A method for dynamic evaluation of sustainable development of a river basin shoreline

Abstract

The application provides a watershed shoreline sustainable development dynamic evaluation method, comprising the following steps: constructing a water environment carrying capacity and shoreline green development index judgment matrix; dynamically analyzing a weight vector through a neural network; synthesizing a carrying capacity index and a green development index based on the weight; dynamically correlating the two indexes by establishing a coupling function; and outputting a sustainable development index. Four major capabilities are broken through and jumped: a spatial correction matrix breaks through administrative barriers and enhances cross-border consistency; a multi-task neural network self-adapts weight adjustment in dry seasons; an entropy value gate fusion suppresses index noise; a pulse neural network coupling function avoids critical point distortion; and a digital twin body constructs an evolutionary benchmark. The method forms a "spatial coupling-dynamic weighting-intelligent synthesis-collaborative correlation-twin evolution" full-chain innovation, solves the problems of evaluation fragmentation, rule rigidity, index imbalance and management disconnection, and promotes the watershed management to adapt to the collaborative paradigm.

Description

Technical Field

[0001] This invention relates to the field of watershed shoreline management technology, and in particular to a dynamic evaluation method for sustainable development of watershed shorelines. Background Technology

[0002] Traditional assessments of sustainable development of river basin shorelines face three major bottlenecks: fragmented assessments due to administrative boundaries in transboundary basins; static weighting models struggling to adapt to dynamic changes in the hydrological environment; and the inability of linear index synthesis methods to handle imbalances in the contributions of complex system indicators. Existing technologies rely on expert experience to construct fixed-rule judgment matrices, ignoring spatial correlations and resulting in insufficient regional collaborative representation capabilities. Weight allocation depends on historical statistical patterns, failing to respond to abrupt changes such as the dry season; sub-dimensional summation is affected by noise indicators, reducing index stability; and assessment results are disconnected from governance decisions, lacking a dynamic feedback mechanism. These shortcomings make traditional methods prone to critical point misjudgments, cross-regional incomparability, and lagging management measures when dealing with the nonlinear evolution of river basin systems, hindering precise governance for sustainable development. Summary of the Invention

[0003] To address the technical problems existing in the background art, this invention proposes a dynamic evaluation method for sustainable development of watershed shorelines, comprising:

[0004] S1. Construct judgment matrices for the water environment carrying capacity index and the green development level index of the target watershed, respectively;

[0005] S2. Dynamically analyze the judgment matrix through a neural network model and output the dynamic weight vector of the carrying capacity index and the green development level index;

[0006] S3. Based on dynamic weight vector synthesis, the carrying capacity index and the shoreline green development level index are combined, and the index adopts a non-linear fusion mechanism to integrate sub-dimensional indicators;

[0007] S4. Establish a dynamic correlation between the carrying capacity index and the shoreline green development level index using a coupling function;

[0008] S5. Output a sustainable development index that represents the degree of fit between carrying capacity and development level.

[0009] In S2, the neural network model is a multi-task learning architecture, which specifically includes: the feature extraction layer learning the implicit rules of the judgment matrix; the weight prediction branch generating dynamic weight vectors; and the matrix reconstruction branch constraining the feature extraction process.

[0010] Specifically, the coupling function in S4 is: ,in, The normalized bearing capacity index This is a normalized index of the level of green development of the shoreline. Here, is the adaptive adjustment coefficient. Used to control the smoothness of function curvature. The output is a sustainable development index.

[0011] Specifically, the nonlinear fusion mechanism in S3 includes: calculating the entropy value of the sub-dimension index weight vector. ,in, A higher entropy value indicates a more dispersed weight distribution; entropy-based gating controls the contribution of sub-dimensions. ; ,in, Specifically Activation function Output range , Specifically, it refers to the weighted entropy value of the carrying capacity sub-dimension. Specifically, it refers to the weight entropy value of the pressure-bearing sub-dimension. Specifically, it is the carrying capacity sub-index. Specifically, it is the pressure bearing sub-index.

