Electrolytic bath health state assessment method based on multi-dimensional characteristics of electrolyte

Abstract

The invention relates to the technical field of neural network models, in particular to an electrolytic bath health state assessment method based on electrolyte multidimensional characteristics, which comprises the following steps: by calling a graph neural network to aggregate multiple parameter nodes, quantitatively exploring a hidden nonlinear cross correlation effect in multiple physical and chemical indexes to construct a multidimensional topological correlation architecture; the accuracy of multi-dimensional spatial feature mapping is improved by calling a long short-term memory network to superpose an internal department control tensor, comparing norm penalty numerical value separation exceeding extreme value parameters, avoiding the bottleneck of long sequence time sequence feature overlap operation calculation, and controlling a life prediction error root-mean-square extreme value to be within a predetermined security tolerance interval; according to the method, a candidate scalar is separated by splicing cross-dimensional parameter extraction dependency association, prediction and label difference aggregation coefficients are compared to generate an error gradient, the non-planned physical failure shutdown probability is controlled within a minimum tolerance interval, and the smoothness index of a whole life cycle prediction curve and the safety of an operation cycle are improved.

Description

Technical Field

[0001] This invention relates to the field of neural network model technology, and in particular to a method for assessing the health status of an electrolyzer based on multidimensional features of the electrolyte. Background Technology

[0002] The existing scheme for indices the remaining service life of computing equipment involves inputting tensors of multiple known physicochemical indicators into a converged long short-term memory network topology. The fully connected layer of the network architecture then outputs a quantitative evaluation tensor. The goal is to assess the degradation state of polarization overpotential of the electrode plates inside the electrolyzer and output a matrix of remaining usable cycle counts. This achieves the goal of triggering a judgment condition and outputting a maintenance beacon code before the electrolyzer's voltage conversion efficiency parameter decays to a preset safety threshold, thereby controlling the probability of unplanned equipment downtime within a predetermined safety tolerance range. However, the existing unidirectional time-series mapping logic only focuses on inferring historical time-step dependencies, neglecting the integration of multiple physical parameters. The chemical index spatial topology is cross-coupled and correlated, and the residuals of overestimation and underestimation in the life assessment are treated equally. The backpropagation optimization is performed according to the loss function surface optimization theory, which makes the computational topology structure very easy to approach the mean of smooth decay samples. This masks the sudden nonlinear deterioration parameter fluctuations inside the equipment, causing the unidirectional loop network topology to still be derived from the historical smooth sequence to map the state. It outputs a false optimistic matrix of remaining available loop cycles, which prevents the maintenance beacon code from being triggered before the real dangerous critical point. As a result, the equipment directly crosses the safety threshold and causes unplanned physical downtime, resulting in the overall production line shutdown and high costs for repairing damaged core components. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method for assessing the health status of electrolyzers based on the multidimensional characteristics of electrolyte.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a method for assessing the health status of an electrolyzer based on the multidimensional characteristics of the electrolyte, comprising the following steps: Step 1: Based on multiple direct-reading scalar values ​​of electrolytic cell terminal voltage and electrolyte pH test values, assign matrix mapping, extract node comparison parameters, associate flow rate alignment timestamps and remove misaligned items, splice parameter architecture, and establish a multi-dimensional parameter embedding dictionary for electrolytic cells; Step 2: Based on the multidimensional parameter embedding dictionary of the electrolytic cell, call the graph neural network aggregation node, compare adjacent product values, retain parameters that exceed the valley limit, accumulate tensor product scalars, merge corrosion depth recombination features, and generate heterogeneous space coupling feature matrix. Step 3: Based on the heterogeneous space coupling feature matrix, call the long short-term memory network to superimpose the gated tensor, compare the norm penalty values ​​and separate the parameters that exceed the extreme values, correlate porosity overlap operation, fuse the hidden layer element states, and obtain the orthogonal constraint long-term memory evolution tensor. Step 4: Based on the heterogeneous space coupling feature matrix and the orthogonal constraint long-term memory evolution tensor, concatenate cross-dimensional parameters and extract dependencies, separate parameters to generate candidate scalars, decouple the output state sequence and integrate the active area array to obtain the life cycle decay prediction array. Step 5: Based on the life cycle degradation prediction array, compare the predicted difference with the label by the real life cycle label tensor, aggregate the difference and coefficient to generate the error gradient, adjust the node bias matrix values ​​and align the voltage frequency to reset the weight tensor, and output the electrolytic cell health quantification distribution column.

[0005] As a further aspect of the present invention, the multidimensional parameter embedding dictionary of the electrolyzer includes multiple direct-reading scalars of electrolyzer terminal voltage, multiple electrolyte pH test values, multiple feature node boundary mapping arrays, and multiple cathode liquid flow rate parameters. The heterogeneous space coupling feature matrix includes multiple node inner product mapping scalar sets, multiple over-limit adjacency weight parameters, and an electrode corrosion depth matrix. The orthogonal constraint long-term memory evolution tensor includes multiple gated penalty feature tensors, multiple porosity overlapping coupling arrays, and multiple hidden layer output tensor parameters. The life cycle decay prediction array specifically includes multiple decoupled state mapping sequences, multiple catalyst layer active specific surface area parameters, and multiple global correlation weight tensors. The electrolyzer health quantification distribution column includes a multidimensional life cycle error gradient matrix, a hidden node bias parameter matrix, a voltage fluctuation frequency parameter scale, and multiple network connection weight distribution tensors.

[0006] As a further aspect of the present invention, the specific steps for establishing the multidimensional parameter embedding dictionary of the electrolytic cell are as follows: Based on multiple direct-reading scalar values ​​of electrolytic cell terminal voltage and electrolyte pH test values, the numerical range is segmented, the matrix dimension coordinates are mapped and the differences of extreme values ​​within the sequence are compared, the boundary index is extracted, the node mapping is reorganized, and a feature node boundary mapping array is generated. Based on the feature node boundary mapping array, the cathode liquid flow rate parameter is imported, the node timestamp and the flow rate timestamp are compared, the time difference value is calculated and items with differences exceeding the tolerance limit are removed, the retained parameter and flow rate parameter architecture are spliced ​​together, and a multi-dimensional parameter embedding dictionary for the electrolytic cell is established.

