Dynamic evaluation method for sustainable development of watershed shoreline

Abstract

The invention provides a watershed shoreline sustainable development dynamic evaluation method. The method comprises the following steps: constructing a water environment bearing capacity and shoreline green development index judgment matrix; dynamically analyzing the weight vector through a neural network; synthesizing a bearing capacity index and a green development index based on weight nonlinearity; establishing a coupling function to dynamically associate the two indexes; and outputting a sustainable development index. The breakthrough is that the space correction matrix breaks through the administrative barrier and enhances the transboundary consistency; carrying out self-adaptive weight adjustment in the dry flood season by the multi-task neural network; entropy gating fusion is carried out to suppress index noise; a pulse neural network coupling function avoids critical point distortion; the digital twin constructs an evolutionary benchmark. A full-chain innovation of space coupling, dynamic empowerment, intelligent synthesis, collaborative association and twinning evolution is formed, the problems of evaluation fragmentation, rule stiffness, index imbalance and management disjunction are solved, and basin treatment is promoted to be converted into an adaptive collaborative normal form.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of watershed shoreline management, and particularly relates to a watershed shoreline sustainable development dynamic evaluation method. BACKGROUND

[0002] There are three major bottlenecks in the traditional watershed shoreline sustainable development evaluation: cross-border watershed evaluation fragmentation caused by administrative boundary segmentation, static weighting model difficult to adapt to dynamic changes in hydrological environment, and linear index synthesis method unable to handle the imbalance of complex system index contribution. The existing technology relies on expert experience to construct a fixed rule judgment matrix, ignores spatial correlation, and makes the matrix area collaborative representation ability insufficient; the weight distribution relies on historical statistical law and cannot respond to mutation scenarios such as dry season; the sub-dimension addition synthesis is disturbed by noise indicators, reducing the stability of the index; the evaluation result is disconnected with the management decision, and lacks a dynamic feedback mechanism. These defects make the traditional method prone to critical point misjudgment, cross-regional incomparability, and management measures lagging behind when dealing with nonlinear evolution of watershed system, which restricts the precise management of sustainable development. SUMMARY

[0003] To solve the technical problems in the background art, the present application provides a watershed shoreline sustainable development dynamic evaluation method, which comprises: S1, constructing a judgment matrix for the target watershed water environment carrying capacity index and the shoreline green development level index respectively; S2, dynamically analyzing the judgment matrix through a neural network model to output a dynamic weight vector of the carrying capacity index and the green development level index; S3, synthesizing the carrying capacity index and the shoreline green development level index based on the dynamic weight vector, wherein the index integrates sub-dimension indicators using a nonlinear fusion mechanism; S4, establishing a coupling function to dynamically correlate the carrying capacity index and the shoreline green development level index; S5, outputting a sustainable development index representing the carrying capacity and development level adaptation degree.

[0004] In S2, the neural network model is a multi-task learning architecture, which specifically includes: a feature extraction layer learns the implicit rules of the judgment matrix; a weight prediction branch generates a dynamic weight vector; and a matrix reconstruction branch constrains the feature extraction process.

[0005] In S4, the coupling function is specifically: wherein, is the normalized carrying capacity index, is the normalized shoreline green development level index, is an adaptive adjustment coefficient, wherein, is used to control the curvature smoothness of the function, is the output sustainable development index.

[0006] wherein, the nonlinear fusion mechanism in S3 specifically comprises: calculating the entropy value of the sub-dimension index weight vector wherein, the higher the entropy value, the more dispersed the weight distribution; and controlling the sub-dimension contribution degree based on the entropy value wherein, specifically an activation function, an output value range , specifically the weight entropy value of the carrying capacity sub-dimension, specifically the weight entropy value of the carrying pressure sub-dimension, specifically the carrying capacity sub-index, specifically the carrying pressure sub-index.

[0007] wherein, the adaptive adjustment coefficient is generated specifically comprising: inputting the historical sustainable development index into a pulse neural network; and encoding the phase amplitude information of the time series, wherein the amplitude calculation function is: wherein, specifically the sustainable development index at the moment, specifically the sustainable development benchmark value, specifically the index decay coefficient, and the amplitude peak value is adjusted according to the pulse amplitude peak value , positively correlated with the amplitude peak value.

