Sludge deep dehydration process parameter intelligent optimization method

Abstract

The invention relates to an intelligent optimization method for sludge deep dehydration process parameters. Key parameters are collected in real time through an Internet of Things architecture, a multi-dimensional dynamic working condition data set is constructed by using a sliding time window and standardization processing, data quality is improved through abnormal value filtering and noise smoothing, a CNN-GRU hybrid model is input, and precise prediction of a working condition evolution trend is realized. On the basis of a prediction result, a trend guide feedback channel, a preference evolution feedback channel and a stability feedback channel are constructed, a multi-objective optimization weight is adjusted in a self-adaptive mode through normalization and weighted fusion, and a high-correlation optimal process parameter combination and closed-loop feedback control are obtained by combining NSGA-II algorithm multi-objective optimization and trend alignment degree screening. According to the scheme, the data perception and multi-target adaptive optimization capability of the sludge deep dehydration system is effectively enhanced, and the operation efficiency, the energy consumption utilization and the system stability are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent optimization technology for sludge treatment processes, and in particular to an intelligent optimization method for sludge deep dewatering process parameters. Background Technology

[0002] Parameter optimization in sludge deep dewatering processes has received widespread attention in recent years as a key technical step to improve sludge treatment efficiency, reduce energy consumption, and minimize reagent consumption. Most existing intelligent optimization systems employ multi-objective optimization strategies, comprehensively balancing parameters around multiple process objectives such as minimum energy consumption, maximum treatment efficiency, and minimum reagent consumption. Mainstream technical solutions typically rely on fixed-weight multi-objective optimization algorithms to achieve autonomous adjustment of process parameters, such as NSGA-II and its variants, fuzzy rule optimization, and evolutionary strategies. These solutions utilize real-time data on several key process parameters (such as influent sludge moisture content, organic matter content, and pH value), combined with rich operational experience and historical operating records, to optimize the sludge dewatering system, achieving a certain degree of energy efficiency and quality improvement. Some advanced systems have introduced expert systems or machine learning models trained on historical data to assist in parameter recommendation and decision support, ushering in a new direction for digital and intelligent operation and maintenance.

[0003] However, current intelligent optimization systems for sludge dewatering generally rely on static or semi-dynamic weight allocation strategies. This means that during multi-objective optimization, the weights of objectives such as energy consumption, efficiency, and reagent consumption are either manually preset or based on rule-based weight switching for a limited number of typical operating conditions. While such fixed-weight models can achieve ideal parameter combinations under some known operating conditions, they exhibit the following significant limitations in complex dynamic and unsteady-state production environments: Most existing optimization systems focus on learning from historical data or static classification patterns. When operating conditions evolve rapidly, sudden disturbances occur, or new operating states emerge, the system's response to unknown operating conditions is often rigid, neglecting the prediction of future operating trend and the forward-looking weight adjustment of process requirements, resulting in optimization results that cannot be aligned with actual operating objectives in a timely manner.

[0004] Excessive manual intervention or rule-based weight adjustments are easily limited by subjective experience, making it difficult to continuously capture changes in operators' actual control tendencies, thus hindering their widespread adoption in intelligent scheduling and unattended scenarios. While some systems introduce some feedback, it mostly remains at the level of result feedback, lacking a multi-level, proactive adjustment mechanism for the weight adaptation process. Summary of the Invention

[0005] This application provides an intelligent optimization method for sludge deep dewatering process parameters, which aims to solve one of the problems or issues of the existing technology mentioned in the background section.

[0006] The intelligent optimization method for sludge deep dewatering process parameters provided in this application specifically includes: S1: Real-time acquisition of sludge moisture content, organic matter content, pH value, temperature and equipment load status parameters in the sludge deep dewatering system, and construction of near-time sequence feature sequences using a sliding time window method to form a multi-dimensional dynamic operating condition dataset.

[0007] S2: Perform normalization and outlier filtering on the near-time-series feature sequence, use the moving average algorithm to eliminate sensor noise interference, and generate a standardized time-series feature matrix as the input for subsequent models.

[0008] S3: The standardized temporal feature matrix constructed based on the sliding time window is input into the CNN-GRU hybrid model. The hybrid model extracts local fluctuation features through convolutional layers, captures time dependencies through GRU layers, and outputs a working condition evolution prediction vector.

[0009] S4: Based on the predicted vector of the working condition evolution, a three-layer feedback channel is constructed: trend-guided feedback maps the predicted trend to the target priority offset based on the fuzzy rule engine; preference evolution feedback captures the operation control tendency through the online Bayesian update mechanism; and stability feedback uses the rolling Horizon simulation mechanism to evaluate the multi-step performance consistency of candidate solutions.

[0010] S5: Normalize the three types of feedback signals respectively and input them into the weighted fusion layer. The weighted fusion layer controls the contribution ratio of each feedback channel through trainable attention weight parameters and outputs a dynamic weight vector that acts on the multi-objective evaluation function.

[0011] S6: Construct a multi-objective optimization function based on dynamic weight vectors, use the NSGA-II algorithm to search for Pareto optimal solution set in the solution space, generate candidate process parameter combinations, calculate the 'trend alignment' index of each solution, and screen the optimal solution that is highly correlated with the predicted evolution direction of the operating conditions.

[0012] S7: Send the selected optimal combination of process parameters to the control system, perform parameter adjustment operations, and continuously monitor the deviation rate between the actual operating status and the optimization target. When the deviation rate exceeds the preset threshold, trigger the weight feedback mechanism to recalculate the dynamic weight.

[0013] The intelligent optimization method for sludge deep dewatering process parameters provided in this application has the following beneficial effects: (1) To address the technical shortcomings of traditional sludge dewatering systems that rely on fixed weights in multi-objective optimization and are difficult to adapt to dynamic operating conditions, this solution significantly improves the system's adaptive decision-making ability in complex operating environments by constructing a dynamic weight generation mechanism based on context awareness and multi-level feedback fusion. Existing technologies typically use static weighting or manually preset operating conditions to balance energy consumption, efficiency, and reagent consumption, which can easily lead to increased objective conflicts, control lag, and even performance degradation. This invention abandons the reliance on prior classification and manual intervention, and instead uses real-time parameters such as sludge moisture content, organic matter content, pH value, temperature, and equipment load to construct a time-series feature sequence within a sliding time window. A CNN-GRU hybrid model is introduced to extract local fluctuation features and predict short-term operating conditions trends, forming a forward-looking trend-sensitive operating condition vector. This design enables the system to actively identify and predict the direction of change of key process parameters, providing a scientific basis for subsequent dynamic weight adjustment, and effectively overcoming the passivity and delay problems caused by traditional methods that only respond to the current state.

