An ecological restoration analysis method and system based on fish passage data of a fishway

Abstract

The application discloses an ecological restoration analysis method and system based on fishway fish passing data, relates to the field of water ecology management, and comprises the following steps: a sensing module is used to acquire fish passing parameters and fishway water environment parameters of each monitoring section in the fishway, and generate fish passing situation data set organized in time sequence; a construction module is used to acquire the fish passing parameters in the fish passing situation data set, extract passing efficiency characteristic value and time distribution characteristic value, and construct a restoration baseline representing the fish passing state of the fishway; by correlating the real-time fish passing situation with the water environment parameters, the application quantifies the influence direction and sensitivity of each factor on the passing efficiency, locates the non-standard section and the corresponding deviation parameters based on the history and expectation, then calculates the repair adjustment amount according to the sensitivity coefficient, generates a targeted scheme, and finally completes accurate regulation and control, thereby reducing the trial and error cost.

Description

Technical Field

[0001] This invention relates to the field of water ecological governance technology, specifically to an ecological restoration analysis method and system based on fish passage data. Background Technology

[0002] While water conservancy and hydropower projects bring comprehensive benefits, they also sever the longitudinal connectivity of rivers, leading to obstructed fish migration routes, habitat fragmentation, and a serious threat to aquatic biodiversity. Fishways, as an important engineering measure to mitigate this obstruction effect, need to be verified through systematic monitoring of fish passage efficiency after construction to determine whether they truly fulfill their intended ecological functions.

[0003] Patent application No. 202210821821.6 discloses a method and system for evaluating the effectiveness of fish passages. This application aims to quantitatively evaluate fish-attracting areas, inlets / outlets, and pond sections of fish passages by establishing an indicator system that includes fish accessibility indicators and hydraulic condition assessment indicators. While this method can assess the fish passage performance of various parts of the fish passage and diagnose problem locations, the evaluation dimension is still primarily based on physical accessibility, and a systematic correlation has not yet been established between fish passage monitoring data and the effectiveness of watershed-scale ecological restoration.

[0004] Based on the above and existing technologies, it is evident that existing technologies mainly focus on the collection and statistical aspects of fish passage data, and cannot establish a quantitative correlation between fish passage monitoring data and ecological restoration effectiveness. The formulation of fish passage optimization and ecological restoration measures still relies on experience-based judgment.

[0005] To this end, we propose an ecological restoration analysis method and system based on fish passage data. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides an ecological restoration analysis method and system based on fish passage data, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions;

[0008] This invention discloses an ecological restoration analysis system based on fish passage data, comprising:

[0009] The system comprises the following modules: a perception module to acquire fish passage parameters and water environment parameters at various monitoring sections within the fishway, generating a time-series organized fish passage status dataset; a construction module to acquire fish passage parameters from the fish passage status dataset, extract passage efficiency feature values ​​and time distribution feature values, and construct a restoration baseline characterizing the fishway's fish passage status; an analysis module to perform correlation analysis between the fishway water environment parameters and the restoration baseline, analyze the response relationship of each water environment parameter to passage efficiency feature values ​​and time distribution feature values, and generate an ecological response correlation matrix; a mapping module to map real-time passage parameters in the fish passage status dataset to the ecological restoration effectiveness dimension, using the ecological response correlation matrix as a constraint, generating a set of quantitative indicators for ecological restoration effectiveness; a location module to compare the set of quantitative indicators for ecological restoration effectiveness with the restoration baseline item by item, and locate fishway sections where fish passage effectiveness does not meet the baseline requirements and the corresponding deviations in water environment parameters based on the comparison results; and a generation module to apply the fishway sections and water environment parameter deviations output by the location module to generate fishway water environment restoration parameter adjustment schemes for each deviation.

[0010] The perception module is interconnected with the construction module via a local area network. The construction module is interconnected with the parsing module via a local area network. The parsing module is interconnected with the mapping module via a local area network. The mapping module is interconnected with the positioning module via a local area network. The positioning module is interconnected with the generation module via a local area network.

[0011] Furthermore, the sensing module includes a water level sensing unit, a flow velocity sensing unit, a dissolved oxygen sensing unit, a temperature sensing unit, and a video acquisition unit, which are arranged longitudinally along the fishway at intervals at each monitoring section.

[0012] The water level sensing unit, flow velocity sensing unit, dissolved oxygen sensing unit, and temperature sensing unit synchronously collect the water level, flow velocity, dissolved oxygen, and temperature values ​​of each monitoring section at a preset sampling frequency, which are used as parameters of the fishway water environment.

[0013] The video acquisition unit acquires fish passing images of each monitoring section at a preset frame rate, performs background subtraction processing on each frame of the fish passing images to separate the fish target area, performs contour detection and morphological feature matching on the fish target area to identify individual fish, performs target tracking and deduplication on the same individual fish in consecutive frames, counts the number of individual fish passing through each monitoring section per unit time as the number of fish passing tails, and extracts the body length data of each individual fish passing based on the preset conversion relationship between the pixel size of the calibration reference object in the fish passing images and the actual size to form the fish passing body length distribution. The number of fish passing tails and the fish passing body length distribution are used as fish passage parameters.

[0014] The sensing module aligns the fish passage water environment parameters and fish passage parameters obtained at the same sampling time from the same monitoring section with the timestamps to generate a fish passage status dataset organized in time series.

[0015] Furthermore, during the construction module's operation phase, the number of fish passing through each monitoring section at each sampling time is read from the fish passage situation dataset. Taking the number of fish passing through the first monitoring section at the fish passage inlet as the benchmark value, the number of fish passing through each monitoring section is compared with the benchmark value, and the resulting ratio is used as the section throughput rate of each monitoring section at each sampling time. Then, the average throughput rate of the same monitoring section at each sampling time is taken as the average throughput rate of the monitoring section. The average throughput rate of all monitoring sections constitutes the passage efficiency feature value.