[0012] Wherein, the adaptive adjustment coefficient is generated. Specifically, this includes: inputting the historical sustainable development index into a spiking neural network; encoding the phase amplitude information of the time series, with the amplitude calculation function being: ,in, Specifically, the first Sustainable Development Index Specifically, these are sustainable development benchmarks. Specifically, it is the exponential decay coefficient, adjusted according to the peak pulse amplitude. , The value is positively correlated with the peak amplitude.

[0013] S1 further includes: constructing a cross-boundary watershed fuzzy cognitive map; and generating spatial correlation factors through map reasoning. ;use Correction judgment matrix elements, where, Specifically, it refers to the spatial correlation factor, where, when When, strengthen the connection; when At that time, the association is suppressed.

[0014] Following S5, it also includes: building a sustainable development digital twin; generating benchmark indices through Monte Carlo simulation. When the deviation between the actual index and the benchmark index exceeds a threshold When this occurs, model self-correction is triggered, where... Specifically, it is the twin simulation benchmark index. Specifically, this refers to the dynamic tolerance threshold. It adaptively adjusts as the number of evaluations increases.

[0015] This invention achieves a breakthrough in four key capabilities: breaking down administrative barriers through a spatial correction judgment matrix to enhance the consistency of cross-boundary watershed evaluation; utilizing a multi-task neural network to dynamically analyze weights, enabling the weights of carrying capacity and green development indicators to adapt to the transition between dry and flood seasons; employing an entropy-gated nonlinear fusion mechanism to suppress redundant indicator interference and improve the index's noise resistance; using a coupling function driven by a spiking neural network to capture system mutation characteristics and avoid critical point evaluation distortion; and constructing an evolutionary benchmark using digital twins and Monte Carlo simulations, continuously optimizing model accuracy through bidirectional parameter backtracking. Ultimately, this forms a complete innovation chain of "spatial coupling - dynamic weighting - intelligent synthesis - collaborative association - twin evolution," addressing industry pain points such as fragmented evaluation, rigid static rules, imbalanced indicator contributions, and management disconnect, thus driving the transformation of watershed governance from adversarial control to an adaptive and collaborative paradigm. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the workflow structure of a dynamic evaluation method for sustainable development of watershed shorelines proposed in this invention. Detailed Implementation

[0017] Reference Figure 1 This invention proposes a dynamic evaluation method for sustainable development of river basin shorelines, comprising:

[0018] S1. Construct judgment matrices for the water environment carrying capacity index and the shoreline green development level index of the target watershed, respectively. Construct a fuzzy cognitive map of the transboundary watershed; generate spatial correlation factors through map reasoning. ;use Correction judgment matrix elements, where, Specifically, spatial correlation factors, when When, strengthen the connection; when At that time, the association is suppressed.

[0019] First, initial judgment matrices are constructed for the water environment carrying capacity index and the shoreline green development level index of the target watershed, respectively, and pairwise comparison relationships are formed through expert scoring or historical data analysis. Then, a cross-boundary watershed fuzzy cognitive map is constructed, which integrates multi-source geographic information data based on the river system topology and administrative division relationships. Finally, a map inference engine is used to quantify the spatial correlation strength and generate spatial correlation factors. Next, spatial correlation factors were used. Dynamically correct the elements of the initial judgment matrix: when When the value is greater than zero, the correlation between indicators in adjacent regions is enhanced. When the value is less than zero, data interference from irrelevant areas is suppressed; finally, the spatially corrected judgment matrix is ​​output. This process breaks down information barriers at administrative boundaries through fuzzy cognitive mapping, transforming traditional isolated matrix construction into spatial coupling analysis, solving the fragmentation problem of cross-boundary watershed evaluation, improving the regional collaborative representation capability of the matrix, and providing spatially consistent basic data support for subsequent dynamic weighting.

[0020] S2. The judgment matrix is ​​dynamically analyzed through a neural network model, and the dynamic weight vectors of the carrying capacity index and the green development level index are output.

[0021] 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 dynamic weight vectors; and a matrix reconstruction branch constraining the feature extraction process.

