Fish habitat water flow structure repairing method

Abstract

The invention discloses a fish habitat water flow structure restoration method, and belongs to the technical field of water conservancy and hydropower ecological protection and fishery resource protection. The method comprises the following steps: S01, analyzing the climate change trend of a target water area; s02, establishing a fish demand model; s03, predicting possible flow velocity change, depth change and area expansion and reduction conditions of the target water area by adopting hydrodynamic simulation software; s04, a set of self-adjustable water flow structure repairing scheme is designed; s05, the water flow structure repairing scheme is visually displayed; s06, carrying out water flow structure repair scheme trial points in the target water area, and adjusting and perfecting a water flow structure repair scheme; and S07, evaluating the effect of the repair measures, and adjusting and optimizing the water flow structure repair scheme according to the evaluation result. Climate change, fish ecological requirements and hydrodynamic simulation are deeply coupled, a self-adjustment optimization mechanism is integrated in the restoration method, and an innovative solution is provided for long-term stability of the fish habitat.

Description

TECHNICAL FIELD

[0001] The present application relates to a fish habitat water flow structure repair method, belonging to the technical field of ecological protection of water conservancy and hydropower and fishery resources protection. BACKGROUND

[0002] Fish habitat refers to the environment where fish live and reproduce, mainly including freshwater environment and seawater environment. Freshwater environment includes rivers, lakes, ponds, streams and wetlands, while seawater environment includes oceans, coral reefs, seagrass beds and deep sea, and the characteristics of fish habitat include diversity, temperature seeking and migratory behavior. In order to ensure the aggregation and survival of fish population, in the natural environment or artificial environment with large water level difference and unstable water flow, fish population is difficult to seek refuge and survive in the environment, including key life processes such as foraging and spawning, and ecological improvement and repair of specific areas are often needed.

[0003] However, due to the intensification of greenhouse effect in recent years, some extreme weather frequently occurs, and even after ecological improvement and repair of some specific areas, due to lack of prediction of climate change and self-adjusting ability of water flow structure repair scheme, many ecological improvement and repair schemes have poor adaptability after implementation, such as drought, pollution, water dike collapse, heavy rain leading to increase of water flow, possible future changes of water flow, changes of water depth and changes of water area, which can make a large part of fish habitat water flow structure repair schemes fail to achieve the expected effect after implementation, and to a certain extent, cause waste of funds and resources.

[0004] For example, the Chinese patent document with publication number CN118071035B discloses a fish habitat water flow structure repair method, which specifically includes the following steps: step one, a water body information management unit obtains water body information, and divides the water area to be repaired into multiple investigation areas according to the water body information; step two, a water organism investigation unit samples and photographs the target position of the investigation area to obtain a water organism sampling image, and the water organism investigation unit obtains fish information in the water body according to the water organism sampling image; step three, a habitat selection unit selects the best repair scheme of the fish habitat water flow structure according to the fish information and the water body information; and step four, a habitat repair unit repairs the target water area according to the best repair scheme. Precise estimation of fish information of the entire water area to be repaired selects the best repair scheme, which ensures the accuracy of the best repair scheme and avoids the problem of insufficient fish habitat or resource waste.

[0005] However, the above method does not take climate change into the repair scheme, resulting in poor adaptability of the repair scheme after implementation, and lack of self-adaptive adjustment and optimization of environmental changes and continuous evaluation of engineering effect. SUMMARY

[0006] To solve the above technical problems, the present application provides a fish habitat water flow structure repair method.

[0007] The present application is realized by the following technical solutions:

[0008] A fish habitat water flow structure repair method, comprising the following steps:

[0009] S01: Based on the historical climate data and future climate prediction data of the target water area, analyze the climate change trend of the target water area;

[0010] S02: Based on a plurality of water biological survey information including water body information, fish species and population distribution information of the target water area, combined with fish biological characteristics and ecological information, establish a fish demand model;

[0011] S03: Combined with the results of climate change trend and fish demand model, use hydrodynamic simulation software to predict the flow rate change, depth change and area expansion and shrinkage that may occur in the target water area at different time points in the future;

[0012] S04: According to the simulation results of step S03, design a self-adjusting water flow structure repair scheme including adding artificial reefs, adjusting river bed shape, adjusting river direction and width;

