Multi-factor coupling fishway effect indirect evaluation and problem diagnosis method

Abstract

This invention provides a multi-factor coupled method for indirect evaluation and problem diagnosis of fishway effectiveness, relating to the field of ecological water conservancy. The method includes: Step S1: Constructing a fishway effectiveness evaluation index system; Step S2: Establishing a dynamically updated evaluation index dataset; Step S3: Screening and identifying key evaluation indicators; Step S4: Constructing a fishway effectiveness evaluation model; Step S5: Constructing a fishway problem diagnosis model; Step S6: Conducting effectiveness evaluation and problem diagnosis of specific fishway projects. This invention can indirectly and quantitatively evaluate the fish passage effect of fishways based on fishway environmental factors and operational management factors, and can diagnose and identify the key driving factors of problems existing in specific fishway projects, providing technical support for water ecological protection work.

Description

Technical Field

[0001] This invention belongs to the field of ecological water conservancy, specifically involving a method for indirect evaluation and problem diagnosis of fishway effects through multi-factor coupling. Background Technology

[0002] In the process of developing and utilizing rivers, numerous water conservancy projects have been constructed, playing a vital role in flood control, water supply, power generation, and navigation. However, these projects have also profoundly altered the upstream and downstream water flow environment, causing ecological problems such as obstructed fish migration, decreased watershed connectivity, and reduced aquatic biodiversity. Fishways, as an important engineering technology, provide migration and exchange pathways for fish and other aquatic organisms, and are a crucial engineering measure for improving watershed habitat connectivity. However, the actual operational effectiveness of fishways still needs improvement.

[0003] Currently, the evaluation of fishway effectiveness mainly employs direct monitoring methods such as netting and pitting-in-the-tide (PIT). By statistically analyzing the number of target fish species and the passage ratio, the effectiveness of fish passage can be directly characterized. Simultaneously, fishway monitoring has also accumulated data on environmental factors and operational management factors.

[0004] However, existing fishway monitoring and evaluation work still has shortcomings, including: the number of target fish species monitored in a single fishway project is limited, the quality of data acquisition and the representativeness of direct evaluation results are low; the monitoring workload is large, the cycle is long, and the cost is high; the evaluation results are difficult to directly reveal the specific links in the fishway problems, and the ability to diagnose problems is lacking; the evaluation work rarely considers the impact of operation and management level.

[0005] Unlike directly monitoring fish passage effectiveness, monitoring and surveying fishway environmental factors and operational management levels requires less work and yields higher-quality data. This invention, based on a fishway effectiveness evaluation index system and a basic dataset, quantifies the mathematical relationship between fishway environmental factors and direct fish passage effectiveness, establishing a new method for fishway effectiveness evaluation and diagnosis. By coupling environmental factors and operational management factors, it achieves indirect and efficient evaluation of fishway effectiveness and diagnoses and identifies key driving factors affecting fishway effectiveness. Summary of the Invention

[0006] The purpose of this invention is to overcome the above-mentioned shortcomings of the prior art and provide a multi-factor coupled method for indirect evaluation and problem diagnosis of fishway effects. This method establishes a mathematical relationship between fishway environmental factors and the direct fish passage effect of the fishway, and can indirectly evaluate the fish passage effect based on fishway environmental factors. It can also diagnose and identify the key driving factors of problems existing in typical fishways, providing technical support for aquatic ecological protection work.

[0007] To achieve the above objectives, this invention provides a method for indirect evaluation and problem diagnosis of fishway effectiveness based on multi-factor coupling, comprising the following steps:

[0008] Step S1: Construct a fishway effectiveness evaluation index system, which includes an indirect index layer and a direct index layer;

[0009] Step S2: Establish an evaluation index dataset and dynamically update the data samples within the evaluation index dataset;

[0010] Step S3: Screen and identify key evaluation indicators;

[0011] Step S4: Construct a fishway effectiveness evaluation model;

[0012] Step S5: Construct a diagnostic model for fishway problems;

[0013] Step S6: Based on the fishway effect evaluation model constructed in Step S4 and the fishway problem diagnosis model constructed in Step S5, conduct effect evaluation and problem diagnosis of specific fishway projects.

[0014] Furthermore, step S1 includes the following sub-steps:

[0015] S11: Determine the definition and calculation method of the indicators based on the current evaluation objectives, data availability, and fish biological attributes;

[0016] S12: Based on the definition and attributes of the indicators, the indicators that characterize hydraulic conditions, environmental factors and internal structure are classified into the indirect indicator layer, and the indicators that characterize the effectiveness of fish passage are classified into the direct indicator layer. The indirect indicator layer and the direct indicator layer constitute the fish passage effect evaluation indicator system.