[0022] First, the spatial correction 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 by convolutional kernels and attention mechanisms to learn the nonlinear implicit rules between indicators and generate a high-dimensional feature tensor. Then, this feature tensor is simultaneously input into the weight prediction branch and the matrix reconstruction branch: the weight prediction branch uses a fully connected layer and a gated recurrent unit to fuse the time series dependencies and outputs a dynamic weight vector; the matrix reconstruction branch decodes the feature tensor through transposed convolution, reconstructs 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 is backpropagated to constrain the parameter update of the feature extraction layer, forming a self-optimizing closed loop.

[0023] This multi-task architecture suppresses the overfitting tendency of neural networks in small-sample scenarios by reconstructing the regularization effect of the branches, and uses the weight prediction branch to capture the spatiotemporal evolution of indicator weights, ultimately outputting a dynamic weight vector with watershed adaptability. This design breaks through the dependence of traditional single-weighting models on static rules, enabling the weight allocation of carrying capacity and green development level indicators to adapt to the dynamic changes of the watershed system, providing a precise quantitative basis for subsequent index synthesis. For example, during the dry season, the weight of water resource constraint indicators is automatically strengthened, while during the flood season, the contribution of ecological resilience indicators is increased, achieving intelligent coordination between evaluation parameters and environmental status.

[0024] S3. The carrying capacity index and the shoreline green development level index are synthesized based on dynamic weight vectors. The index integrates sub-dimensional indicators using a nonlinear fusion mechanism.

[0025] Specifically, the nonlinear fusion mechanism includes: calculating the entropy value of the sub-dimension indicator weight vector. ( (A higher entropy value indicates a more dispersed weight distribution); the contribution of sub-dimensions is controlled based on entropy gating. ; ,in, Specifically Activation function Output range , Specifically, it refers to the weighted entropy value of the carrying capacity sub-dimension. Specifically, it refers to the weight entropy value of the pressure-bearing sub-dimension. Specifically, it is the carrying capacity sub-index. Specifically, it is the pressure bearing sub-index.

[0026] First, the dynamic weight vector output by S2 is received, and the entropy value of the weight distribution is calculated for the two sub-dimensions of carrying capacity and carrying pressure respectively (the entropy value reflects the degree of weight dispersion; a high entropy value indicates that the importance of the indicator is dispersed, while a low entropy value indicates that the weight is concentrated). Then, the entropy values ​​of the carrying capacity sub-dimension and the carrying pressure sub-dimension are input into the activation function to generate standardized gating coefficients. Next, the sub-indices are dynamically weighted using the gating coefficients, and finally, the weighted results are superimposed to generate the carrying capacity index and the shoreline green development level index.

[0027] This mechanism automatically suppresses noise interference from highly discrete sub-dimensions through entropy gating, thereby enhancing the effective contribution of the weighted, concentrated dimensions.

[0028] For example, when the weight of a certain sub-dimension fluctuates randomly, a high entropy value triggers gating closure to prevent redundant indicators from contaminating the index synthesis; while when the weights of key indicators are clustered, low entropy values ​​maintain a high contribution ratio, ensuring that core elements dominate the evaluation results. This design breaks through the rigid constraints of the traditional linear summation method, solves the problem of imbalance in the contribution of indicators in complex systems, and enables the index synthesis to have adaptive anti-interference capabilities, providing a stable quantitative benchmark for sustainable development status assessment.

[0029] S4. Establish a dynamic correlation between the carrying capacity index and the shoreline green development level index using a coupling function.

[0030] The coupling function is specifically as follows: ,in, The normalized bearing capacity index This is a normalized index of the level of green development of the shoreline. Here, is the adaptive adjustment coefficient. Used to control the smoothness of function curvature. The output is a sustainable development index.

[0031] Specifically, generating the adaptive adjustment coefficient k includes: inputting the historical sustainable development index into a pulse neural network; encoding the phase amplitude information of the time series, with the amplitude calculation function being: ,in, Specifically, the first Sustainable Development Index Specifically, these are sustainable development benchmarks. Specifically, it is the exponential decay coefficient, adjusted according to the peak pulse amplitude. , The value is positively correlated with the peak amplitude.