[0013] S05: Use three-dimensional modeling technology and virtual reality technology to visually display the water flow structure repair scheme, so as to facilitate decision makers to intuitively understand the design scheme and its expected effect;

[0014] S06: Carry out pilot test of the water flow structure repair scheme in the target water area, monitor its influence on the local ecosystem, especially the fish population, and then adjust and improve the water flow structure repair scheme;

[0015] S07: After the water flow structure repair scheme is implemented in the target water area, continuously monitor the water quality parameters, biodiversity indicators and fish quantity changes, evaluate the effect of the repair measures, and adjust and optimize the water flow structure repair scheme according to the evaluation results.

[0016] The step S01 comprises the following steps:

[0017] S01a: Collect historical climate data of the target water area, which includes a plurality of data such as air temperature, precipitation, evaporation and wind speed, and the time span covers at least the past thirty years, and form a historical climate data set D h ,

[0018] D h ={C i |i∈[1,n],C i =(Ti , P i , E i , W i ),

[0019] wherein C i is the i-th data point, T i is the air temperature, P i is the precipitation, E i is the evaporation, and W i is the wind speed;

[0020] S01b: Based on historical climate data, using climate model and prediction algorithm, generating future climate prediction dataset D of the target water area f ,

[0021] D f = M(C h , θ),

[0022] wherein C h is the historical climate data, and θ is the model parameter;

[0023] S01c: Statistical analysis of the collected historical climate data and future climate prediction data, and climate trend T is obtained,

[0024]

[0025] wherein C i is the i-th data point of the climate variable, is the average value of the climate variable;

[0026] S01d: Based on the statistical analysis results, a climate change trend model is constructed to predict the future trend of climate change, and the expression of the climate change trend model M c is as follows:

[0027] M c = f(T, δ, α),

[0028] wherein T is the climate trend, δ is the standard deviation of the climate trend, and α is the confidence level of the climate change trend model.

[0029] The step S02 includes the following steps:

[0030] S02a: Conducting comprehensive aquatic biological survey to collect water body information, fish species and population distribution information of the target water area, wherein the water body information includes multiple water quality parameters such as dissolved oxygen, pH value, nitrogen and phosphorus content, water temperature and bottom type, and the standard sampling and analysis method used in the survey process is expressed as follows:

[0031] I ω = {(Q i, M i ) | i ∈ [1, m], Q i , M i},

[0032] where I ω is the water body information set, Q i is the i-th sampling point, M i is the corresponding analysis method;

[0033] S02b: According to the collected fish species and population distribution information, combined with the biological characteristics of fish living habits, breeding habits, diet, and the ecological information of suitable growth water temperature, flow rate, and bottom conditions, data integration and preprocessing are carried out to obtain the preprocessed data set D p ,

[0034] D p = {(B j , E j ) | j ∈ [1, k], B j , E j},

[0035] where B j is the biological characteristics of the j-th fish, and E j is the corresponding ecological information;

[0036] S02c: Use multivariate statistical analysis method and niche theory to establish fish demand model, through which the specific demand of different fish to the habitat environment is quantified, and the fish demand model M d is represented as:

[0037]

[0038] where W j is the demand weight of the j-th fish, B j and E j are the biological characteristics and ecological information of the j-th fish, respectively.

[0039] The step S03 includes the following steps:

[0040] S03a: Select appropriate hydrodynamic simulation software, and the evaluation criteria for software selection are represented as:

[0041] S s = {S i | i ∈ [1, n], S i = (C i , A i , P i )},

[0042] where S s is the software selection set, Si is the i-th candidate software, C i is the accuracy of the software, A i is the applicability of the software, P i is the performance of the software;

[0043] S03b: Input climate change trend data and fish demand model results into the hydrodynamic simulation software, and set simulation parameters: time span, time step and spatial resolution, forming a simulation parameter set P m ,

[0044] P m = {(T s , T t , S r )|T s , T t , S r},

[0045] where T s is the simulation start time, T t is the simulation end time, and S r is the spatial resolution;

[0046] S03c: Run the hydrodynamic simulation software to predict the changes in flow velocity, depth and area expansion and contraction that may occur in the target water area in the future, and then represent the analysis and verification process of the prediction results as:

[0047] R p = {(V f , D f , A f )|V f , D f , A f},

[0048] where R p is the prediction result set, V f is the flow velocity prediction, D f is the depth prediction, and A f is the area change prediction. Analyze the prediction results and compare them with historical data, and conduct field verification to ensure the accuracy and reliability of the prediction results.