[0017] In step S11 above, the selected indicators should be linked to the biological attributes of fish as much as possible, and the dimensional influence of composite indicators should be eliminated in the form of ratios, so as to improve the adaptability of the fishway effect evaluation model in evaluating different watersheds and different fish passage objects, and improve the evaluation accuracy.

[0018] The direct indicator layer mainly characterizes the direct fish passage effect of the fishway, including fish abundance, fish passage effectiveness, fish adaptability and other fish passage effectiveness evaluation indicators.

[0019] The established indicator system is shown in the table below:

[0020] Table 1. Construction of the Indicator System

[0021]

[0022]

[0023] Furthermore, step S2 includes the following sub-steps:

[0024] S21: Collect fishway monitoring data samples through data analysis and keep the data samples dynamically updated, including the following channels: domestic and foreign literature database search, fishway engineering design reports, fishway effect monitoring and evaluation project reports, and field surveys;

[0025] S22: Convert the monitoring data according to the definition and calculation method of the indirect indicator layer and direct indicator layer in the fishway effect evaluation index system described in step S1 to form an indicator dataset;

[0026] The data samples collected in the above steps are the original monitoring data of fish passages monitored in my country, including indicators such as habitat factors, hydraulic conditions, structural parameters, fish passage efficiency, fish resources, and biological adaptability. The specific monitoring parameters are matched with the indicator system constructed in step S1.

[0027] Furthermore, step S3 includes the following sub-steps:

[0028] S31: Perform Pearson correlation analysis on the indirect indicator layer indicators, the expression of which is as follows:

[0029]

[0030] In the formula, x ki This represents the k-th sample value of index i. δ represents the sample mean of index i. ij This represents the Pearson correlation coefficient between index i and index j;

[0031] S32: Based on the established screening principles, identify key evaluation indicators. The screening principles are as follows: If there are several groups of indicators with extremely significant correlation (P≤0.01) in the indirect indicator layer, it indicates that there is an overlap between the concepts of the indicators. Remove one indicator i from each group until there is no extremely significant correlation in the indirect indicator layer.

[0032] S33: The retained indicators after screening will be used as the indirect indicator layer of the subsequent evaluation model;

[0033] Step S3 uses correlation analysis to screen indicators, with the main purpose of minimizing the overlapping evaluation effect between indicators in the subsequent model and reducing the coupling degree within the indirect indicator layer.

[0034] Furthermore, step S4 includes the following sub-steps:

[0035] S41: The fishway effect evaluation model consists of two parts: an indirect indicator layer and a direct indicator layer. Canonical correlation analysis is performed on the determined indirect and direct indicator layers. Assuming that the indirect indicator layer Y consists of m indicators and the direct indicator layer X consists of n factors, the correlation between system X and system Y can be transformed into the correlation between two variables S and T, formed by a linear combination, as shown in the following expression:

[0036] S=α·X=α1x1+α2x2+…+α n x n (2)

[0037] T=β·Y=β1y1+β2y2+…+β m y m (3)

[0038] The correlation coefficient ρ(S, T) between variables S and T is expressed as follows:

[0039]

[0040] ρ^(S, T)=max[ρ(α·X, β·Y)] (5)

[0041] In the formula, Cov is the covariance of the linear combination of the two sets of variables, and Var is the variance of the linear combination of variables.

[0042] S42: Test whether the selection of indicators for the fishway effect evaluation model is reasonable. If the significance of the canonical correlation coefficient result ρ^(S,T) is less than 5%, the test is passed. If it is greater than 5%, the test is not passed. This indicates that the selection of indicators is unreasonable or the sample size is insufficient. The model cannot operate normally and the sample size needs to be increased and the indicators need to be re-selected.

[0043] S43: In the fishway effectiveness evaluation model, the weights of the indirect index layer are determined based on the cross-load results. The normalized result of the cross-load is used as the weight vector W of each index within the indirect index layer. The cross-load calculation expression is as follows:

[0044]

[0045]

[0046] S44: Based on the results of canonical correlation analysis, frequency analysis is used to determine the index values ​​of the fish passage effect evaluation model. The sample values ​​of the indirect index layer are used as the horizontal axis, and the sample values ​​of the direct index layer are used as the vertical axis. This determines the range of fish passage effect corresponding to the indirect index layer index.

