Fish migration path prediction and fishway optimization method based on big data

Abstract

The invention discloses a fish migration path prediction and fishway optimization method based on big data, and is suitable for the field of hydroelectric power station ecological protection. According to the method, a heterogeneous sensor network is constructed, fish activity tracks and environment parameters are collected in a fused mode, and high-dimensional feature modeling and fuzzy clustering are utilized to achieve unified expression of multi-source heterogeneous data. Furthermore, in combination with coupling analysis of fish physiological migration driving force and environmental factors, migration paths and important ecological channels are intelligently identified, and multi-objective optimization is performed on the fishway structure based on an evolutionary learning and simulation mechanism. Through dynamic ecological feedback, actual observation data and model prediction are continuously fused and corrected, and accurate prediction of fish migration behaviors and scientific optimization of a fishway structure are realized. The fishway passing rate and ecological protection benefits are effectively increased, and technical support is provided for sustainable operation of a hydroelectric engineering ecological system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of ecological protection of water power station, more particularly to a fish migration path prediction and fishway optimization method based on big data. BACKGROUND

[0002] With the development of social economy and the continuous growth of energy demand, water power station projects have been widely constructed and applied in China and even globally. Water power stations play an important role in providing clean and renewable energy, regulating water resource utilization, and flood control and irrigation. However, the construction of large-scale water conservancy hubs has greatly changed the hydrological conditions and ecological environment of natural rivers, especially the survival and reproduction of fish and other aquatic organisms. Due to the blocking effect of dam bodies and hydraulic structures, fish migration channels are cut off, and many fish populations that rely on migration to complete their life cycle are facing the serious situation of migration obstruction, population decline, and even endangerment and extinction, which further affects the stability of river ecosystems and biodiversity.

[0003] In order to alleviate the impact of hydraulic structures on fish migration, fishways (fishway facilities) are generally used to provide artificial channels for fish to help them overcome engineering obstacles and realize upstream and downstream migration. However, traditional fishway design relies on empirical formulas and field tests, lacking systematic consideration of the behavior characteristics of different fish populations and complex environmental factors, resulting in low fishway passage rate and difficult to guarantee ecological benefits. In addition, with the development of sensors, telemetry and monitoring technology, fish activity trajectory and habitat change data are increasingly rich, but how to effectively integrate multi-source data, accurately simulate and predict fish migration paths, and scientifically optimize fishway structure, is still a technical problem that needs to be solved in the field of water conservancy and hydropower engineering and ecological protection.

[0004] Therefore, combined with advanced technologies such as big data fusion, intelligent analysis and dynamic simulation, aiming at the key problems of fish migration behavior and fishway optimization, the development of efficient and intelligent fish migration path prediction and fishway optimization method will provide strong technical support for improving the ecological friendliness and sustainable development level of water power station projects. SUMMARY

[0005] The present application aims to solve the problem of low fish migration channel passage rate and poor ecological benefits in existing water power station projects due to the lack of precise modeling, prediction and dynamic feedback mechanism of fish diversity migration behavior and complex environmental factors in fishway design, and proposes a fish migration path prediction and fishway optimization method that integrates multi-source big data, intelligent analysis and dynamic ecological feedback, to realize accurate identification of fish migration path, scientific optimization of fishway structure and continuous improvement of ecological protection effect.

[0006] In order to achieve the above object, the present application is realized by adopting the following technical scheme: the method comprises: Intelligent fusion collection of multi-source fish migration data, collecting activity trajectory data and environmental parameter data of fish population in target water area, and performing data preprocessing; Fish migration path potential distribution identification, generating moving trajectory point set of fish in different environmental factors, and establishing coupling factor function between physiological migration driving force and environmental change in fish migration process; Intelligent extraction of migration path cluster, capturing multi-scale and obviously changing typical fish migration path cluster, performing multi-time scale analysis on the cluster path, and outputting important migration channels and nodes; Fishway optimization joint simulation decision, establishing multi-element dynamic connection between fishway structure parameters, external environment parameters and migration efficiency, and linking fishway structure design space and migration behavior simulation results; Dynamic ecological feedback and continuous optimization, realizing dynamic self-adaptive correction of model prediction and actual observation.