[0016] The number of fish passing through each monitoring section at each sampling time is read from the fish passage status dataset. The cumulative number of fish passing through the entire fish passage within each statistical period is calculated using a preset duration as a statistical period. A time-series curve showing the change of fish passage over time is generated. Peak detection is then performed on the time-series curve to extract the peak occurrence time and peak amplitude. The time interval between adjacent peaks is taken as the period length of the migration fluctuation. The difference between the peak and trough values ​​of fish passage within each period length is taken as the amplitude intensity of the migration fluctuation. Finally, the period length and amplitude intensity constitute the time distribution feature value.

[0017] Furthermore, the construction module constructs the baseline restoration operation phase, and obtains the passage efficiency characteristic value and time distribution characteristic value of the fishway in a preset number of sub-time periods within the historical reference time period;

[0018] The passage efficiency characteristic value includes the average throughput of each monitoring section in each sub-period. The average throughput is the mean of the section throughput at each sampling time in the sub-period. The section throughput is the ratio of the number of fish tails passing through the monitoring section at the sampling time to the number of fish tails passing through the first monitoring section at the fishway inlet at the sampling time.

[0019] The time distribution characteristic values ​​include the migration fluctuation cycle length and migration fluctuation amplitude intensity within each sub-period. The migration fluctuation cycle length is the time interval between adjacent peaks of the fish passage volume time series curve within that sub-period, and the migration fluctuation amplitude intensity is the difference between the peak and trough values ​​of the fish passage volume within that sub-period.

[0020] Based on the average pass rate sample values ​​of each monitoring section in a preset number of sub-time periods, calculate the sample mean and sample standard deviation, and determine the first confidence interval of the average pass rate of the monitoring section according to a preset confidence level.

[0021] Based on a preset number of sample values ​​of the migration fluctuation cycle length in sub-periods, calculate the sample mean and sample standard deviation, and determine the second confidence interval according to a preset confidence level;

[0022] Based on a preset number of sample values ​​of the amplitude intensity of migratory fluctuations in sub-periods, the sample mean and sample standard deviation are calculated, and a third confidence interval is determined according to a preset confidence level.

[0023] The upper and lower limits of the first confidence interval, the upper and lower limits of the second confidence interval, and the upper and lower limits of the third confidence interval are combined to form a recovery baseline characterizing the fish passage's openness.

[0024] The recovery baseline includes the threshold range of traffic efficiency characteristic values, the threshold range of migration fluctuation period, and the threshold range of migration fluctuation amplitude.

[0025] Furthermore, the analysis module obtains the fishway water environment parameters and corresponding cross-sectional throughput of each monitoring section at each sampling time. It combines the water level, flow velocity, dissolved oxygen and temperature values ​​of the same monitoring section at each sampling time into a water environment state vector at that time, and uses the cross-sectional throughput of the monitoring section at the same time as the effectiveness label of the state vector.

[0026] A global state space is constructed using the state vectors of all monitored sections at all sampling times. Euclidean distance is used as the distance metric between vectors, and a preset fixed positive integer K is used as the threshold for the number of neighbors. The nearest neighbor search algorithm searches for the K neighboring state vectors of each state vector in the state space, and reads the performance label carried by each neighboring state vector.

[0027] For each water environment parameter dimension, all state vectors are sorted from smallest to largest according to the value of that dimension. The mean value of the effectiveness label of the first half of the state vectors and the mean value of the effectiveness label of the second half of the state vectors are calculated. The sign of the difference between the two is used as the direction of the influence of the water environment parameter on the traffic efficiency.

[0028] For each water environment parameter dimension and each state vector, calculate the sum of the absolute values ​​of the numerical differences between the state vector and its K neighboring state vectors in that dimension, and the sum of the absolute values ​​of the differences between the state vector and its K neighboring state vectors in the performance label. Use the ratio of the two as the local sensitivity of the state vector, and then take the arithmetic mean of the local sensitivity of all state vectors as the sensitivity coefficient of the water environment parameter to the cross-sectional throughput.

[0029] An ecological response correlation matrix is ​​constructed with each monitoring section as a row and each water environment parameter as a column. The sensitivity coefficients and influence directions of each water environment parameter at each monitoring section are then entered into the ecological response correlation matrix.

[0030] Furthermore, the mapping module obtains the real-time passage parameters and the real-time water environment parameters of each monitoring section from the fish passage status dataset, and combines the real-time water level, real-time flow velocity, real-time dissolved oxygen and real-time temperature values ​​of each monitoring section into the real-time state vector of that monitoring section.

[0031] The real-time access parameters of each monitoring section are compared with the median value of the threshold range of the corresponding item in the recovery baseline, and the deviation of the effectiveness in each dimension is calculated.

[0032] Substitute the real-time state vector into the global state space, search for the K nearest historical state vectors of the real-time state vector in the global state space, and use the average of the effectiveness labels carried by the K nearest historical state vectors as the expected traffic effectiveness of the monitoring section.

[0033] The difference between the expected traffic effect and the real-time traffic parameters is used as the deviation of the ecological restoration effect of the monitoring section. The ecological restoration effect deviation of all monitoring sections is used to form the set of quantitative indicators for ecological restoration effect.

[0034] Furthermore, the positioning module reads the deviation of ecological restoration effectiveness of each monitoring section from the quantitative indicator set of ecological restoration effectiveness, compares the deviation of ecological restoration effectiveness of each monitoring section with a preset tolerance threshold, and marks the monitoring section whose ecological restoration effectiveness deviation exceeds the tolerance threshold as a section where fish passage effectiveness has not met the standard.

[0035] For each section where the fish catchment effect did not meet the standard, the positioning module subtracts the real-time water environment parameters of the monitoring section from the median value of the threshold range of the corresponding water environment parameters in the restoration baseline one by one to obtain the measured deviation of each water environment parameter. Then, the water environment parameter item corresponding to the largest absolute value of the measured deviation is determined as the water environment parameter deviation item of that section.