[0008] wherein, S1 further comprises: constructing a fuzzy cognitive map of a cross-boundary basin; generating a spatial correlation factor through map reasoning ; and correcting the elements of the judgment matrix using wherein, specifically the spatial correlation factor, wherein when the correlation is enhanced; and when the correlation is inhibited.

[0009] wherein, S5 further comprises: constructing a sustainable development digital twin; and generating a benchmark index through Monte Carlo simulation ; when the actual index deviates from the benchmark index by more than a threshold value , triggering model self-correction, wherein, specifically the twin simulation benchmark index, specifically the dynamic tolerance threshold, and the evaluation number is increased to adaptively adjust.

[0010] The application breaks through four major capabilities: breaking administrative barriers through spatial correction judgment matrix, enhancing cross-basin evaluation consistency; using multi-task neural network to dynamically analyze weight, making the carrying capacity and green development index weight adaptive to dry season conversion; entropy value gate nonlinear fusion mechanism suppresses redundant index interference, improves index noise immunity; pulse neural network driven coupling function captures system mutation characteristics, avoids critical point evaluation distortion; digital twin and Monte Carlo simulation construct evolutionary benchmark, continuously optimize model accuracy through bidirectional parameter backtracking. Finally, the whole chain innovation of "spatial coupling-dynamic weighting-intelligent synthesis-coupling correlation-twin evolution" is formed, which solves the industry pain points such as fragmented evaluation, static rule rigidity, index contribution imbalance and management disconnection, and promotes the transformation of basin management from antagonistic control to adaptive collaborative paradigm. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 A workflow structure schematic diagram of a basin shoreline sustainable development dynamic evaluation method proposed by the application. DETAILED DESCRIPTION

[0012] REFERENCE Figure 1 The application proposes a basin shoreline sustainable development dynamic evaluation method, comprising: S1, constructing a judgment matrix for the water environment carrying capacity index and the shoreline green development level index of the target basin respectively. Construct a cross-basin fuzzy cognitive map; generate spatial correlation factors through graph reasoning ; correct the elements of the judgment matrix using , wherein Specifically, the spatial correlation factor, when , enhances the correlation; when , suppress the correlation.

[0013] First, the initial judgment matrix is constructed for the water environment carrying capacity index and the shoreline green development level index of the target basin, and the pairwise comparison relationship is formed through expert scoring or historical data analysis; then a cross-basin fuzzy cognitive map is constructed, which integrates multi-source geographic information data based on water system topological structure and administrative division relationship; the spatial correlation strength is quantified through graph reasoning engine to generate spatial correlation factors ; then, the elements of the initial judgment matrix are dynamically corrected using the spatial correlation factors : when is greater than zero, the correlation of adjacent regional indicators is enhanced, and when When less than zero, the data interference of non-related areas is inhibited; finally, the spatial corrected judgment matrix is output. This process breaks down the administrative boundary information barrier through fuzzy cognitive maps, converts the traditional isolated matrix construction into spatial coupling analysis, solves the fragmentation problem of cross-border basin evaluation, enhances the regional collaborative representation ability of the matrix, and provides spatial consistency basic data support for subsequent dynamic weighting.

[0014] S2, dynamically analyzing the judgment matrix through a neural network model to output a dynamic weight vector of the bearing capacity index and the green development level index.

[0015] Among them, the neural network model is a multi-task learning architecture, specifically including: a feature extraction layer learning the implicit rules of the judgment matrix; a weight prediction branch generating a dynamic weight vector; and a matrix reconstruction branch constraining the feature extraction process.