[0014] (2) To further enhance the intelligence and stability of the optimization process, this scheme innovatively designs a three-layer closed-loop feedback structure consisting of trend-guided feedback, preference evolution feedback, and stability feedback, and achieves the coordinated integration of multi-source signals through a weighted fusion layer. Among them, the trend-guided feedback uses a fuzzy rule engine to transform the predicted trend into a target priority offset, automatically increasing the processing efficiency weight under the trend of rising water content, ensuring that the control strategy is aligned with future needs; the preference evolution feedback uses an online Bayesian update mechanism to continuously learn the manual correction behavior of operators in different working conditions, and integrates it as an implicit preference signal into the weight generation process, enhancing the naturalness and practicality of human-machine collaboration; the stability feedback evaluates the performance consistency of candidate solutions in multiple control cycles in the future through rolling Horizon simulation, suppressing aggressive adjustments caused by instantaneous disturbances, and ensuring the smooth operation of the system. After normalization, the contribution ratio of the three types of feedback is dynamically adjusted by the attention mechanism, which not only ensures a sensitive response to sudden changes, but also avoids the risk of control instability caused by drastic weight oscillations, thus achieving an organic unity of foresight, personalization, and robustness.

[0015] (3) In the final solution evaluation stage, this scheme introduces a new evaluation index, "trend alignment," which requires that the recommended control scheme not only meet the optimal balance among multiple objectives but also that its performance evolution path maintains a high correlation with the predicted operating condition trend, further strengthening the system's closed-loop optimization capability and long-term performance consistency. This mechanism encourages the optimization algorithm to choose solutions that can continuously match actual operating needs over a period of time, rather than being limited to instantaneous optima, significantly improving the time continuity and engineering applicability of the control strategy. Combining the above technical means, the overall system achieves a paradigm shift from "passive response - manual intervention" to "active prediction - autonomous optimization," continuously maintaining a dynamic balance between low energy consumption, high efficiency, and low chemical consumption without frequent manual parameter adjustments, greatly improving the intelligence level and operational reliability of the sludge dewatering process. Attached Figure Description

[0016] Figure 1 This is the main flow chart of the intelligent optimization method for sludge deep dewatering process parameters.

[0017] Figure 2 This is a sub-flowchart of the intelligent optimization method for sludge deep dewatering process parameters.

[0018] Figure 3 This is another sub-flowchart of the intelligent optimization method for sludge deep dewatering process parameters. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0021] like Figure 1 As shown, this application provides an intelligent optimization method for sludge deep dewatering process parameters, specifically including: S1: Real-time acquisition of sludge moisture content, organic matter content, pH value, temperature and equipment load status parameters in the sludge deep dewatering system, and construction of near-time sequence feature sequences using a sliding time window method to form a multi-dimensional dynamic operating condition dataset.

[0022] S2: Perform normalization and outlier filtering on the near-time-series feature sequence, use the moving average algorithm to eliminate sensor noise interference, and generate a standardized time-series feature matrix as the input for subsequent models.

[0023] S3: The standardized temporal feature matrix constructed based on the sliding time window is input into the CNN-GRU hybrid model. The hybrid model extracts local fluctuation features through convolutional layers, captures time dependencies through GRU layers, and outputs a working condition evolution prediction vector.

[0024] S4: Based on the predicted vector of the working condition evolution, a three-layer feedback channel is constructed: trend-guided feedback maps the predicted trend to the target priority offset based on the fuzzy rule engine; preference evolution feedback captures the operation control tendency through the online Bayesian update mechanism; and stability feedback uses the rolling Horizon simulation mechanism to evaluate the multi-step performance consistency of candidate solutions.

[0025] S5: Normalize the three types of feedback signals respectively and input them into the weighted fusion layer. The weighted fusion layer controls the contribution ratio of each feedback channel through trainable attention weight parameters and outputs a dynamic weight vector that acts on the multi-objective evaluation function.

[0026] S6: Construct a multi-objective optimization function based on dynamic weight vectors, use the NSGA-II algorithm to search for Pareto optimal solution set in the solution space, generate candidate process parameter combinations, calculate the 'trend alignment' index of each solution, and screen the optimal solution that is highly correlated with the predicted evolution direction of the operating conditions.

[0027] S7: Send the selected optimal combination of process parameters to the control system, perform parameter adjustment operations, and continuously monitor the deviation rate between the actual operating status and the optimization target. When the deviation rate exceeds the preset threshold, trigger the weight feedback mechanism to recalculate the dynamic weight.

[0028] Step S1: Real-time acquisition of parameters such as sludge moisture content, organic matter content, pH value, temperature, and equipment load status in the sludge deep dewatering system, and construction of a near-time series feature sequence using a sliding time window method to form a multi-dimensional dynamic operating condition dataset. Specifically, this includes: S1.1: Deploy a multi-channel sensor network based on the industrial Internet of Things architecture to synchronously collect key parameters such as sludge moisture content, organic matter content, pH value, temperature and equipment load status in the sludge deep dewatering system in order to obtain real-time operating data.

[0029] For the operating environment of the sludge deep dewatering system, a multi-channel sensor network deployment scheme under the industrial Internet of Things architecture is adopted (configuration parameters: sampling frequency of 1 Hz, transmission protocol of Modbus TCP) to realize the synchronous acquisition of multi-dimensional operating parameters such as sludge moisture content, organic matter content, pH value, temperature and equipment load status.

[0030] Furthermore, a high-precision capacitive moisture sensor (measurement range: 0-100%, resolution: 0.1%) and an online ultraviolet spectrometer for organic matter (measurement range: 0-500 mg / L, resolution: 1 mg / L) are used to achieve real-time detection of moisture content and organic matter content, and to obtain the original analog signal output.

[0031] Furthermore, a high-impedance differential pH electrode sensor (measurement range: 0-14, resolution: 0.01) and a thermocouple temperature sensor (type: K, measurement range: 0-100℃, resolution: 0.1℃) are used to realize the digital acquisition of pH value and temperature data and generate corresponding time-stamped data packets.

[0032] Furthermore, a torque-based equipment load monitoring module (accuracy: ±0.5 Nm) is adopted. By inputting the load sensor signal to the local acquisition controller, the operating power of the equipment is calculated in real time to form a numerical sequence of equipment load status.

[0033] The field-programmable gateway performs unified digital encapsulation processing on the signals collected by the various sensors, transforming the multi-source analog and digital signals from the previous step into structured, multi-channel real-time operating data, thus realizing the basic input conditions for multi-dimensional dynamic operating condition data.

[0034] For example, in a sludge deep dewatering workshop in a certain city, a 16-channel multi-data acquisition system was adopted, with the following configuration parameters: sludge moisture content sensor sampling frequency 1 Hz, organic matter content analyzer sampling frequency 0.5 Hz, pH sensor sampling frequency 1 Hz, temperature sensor sampling frequency 1 Hz, and load monitoring module sampling frequency 2 Hz. Each sensor is connected to a central gateway via an industrial Ethernet network. The gateway is equipped with a time synchronization module, using the IEEE 1588 precision time protocol to synchronize all acquisition channels at the millisecond level. During operation, the measured moisture content was 78.4%, organic matter content was 132 mg / L, pH was 6.85, temperature was 28.7℃, and equipment load was 14.3 Nm. The acquisition system encapsulates the above raw data into a five-dimensional vector sequence, caches it locally, and pushes it to the edge server, providing high-precision raw input for subsequent S1.2 timestamp alignment and sliding time window segmentation. With this configuration, the data acquisition latency is stabilized within 15 ms, signal integrity is significantly improved, and the accuracy and stability of subsequent working condition feature extraction and dynamic weight optimization are guaranteed.