[0007] As a further aspect of the present invention, the specific steps for generating the heterogeneous space coupling feature matrix are as follows: Based on the multidimensional parameter embedding dictionary of the electrolytic cell, a multidimensional topological association architecture is constructed through a graph neural network. The node feature sequences are aligned and the internal matrix parameters are extracted. After the cross-product values ​​of the elements are calculated, multiple product scalars are integrated, multiple adjacent node feature space mapping dimensions are divided, and a set of node inner product mapping scalars is generated. Based on the node inner product mapping scalar set, the scalar difference between adjacent nodes is calculated and the valley comparison parameter is extracted. The magnitude of the difference in the comparison values ​​is used to filter out items with lower limits and then filter out parameter channels that exceed the limit. The corresponding parameter values ​​are retained, and the over-limit adjacency weight parameter is established. Based on the aforementioned adjacency weight parameter, the cross-dimensional tensor product values ​​are accumulated, the scalar sum is extracted, and the electrode corrosion depth matrix is ​​retrieved. After splicing the corrosion features, the associated product parameters are merged, and the multi-level spatial architecture matrix is ​​reorganized to generate a heterogeneous spatial coupling feature matrix.

[0008] As a further aspect of the present invention, the graph neural network extracts multiple initial node feature tensors from the multidimensional parameter embedding dictionary of the electrolytic cell, calculates the inner product value of the multiple node feature tensors, dynamically generates asymmetric graph adjacency matrix parameters, aggregates multiple node anisotropic information to allocate hidden layer feature space coordinates, and constructs a multidimensional topological association architecture.

[0009] As a further aspect of the present invention, after splicing the corrosion features, the associated product parameters are merged, the multidimensional mapping coordinates of the electrode corrosion depth matrix are extracted, the cross-dimensional tensor product numerical space dimension of the cross-boundary adjacency weight parameters is matched, the linear transformation operation is called to align the channels of multiple corrosion feature tensors, the multiple aligned corrosion feature vectors are spliced ​​to the boundary of the tensor product numerical array, the superposition scalar of multiple heterogeneous feature channel elements is calculated, the cross-dimensional associated product parameters are fused to extract and merge feature scales, and the multi-level matrix topology architecture is reshaped.

[0010] As a further aspect of the present invention, the specific steps for obtaining the orthogonal constrained long-term memory evolution tensor are as follows: Based on the heterogeneous spatial coupling feature matrix, a long short-term memory network is called to superimpose a gated tensor, calculate the norm penalty difference amplitude feature scalar, extract the multidimensional deviation state distribution feature sequence, integrate the multi-dimensional spatial mapping parameter array, and generate a gated penalty feature tensor. Based on the gated penalty feature tensor, the distribution feature parameters of the time step exceeding the extreme value are separated, the porosity tensor of the diffusion layer is retrieved and compared, and after performing multiple normative parameter overlapping coupling operations, a cross-dimensional array feature architecture is spliced ​​to establish a porosity overlapping coupling array. Based on the porosity overlapping coupling array, multiple hidden layer output tensor parameters are extracted, the corresponding element evolution states are combined and matched, cross-dimensional temporal evolution feature scalars are fused and internal multi-level state parameters are reorganized to obtain orthogonal constrained long-term memory evolution tensors.

[0011] As a further aspect of the present invention, the Long Short-Term Memory network extracts the heterogeneous spatial coupling feature matrix, inputs the feature matrix into the internal forget gate and input gate operation channels, concatenates the current input tensor and the forward hidden state tensor, operates the linear combination parameters, calculates the forget gate control scalar matrix, extracts the input gate retention ratio value, updates the cell state tensor, retrieves the output gate mapping scalar, fuses the internal feature states, and superimposes multiple gate control tensors.

[0012] As a further aspect of the present invention, the specific steps for obtaining the lifecycle decay prediction array are as follows: Based on the heterogeneous space coupling feature matrix and the orthogonal constraint long-term memory evolution tensor, the cross-dimensional channel mapping parameters are spliced ​​together, the internal dependency correlation scalars are extracted and the candidate feature retention ratio is separated, the underlying mapping state sequence is reorganized, and the decoupled state mapping sequence is generated. Based on the decoupled state mapping sequence, the active specific surface area parameter of the catalytic layer is retrieved, the internal node sequence scale is matched, the state distribution evolution characteristics are fused and multiple array components are connected in series, and the global correlation weight tensor is reset to obtain the life cycle decay prediction array.

[0013] As a further aspect of the present invention, the specific steps for outputting the electrolytic cell health quantification distribution are as follows: Based on the life cycle decay prediction array, the real label tensor values ​​are retrieved and the magnitude of the difference between the predicted scalar and the label parameters is calculated. The product of the difference and the penalty coefficient is aggregated, the global deviation mapping distribution parameter is extracted, and a multidimensional life cycle error gradient matrix is ​​generated. Based on the multidimensional lifecycle error gradient matrix, the hidden node bias parameter matrix is ​​adjusted, the voltage fluctuation frequency parameter scaling is matched, multiple network connection weight distribution tensors are corrected, the internal state of the device decay ratio is mapped, and a quantitative distribution column of electrolytic cell health is established.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by calling a graph neural network to aggregate multiple parameter nodes, comparing adjacent product values ​​to retain parameters that exceed the valley limit, and quantitatively exploring the hidden nonlinear cross-correlation effects within multiple physicochemical indicators, a multi-dimensional topological correlation architecture is constructed, breaking the blind spot of single indicator dimension judgment and improving the accuracy of multi-dimensional spatial feature mapping. In this invention, by calling the Long Short-Term Memory network and superimposing the internal gate control tensor, the parameter of exceeding the extreme value is separated by comparing the norm penalty value, thereby avoiding the computational bottleneck of overlapping operations of long sequence time features and controlling the root mean square extreme value of lifetime prediction error within a predetermined safety tolerance range. In this invention, candidate scalars are separated by splicing cross-dimensional parameter extraction dependencies, comparing the prediction and label difference aggregation coefficients to generate error gradients, adjusting the node bias matrix values ​​to align voltage frequency, resetting the weight tensor outputting the electrolytic cell health quantification distribution, controlling the probability of unplanned physical failure downtime within a minimum tolerance range, and improving the smoothness index of the whole life cycle prediction curve and the safety of the operation cycle. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the main steps of the present invention; Figure 2 This is a schematic diagram of step one of the present invention; Figure 3 This is a schematic diagram of step two of the present invention; Figure 4 This is a schematic diagram of step three of the present invention; Figure 5 This is a schematic diagram of step four of the present invention; Figure 6 This is a schematic diagram of step five of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Example 1 Please see Figure 1 This invention provides a technical solution: a method for assessing the health status of an electrolyzer based on the multidimensional characteristics of the electrolyte, comprising the following steps: Step 1: Based on multiple direct-reading scalar values ​​of electrolytic cell terminal voltage and electrolyte pH test values, assign matrix mapping, extract node comparison parameters, associate flow rate alignment timestamps and remove misaligned items, splice parameter architecture, and establish a multi-dimensional parameter embedding dictionary for electrolytic cells; Step 2: Based on the multidimensional parameter embedding dictionary of the electrolytic cell, call the graph neural network aggregation node, compare adjacent product values, retain parameters that exceed the valley limit, accumulate tensor product scalars, merge corrosion depth reorganization features, and generate heterogeneous space coupling feature matrix. Step 3: Based on the heterogeneous space coupling feature matrix, call the long short-term memory network to superimpose the gated tensor, compare the norm penalty values ​​and separate the parameters that exceed the extreme values, correlate porosity overlapping operations, fuse the hidden layer element states, and obtain the orthogonal constrained long-term memory evolution tensor. Step 4: Based on the heterogeneous space coupling feature matrix and the orthogonal constraint long-term memory evolution tensor, splice cross-dimensional parameters and extract dependencies, separate parameters to generate candidate scalars, decouple the output state sequence and integrate the active area array to obtain the life cycle decay prediction array. Step 5: Based on the life cycle degradation prediction array, compare the predicted difference with the label by the real life cycle label tensor, aggregate the difference and coefficient to generate the error gradient, adjust the node bias matrix values ​​and align the voltage frequency to reset the weight tensor, and output the electrolytic cell health quantification distribution column.