[0047] S45: The index values ​​of the fishway effect evaluation model are represented by membership functions. A linear membership function is constructed using fuzzy mathematics. Based on the good and bad intervals of the frequency analysis results, the index change intervals are divided into two types: positive correlation intervals and negative correlation intervals. The membership function expression for the positive correlation interval is as follows:

[0048]

[0049]

[0050]

[0051]

[0052] The expression for the membership function of negative correlation intervals is as follows:

[0053]

[0054]

[0055]

[0056]

[0057] In the formula, θ A (X), θ B (X), θ C (X), θ D (X) represents the membership values ​​for four levels: excellent, good, satisfactory, and poor. X is the measured value of the indirect indicator sample to be evaluated, and M1, M2, M3, and M4 are the maximum membership values ​​for different levels in the positive correlation interval between the indirect and direct indicator layers. The maximum membership values ​​for different levels of the negative correlation interval;

[0058] S46: Substitute the indirect index data of the target fishway into the membership matrix C as follows:

[0059]

[0060] In the formula Let be the membership function value of the i-th indicator at the j-th level;

[0061] S47: Output the evaluation results of the fishway effect evaluation model, using the weighted average multiplication-bounded operator in fuzzy synthesis theory. The fuzzy comprehensive evaluation vector is used to calculate the indirect evaluation value of the fishway's fish passage effect, as shown in the following formula:

[0062]

[0063]

[0064] In the formula, W is the weight vector of each indirect index layer obtained from the cross-load calculation, and Z is the fuzzy comprehensive evaluation vector. j K represents the value in the fuzzy comprehensive evaluation vector. j K represents the value in the evaluation level scoring vector corresponding to the membership degree. j =(100,75,50,25) T The final V value is the indirect evaluation value of the fish passage effect.

[0065] Furthermore, step S5 includes the following sub-steps:

[0066] S51: The expression for the judgment matrix N within the fishway problem diagnostic model is derived from the standard evaluation values ​​required for the evaluated project:

[0067]

[0068]

[0069] In the formula, D i Let K represent the standard evaluation value of the i-th indicator, and let K = (100, 75, 50, 25). T The judgment matrix expression is derived from the standard evaluation value. This represents the standard membership degree of the i-th indicator at the j-th level;

[0070] S52: Based on the results of the operations of the membership matrix, judgment matrix, and evaluation matrix, problem indicators are diagnosed. The expression for the diagnosis matrix is ​​as follows:

[0071]

[0072] In the formula, Q is the diagnostic matrix, Q i To evaluate the diagnostic result of the i-th indicator of the evaluation object, if Q i If Q ≥ 0, then the indicator meets the standard; if Q i If the value is less than 0, then the indicator is a problem indicator;

[0073] S53: List the diagnosed problem indicators and propose improvement suggestions based on the optimization direction in the fishway effectiveness frequency analysis chart to comprehensively improve the fishway effectiveness.

[0074] Furthermore, step S6 includes the following sub-steps:

[0075] S61: Obtain monitoring data required by indirect indicator layer indicators through monitoring methods, and convert them into indicator data according to the indicator layer definition and calculation formula;

[0076] S62: Substitute the indirect index data of the target fishway into the evaluation data, proceed to steps S46-S47, query the membership function image corresponding to each index, establish the membership matrix, and calculate the indirect evaluation value of the fishway passage effect.

[0077] S63: Substitute the indirect index data of the target fishway for evaluation, proceed to steps S52-S53, diagnose the problem indicators of the target fishway, and propose improvement suggestions.

[0078] Furthermore, the data samples in the evaluation index dataset established in step S2 are regularly and dynamically updated. When the data samples are updated, steps S3-S6 are repeated. First, the model is iteratively optimized using the latest data samples, and then the specific fishway project is evaluated and diagnosed. When the data samples are not updated, steps S3-S5 are skipped, and the established model is used to evaluate the effectiveness of the specific fishway project and diagnose the problems.

[0079] The present invention has the following beneficial effects:

[0080] 1. This invention proposes a method for evaluating the effectiveness and diagnosing problems of fish passages based on indirect indicators such as fish passage hydraulic conditions, environmental factors and internal structure. Compared with existing methods that directly monitor and evaluate indicators such as the number of fish passing through, this method has the advantages of being simple to implement, cost-controllable and reliable.