[0016] First, the spatial corrected judgment matrix output by S1 is input into the feature extraction layer of the multi-task learning architecture neural network model, the matrix elements are scanned in parallel through the convolution kernel and the attention mechanism, the non-linear implicit rules between the indexes are learned, and a high-dimensional feature tensor is generated; then, the feature tensor is input into the weight prediction branch and the matrix reconstruction branch at the same time: the weight prediction branch adopts the fusion of fully connected layers and gated recurrent units to capture the time sequence dependence relationship, and outputs a dynamic weight vector; the matrix reconstruction branch decodes the feature tensor through transposed convolution to reconstruct the simulated judgment matrix, and generates a reconstruction loss function by calculating the difference between the original matrix and the reconstructed matrix; the reconstruction loss function reversely propagates to constrain the parameter update of the feature extraction layer, forming a self-optimizing closed loop.

[0017] This multi-task architecture suppresses the overfitting tendency of the neural network in the small sample scenario through the regularization effect of the reconstruction branch, and captures the spatio-temporal evolution law of the index weight through the weight prediction branch, finally outputs a dynamic weight vector with basin adaptability. This design breaks the dependence of traditional single weighting model on static rules, so that the weight distribution of bearing capacity and green development level index can adapt to the dynamic changes of the basin system, providing accurate quantitative basis for subsequent index synthesis. For example, in the dry season, the weight of the water resource constraint index is automatically strengthened, while in the flood season, the contribution degree of the ecological restoration index is improved, realizing the intelligent collaboration of evaluation parameters and environmental state.

[0018] S3, synthesizing the bearing capacity index and the green development level index of the shoreline based on the dynamic weight vector, the index using a nonlinear fusion mechanism to integrate sub-dimension indexes.

[0019] Among them, the nonlinear fusion mechanism specifically includes: calculating the entropy value of the sub-dimension index weight vector The higher the entropy value, the more dispersed the weight distribution is; and ​​wherein, Specifically activation function, output value range , Specifically, the weight entropy value of the carrying capacity sub-dimension, Specifically, the weight entropy value of the carrying pressure sub-dimension, Specifically, the carrying capacity sub-index, Specifically, the carrying pressure sub-index.

[0020] First, the dynamic weight vector output by S2 is received, and the entropy values of the weight distribution are calculated for the two types of sub-dimensions of carrying capacity and carrying pressure (the entropy value reflects the degree of weight dispersion, and a high entropy value indicates that the importance of the index is scattered, and a low entropy value shows that the weight is concentrated); then, the entropy values of the carrying capacity sub-dimension and the entropy values of the carrying pressure sub-dimension are input into the activation function to generate standardized gating coefficients; then, the sub-index is dynamically weighted using the gating coefficients, and finally the weighted results are superimposed to generate the carrying capacity index and the shore green development level index.

[0021] This mechanism automatically suppresses the noise interference of high dispersion sub-dimensions through entropy gating, and enhances the effective contribution of weight concentrated dimensions.

[0022] For example, when the weight of a certain sub-dimension fluctuates randomly, the high entropy value triggers the gating to close, avoiding the pollution of redundant indicators to the index synthesis; while the key indicator weight is concentrated, the low entropy value maintains a high contribution ratio, ensuring that the core elements dominate the evaluation results. This design breaks through the rigid constraints of traditional linear summation method, solves the problem of unbalanced contribution of complex system indicators, makes the index synthesis have self-adaptive anti-interference ability, and provides a stable quantitative benchmark for sustainable development state evaluation.

[0023] S4, a coupling function is established to dynamically associate the carrying capacity index and the shore green development level index.

[0024] The coupling function is specifically: wherein, is the normalized carrying capacity index, is the normalized shore green development level index, is an adaptive adjustment coefficient, wherein, is used to control the curvature smoothness of the function, is the output sustainable development index.

[0025] The adaptive adjustment coefficient k is generated by inputting the historical sustainable development index into a pulse neural network, encoding the phase amplitude information of the time series, and calculating the amplitude function as: wherein, is the sustainable development index at the time, Specifically, the sustainable development benchmark value, Specifically, the exponential decay coefficient is adjusted according to the peak value of the pulse amplitude , The value is positively correlated with the amplitude peak value.