[0018] The multidimensional parameter embedding dictionary for the electrolyzer includes multiple direct-reading scalars of electrolyzer terminal voltage, multiple electrolyte pH test values, multiple feature node boundary mapping arrays, and multiple cathode liquid flow rate parameters. The heterogeneous space coupling feature matrix includes multiple node inner product mapping scalar sets, multiple over-limit adjacency weight parameters, and an electrode corrosion depth matrix. The orthogonal constraint long-term memory evolution tensor includes multiple gated penalty feature tensors, multiple porosity overlapping coupling arrays, and multiple hidden layer output tensor parameters. The life cycle degradation prediction array specifically consists of multiple decoupled state mapping sequences, multiple catalyst layer active specific surface area parameters, and multiple global correlation weight tensors. The electrolyzer health quantification distribution column includes a multidimensional life cycle error gradient matrix, a hidden node bias parameter matrix, a voltage fluctuation frequency parameter scale, and multiple network connection weight distribution tensors.

[0019] The specific steps for establishing a multidimensional parameter embedding dictionary for an electrolyzer are as follows: Based on multiple direct-reading scalar values ​​of electrolytic cell terminal voltage and electrolyte pH test values, the numerical range is segmented, the matrix dimension coordinates are mapped and the differences of extreme values ​​within the sequence are compared, the boundary index is extracted, the node mapping is reorganized, and a feature node boundary mapping array is generated. Based on the feature node boundary mapping array, the cathode liquid flow rate parameter is imported, the node timestamp and the flow rate timestamp are compared, the time difference value is calculated and items with differences exceeding the tolerance limit are removed, the parameter and flow rate parameter architecture are spliced ​​together, and a multi-dimensional parameter embedding dictionary for the electrolyzer is established. Based on multiple direct-reading scalar values ​​of electrolytic cell terminal voltage and electrolyte pH test values, an adaptive K-means clustering algorithm is adopted. The initial cluster center number parameter is set to 5, and the iteration termination error parameter is set to 0.001. The input terminal voltage direct-reading scalar value and pH test value data stream are fed into the clustering calculation process. The Euclidean distance between the sample point and the cluster center is calculated. The cluster center coordinate position is iteratively updated 300 times. The boundary of the numerical interval is determined according to the final cluster center distribution. The statistical distribution variance calculation model is called to calculate the standard deviation scalar of the internal distribution of multiple input terminal voltage direct-reading scalar values. This is multiplied by the confidence interval amplification factor of the offline verification sample cluster to generate a dynamic boundary filtering threshold parameter. The sample point distance is compared with the filtering threshold value. The index number corresponding to the data exceeding the threshold is extracted. The index mapping command is called to segment the out-of-bounds data. The node mapping channel configuration is reorganized to generate a feature node boundary mapping array. Based on the feature node boundary mapping array, the cathode liquid flow rate parameter is imported, and a dynamic time warping algorithm is adopted. The matching window constraint parameter is set to 50 time steps, and the distance metric mode parameter is set to absolute difference. The node timestamp sequence and the flow rate timestamp sequence are input into the alignment calculation process. The cumulative distance matrix value is calculated, the minimum cost alignment path is searched, the time difference corresponding to the path point is calculated, the alignment tolerance threshold parameter is set to 20 milliseconds, the time difference value is compared with the tolerance threshold value, the mask deletion command is executed to remove items exceeding the tolerance threshold, and the tensor splicing operation is performed to merge and retain the parameters and flow rate parameters along the channel dimension. A multi-dimensional parameter embedding dictionary for the electrolyzer is established. The specific scheme for retrieving the electrode corrosion depth matrix, the catalyst layer active specific surface area parameter, and the diffusion layer porosity tensor is as follows: A pre-stored electrolytic cell performance degradation benchmark mapping matrix is ​​established. This mapping matrix includes multiple offline calibration physical degradation trajectory parameters. The specific steps include: retrieving the real-time sampled cumulative current density time integral scalar and the average operating temperature scalar of the cell, using these scalars as the index entry for a multidimensional lookup table; calling a bilinear interpolation algorithm to locate the corresponding operating condition coordinates within the mapping matrix and calculating the current corrosion depth mapping coefficient and porosity evolution scale; performing the initial physical property tensor matrix and evolution scale Hadamard product operation to generate the matching electrode corrosion depth matrix and diffusion layer porosity tensor for the current moment; and then aligning the model input time step to complete the dynamic mapping update of physical parameters. The matching logic specifically involves retrieving the degradation envelope based on the cumulative charge transfer value and extracting the physical property tensor of the corresponding Euler time step node, ensuring that the offline measurement data and the current electrolytic cell operating state are spatiotemporally aligned.