[0081] 2. This invention uses canonical correlation analysis to examine the correlation between the indirect and direct indicator layers, and determines the weights from the mutual influence relationships of the system through cross-loads, thereby increasing the overall explanatory power of indirect indicators in the evaluation of fishway effectiveness. From an objective perspective, it avoids the subjective errors caused by previous methods such as expert scoring.

[0082] 3. The fishway effectiveness evaluation and problem diagnosis model constructed in this invention can be continuously iterated and optimized with the dynamic update of the basic dataset, which improves the quantification of fishway effectiveness evaluation and ensures the accuracy of problem diagnosis. Attached Figure Description

[0083] Figure 1 This is a flowchart of one embodiment of the fishway effectiveness evaluation and problem diagnosis method of the present invention;

[0084] Figure 2 This is a graph showing the significance of Pearson correlations within the indirect indicator layer.

[0085] Figure 3 This is a typical correlation diagram between indirect indicator layer indicators and direct indicator layer indicators;

[0086] Figure 4 A frequency analysis chart of the import velocity index;

[0087] Figure 5A frequency analysis chart of the export flow velocity index;

[0088] Figure 6 This is a frequency analysis graph of the maximum flow velocity index;

[0089] Figure 7 Frequency analysis chart of pool chamber energy dissipation index;

[0090] Figure 8 This is a frequency analysis chart of the water temperature index;

[0091] Figure 9 A frequency analysis chart of the aperture width index;

[0092] Figure 10 A frequency analysis chart of the proportion index of rest pools;

[0093] Figure 11 The graph of the membership function of the inlet velocity index;

[0094] Figure 12 The graph of the membership function of the export velocity index;

[0095] Figure 13 The graph of the membership function of the maximum flow velocity exponent;

[0096] Figure 14 The graph shows the membership function of the energy dissipation index of the pool chamber.

[0097] Figure 15 The graph is the membership function of the water temperature index.

[0098] Figure 16 The graph of the membership function of the aperture width index;

[0099] Figure 17 The graph shows the membership function of the proportion index of the rest pool. Detailed Implementation

[0100] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings. However, these embodiments do not constitute a limitation of the present invention and are merely examples to help those skilled in the art to better understand the content and advantages of the present invention.

[0101] like Figure 1 As shown, this embodiment of the invention provides a method for indirect evaluation and problem diagnosis of fishway effectiveness based on multi-factor coupling, including the following steps:

[0102] Step S1: Construct an evaluation index system for the effectiveness of fish passages.

[0103] Specifically, considering factors such as the current evaluation objectives, data availability, and fish biological attributes, the definitions and calculation methods of the indicators are determined. Based on the definitions and attributes of the indicators, the indicators that characterize hydraulic conditions, environmental factors, and internal structure are classified into the indirect indicator layer, while the indicators that characterize the effectiveness of fish passage are classified into the direct indicator layer. The indicator system is shown in Table 1.

[0104] Table 1. Construction of the Indicator System

[0105]

[0106]

[0107] Step S2: Establish an evaluation index dataset and dynamically update the data samples within the evaluation index dataset.

[0108] Specifically, step S2 includes the following sub-steps:

[0109] S21: Collect fishway monitoring data samples through data analysis and keep the data samples dynamically updated, including the following channels: domestic and foreign literature database searches, fishway engineering design reports, fishway effect monitoring and evaluation project reports, field surveys, etc.

[0110] For ease of explanation, this embodiment collects sample data from dozens of fish passages in China and provides the process for establishing a method for evaluating the effectiveness of fish passages, so that those skilled in the art can better understand it.

[0111] S22: The collected monitoring data is transformed according to the definition and calculation method of the indicator layer to form a fishway effect evaluation indicator dataset. The number of samples in this dataset increases continuously as the scale of fishway effect monitoring expands.

[0112] Step S3: Screen and identify key evaluation indicators.

[0113] Specifically, step S3 includes the following sub-steps:

[0114] S31: Perform Pearson correlation analysis on the indirect indicator layer indicators, the expression of which is as follows:

[0115]

[0116] The correlation analysis results between indices within the indirect indicator layer are as follows: Figure 2 As shown.

[0117] S32: Based on the screening principles established in this invention, key evaluation indicators are identified. The screening principles are as follows: If there are several groups of indicators with extremely significant correlation (P≤0.01) in the indirect indicator layer, it indicates that there is an overlap between the concepts of the indicators. One indicator i in each group is screened out until there is no extremely significant correlation in the indirect indicator layer.