[0007] In one scheme, the data preprocessing comprises: building a heterogeneous sensor network comprising acoustic tags, video monitoring and water quality sensors and the like multi-type terminals, realizing continuous real-time collection of fish population activity trajectory and environmental parameters in the target water area; Setting the data set collected by each sensor network at each time; fuzzy topology clustering, high-dimensional similarity modeling of all observation points, that is, constructing a fuzzy similarity matrix; Then, based on the self-generated feature space mapping, project different source data into a unified high-dimensional feature space through a feature embedding function; Through alternating optimization, finally express all multi-source data of heterogeneous, asynchronous and different accuracy as a unified high-dimensional feature vector set.

[0008] In one scheme, the fish migration path potential distribution identification specifically comprises: Generating moving trajectory point set of fish in different environmental factors by using the fused data, gradually restoring the moving trajectory point set of fish individuals or groups in time sequence according to the high-dimensional fish behavior and environmental feature data set which has been fused, time-space aligned and standardized; Introducing a "migration driving force-environment reaction depth inference model (MDEF-DR Model)": establishing coupling factor function between physiological migration driving force (reproduction, foraging) and environmental change (water flow, temperature) in fish migration process, and dynamically predicting potential migration path distribution probability of fish population under environmental change.

[0009] In one scheme, the intelligent extraction of migration path cluster specifically comprises: An adaptive spatio-temporal density fluctuation cluster algorithm (ASTC Algorithm) is proposed, which integrates a dynamic probability density weighting mechanism and adaptively adjusts the clustering "perception radius" to capture multi-scale and significantly changing typical fish migration path clusters. The cluster path is analyzed in multiple time scales, and the important migration channels and nodes are output, first, the cluster results are grouped according to different time windows (day, week, season) to form a time series cluster set. For each time scale, the spatial connectivity index of the points in the cluster is calculated, and the importance measurement function is used to normalize the density weight of the cluster where the node is located; a set of main migration channel backbones and ecological key nodes with characteristic significance and spatio-temporal stability are output.

[0010] In one scheme, the fishway optimization joint simulation decision includes: First, a multi-element dynamic connection model between the fishway structure parameters, external environment parameters and fish migration efficiency is established, the fishway structure is abstracted as a parameter set, representing the fishway length, width, slope and water level difference design variables, and external environment variables such as flow, temperature, water depth and water speed are collected; Based on the important migration channels and nodes, an evaluation function is defined to represent the passing efficiency of fish under specific structure and environment conditions, and by combining historical data, physical modeling and fish migration behavior simulation, a high-dimensional nonlinear response surface model is established to jointly fit the functional relationship between structure and environment conditions and individual or group passing probability. A fishway-migration behavior feedback adaptive optimization algorithm is proposed, which takes structure parameters and simulation environment as double inputs, and aims to maximize fish passing rate and improve ecological diversity index as optimization objectives. In the optimization process, a multi-objective evolutionary learning mechanism is used - for each generation of structure parameter candidates, the fish migration intelligent simulation module is automatically called to dynamically simulate the behavior distribution and passing situation of the fish group under the current structure and environment.

[0011] In one scheme, the dynamic ecological feedback and continuous optimization includes: The deviation between the actual new migration path and the passing efficiency collected and the model prediction is calculated; Synchronously introduce an adaptive correction algorithm to dynamically integrate the new observation data into the original model with a weight, and use the weighted least squares method to quickly fine-tune the network parameters.

[0012] The present application has the following advantages: 1. Efficient fusion of multi-source heterogeneous data: by establishing a heterogeneous sensor network composed of multiple types of terminals such as acoustic tags, video surveillance and water quality sensors, the fish migration activities and environmental parameters are continuously, real-time and accurately collected, greatly improving the comprehensiveness and timeliness of the data.

[0013] 2. High-precision modeling and prediction of fish migration paths: Based on the fused high-dimensional data, the coupling relationship between different fish populations and various environmental factors can accurately identify the potential distribution of fish migration paths and typical migration channels, achieving scientific prediction of fish migration behavior in complex ecological systems.