[0036] Furthermore, the generation module reads the sections of fish passage that did not meet the standards and their water environment parameter deviations output by the positioning module, retrieves the sensitivity coefficient and influence direction of the corresponding water environment parameter for that section from the ecological response correlation matrix, divides the measured deviation of the water environment parameter deviation by the sensitivity coefficient, and uses the quotient as the repair adjustment amount of the water environment parameter for that section. The adjustment direction of the water environment parameter is determined by the influence direction, and the adjustment range of the water environment parameter is determined by the repair adjustment amount. This generates a fish passage water environment repair parameter adjustment scheme that includes the substandard section identifier, the type of adjustment parameter, the adjustment direction, and the adjustment range.

[0037] The generation module outputs the adjustment plan to the fishway water environment control and execution agency.

[0038] On the other hand, an ecological restoration analysis method based on fish passage data includes:

[0039] Sensing and camera units were deployed along each section of the fishway to simultaneously collect water environment parameters and images of fish passing through. The number of fish and their body length were extracted through image processing, and the fish passage status dataset was generated by aligning the data with timestamps. The passage rate of each section was calculated based on the first section at the inlet, and the average value was taken to obtain the passage efficiency feature value. The fish passage volume of each period was statistically analyzed to generate a time series curve. The migration cycle and amplitude were extracted by peak detection to form the time distribution feature value. The confidence interval was calculated from the historical reference time period data and combined to form the recovery baseline. The water environment parameters at each time point were combined into a state vector and a global space was constructed with the section passage rate as the label. The direction of influence and local sensitivity of each parameter were determined by nearest neighbor search, and the average value was taken as the sensitivity coefficient to establish an ecological response correlation matrix. The real-time access parameters are compared with the median values ​​of each threshold in the restoration baseline to obtain the deviation of effectiveness in each dimension. The real-time state vector is substituted into the global space to search for the neighboring historical vectors and take the average of the labels as the expected access effectiveness. The difference between the expected effectiveness and the real-time access parameters is taken as the deviation of ecological restoration effectiveness, and the results are summarized into a set of effectiveness quantification indicators. The effectiveness deviation of each section is compared with the tolerance threshold, and those exceeding the threshold are marked as substandard sections. The real-time parameters of each section are subtracted from the median values ​​of the baseline threshold, and the one with the largest deviation is taken as the deviation item of water environment parameters. The sensitivity coefficient and influence direction of the corresponding parameters of the substandard sections are retrieved, and the adjustment amount is obtained by dividing the deviation by the sensitivity coefficient. The adjustment direction is combined to generate a restoration plan and output to the implementing agency.

[0040] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:

[0041] This invention analyzes the correlation between fish passage parameters monitored in real time within the fishway and aquatic environment parameters, decomposing fluctuations in passage efficiency into specific factors such as water level, flow velocity, dissolved oxygen, and temperature. This clarifies the direction and sensitivity of each environmental variable's influence on fish passage effectiveness. This response quantification method based on local state space proximity search means that subsequent effectiveness evaluation no longer relies on a single indicator, but rather combines the expected passage level under similar historical conditions. Therefore, the generated deviation is more likely to reflect the true recovery progress rather than random fluctuations.

[0042] In addition, the system compares the deviation amount with the tolerance threshold on a cross-section basis, directly locating the specific sections with poor performance, and automatically locking the environmental parameters most closely related to them. This avoids the blindness of manual investigation. Based on this, according to the sensitivity coefficient of the parameter in that section and the measured deviation range, the system directly calculates the amount and direction of the required adjustment, forming a targeted parameter adjustment plan for the implementing agency. The overall plan reduces the time cost of trial and error adjustment in ecological restoration and improves the spatial accuracy of intervention measures, enabling limited restoration resources to be concentrated on the locations and parameters that truly need improvement. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A schematic diagram of an ecological restoration analysis system based on fish passage data;

[0045] Figure 2 This is a flowchart illustrating an ecological restoration analysis method based on fish passage data. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0047] The present invention will be further described below with reference to embodiments.

[0048] Example 1:

[0049] This embodiment presents an ecological restoration analysis system based on fish passage data, such as... Figure 1 As shown, it includes:

[0050] The perception module is used to acquire fish passage parameters and fishway water environment parameters at each monitoring section in the fishway, and generate a fish passage status dataset organized in time series.

[0051] The sensing module includes water level sensing units, flow velocity sensing units, dissolved oxygen sensing units, temperature sensing units, and video acquisition units that are arranged at intervals along the longitudinal direction of the fishway at each monitoring section.

[0052] The water level sensing unit, flow velocity sensing unit, dissolved oxygen sensing unit, and temperature sensing unit synchronously collect the water level, flow velocity, dissolved oxygen, and temperature values ​​of each monitoring section at a preset sampling frequency, which are used as parameters of the fishway water environment.

[0053] The video acquisition unit acquires fish passing images at each monitoring section at a preset frame rate. The background difference processing is performed on each frame of the fish passing images to separate the fish target area. Contour detection and morphological feature matching are performed on the fish target area to identify individual fish. The same fish individual in consecutive frames is tracked and deduplicated. The number of fish individuals passing through each monitoring section per unit time is counted as the number of fish passing tails. Based on the conversion relationship between the preset calibration reference pixel size and the actual size in the fish passing images, the body length data of each fish passing is extracted to form the fish body length distribution. The number of fish passing tails and the fish body length distribution are used as fish passage parameters.

[0054] Preferably, the specific process of the video acquisition unit processing the fish passage images is as follows: Background subtraction processing uses a Gaussian mixture model to establish a background model. For each frame of the image, the difference between the current pixel value and the mean value of the background model is calculated pixel by pixel. When the difference exceeds a preset threshold, the pixel is marked as a foreground target area. The background model is updated with the frame sequence according to a preset learning rate. Contour detection uses the Canny edge detection algorithm to extract the edge contour of the target area, and noise is removed by filtering through contour area and aspect ratio. Morphological feature matching pre-establishes a standard body contour template library for the target fish species in the fish passage. The detected fish contours are matched with the template library in shape context. Those with a matching degree exceeding a preset threshold are identified as individual fish. Target tracking uses an inter-frame association matching method based on intersection-union ratio (IU). The IU of the bounding boxes of the fish targets detected in consecutive frames is calculated. When the IU is greater than a preset threshold, it is determined to be the same individual fish. The same individual fish is counted only once when it first appears, and is not counted again in subsequent frames.