[0118] Depend on Figure 2 It can be seen that there are 3 sets of indicators that meet the screening criteria, that is, there is a highly significant correlation between the indicators. These are dissolved gas saturation index and water temperature index, rest pool ratio index and pool width ratio index, and pool chamber size index and pool chamber energy dissipation index. Among the 3 sets, dissolved gas saturation index, pool width ratio index and pool chamber size index are selected for screening.

[0119] S33: The selected indicators are used as the indirect indicator layer for the subsequent evaluation model construction process. The results of the indicator selection for the evaluation model are shown in Table 2.

[0120] Table 2. Results of Evaluation Model Index Screening

[0121]

[0122] Step S4: Construct a fishway effectiveness evaluation model.

[0123] Specifically, step S4 includes the following sub-steps:

[0124] S41: The evaluation model consists of two parts: an indirect indicator layer and a direct indicator layer. Canonical correlation analysis is performed on the determined indirect and direct indicator layers. Assuming the indirect indicator layer Y consists of m indicators and the direct indicator layer X consists of n factors, the correlation between system X and system Y can be transformed into the correlation between two variables S and T, formed by a linear combination, as shown in the following expression:

[0125] S=α·X=α1x1+α2x2+…+α n x n

[0126] T=β·Y=β1y1+β2y2+…+β m y m

[0127] In the formula α n and β n All are linear combination constants.

[0128] The correlation coefficient ρ(S, T) between variables S and T is expressed as follows:

[0129]

[0130] ρ^(S, T)=max[ρ(α·X, β·Y)]

[0131] In the formula, Cov is the covariance of the linear combination of the two sets of variables, and Var is the variance of the linear combination of variables.

[0132] S42: To verify whether the selection of indicators in the evaluation model is reasonable, the correlation coefficient ρ^(S,T) = 0.98 between the fish passage environment Y and the fish passage effect X, calculated from the existing monitoring data, shows a strong positive correlation. The significance P = 0.008 < 0.05, indicating that the system test is passed and the selection of indicators is reasonable.

[0133] S43: The weights of the indirect index layer in the evaluation model are determined based on the cross-load results. The normalized result of the cross-load is used as the weight vector W of each index in the indirect index layer. The cross-load calculation expression is as follows:

[0134]

[0135]

[0136] The results of cross-load calculation and weight distribution are shown in Table 3.

[0137] Table 3. Calculation and Weight Distribution of Cross Loads

[0138] Indirect indicator layer Cross load values weight value Import flow rate index 0.792 0.226 Export velocity index 0.239 0.068 Maximum flow rate index 0.519 0.148 Pool energy dissipation index 0.529 0.151 Water temperature index 0.509 0.145 Hole width index 0.571 0.163 Rest pool ratio index -0.353 0.101

[0139] In this embodiment, the weight vector W = (0.226, 0.068, 0.148, 0.151, 0.145, 0.163, 0.101).

[0140] S44: Based on the correlation coefficients obtained from canonical correlation analysis, the correspondence between the indirect and direct indicator layers is determined. The indicator with the largest absolute value of its correlation coefficient is taken as the corresponding indicator and used as a reference for frequency analysis to determine the range of effectiveness. The correlation analysis results between the indirect and direct indicator layers are as follows: Figure 3 As shown.

[0141] Depend on Figure 3 The corresponding results of the two indicator layers, indirect and direct, are shown in Table 4.

[0142] Table 4. Results of the Two-Indicator Layer Model

[0143]

[0144]

[0145] The frequency analysis method is used to determine the range of fish-catching performance corresponding to the indirect indicator layer by using the sample values ​​of the indirect indicator layer as the x-axis and the sample values ​​of the direct indicator layer as the y-axis.

[0146] Frequency analysis results are as follows Figure 4-10 As shown.

[0147] S45: Represent the index values ​​of the evaluation model using membership functions, construct membership functions using fuzzy evaluation methods, and divide the index change intervals into two types: positive correlation intervals and negative correlation intervals based on the good and bad intervals of the frequency analysis results.

[0148] The membership function expressions for positive correlation intervals are shown in equations (8)-(11), and the membership function expressions for negative correlation intervals are shown in equations (12)-(15).

[0149] The membership function is linear. To avoid abrupt changes in membership at the endpoints of the interval, the membership degree of each level is set to 1 at the midpoint of each level interval and to 0 at the midpoint of adjacent level intervals.

