the target sub-production cycle and dividing by the number of data points. Repeat this process for all constraint indicators to obtain a complete set of average forecast data for the constraint indicators within the target sub-production cycle.

[0072] The processed average forecast data is used as the input value for the constraint indicator and substituted into the response model for each previously constructed response indicator. For each response model, the variable form of the operational indicator is maintained. Through the model's calculation logic, the preliminary forecast value of each response indicator is obtained under the average forecast data of the constraint indicator.

[0073] Based on the production goals and ecological protection requirements of the marine ranch, multiple optimization objectives are determined. For example, maximizing fishery yield, minimizing environmental pollution (measured by response indicators such as water pollution), and maintaining biodiversity are considered. These objectives are converted into mathematical functions and used as the optimization objective functions of the multi-objective optimization algorithm. The weights of each objective function are clearly defined. The weights can be determined using the Analytic Hierarchy Process (AHP), where experts in fields such as marine ecology and fishery production are invited to rate the importance of each objective, construct a judgment matrix, and calculate the weights of each objective. Alternatively, the entropy method can be used to objectively determine the weights based on the degree of variation of each objective in historical data.

[0074] Based on the actual production conditions and resource constraints of marine ranching, the range of values ​​for each operational indicator, known as the feasible domain, should be clearly defined. For example, aquaculture density cannot exceed the carrying capacity of the ocean area, and feed supply must be within reasonable economic and ecological limits. These feasible domains serve as constraints on the operational indicators in the multi-objective optimization algorithm.

[0075] Select an appropriate multi-objective optimization algorithm, such as the non-dominated sorting genetic algorithm-II (NSGA-II) or the multi-objective particle swarm optimization algorithm (MOPSO). Taking the NSGA-II algorithm as an example, a set of operational indicator parameter combinations that satisfy the feasible domain of the operational indicators is randomly generated as the initial population. Each parameter combination represents a potential production strategy. For each individual (operational indicator parameter combination) in the initial population, it is substituted into the response model and, combined with the average predicted data of the constraint indicators, the predicted value of each response indicator is calculated. Based on these predicted values, a comprehensive multi-objective evaluation score is calculated for each individual according to the defined multi-objective function and weights. Then, genetic operations such as selection, crossover, and mutation are used to generate the next generation of the population. This process is repeated for multiple rounds until the algorithm termination criteria (such as reaching the maximum number of iterations or population convergence) are met.

[0076] After the algorithm terminates, the individuals in the non-dominated solution set are considered the optimal strategies. A certain number of strategies (determined based on actual needs) are selected from the non-dominated solution set. Each strategy consists of a specific combination of operational indicator parameters, a predicted response indicator value calculated using the response model, and a multi-objective comprehensive evaluation score calculated based on the predicted response indicator value. Together, these strategies form the optimal strategy set for the target sub-production cycle.

[0077] This embodiment substitutes the average predicted data into the response model, and uses a multi-objective optimization algorithm in combination with the feasible domain of operational indicators to generate an optimal strategy set. Under the premise of considering actual production constraints, it can give full play to the response model's ability to accurately characterize the relationship between indicators, balance multiple goals such as ecological protection and production efficiency, and screen out operational indicator parameter combinations that take into account multiple needs. Each strategy in the optimal strategy set is accompanied by a response indicator prediction value and a multi-objective comprehensive evaluation score, which not only intuitively shows the expected ecological and production effects after the implementation of the strategy, but also provides a clear judgment standard for the pros and cons of the strategy through quantitative scoring.

[0078] A possible implementation of the embodiment of the present application is to dynamically adjust the global strategy based on production data of the next production cycle, including: During the production process of the next production cycle, the sub-production data of each sub-production cycle is obtained, and it is determined whether there is a deterioration response indicator in the sub-production data of the current sub-production cycle; If so, determine the operational indicator to be adjusted and the parameter adjustment method based on the response model of the deterioration response indicator; In the global strategy, the sub-strategy for the next sub-production cycle of the current sub-production cycle is updated based on the parameter adjustment method of the operational indicator to be adjusted.