[0049] In step S04, when designing the water flow structure repair scheme, a system engineering method is used to ensure that the scheme achieves an optimal balance in terms of structural stability, ecological compatibility and economic benefits.

[0050] In step S05, when visualizing the water flow structure repair scheme, virtual reality technology is combined to create an interactive simulation environment, making it easier for decision-makers to evaluate the expected effects of the design scheme from multiple angles.

[0051] In step S06, when conducting a pilot project for the water flow structure restoration scheme in the target water area, a controlled experimental design is adopted to facilitate the assessment of the impact of the water flow structure restoration scheme on the ecosystem.

[0052] In step S07, a comprehensive monitoring system is established to continuously monitor water quality parameters and biodiversity indicators.

[0053] During the repair of water flow structures, a decision support system should be developed to provide dynamic adjustment suggestions for the repair of water flow structures based on monitoring data and assessment results.

[0054] At different stages of water flow structure repair, corresponding emergency plans should be developed to deal with possible extreme weather events or unexpected situations, and to ensure the smooth implementation of the water flow structure repair plan.

[0055] The beneficial effects of this invention are as follows:

[0056] First, multi-factor coupling analysis: breaking through the traditional analysis of single hydrological or biological factors, it deeply couples climate change, fish ecological needs, and hydrodynamic simulation, solving the problem of insufficient dynamic interaction response of multiple variables in existing technologies.

[0057] Second, decision support is upgraded: by lowering the decision-making threshold through visualization technology, and combining pilot feedback with long-term monitoring to form a "design-optimization" cycle, it is more scientific and engineering-practical than the static repair solutions in existing technologies.

[0058] Third, the technological solution is forward-looking: it incorporates a "self-adjustment and optimization" mechanism into the restoration method, anticipating the continued impact of future environmental changes on water areas, and providing an innovative solution for the long-term stability of fish habitats. Attached Figure Description

[0059] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0060] The technical solution of the present invention is further described below, but the scope of protection is not limited to what is described.

[0061] like Figure 1 As shown, the method for restoring the water flow structure of a fish habitat according to the present invention includes the following steps:

[0062] S01: Analyze the climate change trend of the target water area based on historical climate data and future climate prediction data;

[0063] S02: Based on a plurality of water biological survey information including water body information, fish species and population distribution information of the target water area, combined with fish biological characteristics and ecological information, a fish demand model is established;

[0064] S03: Combined with the results of climate change trend and fish demand model, water dynamic simulation software is used to predict the possible flow rate changes, depth changes and area expansion and contraction of the target water area at different time points in the future;

[0065] S04: According to the simulation results of step S03, a self-adjusting water flow structure repair scheme is designed, which includes adding artificial reefs, adjusting river bed shape, adjusting river direction and width;

[0066] S05: Three-dimensional modeling technology and virtual reality technology are used to visualize the water flow structure repair scheme, which is convenient for decision makers to intuitively understand the design scheme and its expected effect;

[0067] S06: Pilot the water flow structure repair scheme in the target water area, monitor its impact on the local ecosystem, especially the fish population, and then adjust and improve the water flow structure repair scheme;

[0068] S07: After the implementation of the water flow structure repair scheme in the target water area, the water quality parameters, biodiversity indicators and fish population changes are continuously monitored, the effect of the repair measures is evaluated, and the water flow structure repair scheme is adjusted and optimized according to the evaluation results.