[0150] The critical points in the membership functions of each index in the indirect index layer are determined by frequency analysis, such as... Figure 4-10 The following is confirmed:

[0151] Import velocity index: M1 = 0.75, M2 = 0.53, M3 = 0.3, M4 = 0.15

[0152] Export velocity index: M1 = 2.6, M2 = 1.7, M3 = 1.1, M4 = 0.73

[0153] Maximum velocity index: M1 = 0.98, M2 = 0.73

[0154] Energy dissipation index of the pool chamber: M1 = 0.95, M3 = 0.55.

[0155] Water temperature index: M1 = 0.98, M2 = 0.88, M3 = 0.78, M4 = 0.6.

[0156] Hole width index: M1 = 11.5, M2 = 8.5, M3 = 6.3, M4 = 4.8

[0157] Rest pool ratio index: M1 = 0.125, M2 = 0.055, M3 = 0.03, M4 = 0.01

[0158] The membership function graphs of each indicator in the indirect indicator layer were finally determined as follows: Figure 11-17 As shown.

[0159] The subsequent steps S46-S47 involve the process of inputting specific fishway engineering data, which will be explained in detail in step S6.

[0160] Step S5: Construct a diagnostic model for fishway problems.

[0161] Specifically, step S5 includes the following sub-steps:

[0162] S51: The expression for the judgment matrix N within the diagnostic model, derived from the standard evaluation values ​​required for the evaluated project, is as follows:

[0163]

[0164]

[0165] In the formula, D i Let K represent the standard evaluation value of the i-th indicator, and let K = (100, 75, 50, 25). T The judgment matrix expression is derived from the standard evaluation value. This represents the standard membership degree of the i-th indicator at the j-th level.

[0166] In this embodiment, the standard evaluation value D of the i-th indicator is defined. i With all scores equal to 60, the expression for matrix N is as follows:

[0167]

[0168] The subsequent steps S52-S53 involve the process of inputting specific fishway engineering data, which will be explained in detail in step S6.

[0169] Step S6: Conduct an evaluation of the effectiveness of the specific fishway project and diagnose any problems.

[0170] The actual fishway evaluated in this embodiment is a fishway in the upper reaches of the Yellow River Basin. The design and monitoring data of this fishway will be used as an example for subsequent explanations.

[0171] Specifically, step S6 includes the following sub-steps:

[0172] S61: The monitoring data required by the indirect indicator layer is obtained through monitoring methods and transformed into indicator data according to the indicator layer definition and calculation formula. The monitoring data transformation results of this fishway example are shown in Table 5.

[0173] Table 5. Results of Data Conversion for Examples of Indirect Indicators

[0174]

[0175]

[0176] S62: Substitute the indirect index data of the target fishway into the evaluation data, proceed to steps S46-S47, query the membership function image corresponding to each index, establish the membership matrix, and calculate the indirect evaluation value of the fishway passage effect.

[0177] The membership matrix C is constructed as follows:

[0178]

[0179] The output model evaluation results are obtained using the weighted average multiplication-bounded operator from fuzzy synthesis theory. The fuzzy comprehensive evaluation vector is used to calculate the indirect evaluation value of the fishway's fish passage effect, as shown in the following formula:

[0180]

[0181]

[0182] In the formula, W is the weight vector of each indirect index layer obtained from the cross-load calculation, and Z is the fuzzy comprehensive evaluation vector. j K represents the value in the fuzzy comprehensive evaluation vector. j K represents the value in the evaluation level scoring vector corresponding to the membership degree. j =(100,75,50,25) T The final V value is the indirect evaluation value of the fish passage effect.

[0183] The indirect evaluation value of the fish passage effect is divided into 4 levels: 0-25, 25-50, 50-75, and 75-100, which correspond to poor, qualified, good, and excellent levels, respectively. The indirect evaluation value of the fish passage effect is 62.73, which is between 50 and 75. Therefore, the result is judged as good fish passage effect.

[0184] S63: Substitute the indirect index data of the target fishway for evaluation, proceed to steps S52-S53, diagnose the problem indicators of the target fishway, and propose improvement suggestions.

[0185] Based on the results of the operations on the membership matrix, judgment matrix, and evaluation matrix, problem indicators are diagnosed, and the diagnostic matrix is ​​calculated as follows:

[0186]

[0187] In the formula, Q is the diagnostic matrix, Q i To evaluate the diagnostic result of the i-th indicator of the evaluation object, if Q i If Q ≥ 0, then the indicator meets the standard; if Q i If the value is less than 0, then the indicator is a problem indicator.

[0188] The results show that Q only has two indicators. i <0 indicates a problem indicator, namely the inlet flow rate index and the rest pool ratio index.