[0014] 3. Scientific optimization of fishway structure: Through the multi-element dynamic correlation analysis of fishway structure parameters, external environment and fish migration efficiency, combined with intelligent simulation and multi-objective optimization algorithm, the rationality and passage rate of fishway design are improved, and the adaptability to different fish populations and environmental changes is enhanced.

[0015] 4. Dynamic improvement of ecological protection benefits: Introducing a dynamic ecological feedback mechanism, through adaptive model correction, the continuous optimization and adjustment between the prediction model and the actual observation are realized, effectively protecting and improving the ecological diversity of fish populations and the overall health of aquatic ecosystems.

[0016] Overall, the present application can significantly improve the scientificity and ecological friendliness of fishway design for hydropower stations, providing innovative technical means for protecting the sustainable development of fish populations and the stable operation of aquatic ecosystems. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is the method flowchart of the present application; Figure 2 is the fishway optimization joint simulation decision flowchart of the present application. DETAILED DESCRIPTION

[0018] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. The drawings show typical embodiments of the present application. However, the present application can be implemented in many different forms, and is not limited to the embodiments described in the present application. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0019] Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used in the present application in the specification of the present application are only for the purpose of describing the specific embodiments of the present application, and are not intended to limit the present application. In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. The drawings show typical embodiments of the present application. However, the present application can be implemented in many different forms, and is not limited to the embodiments described in the present application. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0020] As Figure 1As shown, a fish migration path prediction and fishway optimization method based on big data. The specific steps are as follows: Step 1: Intelligent fusion and collection of multi-source fish migration data Build a multi-type sensor network (such as acoustic tags, video monitoring, water quality sensors, etc.), and collect real-time fish population activity trajectory data and environmental parameter data in the target water area.

[0021] According to the geographical environment and fish activity distribution of the target water area, scientifically plan the layout points and layout density of the sensors. Select different types of sensor devices, including acoustic tags for tracking fish movement, underwater video monitoring nodes for collecting fish group images, and water quality sensors for monitoring flow rate, temperature, dissolved oxygen, and other water environment information. According to the body length and activity ability of fish, acoustic tags are installed on representative fish bodies. Each tagged individual will emit acoustic signals carrying position information in real time during activity, and the acoustic receiving array arranged in the water body will receive the signals in real time, realizing the accurate collection of fish individual trajectory.

[0022] At the same time, high-resolution underwater video monitoring devices are deployed at key nodes of the waterway, flow convergence area and fishway outlet, etc. Real-time capture and record fish group movement trajectory and behavior characteristics are realized by using intelligent edge recognition technology. Video data is preliminarily processed by local computing unit to extract fish group appearance position, speed, quantity and other information, reducing the pressure of back-end data processing. For the collection of environmental data, multiple groups of water quality sensors are arranged at different depth layers and flow rate gradient intervals to collect flow rate, water temperature, dissolved oxygen content, turbidity and other parameters at regular intervals, and synchronously transmit them to the central data management platform.

[0023] All sensor nodes are integrated into wireless or wired networking system to ensure real-time and reliability of data collection, and the clock is uniformly calibrated through distributed data synchronization protocol to solve the asynchrony problem of multi-source data. The entire collection network has automatic fault monitoring and remote maintenance capability, and can dynamically adjust the working parameters of part of the sensors according to the environmental changes. The multi-type sensor network constructed in this way not only can comprehensively cover the key path of fish migration, but also lays a solid foundation of original data for subsequent data fusion and behavior analysis.

[0024] In the intelligent fusion and collection stage of multi-source fish migration data, first, a heterogeneous sensor network including acoustic tags, video monitoring and water quality sensors is built to realize continuous and real-time collection of fish population activity trajectory and environmental parameters in the target water area. Among them, the data set collected by the sensor network at each time t is set as , wherein represents the positioning data of the i-th acoustic tag, represents the trajectory data extracted by the j-th video monitoring node, The environmental parameter data monitored by the kth water quality sensor. Data with different space-time, different accuracy and sampling frequency face problems such as asynchrony, noise and information redundancy.