[0032] First, the system receives the normalized carrying capacity index and the normalized shoreline green development level index output from S3. Simultaneously, it initiates the adaptive adjustment coefficient generation process: the historical sustainable development index sequence is input into a spiking neural network. This network extracts temporal fluctuation characteristics through a phase encoder and uses an amplitude calculation function to quantify the degree of deviation of the index from the benchmark value at each moment. The higher the frequency of historical index fluctuations, the faster the pulse amplitude decays. The adaptive adjustment coefficient is determined based on the peak value of the pulse amplitude; the larger the peak amplitude, the higher the coefficient value. Subsequently, the carrying capacity index, green development level index, and adaptive adjustment coefficient are input into a coupling function to calculate the sustainable development index. The denominator of this function dynamically adjusts the curvature smoothness through coefficients: when the coefficient increases, the transition in the critical region of the function curve is smoother; when the coefficient decreases, the function is more sensitive to index differences. The numerator introduces a logarithmic compensation mechanism to amplify the adaptation differences between carrying capacity and development level. Finally, a sustainable development index with both dynamic balance and critical response is output. This scheme captures the abrupt response characteristics of the watershed system through a spiking neural network, enabling the coefficients to adapt to the intensity of historical fluctuations. For example, when a sudden shift from drought to flood triggers a high-frequency pulse, the coefficient automatically increases to avoid evaluation distortion near the critical point; while low-frequency fluctuations during stable periods reduce the coefficient to improve resolution. Curvature adjustment of coupling functions breaks through the traditional static threshold limitation, solves the quantitative problem of nonlinear correlation between bearing capacity and development level, and realizes the paradigm shift from adversarial evaluation to collaborative evaluation.

[0033] S5. Output a sustainable development index that represents the degree of fit between carrying capacity and development level.

[0034] First, the system receives the raw values ​​of the sustainable development index from S4; through adaptive normalization, the index is mapped to a standard range of -1 to 1, where negative values ​​indicate an imbalance between carrying capacity and development level, while positive values ​​reflect synergistic improvement; the absolute value quantifies the degree of adaptation. Next, a visual interactive interface is constructed; a spatiotemporal distribution heatmap of the index is dynamically rendered; critical warning areas are highlighted; and the index sequence is stored in the watershed sustainable development database, establishing a historical status tracing channel. Finally, it is linked to the watershed management decision-making system; when the index continuously falls below the dynamic warning threshold, optimization suggestions for governance schemes are automatically triggered; when the index jumps into the positive range, an ecological compensation policy simulation report is generated. This output mechanism eliminates scale differences between different watersheds through normalization, making cross-regional evaluation results comparable; for example, the indices of small mountain watersheds and large plain river systems can be directly compared to coordinate evolution stages; the visual heatmap, combined with a geographic information system, locates vulnerable shoreline segments, guiding precise allocation of governance resources; the index sequence accumulated in the database is used to mine long-term evolution patterns through machine learning, overcoming the limitations of traditional static evaluation lacking temporal correlation, and achieving a leap from single-point assessment to process tracking. The index is linked to the decision-making system in real time; it transforms academic evaluation into management action; and it can solve the industry pain point of the disconnect between scientific research results and application scenarios.

[0035] S6. Construct a digital twin for sustainable development; generate benchmark indices through Monte Carlo simulation. When the deviation between the actual index and the benchmark index exceeds a threshold At that time, model self-correction is triggered. Among them, Specifically, it is the twin simulation benchmark index. Specifically, this refers to the dynamic tolerance threshold. It adaptively adjusts as the number of evaluations increases.

[0036] First, an integrated algorithm module (S1 to S5) is constructed to create a digital twin for sustainable development. This twin receives real-time dynamic data from multiple sources, including watershed hydrology, meteorology, and management policies. During the initialization phase, a benchmark index is generated through Monte Carlo simulation: the simulation process randomly combines environmental factors and human activity parameters, performing tens of thousands of scenario simulations, and using the median value of the interval with the highest probability density as the benchmark index. Then, the actual sustainable development index output by S5 is compared with the benchmark index: when the absolute value of the deviation exceeds the dynamic tolerance threshold, a model self-correction mechanism is triggered. The dynamic tolerance threshold is initially set to the system default value and is automatically updated after each evaluation cycle. The update rule follows the statistical distribution characteristics of historical deviations, and the more evaluation cycles, the stricter the threshold convergence. The self-correction process involves two-way linkage of key modules: feeding back weight correction signals to the S2 dynamic weight analysis layer, requiring recalibration of the indicator importance ranking; and sending parameter optimization instructions to the S4 coupling function layer to adjust the response sensitivity of the adaptive adjustment coefficients; ultimately, the twin output approximates the actual observed values.