[0035] S1.2: Timestamps are used to perform time-series alignment processing on the collected multidimensional parameter data to eliminate time-series misalignment caused by differences in the sampling periods of each sensor and generate a time-consistent multidimensional raw data sequence.

[0036] S1.3: Perform a sliding window segmentation operation on the time-consistent multidimensional original data sequence according to the preset time window length to generate continuous near-time sequence feature windows to capture the local dynamic change characteristics of the working conditions.

[0037] S1.4: Perform zero-mean standardization and range normalization on the multidimensional parameter data within each sliding window to eliminate the differences in the dimensions of each parameter and generate a standardized multidimensional time-series feature vector sequence.

[0038] S1.5: The standardized multidimensional temporal feature vector sequence is stacked in chronological order into a three-dimensional tensor structure to form a multidimensional dynamic working condition dataset for subsequent CNN-GRU hybrid model input.

[0039] The multidimensional time-series feature vector sequence, after zero-mean standardization and range normalization, is subjected to tensor quantization reconstruction processing according to the acquisition time index order (parameters: time series length T, feature dimension F, window step size Δt), so as to map the two-dimensional feature matrix to a three-dimensional tensor structure for time-series deep modeling.

[0040] Furthermore, a dimensional rearrangement method is employed (parameters: axis sequence is [window index, time step, feature dimension]) to achieve consistency in the order of elements within the tensor along the time dimension, and to obtain preliminary three-dimensional tensor data that satisfies the CNN input format.

[0041] Furthermore, a tensor slicing and padding algorithm (parameter: padding length P, padding value is zero) is applied to pad the time segments that do not meet the preset window length and generate a set of time windows with consistent length, ensuring the integrity of the neighborhood coverage for subsequent convolution calculations.

[0042] Furthermore, by using the tensor normalization operator (parameter: L2 norm normalization), the scaling of the eigenvector magnitudes at each time step of the three-dimensional tensor is unified, so as to reduce the interference of the difference in eigenvector magnitudes on the model weight update, and generate the normalized three-dimensional tensor representation.

[0043] Furthermore, a tensor compression coding method (parameter: sparse coding threshold λ) is adopted to store the feature dimensions with high redundancy information in a sparse manner in the tensor structure, thereby optimizing the storage efficiency of input data and reducing the computational load of the model.

[0044] Through the above tensor quantization reconstruction and normalization process, the standardized multidimensional temporal feature vector sequence obtained in the previous step is transformed into a multidimensional dynamic working condition dataset with unified time order, unified dimensions, and adaptability to the joint modeling requirements of convolution and recurrent units, thereby achieving matching and optimization of input data structure and model architecture.

[0045] Step S2: Normalize and filter outliers on the near-time-series feature sequence, use a moving average algorithm to eliminate sensor noise interference, and generate a standardized time-series feature matrix as input for subsequent models. Specifically, this includes: S2.1: Perform min-max normalization on the parameters of each dimension in the near-time-series feature sequence obtained from step S1 to map the multi-source heterogeneous data such as sludge moisture content, organic matter content, pH value, temperature and equipment load status to the [0,1] interval, so as to eliminate the influence of different physical dimensions on model training and obtain the normalized standardized feature vector.

[0046] For each dimension parameter in the near-temporal feature sequence output from step S1, a min-max normalization method (parameters: original values ​​of each parameter, minimum and maximum values ​​of the corresponding dimension) is used to linearly map the numerical ranges of different parameters to a unified [0,1] interval. This method is defined mathematically as follows: in, These are the original parameter values. The minimum value of this dimension. The maximum value of this dimension. This is the normalized value.

[0047] Furthermore, a batch calculation strategy based on a sliding time window (window length equal to the time window length set in S1) is used to achieve batch normalization of data of the same dimension within each time window, and to obtain the preliminary normalization matrix corresponding to each window. This strategy avoids compressing small fluctuations caused by global extrema by dynamically calculating the minimum and maximum values ​​within each window.

[0048] Furthermore, a numerical stability constraint mechanism is adopted (constraint condition: if...). If the value is less than a set threshold, the denominator is set to 1. This enables numerical processing of approximately constant sequences and avoids distortion of normalization results due to an excessively small denominator.

[0049] Furthermore, the batch normalization results are reassembled into a global normalized feature vector sequence in the original time series order, and precision truncation is performed (the truncation number is set to three decimal places) to generate a standardized multidimensional time series feature vector set.

[0050] The above-mentioned min-max normalization algorithm transforms the multidimensional heterogeneous operating condition parameters output in the previous step into standardized feature data under a unified dimension, thereby eliminating the impact of physical dimension differences on the subsequent training of the CNN-GRU model.

[0051] For example, in a sludge deep dewatering system, the acquisition time window is set to 60 seconds. The minimum value of the acquired sludge moisture content data within a certain window is 72.5, and the maximum value is 78.3. Normalization is performed on the original value of 75.0, resulting in the normalized value: ,Right now The minimum value of the device load status data within the same window is 45, and the maximum value is 60. Normalization is performed on the original value of 50, resulting in the normalized value: ,Right now After all five dimensions of parameters are normalized and concatenated into a one-dimensional vector, a standardized feature vector set of length 5 is formed to support the outlier detection processing of S2.2. This configuration significantly improves the homogeneity and feature comparability of the model input in actual operation, thereby enhancing the adaptability and computational accuracy of the multi-objective optimization module under different operating conditions.

[0052] S2.2: Based on the normalized standardized feature vector, a sliding window-based Z-score outlier detection algorithm is applied to identify and mark data points that exceed ±3 times the standard deviation as potential outlier candidates to improve the reliability of the data sequence.

[0053] S2.3: Perform linear interpolation-based filtering on the identified outlier candidates, construct local trend lines using the values ​​of adjacent time points, and calculate the interpolation results to replace the outliers, thereby generating a time series feature sequence after outlier correction, improving data continuity and consistency.

[0054] S2.4: Based on the time series feature sequence after outlier correction, the N-order moving average algorithm is used to smooth the data in each dimension to suppress short-term fluctuations caused by sensor noise, obtain smoothed time series feature subsequences, and improve data stability.

[0055] S2.5: Reorganize the smoothed temporal feature subsequences according to the original time series structure to form a standardized temporal feature matrix with a unified time alignment relationship, which serves as the input tensor for the subsequent CNN-GRU hybrid model and supports the generation of the working condition evolution prediction vector.

[0056] Based on the smoothed time-series feature subsequence obtained from step S2.4, the input data is a multidimensional parameter sequence that has undergone outlier correction and noise suppression. The data at each time step has eliminated short-term fluctuations but has not yet been reorganized into a unified structure.

[0057] A time-series reconstruction algorithm (parameters: original timestamp sequence, smoothed feature subsequence) is used to map the position of each dimension of features on the time axis, ensuring that the smoothed data corresponds one-to-one with the original collection time point.

[0058] Furthermore, by using a dimension mapping table generator (parameters: smooth feature dimension identifier, original feature dimension identifier), the structure alignment of each feature dimension is achieved, and a temporary reorganization matrix containing all dimensions and all time steps is obtained.