[0189] Based on the optimization directions shown in the fishway effectiveness frequency analysis chart, diagnostic opinions are proposed for the two problematic indicators. Figure 4It can be seen that the average flow velocity in the fishway inlet area needs to be increased by approximately 0.3 m / s or more to achieve the desired standard effect. Figure 10 It can be seen that the proportion of the fishway rest pool needs to be increased by 4% of the total length in order to achieve the desired standard effect.

[0190] The data samples in step S2, which establishes the dataset, are updated dynamically and periodically. When the dataset is updated, steps S3–S6 are repeated. First, the model is iteratively optimized using the latest dataset, and then the specific fishway project is evaluated and diagnosed. When the dataset is not updated, steps S3–S5 can be skipped, and the already constructed model can be used to evaluate the effectiveness of the specific fishway project and diagnose problems.

[0191] This invention proposes a method for evaluating the effectiveness and diagnosing problems of fish passages based on indirect indicators such as hydraulic conditions, environmental factors, and internal structure. This method is based on statistical analysis of a large number of existing datasets, dynamically determines the weights and suitability of key indicators, realizes the quantitative evaluation of fish passage effectiveness and the accurate diagnosis of potential problems, and continuously iterates and optimizes with the dynamic updating of the basic dataset.

[0192] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for indirect evaluation and problem diagnosis of fishway effectiveness through multi-factor coupling, characterized in that, Includes the following steps: Step S1: Construct a fishway effectiveness evaluation index system. The fishway effectiveness evaluation index system includes an indirect index layer and a direct index layer. Indicators that characterize hydraulic conditions, environmental factors and internal structure are classified as indirect index layers, while indicators that characterize the effectiveness of fish passage are classified as direct index layers. Step S2: Establish an evaluation index dataset and dynamically update the data samples within the evaluation index dataset; Step S3: Screen and identify key evaluation indicators; Step S4: Construct a fishway effectiveness evaluation model; Step S5: Construct a diagnostic model for fishway problems; Step S6: Based on the fishway effect evaluation model constructed in Step S4 and the fishway problem diagnosis model constructed in Step S5, conduct effect evaluation and problem diagnosis of specific fishway projects. Step S4 includes the following sub-steps: S41: The fishway effectiveness evaluation model consists of two parts: an indirect indicator layer and a direct indicator layer. Canonical correlation analysis is performed on the determined indirect and direct indicator layers. The indirect indicator layer... Depend on Composed of several indicators, direct indicator layer Depend on Composed of several factors, the system With the system The correlation between them can be transformed into two variables. and The correlation between them is formed by linear combination, and is expressed as follows: （2）； （3）； variable and correlation coefficient The expression is as follows: （4）； （5）； In the formula, Let covariance be the linear combination of two sets of variables. The variance of the linear combination of variables; S42: Examine whether the selection of the index layer for the fishway effectiveness evaluation model is reasonable. If the canonical correlation coefficient results are... If the significance is less than 5%, the test is passed; if it is greater than 5%, the test is failed. This indicates that the indicator selection is unreasonable or the sample size is insufficient, and the model cannot operate normally. The sample size needs to be increased and the indicators need to be re-selected. S43: In the fishway effectiveness evaluation model, the weights of the indirect indicator layer are determined based on the cross-load results, and the normalized results of the cross-loads are used as the weight vectors of each indicator within the indirect indicator layer. The expression for calculating cross loads is as follows: （6）； （7）； S44: Based on the results of canonical correlation analysis, frequency analysis is used to determine the index values ​​of the fish passage effect evaluation model. The sample values ​​of the indirect index layer are used as the horizontal axis, and the sample values ​​of the direct index layer are used as the vertical axis. This determines the range of fish passage effect corresponding to the indirect index layer index. S45: The index values ​​of the fishway effect evaluation model are represented by membership functions. A linear membership function is constructed using fuzzy mathematics. Based on the good and bad intervals of the frequency analysis results, the index change intervals are divided into two types: positive correlation intervals and negative correlation intervals. The membership function expression for the positive correlation interval is as follows: （8）； （9）； （10）； （11）； The expression for the membership function of negative correlation intervals is as follows: （12）； （13）； （14）； （15）； In the formula, , , , Each corresponds to a membership value of one of the four levels: excellent, good, satisfactory, and poor. These are the measured values ​​of the indirect indicator layer indicators that need to be evaluated. , , , This represents the maximum membership value at different levels within the interval where the indirect and direct indicator layers are positively correlated. , , , The maximum membership values ​​for different levels of the negative correlation interval; S46: Substitute the indirect index data of the target fishway into the evaluation data to establish the membership matrix. As shown in the following formula: （16）； In the formula For the first The first indicator Membership function values ​​for each level; S47: Output the evaluation results of the fishway effect evaluation model, using the weighted average multiplication-bounded operator in fuzzy synthesis theory. The fuzzy comprehensive evaluation vector is used to calculate the indirect evaluation value of the fishway's fish passage effect, as shown in the following formula: （17）； （18）； In the formula, W is the weight vector of each indirect index layer obtained from the cross-load calculation, and Z is the fuzzy comprehensive evaluation vector. The values ​​in the fuzzy comprehensive evaluation vector are... The values ​​in the evaluation level scoring vector corresponding to the membership degree are: The final result The value is an indirect evaluation value of the fishway's effectiveness in facilitating fish passage. Step S5 includes the following sub-steps: S51: The judgment matrix within the fishway problem diagnostic model is derived from the standard evaluation values ​​required for the evaluated project. The expression: （19）； （20）； In the formula, Indicates the first Standard evaluation values ​​for each indicator, evaluation matrix The judgment matrix expression is derived from this standard evaluation value. Indicates the first The first indicator Standard membership degree at each level; S52: Based on the results of the operations of the membership matrix, judgment matrix, and evaluation matrix, problem indicators are diagnosed. The expression for the diagnosis matrix is ​​as follows: （21）； In the formula, For the diagnostic matrix, For the evaluation object The diagnostic results of each indicator, if If the indicator meets the standard, then the indicator meets the standard; if If so, then this indicator is a problem indicator; S53: List the diagnosed problem indicators and propose improvement suggestions based on the optimization direction in the fishway effectiveness frequency analysis chart to comprehensively improve the fishway effectiveness.