[0069] The step S01 includes the following steps:

[0070] S01a: Collect historical climate data of the target water area, including a plurality of data such as air temperature, precipitation, evaporation and wind speed, with a time span of at least the past thirty years, and form a historical climate data set D h ,

[0071] D h = {C i |i∈]1,n],C i =(T i ,P i ,E i ,W i )},

[0072] Wherein, C i is the i-th data point, T i is the air temperature, P i is the precipitation, E i is the evaporation, and W i is the wind speed;

[0073] S01b: Based on historical climate data, generate a future climate prediction dataset D for the target water area using climate models and prediction algorithms f ,

[0074] D f = M(C h , θ),

[0075] where C h is historical climate data, and θ is model parameters;

[0076] S01c: Perform statistical analysis on the collected historical climate data and future climate prediction data, including calculating mean, standard deviation, and trend analysis, to identify the statistical characteristics of climate change, and obtain climate trend T,

[0077]

[0078] where C i is the climate variable of the i-th data point, is the mean of the climate variable;

[0079] S01d: Based on the results of statistical analysis, construct a climate change trend model to predict the direction of future climate change, the expression of climate change trend model M c is:

[0080] M c = f(T, δ, α),

[0081] where T is the climate trend, δ is the standard deviation of the climate trend, and α is the confidence level of the climate change trend model.

[0082] In step S01a, the collected historical climate data covers at least the past thirty years, ensuring the representativeness and accuracy of the climate data used, and providing a reliable basis for future climate prediction.

[0083] In step S01b, through climate models and prediction algorithms, the future climate change can be scientifically predicted, providing forward-looking information for ecological restoration.

[0084] In step S01c, through statistical analysis, the statistical characteristics of climate change are identified, providing necessary statistical information for constructing the climate change trend model.

[0085] In step S01d, by constructing the climate change trend model, the direction of future climate change is predicted, which has guiding significance for formulating and adjusting the water flow structure restoration scheme.

[0086] Overall, step S01 provides scientific climate change prediction for the water flow structure repair scheme through data collection, analysis and modeling of the system, which helps to improve the effectiveness and adaptability of the formulated water flow structure repair scheme.

[0087] The step S02 includes the following steps:

[0088] S02a: Conduct comprehensive aquatic biological survey to collect water body information, fish species and population distribution information of the target water area, wherein the water body information includes multiple water quality parameters such as dissolved oxygen, pH value, nitrogen and phosphorus content, water temperature and bottom type, and the standard sampling and analysis method used in the survey process is expressed as follows:

[0089] I ω = {(Q i , M i )|iφ[1, m], Q i , M i},

[0090] wherein I ω is the water body information set, Q i is the i-th sampling point, and M i is the corresponding analysis method;

[0091] S02b: According to the collected fish species and population distribution information, combined with the biological characteristics of fish living habits, breeding habits, diet, and the ecological information of suitable growth water temperature, flow rate and bottom conditions, data integration and preprocessing are carried out to obtain the preprocessed data set D p ,

[0092] D p = {(B j , E j )|j∈[1, k], B j , E j},

[0093] wherein B j is the biological characteristics of the j-th fish, and E j is the corresponding ecological information;

[0094] S02c: Use multivariate statistical analysis method and niche theory to establish fish demand model, and quantify the specific demand of different fish for habitat environment through the model, and the fish demand model M d is expressed as:

[0095]

[0096] wherein W j is the demand weight of the j-th fish, B j and E jare the biological characteristics and ecological information of the jth fish, respectively. Fish demand model M d for predicting and evaluating the response of fish to habitat environmental changes.

[0097] In step S02a, the accuracy and reliability of the data are ensured by using standard sampling and analysis methods, providing a solid data foundation for subsequent analysis.

[0098] In step S02b, according to the collected fish species and population distribution information, combined with the biological characteristics and ecological information of fish, the demand of fish for habitat environment is deeply understood, providing scientific basis for the protection and management of aquatic ecosystem.

[0099] In step S02c, the fish demand model M d quantifies the specific demand of different fish for habitat environment, which helps to predict and evaluate the impact of environmental changes on fish population, and provides decision support for water flow structure restoration scheme. By understanding the demand of fish for environment, water flow structure restoration scheme can be better designed to maintain and improve the health of aquatic ecosystem.

[0100] The step S03 includes the following steps:

[0101] S03a: Select appropriate hydrodynamic simulation software, and the evaluation criteria for software selection are represented as:

[0102] S s ={S i |i∈[1,n],S i =(C i ,A i ,P i )},

[0103] where S s is the software selection set, S i is the ith candidate software, C i is the accuracy of the software, A i is the applicability of the software, and P i is the performance of the software.