[0059] Furthermore, a tensor quantization recombination method (parameters: matrix dimension composition rules, time axis length) is adopted to convert the recombination matrix into a three-dimensional tensor structure, where the first dimension is the time step index, the second dimension is the feature dimension, and the third dimension is the data value unit, and a working condition representation tensor with a unified index system is generated.

[0060] Furthermore, a time alignment consistency check algorithm (parameters: original timestamp, reconstructed tensor time axis) is used to check the continuity and uniformity of the time axis, and linear interpolation compensation is performed when abnormal intervals are found to generate tensor data with consistent time intervals.

[0061] Through the matrix recombination and tensor quantization methods described above, the smoothed feature sequence from the previous step is transformed into a standardized temporal feature matrix with a unified time alignment relationship, enabling the subsequent CNN-GRU hybrid model to stably extract the evolution features of the working conditions.

[0062] like Figure 2As shown, step S3 involves inputting a standardized temporal feature matrix constructed based on a sliding time window into a CNN-GRU hybrid model. This hybrid model extracts local fluctuation features through convolutional layers, captures temporal dependencies through GRU layers, and outputs a predicted vector for the evolution of operating conditions. Specifically, this includes: S3.1: Based on the standardized time-series feature matrix constructed by the sliding time window, a one-dimensional convolutional neural network (CNN) is used to perform local fluctuation feature extraction processing on the matrix to identify the local change patterns of each process parameter within a short time window and obtain the local fluctuation feature vector.

[0063] The standardized time-series feature matrix generated in step S2 is used as input data. The matrix contains a multi-dimensional process parameter time-series vector after normalization, outlier filtering and smoothing. The time dimension is consistent with the sampling interval to ensure the synchronization and dimensional uniformity of each parameter.

[0064] A one-dimensional convolutional neural network (with a kernel size of 3, a stride of 1, and the number of channels adjusted according to the dimension of the input parameters) is used to perform convolution operations on the standardized temporal feature matrix to automatically extract local change patterns within a short time window and obtain a preliminary local feature response matrix.

[0065] Furthermore, by introducing multi-scale convolution kernels (different sizes of 3, 5, and 7) into the convolution operation and performing parallel computation, the local fluctuation features under different time spans are captured, and a multi-scale local fluctuation feature set is obtained, thereby enhancing the ability to perceive subtle trend changes in the working condition.

[0066] Furthermore, the channel normalization algorithm (Batch Normalization, with a batch size of 64) of the convolutional layer output feature map is used to standardize the mean and variance of the feature distribution of each channel, suppressing the training instability caused by different parameter dimensions or distribution shifts, and outputting a normalized local feature tensor.

[0067] Furthermore, by performing a weighted residual connection on the feature map after the convolution operation, the current convolution result is fused with a specific mapping vector of the input matrix with a weight of 0.3 to retain the low-frequency trend information in the original signal and generate a fused local fluctuation feature vector.

[0068] By using the above one-dimensional convolutional neural network feature extraction processing method, the standardized time series matrix of the previous step is transformed into a local fluctuation feature vector that can characterize the change pattern of operating parameters in a short period of time, so as to realize the high-sensitivity feature input required for subsequent GRU layer time-dependent modeling.

[0069] For example, under the input of five-dimensional parameters—sludge moisture content, organic matter content, pH value, temperature, and equipment load status—for a certain sludge deep dewatering system, the time window length of the standardized temporal feature matrix is ​​set to 10 sampling points, with a sampling interval of 5 seconds. A one-dimensional CNN with a kernel size of 3 is used for computation, the number of convolutional kernels is set to 32, the stride is 1, and the activation function is temporarily disabled, outputting only the linear convolutional response. In the multi-scale convolution module, the kernel sizes are set to 3, 5, and 7, and the number of output channels per scale is 16, 16, and 8, respectively, which are then concatenated to form a multi-scale local feature set with a total of 40 channels. Batch normalization calculates the sliding batch mean on each channel. and variance And execute the standardized formula: ,in For channel feature values, This is the batch average. The standard deviation is the batch size. In the residual connection part, the linear mapping results between the convolutional output features and the input matrix are weighted and fused element-wise with a weight of 0.3. The final output local fluctuation feature vector has a length of 40, while the time dimension remains at 10. Field verification shows that this local fluctuation feature significantly improves the ability to detect short-term spikes in moisture content or sudden drops in equipment load in subsequent GRU modeling, resulting in a significant improvement in optimization performance. The system is also better able to match the changing trends of actual operating conditions during dynamic weight adjustments.

[0070] S3.2: Perform ReLU activation function and max pooling operation on the local fluctuation feature vector to compress the feature dimension and enhance the representation ability of key fluctuation patterns, and generate compressed local feature representation.

[0071] S3.3: Input the compressed local feature representation into the gated recurrent unit (GRU) layer, perform time-dependent modeling based on the serialized features to capture the global dynamic features of the evolution of each process parameter over time, and generate a time-dependent feature sequence.

[0072] The input is a local feature representation after ReLU activation and max pooling compression, which is arranged in chronological order and retains the core information of the local fluctuation pattern.

[0073] A gated recurrent unit (GRU) network structure is adopted (parameters: number of hidden units H, weight matrices of update gate and reset gate initialized in Xavier mode). The optimal number of hidden units in the GRU layer is 256. Temporal dependency modeling is performed on local feature sequences to establish nonlinear dynamic correlation between process parameters at different time steps.

[0074] S3.4: Perform attention mechanism weighted fusion processing on the time-dependent feature sequence to enhance the attention to key time step features and output the weighted time evolution feature vector.

[0075] S3.5: Based on the weighted time evolution feature vector, nonlinear mapping and linear regression are performed through a fully connected layer to predict the direction of operating condition evolution in the next 1-2 control cycles, and output the operating condition evolution prediction vector for use by the trend guidance feedback module.

[0076] Based on the weighted time evolution feature vector, a fully connected network layer (parameters: 64 hidden nodes, activation function is tanh) is used to implement nonlinear mapping processing, which transforms the multidimensional time feature vector into a high-dimensional abstract feature space to enhance the model's ability to fit complex working conditions.

[0077] Furthermore, a multidimensional feature regression mapping is performed through a second fully connected layer (parameters: the number of output nodes is the number of prediction cycles * the dimension of the operating condition parameters, and the activation function is linear), which compresses the high-dimensional abstract features and maps them to the operating condition prediction output space of the future control cycle, generating a continuous prediction value matrix.

[0078] Furthermore, the prediction results are supervised and optimized using the minimum mean square error (MSE) loss function, whereby MSE is defined as: in, To predict the sample size, For operating condition parameters, This represents the true value of the j-th parameter in the i-th sample. This is the corresponding predicted value.

[0079] Furthermore, gradient backpropagation updates are performed on the weight parameters of the fully connected layer and the previous CNN-GRU layer using the Adam optimization algorithm (parameters: initial learning rate 0.001, β1=0.9, β2=0.999) to reduce prediction error and improve the stability of trend prediction.

[0080] Dropout regularization (parameter: dropout rate 0.2) is used to randomly deactivate the output of the regression mapping layer to prevent overfitting caused by sample redundancy under similar operating conditions.

[0081] By using the chain algorithm described above, the weighted time evolution feature vector is transformed into a multi-parameter operating condition evolution prediction vector covering the next 1-2 control cycles, thereby achieving a high-precision alignment between the prediction results and the actual trend.