[0055] The sensing module aligns the fishway water environment parameters and fish passage parameters obtained at the same monitoring section and at the same sampling time according to the timestamp, and generates a fish passage status dataset organized in time series.

[0056] The module is used to obtain fish passage parameters from the fish passage status dataset, extract passage efficiency feature values ​​and time distribution feature values, and construct a recovery baseline that characterizes the fish passage status.

[0057] During the construction module operation phase, the number of fish passing through each monitoring section at each sampling time is read from the fish passage status dataset. Taking the number of fish passing through the first monitoring section at the fish passage inlet as the benchmark value, the number of fish passing through each monitoring section is compared with the benchmark value. The resulting ratio is used as the section throughput rate of each monitoring section at each sampling time. Then, the average throughput rate of the same monitoring section at each sampling time is taken to obtain the average throughput rate of the monitoring section. The average throughput rate of all monitoring sections constitutes the passage efficiency feature value.

[0058] The number of fish passing through each monitoring section at each sampling time is read from the fish passage situation dataset. The cumulative number of fish passing through the entire fish passage within each statistical period is calculated using a preset duration as a statistical period. A time series curve of fish passing through the fish passage over time is generated. Peak detection is then performed on the time series curve to extract the peak occurrence time and peak amplitude. The time interval between adjacent peaks is taken as the period length of the migration fluctuation. The difference between the peak and trough values ​​of fish passing through the fish passage within each period length is taken as the amplitude intensity of the migration fluctuation. Finally, the period length and amplitude intensity are used to construct the time distribution feature value.

[0059] The construction module constructs the baseline restoration operation phase, and obtains the passage efficiency characteristic value and time distribution characteristic value of the fishway in a preset number of sub-time periods within the historical baseline period;

[0060] The passage efficiency characteristic value includes the average throughput of each monitoring section in each sub-period. The average throughput is the mean of the section throughput at each sampling time in the sub-period. The section throughput is the ratio of the number of fish tails passing through the monitoring section at that sampling time to the number of fish tails passing through the first monitoring section at the fishway inlet at that sampling time.

[0061] The temporal distribution characteristic values ​​include the migration fluctuation period length and migration fluctuation amplitude intensity within each sub-period. The migration fluctuation period length is the time interval between adjacent peaks of the fish passage volume time series curve within that sub-period, and the migration fluctuation amplitude intensity is the difference between the peak and trough values ​​of the fish passage volume within that sub-period.

[0062] Based on the average pass rate sample values ​​of each monitoring section in a preset number of sub-time periods, calculate the sample mean and sample standard deviation, and determine the first confidence interval of the average pass rate of the monitoring section according to a preset confidence level.

[0063] The preset confidence level is 95%, which is a commonly used confidence level in the field of ecohydrology to determine the normal fluctuation range of environmental indicators. This value can take into account both the statistical significance requirements and the actual limitations of the sample size of the monitoring data. For periods with a sample size of less than 30, the t-distribution can be used instead of the normal distribution to calculate the confidence interval.

[0064] Based on a preset number of sample values ​​of the migration fluctuation cycle length in sub-periods, calculate the sample mean and sample standard deviation, and determine the second confidence interval according to a preset confidence level;

[0065] Based on a preset number of sample values ​​of the amplitude intensity of migratory fluctuations in sub-periods, the sample mean and sample standard deviation are calculated, and a third confidence interval is determined according to a preset confidence level.

[0066] The upper and lower limits of the first confidence interval, the upper and lower limits of the second confidence interval, and the upper and lower limits of the third confidence interval are combined to form a recovery baseline that characterizes the fish passage's openness.

[0067] The baseline restoration includes the threshold range of traffic efficiency characteristic values, the threshold range of migration fluctuation period, and the threshold range of migration fluctuation amplitude.

[0068] The analysis module is used to perform correlation analysis between fishway water environment parameters and restoration baseline, analyze the response relationship of each water environment parameter to the characteristic values ​​of passage efficiency and time distribution, and generate an ecological response correlation matrix.

[0069] The analysis module obtains the fishway water environment parameters and corresponding cross-sectional throughput of each monitoring section at each sampling time. It combines the water level, flow velocity, dissolved oxygen and temperature values ​​of the same monitoring section at each sampling time into a water environment state vector at that time, and uses the cross-sectional throughput of the monitoring section at the same time as the effectiveness label of the state vector.

[0070] A global state space is constructed using the state vectors of all monitored sections at all sampling times. Euclidean distance is used as the distance metric between vectors, and a preset fixed positive integer K is used as the threshold for the number of neighbors. The nearest neighbor search algorithm is used to search for the K neighboring state vectors of each state vector in the state space, and the performance labels carried by each neighboring state vector are read.

[0071] The value of the fixed positive integer K is determined by the product of the total number of monitoring sections and the total number of historical sampling times. Specifically, K is the square root of the product and rounded up, and K is not less than 3. Under typical working conditions where the number of monitoring sections is 5 to 10 and the number of historical sampling times is 1000 to 5000, the value of K is in the range of 8 to 15. For cases where the amount of data is insufficient, the value of K is 10% of the total number of state vectors and rounded down, but not less than 3.

[0072] For each water environment parameter dimension, all state vectors are sorted from smallest to largest according to the value of that dimension. The mean value of the effectiveness label of the first half of the state vectors and the mean value of the effectiveness label of the second half of the state vectors are calculated. The sign of the difference between the two is used as the direction of the influence of the water environment parameter on the traffic efficiency.