[0020] The specific steps for generating the heterogeneous space coupling feature matrix are as follows: Based on the multidimensional parameter embedding dictionary of the electrolytic cell, a multidimensional topological association architecture is constructed through graph neural network. The node feature sequences are aligned and the internal matrix parameters are extracted. After the cross-product values ​​of the elements are calculated, multiple product scalars are integrated, multiple adjacent node feature space mapping dimensions are divided, and a set of node inner product mapping scalars is generated. Based on the scalar set of node inner product mapping, the scalar difference between adjacent nodes is calculated and the valley comparison parameter is extracted. The difference magnitude of the comparison values ​​is used to filter out the low limit value items and then filter the parameter channels that exceed the limit. The corresponding parameter values ​​are retained and the over-limit adjacency weight parameter is established. Based on the cross-boundary adjacency weight parameter, the cross-dimensional tensor product values ​​are accumulated, the scalar sum is extracted and the plate corrosion depth matrix is ​​retrieved, the corrosion features are spliced ​​and the associated product parameters are merged, the multi-level spatial architecture matrix is ​​reorganized, and the heterogeneous spatial coupling feature matrix is ​​generated. Based on the multidimensional parameter embedding dictionary of the electrolytic cell, a graph neural network algorithm is adopted. The network layer definition command is called to set the input dimension parameter to 128, the output dimension parameter to 64, and the number of attention heads to 4. The multidimensional parameter embedding dictionary of the electrolytic cell is imported into the tensor conversion command to convert the numerical format. The sequence filling command is called to set the filling numerical parameter to 0. The node feature sequence alignment operation is performed, and the internal matrix parameter extraction operation is performed. The graph structure data is split to extract the edge index tensor and the node feature tensor. The matrix multiplication operation command is called to perform the multiplication operation of the node feature tensor and the weight tensor. The preset activation operation parameters are set. The activation extreme value distribution state is tested by reading the historical 500 experiments and the negative half-axis slope parameter of the linear unit with leakage correction is fixed to 0.2. The activation function is imported to calculate the node association weight. The element cross product numerical operation operation is performed, and multiple product scalar integration operation is performed. The batch matrix multiplication command is called to accumulate the coefficients of the anisotropic attention weight matrix. The tensor reshaping command is called to set the dimension reconstruction parameter list to divide the multiple adjacent node feature space mapping dimensions. The feature tensors are allocated to the continuous memory address space to generate the node inner product mapping scalar set. Based on the node inner product mapping scalar set, an adaptive differential filtering algorithm is adopted. The tensor subtraction operation command is called to input the feature tensors of adjacent nodes, performing scalar difference calculations between adjacent nodes and valley value comparison parameter extraction. The absolute value calculation command is called to process the difference tensor and extract absolute values. The global minimum value search command is called to traverse the feature channels of the difference tensor to extract the minimum valley values. A preset valley lower limit scalar is imported, along with multiple offline rated power operation archive matrices. A Gaussian distribution fitting model is called to extract the minimum mean scalar of the multiple inner product mapping scalar array, and multiple discrete distributions are subtracted. The algorithm scales the scale, generates dynamic valley lower limit parameters, calls the tensor comparison command to compare the magnitude of numerical differences, filters out low-limit numerical items, generates a Boolean mask matrix to mark the index positions below the limit, calls the tensor mask filling command to set the filling value to 0 to filter out low-limit feature vectors, calls the non-zero element extraction command to filter out parameter channels that exceed the limit, executes the tensor slicing command to locate the index positions of non-zero channels, extracts the corresponding tensor dimensions, retains the corresponding parameter values, calls the tensor splicing command to combine multiple non-zero parameters to reshape the feature weight distribution state, and establishes the adjacent weight parameters that exceed the limit. Based on the over-limit adjacency weight parameter, a multi-level tensor splicing algorithm is adopted. The dimensionality reduction and summation operation command is called, setting the dimension aggregation parameter to 2, to perform tensor dimensionality reduction and aggregation on the over-limit adjacency weight parameters. Cross-dimensional tensor product numerical accumulation is performed, and scalar sum extraction is carried out. The tensor trace calculation command is called to extract the scalar sum value of the diagonal elements. The disk read command is called to parse the local storage electrode corrosion depth matrix file and load the multi-dimensional numerical array. A preset corrosion alignment coefficient is set. The Taylor expansion approximation algorithm is called to fit multiple offline electrode physical decay trajectory curves, extract the scalar maxima of the first derivative, and map them to generate... The dynamic corrosion alignment scalar is performed by calling the tensor scalar multiplication command to multiply the plate corrosion depth matrix with the corrosion alignment coefficient to adjust the numerical distribution scale. The tensor splicing command is then called to set the splicing dimension axis parameter to negative 1, and corrosion feature splicing is performed. The associated product parameter merging operation is also performed, and the alignment feature tensor is spliced ​​to the end of the product tensor. The Hadamard product calculation command is called to merge the associated product parameter feature points. The tensor flattening and combination command is called to set the target dimension tuple parameter to reorganize the multi-level spatial architecture matrix. The fusion matrix is ​​allocated to the shared memory space of the main computing node to generate the heterogeneous spatial coupling feature matrix.

[0021] A graph neural network is used to extract multiple initial node feature tensors from a multidimensional parameter embedding dictionary of the mapping electrolytic cell. The inner product values ​​of the multiple node feature tensors are calculated to dynamically generate the asymmetric graph adjacency matrix parameters. The anisotropic information of multiple nodes is aggregated to allocate the feature space coordinates of the hidden layer and construct a multidimensional topological association architecture.

[0022] After splicing corrosion features and merging associated product parameters, multidimensional mapping coordinates of the plate corrosion depth matrix are extracted. Cross-dimensional tensor product numerical space dimensions of the cross-boundary adjacency weight parameters are matched. Linear transformation operation is called to align multiple corrosion feature tensor channels. Multiple aligned corrosion feature vectors are spliced ​​to the boundary of the tensor product numerical array. The superposition scalar of multiple heterogeneous feature channel elements is calculated. Cross-dimensional associated product parameters are fused to extract and merge feature scales, and the multi-level matrix topology architecture is reshaped.