[0104] S03b: Input climate change trend data and fish demand model results into the hydrodynamic simulation software, and set simulation parameters: time span, time step and spatial resolution, to form the simulation parameter set P m ,

[0105] P m ={(T s ,T t ,S r )|T s ,T tS r},

[0106] where T s is the simulation start time, T t is the simulation end time, and S r is the spatial resolution;

[0107] S03c: Run the hydrodynamic simulation software to predict the changes in flow velocity, depth, and area expansion and contraction that may occur in the target water area in the future, and then represent the analysis and verification process of the prediction results as:

[0108] R p = {(V f ,D f ,A f )|V f ,D f ,A f},

[0109] where R p is the prediction result set, V f is the flow velocity prediction, D f is the depth prediction, and A f is the area change prediction. The prediction results are analyzed and compared with historical data, and field verification is performed to ensure the accuracy and reliability of the prediction results.

[0110] The hydrodynamic simulation software carefully selected in step S03a accurately simulates the dynamic characteristics of the target water area, providing a scientific basis for the development of fish habitat ecological restoration schemes.

[0111] Reasonably setting the simulation parameters in step S03b helps improve the accuracy and reliability of the simulation results, making the simulation results more realistic.

[0112] In step S03c, the hydrodynamic simulation software is run to predict the changes in flow velocity, depth, and area expansion and contraction that may occur in the target water area in the future, providing important forward-looking information for developing fish habitat flow structure restoration schemes. This helps to plan ahead and take measures to verify the results. By comparing the prediction results with historical data and necessary field verification, the accuracy and reliability of the prediction are ensured, and the effectiveness and practicality of the fish habitat flow structure restoration scheme are improved.

[0113] In step S04, when designing the flow structure restoration scheme, the system engineering method is used to ensure the optimal balance of structural stability, ecological compatibility, and economic benefits.

[0114] By adopting the system engineering method, the stability of the fish habitat water flow structure restoration scheme in the physical structure can be ensured, the restoration measures can be prevented from being washed away by the flow or other natural factors, and the restoration scheme can be ensured to improve the water flow structure without causing negative impacts on the water ecosystem and promoting the recovery and improvement of biodiversity, while ensuring that the restoration scheme is optimal in terms of cost-effectiveness, i.e. achieving the expected ecological restoration effect with the least economic investment. The system engineering method can consider multiple factors and achieve comprehensive optimization, such as the interaction of structure, ecology and economy, so as to design a restoration scheme with the best comprehensive effect, and through systematic analysis and evaluation, the risk can be reduced and the success rate of the implementation of the restoration scheme can be improved. The system engineering method helps to rationally allocate and use resources, avoid waste of resources, improve resource utilization efficiency, and the designed restoration scheme not only focuses on short-term effect but also considers long-term sustainability to ensure the durability of the restoration effect. The restoration scheme designed by the system engineering method is more adaptable and can better cope with future environmental changes and challenges.

[0115] In the step S05, when the water flow structure restoration scheme is visually displayed, a virtual reality technology is combined to create an interactive simulation environment, which facilitates decision-makers to intuitively evaluate the expected effect of the design scheme from multiple angles.

[0116] Through the virtual reality technology, decision-makers can intuitively view and evaluate the effect of the water flow structure restoration scheme in a three-dimensional virtual environment instead of relying only on flat drawings or text descriptions. At the same time, the virtual reality technology allows users to observe the restoration scheme from different angles and scales, which helps to find potential problems or deficiencies in the design. Specifically, decision-makers can understand the details of the water flow structure restoration design scheme more deeply through interactive operations such as moving, rotating and zooming, so as to make more accurate evaluation. Through the intuitive three-dimensional display, decision-makers can understand and evaluate the design scheme more quickly, improve the speed and efficiency of decision-making, and the immersive experience provided by the virtual reality technology can help decision-makers understand the expected effect of the scheme more comprehensively and enhance their confidence in the implementation of the scheme. At the same time, the interactive virtual reality environment can serve as a communication tool to help team members or stakeholders with different backgrounds better understand and discuss the design scheme. Before actual construction, the simulation and evaluation of the scheme through the virtual reality technology can reduce the rework and modification caused by improper design, thereby reducing project risks. If necessary, this interactive display can also be used for public participation to help the public better understand the project, collect feedback and improve the public acceptance of the project.