[0082] For example, in the operation scenario of a sludge deep dewatering system, the weighted time evolution feature vector has a dimension of (32×128). The first fully connected layer has 64 hidden units and tanh activation; the second fully connected layer has 10 output nodes, corresponding to the predicted values ​​of 5 operating parameters in the next 2 control cycles. The training dataset contains 2000 control cycle samples. The loss function is calculated using the above MSE formula. Under the conditions of n=2000 and m=5, the Adam optimization algorithm iterates for 500 rounds with an initial learning rate of 0.001. After training, the mean absolute error of the predicted output matrix on the test set is less than 0.05 (physical quantity unit is normalized value). Dropout processing significantly suppresses the overfitting phenomenon of prediction under high-frequency fluctuations. The final generated operating condition evolution prediction vector is verified in the subsequent trend-guided feedback module to be highly consistent with the actual operating condition change trajectory, improving the accuracy of dynamic weight adjustment and the foresight of system optimization.

[0083] like Figure 3 As shown, step S4 involves constructing a three-layer feedback channel based on the predicted vector of the operating condition evolution: trend-guided feedback maps the predicted trend to a target priority offset using a fuzzy rule engine; preference evolution feedback captures operational control tendencies through an online Bayesian update mechanism; and stability feedback evaluates the multi-step performance consistency of candidate solutions using a rolling Horizon simulation mechanism. Specifically, this includes: S4.1: Based on the operating condition evolution prediction vector, a trend-guided feedback channel is constructed. Using a predefined fuzzy rule engine, the predicted trend information is mapped to the priority offset of each optimization objective, so as to dynamically adjust the objective weight allocation in the multi-objective optimization process.

[0084] The input operating condition evolution prediction vector serves as the initial data source for the trend-guided feedback channel.

[0085] A trend classification analysis method (parameters: prediction vector dimension, time span, fluctuation threshold) is adopted to classify the evolution direction of each process parameter in the prediction vector and generate a trend symbol matrix as input for the subsequent fuzzy rule engine.

[0086] Furthermore, by using the fuzzy membership degree calculation method (parameters: shape coefficient of the triangular membership function, parameter target interval), the fuzzy mapping relationship between the trend symbol matrix and the influencing factors of each optimization objective is transformed, and the trend membership degree matrix is ​​obtained, which quantifies the influence intensity of different trends on each objective.

[0087] Furthermore, a fuzzy inference mechanism (parameters: rule base size, inference method is Mamdani type, set operation method is min-max) is adopted to match the trend membership matrix with the multi-objective weight offset rules in the predefined rule base and generate the target weight offset vector. The rule base is designed based on domain knowledge and operational experience.

[0088] Furthermore, by using a weight smoothing control algorithm (parameters: smoothing coefficient λ, minimum change threshold ε), dynamic smoothing and threshold truncation of the target weight offset vector are achieved, and a stable weight adjustment vector is generated to reduce drastic weight changes caused by instantaneous trend fluctuations.

[0089] By using a fuzzy rule engine and smooth control, the predicted trend results from the previous step are transformed into target priority offsets that can be directly applied to the multi-objective evaluation function, enabling real-time dynamic adjustment of weight allocation and improving optimization accuracy under complex dynamic conditions.

[0090] For example, in the operation scenario of a certain sludge deep dewatering system, the input operating condition evolution prediction vector includes a predicted influent sludge moisture content of 0.78 with a future decrease of -0.03, a predicted organic matter content of 0.25 with a future increase of 0.02, and a predicted pH value of 6.8 with a future decrease of -0.1. When using the trend classification analysis method, the influent sludge moisture content is classified as decreasing, the organic matter content as increasing, and the pH value as decreasing, forming a symbol matrix [ 1, +1, 1). In the fuzzy membership calculation process, the peak position of the triangular membership function is selected to correspond to the median of the optimal interval for each objective. For example, the membership degree of the treatment efficiency objective to the decrease in the moisture content of the influent is calculated. The membership degree of energy consumption targets to the increase in organic matter content The membership degree of the target to the decrease in pH value due to the reduction of the agent Construct a membership matrix. Use a Mamdani-type inference method to match the rule base "increase treatment efficiency weight when moisture content and pH decrease" and "decrease reagent weight when organic matter content increases," outputting a weight offset vector [+0.12, 0.05, 0.07]. Smoothing control was performed using a smoothing coefficient λ=0.6 and a minimum change threshold ε=0.02, resulting in a stable weight adjustment vector [+0.072, 0.03, [0.042], as a trend-guided feedback output, is directly input into the dynamic weight fusion module. Operation verification shows that the processing efficiency is significantly improved under this working condition, while energy consumption and reagent usage remain within a stable range.

[0091] S4.2: Based on historical operation records and current trend stages, construct a preference evolution feedback channel. Through an online Bayesian update mechanism, continuously capture the changing trend of operators' preferences for process parameter adjustments and generate an operation tendency evolution vector as a feedback signal.

[0092] S4.3: Based on the multi-objective candidate solution set and the system dynamic response model, a stability feedback channel is constructed. The rolling horizon simulation mechanism is used to perform multi-step prediction and scoring of the performance consistency of candidate solutions in multiple future control cycles in order to evaluate their system stability performance.

[0093] Based on the operating condition evolution prediction vector obtained from step S3 and the multi-objective candidate solution set generated from step S6, a system dynamic response model (parameters: control cycle length, state transition matrix, external disturbance estimation vector) is used to simulate the state change trajectory of each candidate solution in multiple future control cycles, thereby obtaining multi-step time series response data.

[0094] Furthermore, by using the rolling horizon simulation mechanism (parameters: prediction step size h, rolling step Δ, and control input sequence length L), the progressive rolling prediction of each candidate solution is realized. Each rolling window uses the prediction end state of the previous window as the initial condition of the current window, and the operating condition evolution prediction vector and the disturbance estimation vector are superimposed and input into the system dynamic response model to generate a continuous future operating state estimation sequence.

[0095] Furthermore, for each candidate solution's rolling prediction results, time-series performance metrics for each core optimization objective are calculated, including energy consumption, processing efficiency, and reagent usage sequences. A performance consistency score is then calculated using the consistency evaluation formula. Score= in, To predict the total step size, For the k-th prediction time step, This is the square of the performance fluctuation value at that time step. This formula quantifies the performance fluctuation of the sequence; the smaller the fluctuation value, the higher the consistency.

[0096] Furthermore, to prevent short-term abnormal fluctuations from affecting the scoring results, a weighted smoothing process (parameter: weight decay factor λ) is adopted to perform a weighted average on the consistency scores. The weights decay as the prediction step size increases, thereby improving the stability of the scores and making them more consistent with long-term trends.

[0097] By using rolling Horizon simulation and consistency quantization, the candidate solution sequence from the previous step is transformed into a stability scoring matrix, enabling accurate evaluation of the stability performance of each candidate solution over multiple prediction cycles.

[0098] S4.4: Normalize the three types of output signals—trend-guided feedback, preference evolution feedback, and stability feedback—to eliminate the dimensional differences between different feedback channels and generate a feedback normalization vector with unified dimensions.