[0079] In this embodiment, the response indicator data of the current sub-production cycle is compared with a preset deterioration condition. If a response indicator data satisfies the deterioration condition, for example, the value reaches a deterioration threshold, it is determined to be a deterioration response indicator.

[0080] For the determined deterioration response indicators, the corresponding response model is retrieved. Using the sensitivity analysis method, the sensitivity coefficient of each operational indicator to the deterioration response indicator is calculated. The sensitivity coefficient reflects the degree of change of the deterioration response indicator when the operational indicator changes by one unit. For example, by making a small disturbance to the operational indicator in the response model, observing the change of the deterioration response indicator, and calculating the ratio of the change between the two, the sensitivity coefficient is obtained. According to the size of the sensitivity coefficient, the operational indicators with a greater impact on the deterioration response indicator are selected as the operational indicators to be adjusted. A preset number of operational indicators with a high sensitivity coefficient ranking are selected for adjustment. The preset number can be 3-5.

[0081] Based on the response model, combined with the constraint indicator data of the current sub-production cycle and the standard range of the deterioration response indicator, the parameter adjustment direction and adjustment range of the operational indicator to be adjusted are calculated through optimization algorithms (such as gradient descent method and Newton method).

[0082] Within the established global strategy, locate the sub-strategy corresponding to the next sub-production cycle after the current one. Apply the calculated parameter adjustment method for the operational indicator to be adjusted to the located sub-strategy. For example, if the operational indicator to be adjusted is stocking density, and the calculation shows that it needs to be reduced by 10%, then directly modify the stocking density parameter in the sub-strategy to 90% of the original parameter.

[0083] After updating a sub-strategy, simulate and validate it using the response model. Substitute the updated operational indicator parameters and the current constraint indicator data into the response model to calculate the predicted values ​​of each updated response indicator. This evaluates whether the updated sub-strategy effectively improves the deteriorating response indicator and meets the marine ranch's production goals and ecological requirements. If not, repeat the above steps to further adjust parameters or reselect the operational indicator to adjust.

[0084] By identifying deterioration-responsive indicators, this embodiment can promptly capture abnormal changes in the marine ranch ecosystem and achieve dynamic monitoring of the production process. When a deterioration-responsive indicator is discovered, the operational indicator to be adjusted and the parameter adjustment method are determined based on its response model. With the help of the model, the relationship between indicators is accurately analyzed to ensure that the adjustment plan has a scientific basis and is targeted. The sub-strategy for the next sub-production cycle is updated in the global strategy, so that the production strategy can quickly adapt to changes in the ecological environment and avoid production losses caused by ecological deterioration.

[0085] An electronic device is provided in an embodiment of the present application, such as Figure 2 As shown, Figure 2 The electronic device 200 shown includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, for example, via a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in actual applications, the number of transceivers 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation on the embodiments of the present application.

[0086] Processor 201 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 201 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0087] The bus 202 may include a path for transmitting information between the above components. The bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 202 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but it does not mean that there is only one bus or one type of bus.

[0088] The memory 203 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0089] The memory 203 is used to store application code for executing the solution of the present application, and is controlled by the processor 201. The processor 201 is used to execute the application code stored in the memory 203 to implement the content shown in the embodiment of the ecological service evaluation method of the marine ranch.

[0090] Figure 2 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0091] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the contents shown in the aforementioned embodiment of the method for evaluating ecological services of marine ranches.

[0092] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0093] An embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the contents shown in the aforementioned embodiment of the method for evaluating ecological services of marine ranches are implemented.

[0094] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for evaluating the ecological services of marine ranches, characterized in that: include: Obtaining historical production data and current production data of the marine ranch during its historical production period, wherein both the historical production data and the current production data include data of operational indicators; generating an ecological service evaluation result for the current production cycle based on the historical production data and the current production data; Formulate a global strategy for the next production cycle targeting the operational indicators; The next production cycle is executed based on the global strategy, and the global strategy is dynamically adjusted based on production data of the next production cycle.