[0117] In the step S06, when the water flow structure restoration scheme is piloted in the target water area, a control experiment design is adopted to facilitate the evaluation of the impact of the water flow structure restoration scheme on the ecosystem.

[0118] Through the control experiment design, the ecological changes before and after the implementation of the water flow structure restoration scheme can be systematically compared, and the differences between the control areas without the water flow structure restoration scheme can be compared, so as to scientifically evaluate the effect of the restoration measures, and the control experiment can help to verify the theoretical hypothesis of the restoration measures, that is, whether the restoration measures can achieve the expected ecological restoration effect; through the experimental design, the data can be more targetedly collected, which helps to understand the action mechanism of the restoration measures and the response of the ecological system, wherein the successful experience of the pilot project can provide reference for the ecological restoration projects in a larger range, promote the effective promotion and application of the restoration measures; through the control experiment, the negative effects that may be brought by the restoration measures can be found in time, so as to take measures to reduce the risk, and the experimental results can accumulate valuable data and cases for the scientific research in the field of ecological restoration, promote the progress of scientific knowledge, scientific experimental design and objective evaluation of results can increase the credibility of the restoration project, win the support of the public and stakeholders, and the experimental results can provide scientific basis for the relevant policy making, help to form evidence-based policy making.

[0119] A comprehensive monitoring system is established in step S07 to continuously monitor water quality parameters and biodiversity indicators.

[0120] By monitoring multiple water quality parameters and biodiversity indicators, the influence of the water flow structure restoration scheme on the water environment and biodiversity can be comprehensively evaluated to ensure the comprehensiveness and accuracy of the evaluation, and continuous monitoring helps to track the progress of the restoration project and understand the restoration status of the ecological system in time, and the comprehensive monitoring system can provide early warning, so that once the water quality or biodiversity indicators are abnormal, timely measures can be taken. In addition, the comprehensive monitoring system provides a scientific basis for the management of the restoration project, which helps to develop and adjust the management strategy, and through continuous monitoring, the restoration project can achieve the expected effect, and problems that may arise can be found and solved in time; the monitoring data can help managers understand the actual effect of the restoration measures, so as to optimize the management strategy and restoration scheme, and monitoring biodiversity indicators helps to protect and manage key species in the ecological system and promote the restoration of biodiversity. The comprehensive monitoring system also helps to establish a long-term ecological protection mechanism to ensure the durability of the restoration effect, and combined with the monitoring results, resources and funds can be more reasonably allocated to improve the cost-effectiveness of the restoration project.

[0121] In the process of water flow structure restoration, a decision support system is developed to provide dynamic adjustment suggestions for water flow structure restoration based on monitoring data and evaluation results.

[0122] By analyzing real-time monitoring data, the decision support system can provide immediate decision support for managers to quickly respond to environmental changes, where the decision support system can optimize the restoration plan according to the actual response of the monitored ecosystem, making it more in line with the actual situation; if unpredictable environmental changes occur during the restoration process, the decision support system helps to improve the adaptability of the restoration plan; in addition, by analyzing monitoring data, the system can identify potential risks and propose prevention or mitigation measures, and the system can also help managers allocate human, material and financial resources reasonably according to the restoration effect and resource utilization efficiency.

[0123] At different stages of the flow structure restoration, corresponding emergency plans are developed to deal with possible extreme weather events or unexpected situations, ensuring the smooth implementation of the flow structure restoration plan.

[0124] By developing emergency plans in advance, risks that may be encountered during the restoration project can be identified and prevented, including extreme weather events and unexpected situations, thereby reducing the impact of these risks on the project. In the event of extreme weather events or unexpected situations, emergency plans provide guidelines and steps for quick response, ensuring that effective measures can be taken in a timely manner to mitigate the impact of the event, and ensuring that all personnel involved in the restoration project can quickly evacuate to a safe area in the event of an emergency, maximizing personnel safety. By taking predetermined emergency measures, the loss of restoration project equipment and materials can be minimized; in addition, emergency plans help to quickly resume normal work after extreme events, ensuring that the progress of the restoration project will not be significantly delayed due to unexpected events, and are beneficial to prevent secondary environmental pollution caused by extreme weather events or unexpected situations, protect the ecological environment, ensure that the restoration project can be carried out as planned, avoid investment losses due to unexpected events, and protect investment returns.