[0023] The specific steps to obtain the orthogonal constrained long-term memory evolution tensor are as follows: Based on the heterogeneous spatial coupling feature matrix, a long short-term memory network is called to superimpose a gated tensor, calculate the norm penalty difference amplitude feature scalar, extract the multidimensional bias state distribution feature sequence, integrate the multi-dimensional spatial mapping parameter array, and generate the gated penalty feature tensor. Based on the gated penalty feature tensor, the distribution feature parameters of the time step exceeding the extreme value are separated, the porosity tensor of the diffusion layer is retrieved and compared, and after performing multiple normed parameter overlapping coupling operations, a cross-dimensional array feature architecture is spliced ​​to establish a porosity overlapping coupling array. Based on the porosity overlapping coupling array, multiple hidden layer output tensor parameters are extracted, the corresponding element evolution states are combined and matched, cross-dimensional temporal evolution feature scalars are fused and internal multi-level state parameters are reorganized to obtain the orthogonal constraint long-term memory evolution tensor. Based on the heterogeneous spatial coupling feature matrix, a Long Short-Term Memory (LSTM) network algorithm is employed. The network layer instantiation command sets the input feature dimension to 64, the number of hidden layer nodes to 128, and the number of recurrent network layers to 2. The heterogeneous spatial coupling feature matrix is ​​input to the network's forward propagation channel. A gating tensor superposition operation is performed. A matrix multiplication command is called to calculate the product of the forget gate input, output, and weight parameter matrices with the hidden state tensor. The Sigmoid activation function is called to process the product matrix and extract the gating activation tensor in the 0-1 interval. Finally, the Hadamard product calculation command is called to multiply the gating activation tensor with the corresponding element-wise cell state tensor to extract and update the cell state sequence. The algorithm performs tensor superposition and norm penalty difference magnitude feature scalar calculation. It calls the tensor norm calculation command, sets the norm order parameter to 2, and obtains the absolute value scalar of the difference between the hidden state output tensor and the preset baseline state tensor. It calibrates the preset baseline state tensor by retrieving the extreme values ​​of the hidden state mean tensor distribution of the past 50 healthy electrolytic cell samples throughout their entire life cycle. It calls the array slicing extraction command, sorts the index in descending order according to the absolute value scalar of the difference between the two norms, and extracts the multidimensional deviation state distribution feature sequence. It calls the tensor stacking aggregation command, sets the stacking dimension axis parameter to 0, and integrates the multi-dimensional spatial mapping parameter array configuration into a continuous memory block to generate a gated penalty feature tensor. Based on a gated penalty feature tensor, a time-step extreme value filtering algorithm is employed. This involves calling a global extreme value search command to traverse the tensor's time-step dimension and extract the maximum and minimum scalar values ​​of feature elements. A statistical variance calculation command is then used to extract the internal variance values ​​of the time-step distribution feature parameter matrix. A preset time-step extreme value separation threshold is set. Multiple offline anomaly evolution time matrices are imported, and a kernel density estimation algorithm is used to calculate the upper bound scalar of the cumulative probability density distribution. A dynamic time-step extreme value separation threshold parameter is established, and the separation operation of time-step distribution feature parameters exceeding extreme values ​​is performed. Finally, a tensor value comparison command is used to compare the maximum scalar value with the preset time-step extreme value separation threshold. A Boolean index extraction command is then used to extract values ​​based on the comparison results. The time step features are extracted from the slice index tensor of the time step exceeding the threshold, and multiple normative parameter overlap coupling operations are performed. The disk read interface is called to parse the local storage gas diffusion layer attribute database and load the porosity tensor array. The element-wise division calculation command is called to compare the ratio of the porosity tensor with the separated feature parameter tensor values. The tensor dot product calculation command is called to multiply the ratio matrix with the dot product of the gated feature tensor to extract the normative parameter overlap coupling matrix. The tensor splicing command is called to set the cascaded dimension parameter to negative 1 to splice the overlap coupling matrix with the gated tensor to extract the cross-dimensional array feature architecture. The cross-dimensional array feature architecture is allocated to the master node register address space to establish the porosity overlap coupling array. Based on a porosity-overlapping coupled array, an orthogonal temporal attention mechanism algorithm is employed. The self-attention layer instantiation command sets the number of attention heads to 8 and the key-value pair tensor dimension to 64. The porosity-overlapping coupled array is imported into a linear mapping layer to extract the query tensor, key tensor, and value tensor. A matrix multiplication command is then used to multiply the query tensor with the transpose of the key tensor to extract the original attention distribution matrix. Corresponding element evolution state combination matching is performed. The Softmax normalization function is called to process the original attention distribution matrix and extract the time-step association weight matrix. Finally, the Hadamard product calculation command is used to multiply the time-step association weight matrix with the parameters of the multiple hidden layer output tensors. Extract the corresponding element evolution state matrix and perform internal multi-level state parameter recombination operations. Call the identity matrix generation command to construct dimension-matching identity matrix parameters. Call the tensor subtraction calculation command to subtract the numerical difference between the original attention distribution matrix and transpose matrix product and the identity matrix parameters to extract the orthogonal constraint penalty matrix. Call the tensor addition operation command to fuse the corresponding element evolution state matrix and orthogonal constraint penalty matrix to extract cross-dimensional temporal evolution feature scalars. Call the tensor reconstruction command to set the multi-dimensional tensor shape tuple parameters, combine multiple hidden layer state channels to reconstruct the multi-level structure array, allocate the fused evolution tensor to the lower-level network computing memory nodes, and obtain the orthogonal constraint long-term memory evolution tensor.

[0024] Long Short-Term Memory (LSTM) network extracts heterogeneous spatial coupling feature matrix, inputs the feature matrix to the internal forget gate and input gate operation channels, concatenates the current input tensor and the forward hidden state tensor, operates on linear combination parameters, calculates the forget gate control scalar matrix, extracts the input gate retention ratio value, updates the cell state tensor, retrieves the output gate mapping scalar, fuses internal feature states, and superimposes multiple gate control tensors.