[0073] For each water environment parameter dimension and each state vector, calculate the sum of the absolute values ​​of the numerical differences between the state vector and its K neighboring state vectors in that dimension, and the sum of the absolute values ​​of the differences between the state vector and its K neighboring state vectors in the performance label. Use the ratio of the two as the local sensitivity of the state vector, and then take the arithmetic mean of the local sensitivity of all state vectors as the sensitivity coefficient of the water environment parameter to the cross-sectional throughput.

[0074] An ecological response correlation matrix was constructed with each monitoring section as a row and each water environment parameter as a column. The sensitivity coefficients and influence directions of each water environment parameter at each monitoring section were entered into the ecological response correlation matrix.

[0075] The ecological response correlation matrix is ​​implemented using a relational data table structure. Each record contains the following fields: section identifier, water environment parameter type, sensitivity coefficient, influence direction symbol, and most recent update timestamp. The section identifier corresponds one-to-one with the physical installation location of the monitoring section. The water environment parameter type includes four enumerated values: water level, flow velocity, dissolved oxygen, and temperature. The influence direction symbol uses a positive sign to indicate a positive influence and a negative sign to indicate a negative influence. When the generation module retrieves the sensitivity coefficient and influence direction of the corresponding water environment parameter for this section, it searches the data table using the section identifier and water environment parameter type as joint query conditions, and returns the corresponding sensitivity coefficient value and influence direction symbol.

[0076] The mapping module is used to map the real-time passage parameters in the fish passage status dataset to the ecological restoration effectiveness dimension, using the ecological response correlation matrix as a constraint, and generate a set of quantitative indicators for ecological restoration effectiveness.

[0077] The mapping module obtains the real-time passage parameters and the real-time water environment parameters of each monitoring section from the fish passage status dataset. It combines the real-time water level, real-time flow velocity, real-time dissolved oxygen, and real-time temperature values ​​of each monitoring section into the real-time state vector of that monitoring section.

[0078] The real-time access parameters of each monitoring section are compared with the median value of the threshold range of the corresponding item in the recovery baseline, and the deviation of the effectiveness in each dimension is calculated.

[0079] Substitute the real-time state vector into the global state space, search for the K nearest historical state vectors of the real-time state vector in the global state space, and use the average of the effectiveness labels carried by the K nearest historical state vectors as the expected passage effectiveness of the monitoring section.

[0080] The difference between the expected traffic effect and the real-time traffic parameters is used as the deviation of the ecological restoration effect of the monitoring section. The ecological restoration effect deviation of all monitoring sections is used to form a set of quantitative indicators for ecological restoration effect.

[0081] The positioning module is used to compare the set of quantitative indicators of ecological restoration effectiveness with the restoration baseline item by item, and locate the fish passage sections where the fish passage effectiveness has not met the baseline requirements and the corresponding deviations of water environment parameters based on the comparison results.

[0082] The positioning module reads the deviation of ecological restoration effectiveness of each monitoring section from the quantitative indicators of ecological restoration effectiveness. It compares the deviation of ecological restoration effectiveness of each monitoring section with the preset tolerance threshold and marks the monitoring section whose ecological restoration effectiveness deviation exceeds the tolerance threshold as the section where fish passage effectiveness has not met the standard.

[0083] The tolerance threshold is determined as a percentage of the width of the threshold range of traffic efficiency characteristic values ​​for each monitoring section in the recovery baseline. Specifically, the tolerance threshold is 10% of the difference between the upper and lower limits of the threshold range of traffic efficiency characteristic values ​​for that monitoring section, and this value is not less than 0.02. When the sample standard deviation of traffic efficiency characteristic values ​​in the historical reference period is less than 0.05, the tolerance threshold is 0.05.

[0084] For each section where the fish catchment effect did not meet the standard, the positioning module subtracts the real-time water environment parameters of the monitoring section from the median value of the threshold range of the corresponding water environment parameters in the restoration baseline one by one to obtain the measured deviation of each water environment parameter. Then, the water environment parameter item corresponding to the largest absolute value of the measured deviation is determined as the water environment parameter deviation item of that section.

[0085] The generation module is used to generate a fishway water environment restoration parameter adjustment plan for each deviation item based on the deviation items of the fishway section and water environment parameters output by the positioning module.

[0086] The generation module reads the fish passage sections that fail to meet the fish passage effectiveness standards and their water environment parameter deviations from the positioning module. It retrieves the sensitivity coefficient and influence direction of the corresponding water environment parameter for that section from the ecological response correlation matrix. The measured deviation of the water environment parameter deviation is divided by the sensitivity coefficient, and the quotient is used as the repair and adjustment amount of the water environment parameter for that section. The adjustment direction of the water environment parameter is determined by the influence direction, and the adjustment range of the water environment parameter is determined by the repair and adjustment amount. A fish passage water environment repair parameter adjustment plan is generated, which includes the identification of the substandard section, the type of adjustment parameter, the adjustment direction, and the adjustment range.

[0087] The generation module will output the adjustment plan to the fishway water environment control and execution agency;

[0088] The fishway water environment control actuator includes an electric regulating gate, a variable frequency speed-regulating water pump, and a microporous aeration device installed near each monitoring section of the fishway. The electric regulating gate adjusts the water level and flow rate at the section by changing its opening degree, the variable frequency speed-regulating water pump adjusts the circulating water flow rate in the fishway by changing its operating frequency, and the microporous aeration device adjusts the dissolved oxygen value of the water by changing the air supply. The adjustment plan output by the generation module is sent to the field control unit of the actuator in the form of structured instructions. The field control unit drives the corresponding actuator to act step by step according to the adjustment direction and adjustment range in the adjustment plan, according to the preset adjustment step size, until the measured value of the water environment parameter at the section reaches the target value.

[0089] The perception module interacts with the construction module via a local area network. The construction module interacts with the parsing module via a local area network. The parsing module interacts with the mapping module via a local area network. The mapping module interacts with the positioning module via a local area network. The positioning module interacts with the generation module via a local area network.