[0026] First, the normalized carrying capacity index and the normalized green development level index of the coast are received S3 output; at the same time, the adaptive adjustment coefficient generation process is started: the historical sustainable development index sequence is input into the pulse neural network, the network extracts the time fluctuation characteristics through the phase encoder, and the amplitude calculation function is used to quantify the degree of deviation of the index from the benchmark value at each time; among them, the higher the historical index, the faster the frequency fluctuation of the pulse amplitude; the adaptive adjustment coefficient is determined according to the peak value of the pulse amplitude, and the larger the amplitude peak value, the higher the coefficient value. Then the carrying capacity index, green development level index and adaptive adjustment coefficient are input into the coupling function to calculate the sustainable development index; the denominator of the function dynamically adjusts the curvature smoothness through the coefficient: when the coefficient increases, the function curve transition in the critical region is smooth; when the coefficient decreases, the function is more sensitive to the difference between the indexes; the molecular term introduces a logarithmic compensation mechanism to amplify the adaptive difference between carrying capacity and development level. Finally, the sustainable development index with dynamic balance and critical responsiveness is output. This scheme captures the mutation response characteristics of the basin system through the pulse neural network, so that the coefficient can adapt to the historical fluctuation intensity; for example, when a drought and flood event triggers a high-frequency pulse, the coefficient automatically increases to avoid evaluation distortion near the critical point; while low-frequency fluctuations in the stable period reduce the coefficient to improve resolution. The curvature adjustment of the coupling function breaks through the traditional static threshold limit, solves the quantification problem of the nonlinear relationship between carrying capacity and development level, and realizes the paradigm shift from antagonistic evaluation to collaborative evaluation.

[0027] S5, output the sustainable development index representing the adaptability of carrying capacity and development level.

[0028] First, the sustainable development index raw value output by S4 is received; the index is mapped to the standard interval of negative one to one through adaptive normalization processing; where negative values indicate that the carrying capacity and development level are in an unbalanced state; positive values reflect the coordinated improvement of the two; and the absolute value size quantifies the strength of the adaptation degree. Subsequently, a visual interactive interface is constructed; the spatio-temporal distribution heat map of the index is dynamically rendered; the critical warning area is highlighted; at the same time, the index sequence is stored in the basin sustainable development database; a historical state tracing channel is established. Finally, the basin management decision system is associated; when the index is continuously lower than the dynamic warning threshold; automatically trigger the optimization suggestion push of the management scheme; when the index jumps to the positive interval; generate an ecological compensation policy simulation report. This output mechanism eliminates the scale difference of different basins through normalization; making the cross-regional evaluation results comparable; for example, the indices of small mountainous basins and large plain river systems can be directly compared and coordinated evolution stages; the visual heat map combined with geographic information system locates the vulnerable shoreline segment; guides the precise injection of management resources; the cumulative index sequence in the database is mined for long-period evolution rules through machine learning; breaking through the limitations of traditional static evaluation lacking temporal correlation; realizing the leap from single-point evaluation to process tracking. Real-time linkage of index and decision system; convert academic evaluation into management action; can solve the industry pain point of the disconnection between research achievements and application scenarios.

[0029] S6, construct a sustainable development digital twin; generate a benchmark index through Monte Carlo simulation ; when the actual index deviates from the benchmark index by more than the threshold , trigger model self-correction. Among them, specifically, the twin simulation benchmark index, specifically, the dynamic tolerance threshold, which is adaptively adjusted as the number of evaluations increases.

[0030] First, integrate the algorithm modules of S1 to S5 to construct a sustainable development digital twin; the twin real-time accesses multi-source dynamic data such as basin hydrology, weather, and management policies. In the initialization stage, a benchmark index is generated through Monte Carlo simulation: the simulation process randomly combines environmental factors and human activity parameters, performs 10,000 scenario simulations, and takes the median value of the highest probability density interval as the benchmark index. Then compare the actual sustainable development index output by S5 with the benchmark index: when the absolute deviation exceeds the dynamic tolerance threshold, trigger the model self-correction mechanism; where the dynamic tolerance threshold is initially set to the system default value; the threshold is automatically updated after each evaluation cycle; the update rule follows the statistical distribution characteristics of historical deviations; the more the number of evaluations, the more stringent the threshold converges. The self-correction process is bidirectional linkage with key modules: send weight correction signals back to the S2 dynamic weight resolution layer, requiring to recalibrate the importance of the index; send parameter optimization instructions to the S4 coupling function layer, adjust the response sensitivity of the adaptive adjustment coefficient; finally make the twin output approach the actual observation value.