[0025] The specific steps to obtain the lifecycle degradation prediction array are as follows: Based on heterogeneous space coupling feature matrix and orthogonal constraint long-term memory evolution tensor, cross-dimensional channel mapping parameters are spliced, internal dependency correlation scalars are extracted and candidate feature retention ratios are separated, and the underlying mapping state sequence is reorganized to generate decoupled state mapping sequence. Based on the decoupled state mapping sequence, the active specific surface area parameter of the catalyst layer is retrieved, the internal node sequence scale is matched, the state distribution evolution characteristics are fused and multiple array components are connected in series, and the global correlation weight tensor is reset to obtain the life cycle decay prediction array. Based on the heterogeneous spatial coupling feature matrix and the orthogonally constrained long-term memory evolution tensor, a multidimensional tensor concatenation decoupling algorithm is adopted. The tensor concatenation function is called, setting the concatenation dimension axis parameter to negative one. The heterogeneous spatial coupling feature matrix and the orthogonally constrained long-term memory evolution tensor are input to the feature fusion calculation channel, performing cross-dimensional channel mapping parameter concatenation. A linear feature mapping command is called, setting the input dimension parameter to 128 and the output dimension parameter to 64. The product of the weight matrix parameters and the concatenated tensor parameters is calculated, and an internal dependency correlation scalar extraction operation is performed. A self-attention calculation function is called, setting the attention head parameter to 4 to extract feature weight coefficients. A preset feature retention threshold parameter is set, and multiple offline steady-state parameters are imported. The mapping array is processed by calling the covariance matrix operation engine to extract the minimum scalar of the diagonal elements, establishing a dynamic feature retention threshold parameter, calling the tensor mask comparison command to compare the feature weight coefficients with the preset feature retention threshold parameter, executing the feature discard function to remove the channel feature sequences corresponding to the preset feature retention threshold parameter, and performing a candidate feature retention ratio separation operation. The tensor slicing command is called to set the slicing step size parameter to 2 to extract the remaining feature channel vector array, and the sequence stacking reconstruction command is called to set the sequence length parameter to 50 to concatenate the multi-function extracted channel feature vectors. The underlying mapping state sequence tensor architecture is reorganized, and the reorganized tensor sequence is allocated to the high-frequency memory address pool of the master and slave computing nodes to generate a decoupled state mapping sequence. Based on the decoupled state mapping sequence, a multilayer perceptron regression prediction algorithm is employed. The database read interface loads offline storage device configuration table data, and the numerical extraction function locates the catalytic layer attribute column index to retrieve specific measurement values. The catalytic layer active surface area parameter is retrieved, and the tensor broadcast expansion command is used to set the target shape tuple parameters to match the time step dimension space data of the decoupled state mapping sequence. Internal node sequence scaling is performed, and the Hadamard product calculation command is used to perform element-wise multiplication of the expanded active surface area parameter matrix with the mapping sequence matrix. State distribution evolution feature fusion is also performed. The forward computation network layer is called, setting the number of neurons in the first hidden layer to 256 and the number of neurons in the second hidden layer to 64. The product feature matrix is ​​input to the network topology to calculate the linear combination scalar, and the tensor flattening function is called. The operation compresses the spatial dimension to a one-dimensional feature vector array, calls the feature concatenation function to set the concatenation axis parameter to 0, and performs multiple array component element concatenation operations. It combines the feature vector with the scalar parameter combination sequence of the device's initial rated capacity, sets the preset global weight decay coefficient, and calibrates the preset global weight decay coefficient to 0.005 by fitting the median slope of the full life cycle decay curve of the past 3000 hours of continuous electrolysis test. It calls the adaptive optimizer update command to load the preset global weight decay coefficient and sets the learning rate parameter to 0.001. It performs backpropagation gradient descent calculation to adjust the network connection bias parameters and performs a global correlation weight tensor reset operation. It calls the linear prediction output layer to set the output node dimension parameter to 1 to map the final evaluation floating-point value distribution state, allocates the floating-point value sequence to the result cache register device, and obtains the life cycle decay prediction array. The heterogeneous spatial coupling feature matrix is ​​specifically a 128x128 dimensional square matrix. The matrix row index and column index are associated with the interaction mapping weights of multiple physical entity nodes such as cathode plate, anode plate, proton exchange membrane, and electrolyte. To obtain the orthogonal constraint long-term memory evolution tensor, the hidden layer weight matrix of the long short-term memory network is retrieved and multiplied by its transpose. The Frobenius norm value of the difference between the product matrix and the identity matrix is ​​calculated. The norm loss term constraint threshold is set to 0.000001, forcing the evolution tensor to maintain a linearly independent distribution. To splice the cross-dimensional parameters, the heterogeneous spatial coupling feature matrix and the orthogonal constraint long-term memory evolution tensor are merged along the tensor channel dimension to generate a fusion tensor with a dimension width equal to the sum of multiple mapping dimensions. Then, the multi-head attention mechanism engine is called to calculate the self-attention score matrix of the fusion tensor. A mask removal operation is performed to filter out candidate scalar channels with attention scores below 0.15. The remaining channel tensors are reorganized, and the decoupling of the underlying mapping state sequence is completed. The process of splicing cross-dimensional parameters involves calling a linear transformation operator to align the heterogeneous space coupling feature matrix (64 dimensions) with the orthogonally constrained long-term memory evolution tensor feature (64 dimensions), and setting the total dimension parameter after alignment to 128. Then, a tensor concatenation splicing operation is performed along the negative one axis of the feature dimension to generate a fused feature tensor. Dependency extraction involves inputting the fused feature tensor to a multi-head self-attention mechanism module and calculating the scalar dot product of the query matrix and the transpose of the key matrix, multiplying by a scaling factor (the inverse square root of the feature dimension), and calculating the attention distribution weights. Decoupling the output state sequence involves performing channel segmentation on the fused tensor based on the attention distribution weights, separating candidate sub-vectors for multiple physical decay trajectories corresponding to cathode polarization, anode dissolution, and proton membrane degradation. Integrating the active area array involves performing a Hadamard product operation between the candidate sub-vectors and the scalar Hadamard product of the catalytic layer's active surface area, and then inputting a regression architecture composed of three fully connected layers with a neuron distribution of 256, 128, and 1, mapping the output lifecycle decay prediction array.