[0025] The "multi-source heterogeneous biological behavior data adaptive mapping fusion algorithm" is proposed: based on fuzzy topological clustering and self-generated feature space, the multi-source data with different space-time and accuracy are uniformly expressed in high dimension, and the dynamic fusion of cross-type data is realized, providing a consistent data basis for subsequent analysis.

[0026] The core idea is to first model the high-dimensional similarity of all observation points through fuzzy topological clustering, that is, to construct a fuzzy similarity matrix F, where the matrix element represents the similarity of the mth and nth data points in the multi-feature space. Then, based on the self-generated feature space mapping, different source data is projected into a unified high-dimensional feature space , that is . In the mapping process, the space-time alignment factor and the accuracy confidence weight are introduced, and the overall mapping optimization objective function is: where is the confidence weight of the data source src(x), the first term realizes the spatial similarity of fuzzy topological clustering, and the second term is the space-time alignment loss, is the adjustment coefficient. Through alternating optimization, all heterogeneous, asynchronous and different accuracy multi-source data are finally fused and expressed as a set of unified high-dimensional feature vectors , providing a highly consistent and rich data basis for subsequent analysis of fish migration path distribution patterns and prediction.

[0027] Step 2: Fish migration path potential distribution identification Step 201: Use the fused data to generate the fish movement trajectory point set in different environmental factors.

[0028] In step 201, first, for the high-dimensional fish behavior and environmental feature data set fused, space-time aligned and standardized in step 1, the fish individual or group movement trajectory point set is gradually restored in time sequence. The specific method is to take the spatial position data of each fish at time t as the index, combine the environmental parameters (such as flow rate, water temperature, dissolved oxygen content, etc.) at this time and adjacent time, and combine the position of each fish at each time with the environmental features ​The trajectory points of the same fish or fish group at consecutive time points are connected to form an informationized migration path with environmental context. During this process, interpolation is needed according to the missing points in the data to complete the trajectory using a multivariate interpolation method to ensure continuity and accuracy. Finally, the obtained result is a set of fish movement trajectory points with multiple sources, multiple dimensions and environmental factors, providing detailed basic data for subsequent potential distribution identification.

[0029] Step 202: Introduce the "Migration Driving Force-Environmental Reaction Depth Inference Model (MDEF-DR Model)": Establish a coupling factor function between the physiological migration driving force (such as reproduction, foraging, etc.) and environmental changes (such as water flow, temperature) during fish migration, and dynamically predict the potential migration path distribution probability of fish groups under environmental changes through end-to-end deep representation learning.

[0030] The "Migration Driving Force-Environmental Reaction Depth Inference Model" (MDEF-DR Model) is proposed to intelligently and dynamically predict the potential path distribution probability of fish migration. The core idea of the model is to first establish a coupling factor function between the physiological needs of fish migration (such as reproduction, foraging, etc.) and external environmental factors (such as water flow, water temperature). Define the physiological driving force vector as , whose components can be described by the state of fish individuals (such as maturity, energy demand); the environmental driving force vector is , whose components correspond to the spatial gradient of environmental characteristics (flow rate, water temperature, etc.). The coupling factor function is denoted as , which describes the joint influence of physiology and environmental factors on migration direction and speed, and its mathematical expression can be where is the learnable weight parameter, represents the element-level product.

[0031] Based on the above coupling factor, an end-to-end deep representation learning network is constructed, which takes historical trajectory points and the current physiological and environmental state as input, and automatically learns the decision rules and spatio-temporal dependence structure hidden in the migration behavior through multiple layers of nonlinear mapping. The output of the network is the potential migration probability distribution at each location on the spatial grid G, denoted as . Network training is performed by minimizing the KL divergence loss function between the predicted distribution and the true trajectory distribution. In this way, the model realizes high-precision and dynamic probability prediction of the migration path of fish groups in complex dynamic environments, greatly improving the scientificity and intelligence level of potential distribution identification of fish migration.

[0032] Step 3: Intelligent extraction of migration path clusters Step 301: Based on the path distribution probability of the previous step, the "adaptive spatio-temporal density fluctuation cluster algorithm (ASTC Algorithm)" is proposed: this algorithm integrates a dynamic probability density weighting mechanism and adaptively adjusts the clustering "perception radius", effectively capturing multi-scale and significantly changing typical fish migration path clusters, avoiding the limitations of existing static parameter models such as DBSCAN.