[0090] In this embodiment of the system, the sensing module is deployed along each monitoring section of the fishway to synchronously collect water environment parameters such as water level, flow velocity, dissolved oxygen, and temperature. The video unit synchronously acquires images of fish passing through and counts the number of fish passing through and extracts the body length after background subtraction and target tracking. The environmental and passage data are aligned according to the timestamp to generate a fish passage status dataset.

[0091] The construction module calculates the throughput rate of each section based on the first section at the inlet end and takes the mean as the characteristic value of the passage efficiency. Then, based on the time series curve of the cumulative fish passage volume of the whole line, it extracts the peak interval and peak-valley difference as the time distribution characteristic value. Subsequently, it takes the above characteristic samples of multiple sub-periods within the historical benchmark period to calculate the mean and standard deviation, determines the upper and lower limits of the interval according to the confidence level, and combines them to form the recovery baseline.

[0092] The parsing module combines the environmental parameters at each moment into a state vector and constructs a global state space with the corresponding pass rate as the label. It determines the influence direction of each parameter by searching for the nearest neighbor and comparing the average labels of the front and rear halves. At the same time, it calculates the local sensitivity and takes the average as the sensitivity coefficient of each parameter to form an ecological response correlation matrix.

[0093] The mapping module compares the real-time state vector with the baseline median value to obtain the deviation in each dimension, then searches for neighboring vectors in the historical state space, uses the mean of the neighboring vector labels as the expected passage effect, and uses the difference between the expected and the actual as the deviation of the ecological restoration effect. After summarizing each section, a set of quantitative indicators is formed.

[0094] The positioning module compares the deviation with the tolerance threshold one by one. Those exceeding the threshold are marked as substandard sections. Then, for these sections, the real-time environmental parameters are subtracted from the baseline median value one by one, and the parameter with the largest absolute value is taken as the key deviation item.

[0095] The generation module retrieves the sensitivity coefficient and influence direction of the corresponding parameter for the segment from the correlation matrix, divides the deviation by the coefficient to obtain the repair adjustment amount, and determines the adjustment range and sign by combining the direction. This forms a repair plan that includes the segment identifier, parameter type, adjustment direction and range, and is then issued to the execution mechanism.

[0096] Example 2:

[0097] At the implementation level, based on Example 1, this example refers to... Figure 1 A further detailed description of the ecological restoration analysis system based on fish passage data in Example 1 is provided below:

[0098] An ecological restoration analysis method based on fishway data includes:

[0099] Sensing and camera units are deployed along each section of the fishway to simultaneously collect water environment parameters and images of fish passing through. The number of fish and their body length are extracted through image processing, and the fish passage status dataset is generated by aligning the timestamps.

[0100] The throughput rate of each section is calculated based on the first cross-section of the inlet and the average value is taken to obtain the characteristic value of the passage efficiency; the fish passage volume of the statistical period is used to generate a time series curve, and the migration cycle and amplitude are extracted by peak detection to form the time distribution characteristic value; the confidence interval is calculated by taking the historical benchmark time period data and combined to form the recovery baseline;

[0101] The water environment parameters at each time point are combined into a state vector and a global space is constructed using the cross-sectional throughput rate as a label. The influence direction and local sensitivity of each parameter are determined by the nearest neighbor search, and the mean value is taken as the sensitivity coefficient to establish an ecological response correlation matrix.

[0102] The real-time access parameters are compared with the median values ​​of each threshold in the recovery baseline to obtain the deviation of the effectiveness in each dimension; the real-time state vector is substituted into the global space to search for the neighboring historical vectors and take the average of the labels as the expected access effectiveness; the difference between the expected effectiveness and the real-time access parameters is taken as the deviation of the ecological restoration effectiveness, and the results are summarized into a set of quantitative indicators of effectiveness.

[0103] Compare the deviation of each section with the tolerance threshold, and mark the section that exceeds the threshold as the substandard section; calculate the difference between the real-time parameter of the section and the median value of the baseline threshold, and take the largest deviation as the deviation item of the water environment parameter.

[0104] The sensitivity coefficient and influence direction of the corresponding parameters of the substandard section are retrieved. The amount of deviation is divided by the sensitivity coefficient to obtain the amount of repair and adjustment. The repair plan is generated by combining the adjustment direction and output to the actuator.

[0105] In summary, the system and method in the above embodiments correlate fish passage parameters monitored in real time within the fishway with aquatic environmental parameters, decomposing fluctuations in passage efficiency into specific factors such as water level, flow velocity, dissolved oxygen, and temperature. This clarifies the direction and sensitivity of each environmental variable's impact on fish passage effectiveness. This response quantification method based on local state space proximity search allows subsequent effectiveness evaluation to no longer rely on a single indicator but rather combine expected passage levels under similar historical conditions. Therefore, the generated deviation reflects the true recovery progress rather than random fluctuations. Simultaneously, the system compares the deviation with tolerance thresholds section by section, directly locating specific sections with poor performance and automatically locking the most closely related deviation environmental parameters, avoiding the blindness of manual investigation. Based on this, according to the sensitivity coefficient and measured deviation of the parameter in that section, the required restoration amount and direction are directly calculated, forming a targeted parameter adjustment plan for the implementing agency. The overall plan reduces the time cost of trial and error adjustments in ecological restoration and improves the spatial accuracy of intervention measures, enabling limited restoration resources to be concentrated on the locations and parameters that truly need improvement.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An ecological restoration analysis system based on fish passage data, characterized in that, include: The perception module is used to acquire fish passage parameters and fishway water environment parameters at each monitoring section in the fishway, and generate a fish passage status dataset organized in time series. The module is used to obtain fish passage parameters from the fish passage status dataset, extract passage efficiency feature values ​​and time distribution feature values, and construct a recovery baseline that characterizes the fish passage status. The analysis module is used to perform correlation analysis between fishway water environment parameters and restoration baseline, analyze the response relationship of each water environment parameter to the characteristic values ​​of passage efficiency and time distribution, and generate an ecological response correlation matrix. The mapping module is used to map the real-time passage parameters in the fish passage status dataset to the ecological restoration effectiveness dimension, using the ecological response correlation matrix as a constraint, and generate a set of quantitative indicators for ecological restoration effectiveness. The positioning module is used to compare the set of quantitative indicators of ecological restoration effectiveness with the restoration baseline item by item, and locate the fish passage sections where the fish passage effectiveness has not met the baseline requirements and the corresponding deviations of water environment parameters based on the comparison results. The generation module is used to generate a fishway water environment restoration parameter adjustment plan for each deviation item based on the deviation items of the fishway section and water environment parameters output by the positioning module.