[0099] S4.5: The normalized three types of feedback signals are input into the weighted fusion layer. The contribution ratio of each feedback channel is dynamically adjusted based on the trainable attention weight parameters, and the fused dynamic weight vector is output to drive the real-time optimization of the multi-objective evaluation function.

[0100] Step S5: The three types of feedback signals are normalized and input into a weighted fusion layer. The weighted fusion layer controls the contribution ratio of each feedback channel through trainable attention weight parameters and outputs a dynamic weight vector that acts on the multi-objective evaluation function. Specifically, this includes: S5.1: The trend-guided feedback signal, preference evolution feedback signal, and stability feedback signal are normalized to eliminate the differences in the dimensions and amplitude scales of each feedback signal, thereby obtaining three types of standardized feedback signals and providing a unified input for subsequent fusion processing.

[0101] The trend-guided feedback signal, preference evolution feedback signal, and stability feedback signal are respectively input into the normalization processing unit. The Z-score normalization method (parameters: sample mean μ, sample standard deviation σ) is used to achieve zero centering of each signal in the mean dimension and unit scaling in the variance dimension, thereby eliminating the dimensional differences of the feedback signals.

[0102] Furthermore, by using the minimum-maximum scaling method (parameters: minimum value min, maximum value max), the above standardized results are mapped to the [0,1] interval to ensure that the amplitude of all feedback signals is within a uniform normalized interval and has the same scaling characteristics.

[0103] Furthermore, a robust normalization method (parameters: median med, interquartile range IQR) is adopted to perform scaling adjustments based on the median and interquartile range for feedback signals with abnormal peak fluctuations, thereby reducing the impact of outliers on the overall normalization results and improving signal robustness.

[0104] Furthermore, the normalized feedback signal is smoothed in the time domain by using a sliding window smoothing algorithm (parameters: window length W, weighting coefficient α) to suppress numerical oscillations caused by short-term noise or instantaneous disturbances and obtain a stable normalized signal sequence.

[0105] Through the aforementioned hierarchical normalization algorithm chain, the trend guidance feedback signal, preference evolution feedback signal, and stability feedback signal generated in the previous step are transformed into three types of standardized feedback signals with unified dimensions, unified amplitude range, and time stability. This provides unified input conditions for the subsequent weighted fusion layer and improves the accuracy and comparability of the fusion calculation.

[0106] For example, under a certain deep sludge dewatering condition, the raw data range of the trend-guided feedback signal is 0.8~1.5, and the range of the preference evolution feedback signal is... The stability feedback signal ranges from 200 to 350, with a range of 0.3 to 0.6. The three types of signals are input into the Z-score normalization unit, and their means and standard deviations are calculated separately. For example, the mean of the trend-guided feedback signal... Standard deviation Execute the formula Complete the zero-mean unit variance transformation. Then, based on the min-max scaling method, map the trend-guided feedback signal to the [0,1] interval using the formula. in , Robust normalization is applied to the preference evolution feedback signal to calculate the median. and interquartile difference The normalization results were adjusted to suppress the influence of outliers. Finally, a sliding window smoothing algorithm (window length W=5, weighting coefficient α=0.6) was applied to all normalized signals to obtain smooth outputs of the three types of standardized feedback signal sequences in the time dimension, ensuring numerical stability and comparability during subsequent weighted fusion. In this scenario, the amplitude range of the three types of feedback signals after normalization is all [0,1] and the fluctuation amplitude is significantly reduced, ensuring that the contributions of different feedback channels in the dynamic weighted fusion stage can be accurately calculated based on comparable amplitude scales.

[0107] S5.2: Based on the normalized three types of feedback signals, a weighted fusion layer is constructed. The weighted fusion layer consists of a set of trainable attention weight parameters, which are optimized by the backpropagation algorithm to reflect the relative importance of each feedback channel under the current working condition.

[0108] Based on the input conditions of the normalized trend-guided feedback signal, preference evolution feedback signal, and stability feedback signal, an interpretable attention-weighted fusion method (parameters: number of feedback channels n=3, initial weight vector initialization method is uniform distribution, weight constraint condition Σw=1) is adopted to realize the dynamic modeling and calculation of the contribution of each feedback channel.

[0109] Furthermore, through the attention weight parameterization mechanism (parameters: weight update learning rate η, weight regularization coefficient λ) r The feature representation of each feedback channel is multiplied element-wise with the trainable attention weight matrix to obtain a weighted component matrix, which characterizes the relative influence intensity of different feedback channels under the current operating conditions.

[0110] Furthermore, the backpropagation gradient optimization method is adopted to calculate the gradient of the loss function based on the difference between the fused output and the target weight allocation, and to perform iterative updates on the attention weight parameters to converge to a stable weight configuration result.

[0111] Furthermore, by using the gradient normalization method (parameters: normalization range [0,1], scaling factor κ), nonlinear normalization mapping is performed on each element of the updated weight vector to ensure that the weight values ​​meet the scaling constraints and maintain the numerical stability of the fusion calculation.

[0112] Furthermore, by utilizing the weight sparsity constraint method, sparsification is performed on the elements close to zero in the weight vector to reduce the interference of low-contribution channels, thereby improving the significance of high-contribution channels in the fusion result.

[0113] By employing an interpretable attention-weighted fusion processing method, the three types of normalized feedback signals are transformed into channel contribution parameters within the dynamic weight vector. This enables the multi-objective optimization function to achieve adaptive weight allocation under different operating conditions and provides a stable and interpretable parameter basis for subsequent sub-steps of single-channel weight calculation and global fusion.

[0114] For example, under the operating conditions of a certain sludge deep dewatering system, the input normalized trend-guided feedback signal is [0.68, 0.75, 0.55], the preference evolution feedback signal is [0.40, 0.65, 0.80], the stability feedback signal is [0.72, 0.60, 0.50], and the initial attention weight vector is set to [0.33, 0.33, 0.34]. An element-wise multiplication calculation is performed using an attention parameterization mechanism to obtain the weighted component matrix: trend-guided component [0.2244, 0.2475, 0.1870], preference evolution component [0.1320, 0.2145, 0.2720], and stability component [0.2448, 0.2040, 0.1700]. Based on the difference between the fusion result calculated by the weighted mean square error loss function and the target weight configuration, backpropagation is used to update the weight vector, with the learning rate η set to 0.01 and the regularization coefficient λ... r Set the gradient cutoff threshold τ to 0.001. gSetting the initial value to 0.1, the weight vector is updated to [0.36, 0.31, 0.33] after iteration. Normalization is performed to maintain Σw=1, and the scaling factor κ=1.0. After nonlinear mapping, the weight vector is [0.360, 0.310, 0.330]. A sparsity intensity β=0.05 is introduced for weights below the threshold θ. s Elements with a value of 0.05 are set to zero; in this embodiment, no elements need to be set to zero. The final attention weight parameters are [0.360, 0.310, 0.330]. This weight allocation significantly improves the forward-looking response capability of the trend-guided channel to the optimization function in subsequent fusion calculations and maintains a stable output of highly correlated solutions in multiple rounds of optimization.

[0115] S5.3: Input the normalized trend-guided feedback signal into the weighted fusion layer, use the attention weight parameter to perform weighted calculation on the trend-guided feedback signal, and output the trend-guided weighted feedback component to reflect the dynamic impact of trend changes on target priority under the current working conditions.