[0033] For the fish migration path spatial probability distribution obtained in the previous step, the "adaptive spatio-temporal density fluctuation cluster algorithm (ASTC Algorithm)" is proposed. This algorithm breaks through the limitations of traditional density clustering (such as DBSCAN) relying on fixed perception radius and static density threshold, and can dynamically adapt to the multi-scale distribution characteristics of migration paths in space and time. Specifically, first, a space-time grid is constructed for each grid center point to calculate its probability density , which is derived from the migration path probability distribution of step 2. Further, according to the local variance of the density field and the density gradient of the surrounding points, the adaptive perception radius is defined, where is the base radius, is the adjustment coefficient, is the average density within the neighborhood. Subsequently, for each point, the dynamic weighted neighborhood density is calculated within its adaptive radius, and the weight is related to the distance and density gradient, giving priority to highlighting "density steep change" areas. The clustering core points are automatically distinguished by the density threshold, and multiple high-density core points naturally form clusters. Finally, under the guidance of adaptive density and radius, different scales and time-varying typical migration path clusters are accurately captured, effectively avoiding the problem of cluster adhesion or splitting caused by static parameter setting.

[0034] Step 302: Multi-time scale analysis of cluster paths, output important migration channels and nodes.

[0035] Multi-time scale analysis is performed on the extracted migration path clusters to identify important channels and key nodes in the migration process. First, the cluster results are grouped according to different time windows (such as day, week, season, etc.) to form a time series cluster set . For each time scale , the spatial connectivity index of the points within the cluster is calculated , where is the number of effective migration paths within the cluster, is the number of cluster nodes. At the same time, the importance measure function is used, where is the normalized density weight of the node in the cluster.The spatial nodes that play a key guiding role at different scales are screened out. Finally, a set of main migratory channel backbones and ecological key nodes with significant characteristics and stable space-time are output, providing scientific basis for subsequent fishway optimization design and ecological protection decision-making.

[0036] As shown in Figure 2 Step 4: Fishway optimization joint simulation decision Step 401: Establish the multi-element dynamic relationship between fishway structure parameters, external environment parameters and migration efficiency.

[0037] In step 401, first, a multi-element dynamic relationship model between fishway structure parameters, external environment parameters and fish migration efficiency is constructed. Specifically, the fishway structure is abstracted as a parameter set , representing fishway length, width, slope, water level difference, etc. design variables, while collecting external environment variables such as flow, temperature, water depth, water speed, etc. Based on the important migration channels and nodes output in the previous stage, the evaluation function is defined, which represents the passing efficiency of fish under specific structure and environment conditions. Through historical data, physical modeling and fish migration behavior simulation, the function relationship between structure and environment conditions and individual or group passing probability is jointly fitted to establish a high-dimensional nonlinear response surface model. This model supports fast prediction of the dynamic changes of fish migration efficiency under any fishway structure and environment combination, and serves as the fitness evaluation core of the subsequent optimization algorithm.

[0038] Step 402: Propose the "fishway-migration behavior feedback adaptive optimization algorithm (FMPA Algorithm)": link the fishway structure design space and migration behavior simulation results, and through multi-objective evolutionary learning and real-time environment feedback adjustment mechanism, iteratively optimize fishway parameters to maximize fish passing rate and consider ecological diversity.

[0039] In step 402, the "fishway-migration behavior feedback adaptive optimization algorithm" (FMPA Algorithm) is innovatively proposed to realize deep coupling optimization of fishway structure design and fish behavior simulation. The algorithm takes structure parameters and simulation environment as double inputs, and aims to maximize fish passing rate and improve ecological diversity index as optimization objectives. The specific optimization objective function can be written as , where is the weight coefficient. In the optimization process, a multi-objective evolutionary learning mechanism is adopted. For each generation of structural parameter candidates, the fish migration intelligent simulation module is automatically called to dynamically simulate the behavior distribution and passage of fish in the current structure and environment. The simulation feedback results update the fitness evaluation in real time, and the evolutionary algorithm adjusts the parameters according to the fitness and environmental state changes to form a closed-loop optimization link of structure-behavior-environment. To avoid falling into local optimum, the algorithm also introduces a diversity preservation strategy based on genetic mutation and behavior distribution disturbance to ensure comprehensive exploration of the design space.