[0026] The specific steps for outputting the electrolytic cell health quantification distribution are as follows: Based on the life cycle decay prediction array, the real label tensor values ​​are retrieved and the magnitude of the difference between the predicted scalar and the label parameters is calculated. The product of the difference and the penalty coefficient is aggregated, the global bias mapping distribution parameter is extracted, and a multidimensional life cycle error gradient matrix is ​​generated. Based on the multidimensional life cycle error gradient matrix, the hidden node bias parameter matrix is ​​adjusted, the voltage fluctuation frequency parameter scaling is matched, the distribution tensor of multiple network connection weights is corrected, the internal state is integrated by mapping the equipment decay ratio, and a quantitative distribution column of electrolytic cell health is established. Based on a lifecycle degradation prediction array, a piecewise asymmetric log-hyperbolic cosine loss algorithm is employed. The local tag database read interface is called, with the batch size parameter set to 128 and the tensor dimension parameter set to 64 to import the actual lifecycle tag tensor values. The difference between the predicted scalar and the tag parameters is calculated. The tensor subtraction function is called, with the calculation dimension axis parameter set to 1, to calculate the absolute difference between corresponding elements of the lifecycle degradation prediction array and the actual lifecycle tag tensor. The difference is then multiplied and aggregated with a penalty coefficient. A preset overestimation penalty coefficient parameter is set. A Laplace distribution fitting algorithm is called to map the multinomial offline prediction bias array, and the positive residual skewness scalar is calculated to generate the overestimation penalty coefficient parameter. The calculation is then performed... The negative residual variance scalar generates an underestimated penalty coefficient parameter. The mask comparison calculation command is called to separate the positive and negative difference distribution intervals. The piecewise logarithmic hyperbolic cosine calculation function is called to input the absolute difference value. The tensor dot product multiplication operation command is called to execute the hyperbolic cosine logarithm value and the corresponding interval penalty coefficient parameter to obtain the aggregated product matrix. The global deviation mapping distribution parameter extraction operation is performed. The global average pooling command is called to set the pooling window size parameter to 3x3 and the step size parameter to 1 to extract the spatial hierarchical penalty tensor features. The tensor flattening function is called to compress the pooling matrix dimension to map the global deviation distribution parameter structure. The extracted feature parameters are allocated to the continuous main memory computing address pool to generate a multi-dimensional lifetime error gradient matrix. Based on the multidimensional lifetime error gradient matrix, an adaptive moment estimation network optimization algorithm is adopted. The model optimizer instantiation command sets the initial learning rate parameter to 0.005, the first momentum decay coefficient parameter to 0.9, and the second momentum decay coefficient parameter to 0.999. The backpropagation calculation function is called to input the multidimensional lifetime error gradient matrix to extract the gradient direction scalar. The hidden node bias parameter matrix is ​​adjusted. The parameter update configuration command is called to subtract the gradient direction scalar to reconstruct the values ​​of multiple hidden layer node bias parameters. Voltage fluctuation frequency parameter scaling is performed. The device interface communication command is called to read the real-time sampling sequence of the voltage sensor and set the sampling frequency parameter to 1000 Hz. The fast Fourier transform calculation function is called to process the time-series sampling sequence to extract the peak frequency parameter of the main frequency band. A preset voltage frequency compensation threshold parameter is set. By analyzing historical data (20...),... The voltage extreme value mapping envelope spectrum is calibrated at 00 hours. The preset voltage frequency compensation threshold parameter is 50 Hz. The tensor division calculation command is called to compare the peak frequency parameter of the main frequency band with the preset voltage frequency compensation threshold parameter to obtain the frequency fluctuation mapping scale. The network connection weight distribution tensor correction operation is performed. The tensor scalar multiplication command is called to perform the multiplication operation of the network connection weight feature tensor and the frequency fluctuation mapping scale to reset the node weight parameters. The equipment degradation ratio hierarchical mapping state integration operation is performed. The linear fully connected layer forward propagation calculation command is called to set the output feature channel dimension parameter to 10 to extract the calculation state parameters of the multi-level hidden layer inside the network topology. The tensor stacking cascade command is called to set the cascade dimension axis parameter to 0 to perform the stacking of internal calculation state parameters. The normalized exponential function is called to allocate the multi-level equipment degradation ratio numerical distribution feature of the electrolyzer and establish a quantitative distribution column of electrolyzer health.

[0027] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte, characterized in that, Includes the following steps: Step 1: Based on multiple direct-reading scalar values ​​of electrolytic cell terminal voltage and electrolyte pH test values, assign matrix mapping, extract node comparison parameters, associate flow rate alignment timestamps and remove misaligned items, splice parameter architecture, and establish a multi-dimensional parameter embedding dictionary for electrolytic cells; Step 2: Based on the multidimensional parameter embedding dictionary of the electrolytic cell, call the graph neural network aggregation node, compare adjacent product values, retain parameters that exceed the valley limit, accumulate tensor product scalars, merge corrosion depth recombination features, and generate heterogeneous space coupling feature matrix. Step 3: Based on the heterogeneous space coupling feature matrix, call the long short-term memory network to superimpose the gated tensor, compare the norm penalty values ​​and separate the parameters that exceed the extreme values, correlate porosity overlap operation, fuse the hidden layer element states, and obtain the orthogonal constraint long-term memory evolution tensor. Step 4: Based on the heterogeneous space coupling feature matrix and the orthogonal constraint long-term memory evolution tensor, concatenate cross-dimensional parameters and extract dependencies, separate parameters to generate candidate scalars, decouple the output state sequence and integrate the active area array to obtain the life cycle decay prediction array. Step 5: Based on the life cycle degradation prediction array, compare the predicted difference with the label by the real life cycle label tensor, aggregate the difference and coefficient to generate the error gradient, adjust the node bias matrix values ​​and align the voltage frequency to reset the weight tensor, and output the electrolytic cell health quantification distribution column.

2. The method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte according to claim 1, characterized in that, The multidimensional parameter embedding dictionary of the electrolyzer includes multiple direct-reading scalars of electrolyzer terminal voltage, multiple electrolyte pH test values, multiple feature node boundary mapping arrays, and multiple cathode liquid flow rate parameters. The heterogeneous space coupling feature matrix includes multiple node inner product mapping scalar sets, multiple over-limit adjacency weight parameters, and an electrode corrosion depth matrix. The orthogonal constraint long-term memory evolution tensor includes multiple gated penalty feature tensors, multiple porosity overlapping coupling arrays, and multiple hidden layer output tensor parameters. The life cycle decay prediction array specifically includes multiple decoupled state mapping sequences, multiple catalyst layer active specific surface area parameters, and multiple global correlation weight tensors. The electrolyzer health quantification distribution column includes a multidimensional life cycle error gradient matrix, a hidden node bias parameter matrix, a voltage fluctuation frequency parameter scale, and multiple network connection weight distribution tensors.