[0125] Overall, the fish habitat flow structure restoration method provided by the present application has the following technical effects:

[0126] By collecting key climate parameters such as air temperature, precipitation, evaporation and wind speed, and combining climate change trend models, the basic outline of climate change can be depicted, providing important background information for subsequent flow structure restoration plan design and ecological restoration.

[0127] By analyzing water body information, fish species and population distribution information, and combining biological and ecological information of fish, the specific needs of fish for habitat environment can be mastered, and the fish demand model is crucial for predicting the impact of climate change on fish populations.

[0128] By combining climate change trends and fish demand models, water flow structure restoration schemes can be designed using hydrodynamic simulation software to predict future changes in water flow velocity, depth, and area. This prediction provides a scientific basis for designing water flow structure restoration schemes. Specifically, by analyzing climate change trends, we can understand the future trends of water climate change and ensure that the water flow structure restoration scheme is adapted to actual climate change. By establishing a fish demand model, we can understand the ecological needs of fish and ensure that the water flow structure restoration scheme meets the needs of fish survival and reproduction. By combining hydrodynamic simulation, we can predict future changes in the water environment, making the water flow structure restoration scheme more forward-looking and scientific.

[0129] The water flow structure restoration scheme designed based on simulation results can provide a specific and feasible restoration scheme that can adapt to climate change and help restore and enhance the ecological function of the water area.

[0130] After selecting a range within the target water area to carry out a pilot project and monitoring the impact of the water flow structure restoration scheme on the local ecosystem, the scheme is adjusted based on feedback to ensure the scientificity and effectiveness of the final scheme.

[0131] After the water flow structure restoration scheme is fully implemented in the target water area, water quality, biodiversity, and fish population are continuously monitored to evaluate the restoration effect and make optimization adjustments based on actual conditions. Through continuous monitoring and adjustment, the long-term effect of the restoration measures and the overall health of the ecosystem are improved.

Claims

1. A method of fish habitat flow structure remediation, the method comprising: The method comprises the following steps: S01: analyzing the climate change trend of the target water area based on historical climate data and future climate prediction data of the target water area; S02: establishing a fish demand model based on a plurality of water biological survey information including water body information, fish species and population distribution information of the target water area, and combining fish biological characteristics and ecological information; S03: predicting the flow rate change, depth change, and area expansion and reduction that may occur in the target water area at different time points in the future by combining the climate change trend and the results of the fish demand model, and using hydrodynamic simulation software; S04: designing a self-adjusting water flow structure repair scheme including adding artificial reefs, adjusting the river bed shape, adjusting the river direction and width, and the like, according to the simulation results of step S03; S05: visualizing the water flow structure repair scheme by using three-dimensional modeling technology and virtual reality technology, so as to facilitate decision makers to intuitively understand the design scheme and its expected effect; S06: carrying out a pilot of the water flow structure repair scheme in the target water area, monitoring its influence on the local ecosystem, especially the fish population, and then adjusting and improving the water flow structure repair scheme; S07: after the water flow structure repair scheme is implemented in the target water area, continuously monitoring the water quality parameters, biodiversity indicators and fish quantity change, evaluating the effect of the repair measures, and adjusting and optimizing the water flow structure repair scheme according to the evaluation results.

2. The fish habitat flow structure rehabilitation method of claim 1, wherein: The step S01 comprises the following steps: S01a: collect historical climate data of the target water area, the historical climate data including multiple data such as air temperature, precipitation, evaporation amount and wind speed, a time span covering at least the past thirty years, and forming a historical climate data set D h , D h = {C i | i e [1, n], C i = (T i , P i , E i , W i )}, where C i is the ith data point, T i is the air temperature, P i is the precipitation, E i is the evaporation, and W i is the wind speed. S01b: generating a future climate prediction dataset D for the target water body based on historical climate data, using climate models and prediction algorithms f , D f = M(C h , θ), where C h is historical climate data, and θ are model parameters. S01c: statistically analyzing the collected historical climate data and future climate prediction data to obtain a climate trend T, where C i is the climate variable for the ith data point, is the mean of the climate variable; S01d: based on the statistical analysis result, a climate change trend model is constructed to predict the trend of future climate change, the climate change trend model M c The expression is: M c = f(T, δ, a), Wherein, T is the climate trend, δ is the standard deviation of the climate trend, and α is the confidence level of the climate change trend model.