[0040] Step 403: Virtual simulation verification of optimization results to form a fish pass design recommendation.

[0041] In step 403, the candidate fish pass design scheme generated by optimization is input into a high-precision virtual simulation platform, combining real terrain, hydrodynamic force, fish population, and other multi-factors to carry out multi-scenario behavior response simulation. By comparing the passage rate curve, migration path distribution, and ecological diversity response of fish in the simulation, statistical evaluation indicators such as significance test and robustness analysis are used to comprehensively evaluate the effectiveness and adaptability of each design scheme. Finally, the optimal fish pass structure with high passage rate, optimal ecological benefit, and adaptability to complex environmental changes is selected to form an intelligent fish pass design recommendation scheme for engineering practice, and to provide simulation scenarios and data support for subsequent actual deployment and ecological monitoring.

[0042] Step 5: Dynamic ecological feedback and continuous optimization Step 501: After the deployment of the fish pass is optimized, fish behavior and passage data are continuously collected.

[0043] After the fish pass structure is optimized and simulation verified, an intelligent multi-source sensing network is deployed in the optimized fish pass and key areas upstream and downstream. The new round of data collection system covers acoustic tags, video surveillance, water quality sensors, and other sensors, continuously and seamlessly collecting real-time fish migration behavior data and fish pass passage rate. Various sensor nodes are arranged according to the optimized key path and ecological nodes, not only collecting real-time activity trajectories, population numbers, and behavior patterns of fish, but also monitoring water flow, water temperature, dissolved oxygen, and other environmental variables, ensuring the temporal and spatial continuity and ecological relevance of the data. The collected data is aggregated through a distributed platform to build a complex long-term ecological behavior-environment big data, providing high-value data support for subsequent feedback mechanisms and model retraining.

[0044] Step 502: Introduce an "ecological feedback closed-loop self-learning mechanism": the deviation between the actual observed new migration path and the model prediction is integrated into the adaptive correction algorithm (using dynamic trust weight adjustment and fast model migration fusion), continuously optimizing the prediction and design scheme to form a long-term sustainable optimization capability.

[0045] An "ecological feedback closed-loop self-learning mechanism" is introduced to realize dynamic adaptive correction of model prediction and actual observation. First, the deviation between the actual new migration path and the model prediction is calculated wherein is the predicted path distribution of the current intelligent model, is the real-time observed path. According to the deviation size and time series performance, a dynamic trust weight is set is an adjustment factor used to describe the adaptation speed of the model to new observations. A self-adaptive correction algorithm is introduced simultaneously to dynamically integrate the new observation data into the original model with a weight and quickly fine-tune the network parameters in a weighted minimum loss manner In addition, the idea of transfer learning is used to realize the rapid migration and integration of new and old knowledge in the model parameter space, improving the model's response ability to new spatio-temporal ecological changes. The entire closed loop can maintain "observation-evaluation-self-learning-reoptimization-actual-observation" continuous iteration, through real-time feedback and adaptive model adjustment, constantly optimizing the scientificity, stability and forward-looking of fishway design and behavior prediction, realizing dynamic ecological management and long-term sustainable optimization ability.

[0046] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.