2. The ecological restoration analysis system based on fish passage data according to claim 1, characterized in that, The sensing module includes a water level sensing unit, a flow velocity sensing unit, a dissolved oxygen sensing unit, a temperature sensing unit, and a video acquisition unit, which are arranged at intervals along the longitudinal direction of the fishway at each monitoring section. The water level sensing unit, flow velocity sensing unit, dissolved oxygen sensing unit, and temperature sensing unit synchronously collect the water level, flow velocity, dissolved oxygen, and temperature values ​​of each monitoring section at a preset sampling frequency, which are used as parameters of the fishway water environment. The video acquisition unit acquires fish passing images of each monitoring section at a preset frame rate, performs background subtraction processing on each frame of the fish passing images to separate the fish target area, performs contour detection and morphological feature matching on the fish target area to identify individual fish, performs target tracking and deduplication on the same individual fish in consecutive frames, counts the number of individual fish passing through each monitoring section per unit time as the number of fish passing tails, and extracts the body length data of each individual fish passing based on the preset conversion relationship between the pixel size of the calibration reference object in the fish passing images and the actual size to form the fish passing body length distribution. The number of fish passing tails and the fish passing body length distribution are used as fish passage parameters. The sensing module aligns the fish passage water environment parameters and fish passage parameters obtained at the same sampling time from the same monitoring section with the timestamps to generate a fish passage status dataset organized in time series.

3. The ecological restoration analysis system based on fish passage data according to claim 1, characterized in that, During the operation phase of the construction module, the number of fish passing through each monitoring section at each sampling time is read from the fish passage situation dataset. Taking the number of fish passing through the first monitoring section at the fish passage inlet as the benchmark value, the number of fish passing through each monitoring section is compared with the benchmark value. The resulting ratio is used as the section throughput rate of each monitoring section at each sampling time. Then, the average throughput rate of the same monitoring section at each sampling time is taken to obtain the average throughput rate of the monitoring section. The average throughput rate of all monitoring sections constitutes the passage efficiency feature value. The number of fish passing through each monitoring section at each sampling time is read from the fish passage status dataset. The cumulative number of fish passing through the entire fish passage within each statistical period is calculated using a preset duration as a statistical period. A time-series curve showing the change of fish passage over time is generated. Peak detection is then performed on the time-series curve to extract the peak occurrence time and peak amplitude. The time interval between adjacent peaks is taken as the period length of the migration fluctuation. The difference between the peak and trough values ​​of fish passage within each period length is taken as the amplitude intensity of the migration fluctuation. Finally, the period length and amplitude intensity constitute the time distribution feature value.

4. The ecological restoration analysis system based on fish passage data according to claim 3, characterized in that, The construction module constructs the baseline restoration operation phase, and obtains the passage efficiency characteristic value and time distribution characteristic value of the fishway in a preset number of sub-time periods within the historical reference time period; The passage efficiency characteristic value includes the average throughput of each monitoring section in each sub-period. The average throughput is the mean of the section throughput at each sampling time in the sub-period. The section throughput is the ratio of the number of fish tails passing through the monitoring section at the sampling time to the number of fish tails passing through the first monitoring section at the fishway inlet at the sampling time. The time distribution characteristic values ​​include the migration fluctuation cycle length and migration fluctuation amplitude intensity within each sub-period. The migration fluctuation cycle length is the time interval between adjacent peaks of the fish passage volume time series curve within that sub-period, and the migration fluctuation amplitude intensity is the difference between the peak and trough values ​​of the fish passage volume within that sub-period. Based on the average pass rate sample values ​​of each monitoring section in a preset number of sub-time periods, calculate the sample mean and sample standard deviation, and determine the first confidence interval of the average pass rate of the monitoring section according to a preset confidence level. Based on a preset number of sample values ​​of the migration fluctuation cycle length in sub-periods, calculate the sample mean and sample standard deviation, and determine the second confidence interval according to a preset confidence level; Based on a preset number of sample values ​​of the amplitude intensity of migratory fluctuations in sub-periods, the sample mean and sample standard deviation are calculated, and a third confidence interval is determined according to a preset confidence level. The upper and lower limits of the first confidence interval, the upper and lower limits of the second confidence interval, and the upper and lower limits of the third confidence interval are combined to form a recovery baseline characterizing the fish passage's openness. The recovery baseline includes the threshold range of traffic efficiency characteristic values, the threshold range of migration fluctuation period, and the threshold range of migration fluctuation amplitude.