[0037] This closed-loop design overcomes the deterministic limitations of traditional benchmark construction through Monte Carlo multi-factor combination simulation, enabling the benchmark index to cover extreme scenarios; for example, simulating the combined impact of a once-in-a-decade flood combined with excessive pollution discharge on the level of green development. The dynamic tolerance threshold adaptively tightens with data accumulation, avoiding false triggers in early evaluations due to insufficient samples; the model's self-calibrating bidirectional parameter backtracking mechanism synchronously updates weights and function parameters, solving the system detuning problem caused by single-parameter optimization; for example, when a flood event causes a sudden change in the index, the model synchronously corrects the water resource index weights and coupling function curvature, rather than adjusting a single module in isolation, ensuring the overall robustness of the evaluation system. The continuous evolution of the twin makes the evaluation results both scenario predictive and system stable, providing a scientific framework for long-term watershed management.

[0038] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic evaluation method for sustainable development of watershed shorelines, characterized in that, include: S1. Construct judgment matrices for the water environment carrying capacity index and the green development level index of the target watershed, respectively; S2. Dynamically analyze the judgment matrix through a neural network model and output the dynamic weight vector of the carrying capacity index and the green development level index; S3. Based on dynamic weight vector synthesis, the carrying capacity index and the shoreline green development level index are combined, and the index adopts a non-linear fusion mechanism to integrate sub-dimensional indicators; S4. Establish a dynamic correlation between the carrying capacity index and the shoreline green development level index using a coupling function; S5. Output a sustainable development index that represents the compatibility between carrying capacity and development level; Specifically, the coupling function in S4 is: ,in, The normalized bearing capacity index This is a normalized index of the level of green development of the shoreline. Here, is the adaptive adjustment coefficient. Used to control the smoothness of function curvature. The output is the sustainable development index; The nonlinear fusion mechanism in S3 specifically includes: calculating the entropy value of the sub-dimension index weight vector. ,in, A higher entropy value indicates a more dispersed weight distribution; entropy-based gating controls the contribution of sub-dimensions. ; ,in, Specifically, it is the Sigmoid activation function. Output range , Specifically, it refers to the weighted entropy value of the carrying capacity sub-dimension. Specifically, it refers to the weight entropy value of the pressure-bearing sub-dimension. Specifically, it is the carrying capacity sub-index. Specifically, it is the sub-index of bearing pressure; Generating the adaptive adjustment coefficient k specifically includes: inputting the historical sustainable development index into a spiking neural network; encoding the phase amplitude information of the time series, with the amplitude calculation function being: ,in, Specifically, the first Sustainable Development Index Specifically, these are sustainable development benchmarks. Specifically, it is the exponential decay coefficient, adjusted according to the peak pulse amplitude. , The value is positively correlated with the peak amplitude.

2. The method as described in claim 1, characterized in that, The neural network model in S2 is a multi-task learning architecture, which specifically includes: the feature extraction layer learning the implicit rules of the judgment matrix; the weight prediction branch generating dynamic weight vectors; and the matrix reconstruction branch constraining the feature extraction process.

3. The method as described in claim 1, characterized in that, S1 also includes: constructing a cross-boundary watershed fuzzy cognitive map; generating spatial correlation factors through map reasoning. ;use Correction judgment matrix elements, where, Specifically, it refers to the spatial correlation factor, where, when When, strengthen the connection; when At that time, the association is suppressed.

4. The method as described in claim 1, characterized in that, Following S5, it also includes: building a sustainable development digital twin; generating benchmark indices through Monte Carlo simulation. When the deviation between the actual index and the benchmark index exceeds a threshold When this occurs, model self-correction is triggered, where... Specifically, it is the twin simulation benchmark index. Specifically, this refers to the dynamic tolerance threshold. It adaptively adjusts as the number of evaluations increases.

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