[0047] It should be understood that the above detailed description of the technical solutions of the present application by means of preferred embodiments is illustrative rather than limiting. Those of ordinary skill in the art can modify the technical solutions recorded in the embodiments or make equivalent substitutions for part of the technical features on the basis of the present application; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A big data-based fish migration path prediction and fishway optimization method, characterized in that: The method comprises: Intelligent fusion of multi-source fish migration data, collecting the activity trajectory data and environmental parameter data of fish populations in the target water area, and performing data preprocessing; Fish migration path potential distribution identification, generating fish moving trajectory point sets in different environmental factors, and establishing a coupling factor function between the physiological migration driving force and environmental changes in the fish migration process; Intelligent extraction of migration path clusters, capturing multi-scale and obviously changing typical fish migration path clusters, performing multi-time scale analysis on the cluster paths, and outputting important migration channels and nodes; Fishway optimization joint simulation decision, establishing a multi-element dynamic relationship between fishway structure parameters, external environment parameters and migration efficiency, and linking the fishway structure design space and the migration behavior simulation results; Dynamic ecological feedback and continuous optimization, realizing dynamic self-adaptive correction of model prediction and actual observation. 2.The fish migration path prediction and fishway optimization method based on big data according to claim 1, characterized in that: The data preprocessing comprises: building a heterogeneous sensor network comprising acoustic tags, video monitoring and water quality sensors of multiple types of terminals, realizing continuous real-time collection of fish population activity trajectory and environmental parameters in the target water area; Setting the data set collected by each sensor network at each time; fuzzy topology clustering, high-dimensional similarity modeling of all observation points, that is, constructing a fuzzy similarity matrix; Based on the self-generated feature space mapping, project different source data into a unified high-dimensional feature space through a feature embedding function; Through alternating optimization, finally express all heterogeneous, asynchronous and different accuracy multi-source data as a unified high-dimensional feature vector set. 3.The fish migration path prediction and fishway optimization method based on big data according to claim 1, characterized in that: The fish migration path potential distribution identification specifically comprises: Using the fused data to generate fish moving trajectory point sets in different environmental factors, and gradually restoring the moving trajectory point sets of fish individuals or groups in time sequence according to the high-dimensional fish behavior and environmental feature data set after fusion, spatio-temporal alignment and standardization; Introducing the "migration driving force-environment reaction deep inference model (MDEF-DR Model)": establishing a coupling factor function between the physiological migration driving force (reproduction, foraging) and environmental changes (water flow, temperature) in the fish migration process, and dynamically predicting the potential migration path distribution probability of fish schools under environmental changes. 4.The fish migration path prediction and fishway optimization method based on big data according to claim 1, characterized in that: The intelligent extraction of migration path clusters specifically comprises: Proposing the "adaptive spatio-temporal density fluctuation cluster algorithm (ASTC Algorithm)": integrating a dynamic weighted mechanism of probability density and adaptively adjusting the clustering "perception radius" to capture multi-scale and obviously changing typical fish migration path clusters; Performing multi-time scale analysis on the cluster paths, outputting important migration channels and nodes, first grouping the cluster results according to different time windows (day, week, season) to form a time series cluster set; For each time scale, calculate the spatial connectivity index of the points in the cluster, use the importance measurement function to normalize the density weight of the node cluster, and output a set of main migration channel backbones and ecological key nodes that are characteristic, significant and spatio-temporally stable. 5.The fish migration path prediction and fishway optimization method based on big data according to claim 1, characterized in that: The fishway optimization joint simulation decision comprises: Firstly, a multi-dynamic connection model between fishway structure parameters, external environment parameters and fish migration efficiency is constructed. Fishway structure is abstracted as a parameter set, representing fishway length, width, slope and water level difference design variables, while external environment variables such as flow, temperature, water depth and water speed are collected. Based on important migration channels and nodes, an evaluation function is defined to represent the passing efficiency of fish under specific structural and environmental conditions. Through historical data, physical modeling and fish migration behavior simulation, the functional relationship between structure and environment conditions and individual or group passing probability is jointly fitted to establish a high-dimensional nonlinear response surface model. A "fishway-migration behavior feedback adaptive optimization algorithm" is proposed, which takes structure parameters and simulation environment as double inputs, and aims to maximize fish passing rate and improve ecological diversity indicators as optimization objectives. During the optimization process, a multi-objective evolutionary learning mechanism is used, which automatically calls the fish migration intelligent simulation module for each generation of structure parameter candidates to dynamically simulate the behavior distribution and passing situation of fish groups under the current structure and environment. 6.The fish migration path prediction and fishway optimization method based on big data according to claim 1, characterized in that: The dynamic ecological feedback and continuous optimization includes: For the actual new migration path and passing efficiency collected, the deviation between the model prediction and the actual value is calculated. Synchronously introduce adaptive correction algorithm, dynamically integrate new observation data into the original model with weight, and use weighted least squares loss to quickly fine-tune network parameters.

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