5. The ecological restoration analysis system based on fish passage data according to claim 1, characterized in that, The analysis module obtains the fishway water environment parameters and corresponding cross-sectional throughput of each monitoring section at each sampling time. It combines the water level, flow velocity, dissolved oxygen and temperature values ​​of the same monitoring section at each sampling time into a water environment state vector at that time, and uses the cross-sectional throughput of the monitoring section at the same time as the effectiveness label of the state vector. A global state space is constructed using the state vectors of all monitored sections at all sampling times. Euclidean distance is used as the distance metric between vectors, and a preset fixed positive integer K is used as the threshold for the number of neighbors. The nearest neighbor search algorithm searches for the K neighboring state vectors of each state vector in the state space, and reads the performance label carried by each neighboring state vector. For each water environment parameter dimension, all state vectors are sorted from smallest to largest according to the value of that dimension. The mean value of the effectiveness label of the first half of the state vectors and the mean value of the effectiveness label of the second half of the state vectors are calculated. The sign of the difference between the two is used as the direction of the influence of the water environment parameter on the traffic efficiency. For each water environment parameter dimension and each state vector, calculate the sum of the absolute values ​​of the numerical differences between the state vector and its K neighboring state vectors in that dimension, and the sum of the absolute values ​​of the differences between the state vector and its K neighboring state vectors in the performance label. Use the ratio of the two as the local sensitivity of the state vector, and then take the arithmetic mean of the local sensitivity of all state vectors as the sensitivity coefficient of the water environment parameter to the cross-sectional throughput. An ecological response correlation matrix is ​​constructed with each monitoring section as a row and each water environment parameter as a column. The sensitivity coefficients and influence directions of each water environment parameter at each monitoring section are then entered into the ecological response correlation matrix.

6. The ecological restoration analysis system based on fish passage data according to claim 5, characterized in that, The mapping module obtains the real-time passage parameters and the real-time water environment parameters of each monitoring section from the fish passage status dataset, and combines the real-time water level, real-time flow velocity, real-time dissolved oxygen and real-time temperature values ​​of each monitoring section into the real-time state vector of that monitoring section. The real-time access parameters of each monitoring section are compared with the median value of the threshold range of the corresponding item in the recovery baseline, and the deviation of the effectiveness in each dimension is calculated. Substitute the real-time state vector into the global state space, search for the K nearest historical state vectors of the real-time state vector in the global state space, and use the average of the effectiveness labels carried by the K nearest historical state vectors as the expected traffic effectiveness of the monitoring section. The difference between the expected traffic effect and the real-time traffic parameters is used as the deviation of the ecological restoration effect of the monitoring section. The ecological restoration effect deviation of all monitoring sections is used to form the set of quantitative indicators for ecological restoration effect.

7. The ecological restoration analysis system based on fish passage data according to claim 1, characterized in that, The positioning module reads the deviation of ecological restoration effectiveness of each monitoring section from the set of quantitative indicators of ecological restoration effectiveness, compares the deviation of ecological restoration effectiveness of each monitoring section with the preset tolerance threshold, and marks the monitoring section whose ecological restoration effectiveness deviation exceeds the tolerance threshold as the section where fish passage effectiveness has not met the standard. For each section where the fish catchment effect did not meet the standard, the positioning module subtracts the real-time water environment parameters of the monitoring section from the median value of the threshold range of the corresponding water environment parameters in the restoration baseline one by one to obtain the measured deviation of each water environment parameter. Then, the water environment parameter item corresponding to the largest absolute value of the measured deviation is determined as the water environment parameter deviation item of that section.

8. The ecological restoration analysis system based on fish passage data according to claim 7, characterized in that, The generation module reads the fish passage sections that fail to meet the fish passage effectiveness standards and their water environment parameter deviations output by the positioning module. It retrieves the sensitivity coefficient and influence direction of the corresponding water environment parameter for that section from the ecological response correlation matrix. The measured deviation of the water environment parameter deviation is divided by the sensitivity coefficient, and the quotient is used as the repair and adjustment amount of the water environment parameter for that section. The adjustment direction of the water environment parameter is determined by the influence direction, and the adjustment range of the water environment parameter is determined by the repair and adjustment amount. A fish passage water environment repair parameter adjustment scheme is generated, which includes the substandard section identifier, the type of adjustment parameter, the adjustment direction, and the adjustment range. The generation module outputs the adjustment plan to the fishway water environment control and execution agency.

9. The ecological restoration analysis system based on fish passage data according to claim 1, characterized in that, The perception module is interconnected with the construction module via a local area network. The construction module is interconnected with the parsing module via a local area network. The parsing module is interconnected with the mapping module via a local area network. The mapping module is interconnected with the positioning module via a local area network. The positioning module is interconnected with the generation module via a local area network.

10. An ecological restoration analysis method based on fish passage data, wherein the method is an implementation method of the ecological restoration analysis method and system based on fish passage data as described in any one of claims 1-9, characterized in that, include: Sensing and camera units are deployed along each section of the fishway to simultaneously collect water environment parameters and images of fish passing through. The number of fish and their body length are extracted through image processing and the fish passage status dataset is generated by aligning the timestamps. The throughput rate of each section is calculated based on the first section of the inlet and the average value is taken to obtain the characteristic value of the traffic efficiency. The fish passage volume in the statistical period is used to generate a time series curve. The migration period and amplitude are extracted by peak detection to form the time distribution characteristic value. Calculate confidence intervals from historical baseline data and combine them to form a recovery baseline; The water environment parameters at each time point are combined into a state vector and a global space is constructed using the cross-sectional throughput rate as a label. The influence direction and local sensitivity of each parameter are determined by the nearest neighbor search, and the mean value is taken as the sensitivity coefficient to establish an ecological response correlation matrix. The real-time access parameters are compared with the median values ​​of each threshold in the recovery baseline to obtain the deviation of the effectiveness in each dimension; the real-time state vector is substituted into the global space to search for the neighboring historical vectors and take the average of the labels as the expected access effectiveness; the difference between the expected effectiveness and the real-time access parameters is taken as the deviation of the ecological restoration effectiveness, and the results are summarized into a set of quantitative indicators of effectiveness. Compare the deviation of each section with the tolerance threshold, and mark the section that exceeds the threshold as the substandard section; calculate the difference between the real-time parameter of the section and the median value of the baseline threshold, and take the largest deviation as the deviation item of the water environment parameter. The sensitivity coefficient and influence direction of the corresponding parameters of the substandard section are retrieved. The amount of deviation is divided by the sensitivity coefficient to obtain the amount of repair and adjustment. The repair plan is generated by combining the adjustment direction and output to the actuator.

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