[0031] The closed-loop design breaks through the deterministic limitations of traditional benchmark construction through Monte Carlo multi-factor combination simulation, making the benchmark index cover extreme scenarios; for example, simulating the combined impact of a ten-year flood event superimposed on excessive pollution events on green development levels. Dynamic tolerance thresholds adaptively tighten with data accumulation, avoiding false triggers due to insufficient samples in early evaluations; the model's self-correcting two-way parameter backtracking mechanism synchronously updates weight and function parameters, solving the system dissonance problem caused by single parameter optimization; for example, when a flood event causes index mutation, the model synchronously modifies the water resources index weight and coupling function curvature, rather than isolated adjustment of a single module, ensuring the overall robustness of the evaluation system. The continuous evolution of the twin body makes the evaluation results have both scenario predictability and system stability, providing a scientific sand table for long-term management of the basin.

[0032] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art, according to the technical solution and the inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A dynamic evaluation method for sustainable development of river basin shorelines, characterized in that: include: S1. Construct judgment matrices for the target basin's water environment carrying capacity index and shoreline green development level index respectively; S2. Dynamically analyzing the judgment matrix through a neural network model to output a dynamic weight vector of the carrying capacity index and the green development level index; S3, a composite carrying capacity index and a shoreline green development level index based on a dynamic weight vector, wherein the index integrates sub-dimension indicators using a nonlinear fusion mechanism; S4. Establish a coupling function to dynamically correlate the carrying capacity index and the shoreline green development level index; S5. Output a sustainable development index that represents the degree of compatibility between carrying capacity and development level.

2. The method according to claim 1, wherein The neural network model in S2 is a multi-task learning architecture, which specifically includes: the feature extraction layer learns the implicit rules of the judgment matrix; the weight prediction branch generates a dynamic weight vector; and the matrix reconstruction branch constrains the feature extraction process.

3. The method according to claim 1, wherein The coupling function in S4 is specifically: ,in, is the normalized bearing capacity index, is the normalized coastline green development level index, is the adaptive adjustment coefficient, where , used to control the smoothness of the function curvature, The sustainable development index is the output.

4. The method according to claim 1, wherein The nonlinear fusion mechanism in S3 specifically includes: calculating the entropy value of the weight vector of the sub-dimension indicator ,in, , the higher the entropy value, the more dispersed the weight distribution; based on the entropy value gate regulation sub-dimension contribution ; ,in, Specifically activation function, Output range , Specifically, it is the weight entropy value of the carrying capacity sub-dimension. Specifically, it is the weight entropy value of the bearing pressure sub-dimension, Specifically, the carrying capacity sub-index is Specifically, it is the bearing pressure sub-index.

5. The method according to claim 3, wherein Generating the adaptive adjustment coefficient k specifically includes: inputting the historical sustainable development index into the pulse neural network; encoding the phase amplitude information of the time series, and the amplitude calculation function is: ,in, Specifically, Moment Sustainability Index, Specifically, it is the sustainable development benchmark value. Specifically, it is the exponential attenuation coefficient, which is adjusted according to the peak value of the pulse amplitude. , The value is positively correlated with the peak amplitude.

6. The method according to claim 1, wherein S1 also includes: constructing a fuzzy cognitive map of cross-border watersheds; generating spatial correlation factors through map reasoning; ;use Correction judgment matrix elements, where Specifically, it is the spatial correlation factor, where When , the association is enhanced; when , inhibit association.

7. The method according to claim 1, wherein S5 will also include: building a digital twin for sustainable development; generating benchmark indices through Monte Carlo simulation ; When the actual index deviates from the benchmark index by more than the threshold When , the model self-correction is triggered, where Specifically, it is the twin simulation benchmark index. is the dynamic tolerance threshold, Adaptive adjustment is performed as the number of evaluations increases.

Images