[0116] S5.4: Input the normalized preference evolution feedback signal into the weighted fusion layer, use the attention weight parameter to perform weighted calculation on the preference evolution feedback signal, and output the preference evolution weighted feedback component to reflect the dynamic adjustment effect of the operator's control tendency on the target weight.

[0117] S5.5: Input the normalized stability feedback signal into the weighted fusion layer, use the attention weight parameter to perform weighted calculation on the stability feedback signal, and output the stability weighted feedback component to suppress weight jumps caused by short-term fluctuations and improve the decision stability of the system under non-steady-state operating conditions.

[0118] S5.6: The trend-guided weighted feedback component, the preference evolution weighted feedback component, and the stability weighted feedback component are summed and fused to output the fused dynamic weight vector, which serves as the real-time weight configuration for each optimization objective in the multi-objective evaluation function, thereby realizing the adaptive adjustment of weights during the multi-objective optimization process.

[0119] The input consists of three weighted feedback components after being processed by the weighted fusion layer, corresponding to the three channels of trend guidance, preference evolution, and stability, respectively, and each component is in a state of unified dimension and normalization.

[0120] An element-wise summation method (parameters: component vector length and corresponding index) is used to superimpose the values ​​of the three types of feedback components at the same index position, forming a vector set before fusion. Furthermore, a normalization coefficient correction method (parameters: vector maximum and minimum values) is used to unify the amplitude scale of the fusion result, obtaining a weight vector that can be directly used for the optimization function. Further, a constraint normalization formula is used... To standardize the proportions of each weight, For a certain optimization objective, the fusion weights The weights of all objectives are summed to ensure that the total weight of the multi-objectives is always 1. Furthermore, a weighted smoothing algorithm (parameter: smoothing coefficient α) is used to achieve a smooth transition of the fused weight vector between adjacent control cycles, generating a dynamic weight vector with improved stability. This fusion processing method transforms the results of the previous step into an optimization input that can directly drive the multi-objective evaluation function, achieving adaptive weight adjustment in the multi-objective optimization process of sludge deep dewatering.

[0121] For example, in a certain dehydration operation scenario, the trend-guided weighted feedback component is [0.40, 0.35, 0.25], the preference evolution weighted feedback component is [0.30, 0.40, 0.30], and the stability weighted feedback component is [0.35, 0.25, 0.40]. The vector before fusion is obtained by element-wise summation = [1.05, 1.00, 0.95]. After amplitude normalization, it is mapped to the interval [0, 1], resulting in [1.00, 0.95, 0.90]. Based on the proportional normalization formula... The standardized weights for each objective are calculated to be [0.358, 0.340, 0.302]. A smoothing coefficient is introduced. =0.2, and exponentially weighted smoothing is performed on the weight vector [0.35, 0.33, 0.32] from the previous control cycle to obtain the smoothed dynamic weight vector [0.3526, 0.3340, 0.3134]. This weight vector serves as the immediate input to the multi-objective evaluation function. After optimization by the NSGA-II algorithm, the consistency between the candidate solution and the predicted trend is significantly improved, and the system maintains the stability and practicality of the optimized solution under fluctuating conditions.

[0122] Step S6: Construct a multi-objective optimization function based on the dynamic weight vector, use the NSGA-II algorithm to search for a Pareto optimal solution set in the solution space, generate candidate process parameter combinations, calculate the 'trend alignment' index for each solution, and screen the optimal solution that is highly correlated with the predicted evolution direction of the operating conditions. Specifically, this includes: S6.1: Based on dynamic weight vectors and multi-objective evaluation functions, a weighted multi-objective optimization mathematical model is constructed. The three core objectives of minimum energy consumption, maximum processing efficiency, and minimum reagent consumption are used as optimization variables. The objective function is weighted and fused to generate a multi-objective fitness function expression that can be processed by the NSGA-II algorithm.

[0123] Based on the dynamic weight vector output from step S5 and the input data of the predefined multi-objective evaluation function, a target weighted fusion method (parameters: dynamic weight vector, number of core objectives = 3) is adopted to achieve weighted modeling for the three core optimization objectives of minimum energy consumption, maximum processing efficiency, and minimum reagent consumption.

[0124] Furthermore, by using a normalized weighted calculation method (parameter: weight normalization coefficient Σw=1), the product of each optimization objective and its weight is summed to obtain the total weighted objective value.

[0125] Furthermore, a target fitness function construction method (parameter: fitness function type = weighted linear combination) is adopted to realize the fusion expression of each objective function in a unified evaluation space and generate a fitness function expression with dynamic weight adjustment capability.

[0126] Furthermore, by constructing formulas, the three single-objective functions are... , , Respectively with dynamic weight vector , , Perform the product and construct the weighted multi-objective fitness function using the following MathML formula: ,in For the i-th target weight output from the weighted fusion layer, This represents the corresponding single-objective function value.

[0127] Furthermore, by constructing a formula constraint, the range of process parameters, physical boundaries, and safe operation restrictions are set, and the above fitness function and constraints are combined to form a multi-objective optimization mathematical model that can be directly processed by the improved NSGA-II algorithm.

[0128] Through the above processing method, the dynamic weight vector and multi-objective function structure of the previous step are transformed into fitness evaluation data with real-time adaptability and which can be optimized by evolutionary algorithms, so as to realize the scientific quantification and computable expression of multi-objective optimization problems under complex dynamic conditions.

[0129] For example, during a certain operating cycle of the sludge deep dewatering process, the input dynamic weight vector is [ , , ], where the first objective function Energy consumption per unit of processing capacity (unit: kWh / t), with values ​​ranging from... The second objective function Represents the processing efficiency per unit time (unit: t / h), with a value of The third objective function The representative unit of reagent consumption (unit: kg / t) is taken as a value. The weighted fitness function is calculated using the formula described above: Output total fitness value Under constraints of energy consumption ranging from 10-15 kWh / t, processing efficiency ranging from 7-10 t / h, and reagent consumption ranging from 0.5-1.5 kg / t, this fitness function value is input into the improved NSGA-II algorithm. Combined with fast non-dominated sorting and crowding comparison mechanisms, this significantly improves the multi-objective equilibrium of candidate solutions while satisfying the constraints. After multiple iterations, this fitness expression supports the algorithm outputting a Pareto front solution set that is energy-efficient, highly efficient, and uses low-reagent-consumption, effectively improving optimization accuracy and operational fit under unsteady conditions.

[0130] S6.2: Perform multi-objective optimization calculation based on the NSGA-II algorithm, initialize the population size, crossover rate and mutation rate parameters, adopt a fast non-dominated sorting mechanism and crowding comparison strategy, iteratively evolve the candidate solutions in the solution space, and obtain the Pareto front optimal solution set as the initial recommended set of candidate process parameter combinations.

[0131] S6.3: For each candidate solution in the Pareto front optimal solution set, based on the trend alignment calculation model, input the operating condition evolution prediction vector and the current dynamic weight vector, perform dynamic correlation analysis between the solution and the predicted trend, and generate a trend alignment score for each solution using the Pearson correlation coefficient as the metric.