3. The method for assessing the health status of an electrolyzer based on the multidimensional characteristics of the electrolyte according to claim 1, characterized in that, The specific steps for establishing the multidimensional parameter embedding dictionary of the electrolytic cell are as follows: Based on multiple direct-reading scalar values ​​of electrolytic cell terminal voltage and electrolyte pH test values, the numerical range is segmented, the matrix dimension coordinates are mapped and the differences of extreme values ​​within the sequence are compared, the boundary index is extracted, the node mapping is reorganized, and a feature node boundary mapping array is generated. Based on the feature node boundary mapping array, the cathode liquid flow rate parameter is imported, the node timestamp and the flow rate timestamp are compared, the time difference value is calculated and items with differences exceeding the tolerance limit are removed, the retained parameter and flow rate parameter architecture are spliced ​​together, and a multi-dimensional parameter embedding dictionary for the electrolytic cell is established.

4. The method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte according to claim 1, characterized in that, The specific steps for generating the heterogeneous space coupling feature matrix are as follows: Based on the multidimensional parameter embedding dictionary of the electrolytic cell, a multidimensional topological association architecture is constructed through a graph neural network. The node feature sequences are aligned and the internal matrix parameters are extracted. After the cross-product values ​​of the elements are calculated, multiple product scalars are integrated, multiple adjacent node feature space mapping dimensions are divided, and a set of node inner product mapping scalars is generated. Based on the node inner product mapping scalar set, the scalar difference between adjacent nodes is calculated and the valley comparison parameter is extracted. The magnitude of the difference in the comparison values ​​is used to filter out items with lower limits and then filter out parameter channels that exceed the limit. The corresponding parameter values ​​are retained, and the over-limit adjacency weight parameter is established. Based on the aforementioned adjacency weight parameter, the cross-dimensional tensor product values ​​are accumulated, the scalar sum is extracted, and the electrode corrosion depth matrix is ​​retrieved. After splicing the corrosion features, the associated product parameters are merged, and the multi-level spatial architecture matrix is ​​reorganized to generate a heterogeneous spatial coupling feature matrix.

5. The method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte according to claim 1, characterized in that, The graph neural network extracts multiple initial node feature tensors from the multidimensional parameter embedding dictionary of the electrolytic cell, calculates the inner product value of the multiple node feature tensors, dynamically generates the asymmetric graph adjacency matrix parameter, aggregates the anisotropic information of multiple nodes to allocate the feature space coordinates of the hidden layer, and constructs a multidimensional topological association architecture.

6. The method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte according to claim 4, characterized in that, The process involves splicing corrosion features, merging associated product parameters, extracting multidimensional mapping coordinates of the electrode corrosion depth matrix, matching the cross-dimensional tensor product numerical space dimension of the cross-boundary adjacency weight parameters, calling linear transformation operations to align multiple corrosion feature tensor channels, splicing multiple aligned corrosion feature vectors to the boundary of the tensor product numerical array, calculating the superposition scalar of multiple heterogeneous feature channel elements, fusing cross-dimensional associated product parameters to extract and merge feature scales, and reshaping the multi-level matrix topology architecture.

7. The method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte according to claim 1, characterized in that, The specific steps for obtaining the orthogonal constrained long-term memory evolution tensor are as follows: Based on the heterogeneous spatial coupling feature matrix, a long short-term memory network is called to superimpose a gated tensor, calculate the norm penalty difference amplitude feature scalar, extract the multidimensional deviation state distribution feature sequence, integrate the multi-dimensional spatial mapping parameter array, and generate a gated penalty feature tensor. Based on the gated penalty feature tensor, the distribution feature parameters of the time step exceeding the extreme value are separated, the porosity tensor of the diffusion layer is retrieved and compared, and after performing multiple normative parameter overlapping coupling operations, a cross-dimensional array feature architecture is spliced ​​to establish a porosity overlapping coupling array. Based on the porosity overlapping coupling array, multiple hidden layer output tensor parameters are extracted, the corresponding element evolution states are combined and matched, cross-dimensional temporal evolution feature scalars are fused and internal multi-level state parameters are reorganized to obtain orthogonal constrained long-term memory evolution tensors.

8. The method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte according to claim 1, characterized in that, The Long Short-Term Memory network extracts the heterogeneous spatial coupling feature matrix, inputs the feature matrix into the internal forget gate and input gate operation channels, concatenates the current input tensor and the forward hidden state tensor, operates the linear combination parameters, calculates the forget gate control scalar matrix, extracts the input gate retention ratio value, updates the cell state tensor, retrieves the output gate mapping scalar, fuses the internal feature states, and superimposes multiple gate control tensors.

9. The method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte according to claim 1, characterized in that, The specific steps to obtain the lifecycle degradation prediction array are as follows: Based on the heterogeneous space coupling feature matrix and the orthogonal constraint long-term memory evolution tensor, the cross-dimensional channel mapping parameters are spliced ​​together, the internal dependency correlation scalars are extracted and the candidate feature retention ratio is separated, the underlying mapping state sequence is reorganized, and the decoupled state mapping sequence is generated. Based on the decoupled state mapping sequence, the active specific surface area parameter of the catalytic layer is retrieved, the internal node sequence scale is matched, the state distribution evolution characteristics are fused and multiple array components are connected in series, and the global correlation weight tensor is reset to obtain the life cycle decay prediction array.

10. The method for assessing the health status of an electrolyzer based on multidimensional characteristics of the electrolyte according to claim 1, characterized in that, The specific steps for outputting the quantitative distribution of the electrolytic cell health metric are as follows: Based on the life cycle decay prediction array, the real label tensor values ​​are retrieved and the magnitude of the difference between the predicted scalar and the label parameters is calculated. The product of the difference and the penalty coefficient is aggregated, the global deviation mapping distribution parameter is extracted, and a multidimensional life cycle error gradient matrix is ​​generated. Based on the multidimensional lifecycle error gradient matrix, the hidden node bias parameter matrix is ​​adjusted, the voltage fluctuation frequency parameter scaling is matched, multiple network connection weight distribution tensors are corrected, the internal state of the device decay ratio is mapped, and a quantitative distribution column of electrolytic cell health is established.

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