3. The fish habitat flow structure rehabilitation method of claim 1, wherein: The step S02 comprises the following steps: S02a: carrying out comprehensive water biological survey to collect water body information, fish species and population distribution information of the target water area, wherein the water body information includes a plurality of water quality parameters such as dissolved oxygen, pH value, nitrogen and phosphorus content, water temperature and bottom type, and the standard sampling and analysis method used in the survey process is expressed as follows: I ω = {(Q i , M i )|i∈[1, m], Q i , M i}, wherein I ω is the set of water body information, Q i is the i-th sampling point, M i is the corresponding analysis method; S02b: According to the collected fish species and population distribution information, combined with the biological characteristics of fish living habits, breeding habits, diet, and ecological information of suitable growth water temperature, flow rate, and bottom conditions, data integration and preprocessing are performed to obtain a preprocessed data set D p , D p = {(B j , E j )|j e [1, k], B j , E j}, wherein B j is a biological characteristic of the jth fish, E j is the corresponding ecological information; S02c: using multivariate statistical analysis method and niche theory, establishing fish demand model, quantifying specific demands of different fish on habitat environment through the model, fish demand model M d is represented as: where W j is the demand weight of the jth fish, B j and E j are the biological characteristics and ecological information of the jth fish, respectively.

4. The fish habitat flow structure rehabilitation method of claim 1, wherein: The step S03 comprises the following steps: S03a: selecting appropriate hydrodynamic simulation software, and the evaluation standard of software selection is expressed as: S s = {S i | i ∈ [1, n], S i = (C i , A i , P i )}, where S s is a set of software selections, S i is the i-th candidate software, C i is the accuracy of the software, A i is the applicability of the software, P i is the performance of the software; S03b: input climate change trend data and fish demand model results into the hydrodynamic simulation software and set simulation parameters: time span, time step and spatial resolution, form a simulation parameter set P m , P m = {(T s , T t , S r ) | T s , T t , S r}, where T s is the simulation start time, T t is the simulation end time, S r is the spatial resolution; S03c: running the hydrodynamic simulation software to predict the flow rate change, depth change, and area expansion and reduction that may occur in the target water area in the future, and then the analysis and verification process of the prediction results is expressed as: R p = {(V f ,D f ,A f )|V f ,D f ,A f}, where R p is the set of prediction results, V f is the flow rate prediction, D f is the depth prediction, and A f is the area change prediction. The prediction results are analyzed and compared with historical data, and verified on site to ensure the accuracy and reliability of the prediction results.

5. The fish habitat flow structure rehabilitation method of claim 1, wherein: In the step S04, when designing the water flow structure repair scheme, a system engineering method is used to ensure that the scheme is optimally balanced in terms of structural stability, ecological compatibility and economic benefit.

6. The fish habitat flow structure rehabilitation method of claim 1, wherein: In the step S05, when visualizing the water flow structure repair scheme, an interactive simulation environment is created by combining virtual reality technology, so as to facilitate decision makers to intuitively evaluate the expected effect of the design scheme from multiple angles.

7. The fish habitat flow structure rehabilitation method of claim 1, wherein: In the step S06, when carrying out the pilot of the water flow structure repair scheme in the target water area, a control experiment design is used to facilitate the evaluation of the influence of the water flow structure repair scheme on the ecosystem.

8. The fish habitat flow structure rehabilitation method of claim 1, wherein: A comprehensive monitoring system is established in step S07 to continuously monitor water quality parameters and biodiversity indicators.

9. The fish habitat flow structure rehabilitation method of claim 1, wherein: During the restoration of the water flow structure, a decision support system is developed to provide dynamic adjustment recommendations for the restoration based on monitoring data and evaluation results.

10. The fish habitat flow structure rehabilitation method of claim 1, wherein: At different stages of the restoration of the water flow structure, corresponding contingency plans are developed to deal with possible extreme weather events or unexpected situations, ensuring the smooth implementation of the restoration plan.

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