2. The method for evaluating the ecological services of marine ranches according to claim 1, characterized in that: The historical production data and the current production data also include data of constraint indicators and response indicators; The generating of the ecological service evaluation result of the current production cycle based on the historical production data and the current production data includes: determining a standard range of the responsiveness indicator based on the historical production data; Comparing the data of the response indicator in the current production data with the corresponding standard range to obtain an indicator evaluation result of the response indicator; Obtaining the evaluation dimension to which each responsive indicator belongs, and calculating the dimension evaluation result of each evaluation dimension based on the indicator evaluation result; An ecological service evaluation result of the marine ranch in the current production cycle is generated based on the dimensional evaluation results.

3. The method for evaluating the ecological services of marine ranches according to claim 1, characterized in that: The formulation of a global strategy for the next production cycle of the operational indicator includes: Construct a response model for each response indicator; Predicting based on the data of the constraint indicators in the historical production data to obtain constraint indicator prediction data for the next production cycle; The global strategy is generated based on the constraint indicator prediction data and the response model.

4. The method for evaluating the ecological services of marine ranches according to claim 3, characterized in that: The target response indicator is any response indicator, and constructing a response model for the target response indicator includes: constructing a generalized additive model of the target response indicator as an initial response model; Calculating the importance scores of each operational indicator and each constraint indicator in the initial response model, and adjusting the initial response model based on the importance scores to obtain an intermediate response model; Analyzing the direct effect index of each operational indicator in the intermediate response model; Constructing cross-action items of the operational indicators and adding them into the intermediate response model, and calculating the synergistic effect index of the cross-action items; Constructing an interaction term between the operational indicator and the constraint indicator, and adding the interaction term of the constraint indicator to the intermediate response model, and calculating the moderation effect index of the interaction term; The direct effect index, the synergistic effect index and the regulatory effect index are substituted into the intermediate response model to obtain the response model of the target response indicator.

5. The method for evaluating the ecological services of marine ranches according to claim 3, characterized in that: Generating the global strategy based on the constraint indicator prediction data and the response model includes: Dividing the next production cycle into a plurality of sub-production cycles based on the constraint indicator prediction data; generating an optimal strategy set for each of the plurality of sub-production cycles; Based on the constraint indicator prediction data and optimal strategy set of each of the multiple sub-production cycles, the state space, action space and reward function are defined, and the optimal strategy sequence is generated using the value iteration algorithm, and the obtained optimal decision sequence is used as the global strategy.

6. The method for evaluating the ecological services of marine ranches according to claim 5, characterized in that: The target sub-production cycle is any sub-production cycle, and the optimal strategy set for the target sub-production cycle is generated, including: Calculating the average forecast data of the constraint indicator forecast data within the target sub-production cycle; Substituting the average forecast data into the response model, and generating an optimal strategy set for the target sub-production cycle using a multi-objective optimization algorithm based on the feasible domain of the operational indicator; Each strategy in the optimal strategy set includes: an operational indicator parameter combination, a response indicator prediction value, and a multi-objective comprehensive evaluation score based on the response indicator prediction value.

7. The method for evaluating the ecological services of marine ranches according to claim 1, characterized in that: The dynamically adjusting the global strategy based on the production data of the next production cycle includes: During the production process of the next production cycle, obtaining sub-production data of each sub-production cycle, and determining whether there is a deterioration response indicator in the sub-production data of the current sub-production cycle; If so, determining the operational indicator to be adjusted and the parameter adjustment method based on the response model of the deterioration response indicator; In the global strategy, the sub-strategy for the next sub-production cycle of the current sub-production cycle is updated based on the parameter adjustment method of the operational indicator to be adjusted.

8. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the ecological service evaluation method of the marine ranch as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the marine ranch ecological service evaluation method according to any one of claims 1 to 7.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the steps of the marine ranch ecological service evaluation method according to any one of claims 1 to 7.

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