2. The method for indirect evaluation and problem diagnosis of fishway effectiveness based on multi-factor coupling as described in claim 1, characterized in that: Step S2 includes the following sub-steps: S21: Collect fishway monitoring data samples through data analysis and keep the data samples dynamically updated, including the following channels: domestic and foreign literature database search, fishway engineering design reports, fishway effect monitoring and evaluation project reports, and field surveys; S22: Convert the monitoring data according to the definition and calculation method of the indirect indicator layer and direct indicator layer in the fishway effect evaluation index system described in step S1 to form an indicator dataset.

3. The method for indirect evaluation and problem diagnosis of fishway effectiveness based on multi-factor coupling as described in claim 1, characterized in that: Step S3 includes the following sub-steps: S31: Perform Pearson correlation analysis on the indirect indicator layer indicators, the expression of which is as follows: （1） In the formula, Indicators The Each sample value Indicators The sample mean, Indicators With indicators The Pearson correlation coefficient; S32: Based on the established screening principles, identify key evaluation indicators. The screening principles are as follows: If several groups of indicators in the indirect indicator layer have extremely significant correlations, it indicates that there is overlap between the concepts of the indicators, and one indicator from each group is removed. until no highly significant correlation exists in the indirect indicator layer; S33: The retained indicators after screening will be used as the indirect indicator layer of the subsequent evaluation model.

4. The method for indirect evaluation and problem diagnosis of fishway effectiveness based on multi-factor coupling as described in claim 1, characterized in that: Step S6 includes the following sub-steps: S61: Obtain monitoring data required by indirect indicator layer indicators through monitoring methods, and convert them into indicator data according to the indicator layer definition and calculation formula; S62: Substitute the indirect index data of the target fishway into the evaluation data, proceed to steps S46-S47, query the membership function image corresponding to each index, establish the membership matrix, and calculate the indirect evaluation value of the fishway passage effect. S63: Substitute the indirect index data of the target fishway for evaluation, proceed to steps S52-S53, diagnose the problem indicators of the target fishway, and propose improvement suggestions.

5. The method for indirect evaluation and problem diagnosis of fishway effectiveness based on multi-factor coupling according to claim 1, characterized in that: The data samples in the evaluation index dataset established in step S2 are regularly and dynamically updated. When the data samples are updated, steps S3-S6 are repeated. First, the model is iteratively optimized using the latest data samples, and then the specific fishway project is evaluated and diagnosed. When the data samples are not updated, steps S3-S5 are skipped, and the established model is used to evaluate the effect of the specific fishway project and diagnose the problems.

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