[0132] S6.4: Based on the trend alignment score, a second screening is performed on the Pareto optimal solution set. A trend alignment threshold is set, and candidate solutions with trend correlation below the threshold are eliminated. High-quality solution sets that are highly consistent with the evolution direction of the predicted working condition are retained to form the optimized recommended solution set.

[0133] S6.5: Perform feasibility verification on the optimized recommended solution set. Based on process boundary constraints (such as pH range, temperature limit, maximum equipment load, etc.), perform legality verification on candidate parameter combinations, filter out abnormal solutions that exceed the system's safe operating threshold, and output the final executable optimal process parameter combination.

[0134] The input is a set of recommended solutions filtered by trend alignment. Each solution contains multiple process parameter values ​​and an optimization objective result that matches the dynamic weight vector. A set of process boundary constraints (including allowable pH range, temperature limit, maximum equipment load, safe reagent dosing range, etc.) is used as the criterion for validity verification.

[0135] An interval verification method (parameters: lower limit of pH value, upper limit of pH value) is adopted to determine the validity of pH parameters in candidate solutions and output a pH value boundary verification result matrix.

[0136] Furthermore, the boundary constraint verification of the temperature parameters of the candidate solutions is realized by using a threshold comparison method (parameters: maximum allowable temperature value, minimum allowable temperature value), and the temperature boundary judgment identifier vector is obtained.

[0137] Furthermore, a capacity-load ratio calculation method (parameters: equipment rated load, equipment load value corresponding to candidate solutions) is adopted to realize the feasibility verification of equipment load boundary and generate a set of equipment load safety judgment results.

[0138] Furthermore, based on the method for calculating the safety factor of drug addition (parameters: maximum allowable drug addition amount, minimum drug addition amount), the process safety of drug dosage is verified, and a sequence of legality markers for drug parameters is obtained.

[0139] By performing logical AND operations, the boundary verification result matrices, identifier vectors, and judgment result sets are fused element by element to generate a comprehensive legality judgment matrix, which is used to filter out abnormal candidate solutions that do not meet all process boundary conditions.

[0140] Step S7: The selected optimal process parameter combination is sent to the control system to perform parameter adjustment operations and continuously monitor the deviation rate between the actual operating status and the optimization target. When the deviation rate exceeds a preset threshold, a weight feedback mechanism is triggered to recalculate the dynamic weights. Specifically, this includes: S7.1: Based on the optimal combination of process parameters after screening, a control instruction data packet conforming to the communication protocol format of the control system is generated. The control instructions include key parameters such as adjusting the pressure setpoint of the filter press, the dosage ratio of the conditioner, the operating frequency of the dewatering machine, and the heating temperature threshold, so as to realize closed-loop control of the sludge deep dewatering equipment.

[0141] Based on the selected optimal combination of process parameters, a control command encoding generation method (parameters: process parameter set, protocol type, field mapping rules) is used to convert the multi-objective optimization results into a command data structure that conforms to the control system communication protocol. Furthermore, through a field assembly method (parameters: filter press pressure setpoint, conditioner dosage ratio, dewatering machine operating frequency, heating temperature threshold), the numerical encoding of each process parameter is achieved, resulting in a preliminary data packet composed of multiple command fields.

[0142] Furthermore, through a protocol encapsulation algorithm (parameters: protocol header information, checksum calculation rules, terminator), the initial data packet is embedded into the protocol frame structure, generating a control command packet with a complete message format. Further, through a Cyclic Redundancy Check (CRC) method (parameters: polynomial generation function, start register value), a checksum is generated on the encapsulated data packet, and the checksum is appended to the end of the data packet, forming a final control command data packet that can be directly issued.

[0143] Through the above encoding and encapsulation methods, the optimal combination of process parameters is transformed into protocol-based instruction data that can be recognized and executed by the control system, thereby realizing closed-loop operation control of the sludge deep dewatering equipment.

[0144] S7.2: The control command data packet is transmitted to the PLC controller via industrial Ethernet or fieldbus protocol. The PLC controller performs parameter update operation and records the start timestamp of the current control cycle and the target parameter value as the time alignment reference for subsequent operation status monitoring.

[0145] S7.3: Based on real-time operating data fed back by the PLC controller, including parameters such as actual filter press pressure, reagent dosage, dewatering efficiency and sludge moisture content, construct a performance index vector for the current control cycle to evaluate the degree of achievement of optimization objectives.

[0146] S7.4: Calculate the deviation rate between each dimension parameter in the performance index vector and the corresponding optimization target. Use the weighted Euclidean distance method to fuse the multi-target deviations and generate a comprehensive deviation rate index to quantify the degree of deviation between the current operating state and the expected optimization effect.

[0147] S7.5: Based on the comparison between the comprehensive deviation rate index and the preset dynamic weight feedback trigger threshold, if the comprehensive deviation rate exceeds the threshold, a weight feedback trigger signal is generated, and the current running status data is used as feedback input and sent to the three-layer feedback channel to recalculate the dynamic weight vector, so as to realize the adaptive adjustment of the weight.

[0148] Based on the comprehensive deviation rate index and the preset dynamic weight feedback trigger threshold, a comparison calculation method (parameters: comprehensive deviation rate value, threshold) is used to determine the degree of deviation between the running status and the optimization expectation.

[0149] Furthermore, the triggering condition is Booleanized through a logical threshold determination algorithm (parameter: determination condition = comprehensive deviation rate > threshold), and the weighted feedback triggering flag is obtained.

[0150] Furthermore, a data encapsulation method (parameters: real-time operating parameters of the current control cycle, including filter press pressure, reagent dosage, dewatering efficiency, and sludge moisture content) is adopted to achieve structured encapsulation processing of operating status data, forming a status data package that conforms to the feedback channel input specifications.

[0151] Furthermore, a three-layer feedback channel data input routing algorithm (parameters: trend-guided feedback channel, preference evolution feedback channel, and stability feedback channel) is used to achieve parallel distribution of state data packets and channel-specific preprocessing, and to generate feedback signal update requests corresponding to each channel.

[0152] Furthermore, a dynamic weight recalculation algorithm (parameters: three types of channel feedback signals, weighted fusion mechanism parameters) is adopted to achieve adaptive updating of the dynamic weight vector, and the updated weight vector is output to the multi-objective evaluation function module.

[0153] By using the threshold determination and feedback triggering process described above, the deviation rate quantification result of the previous step is transformed into dynamic weight adjustment decision data, thereby achieving real-time adaptive adjustment of the system's weight allocation.

[0154] For example, in the deep sludge dewatering operation, the comprehensive deviation rate index is: The preset dynamic weight feedback trigger threshold is The comparison operation yields If successful, the logic determination output trigger flag is set to 1. The operating status data packet includes the actual filter press pressure. MPa, dosage of pesticide kg / h, dehydration efficiency t / h, sludge moisture content Parameters such as % are structured and encapsulated into a three-layer feedback channel. The trend-guided feedback channel detects deviations in moisture content from the trend and requires an increase in processing efficiency weight; the preference evolution feedback channel reduces the energy consumption target weight based on recent operator behavior trends; and the stability feedback channel performs performance consistency scoring on candidate weight configurations and suppresses short-term fluctuations in rolling Horizon simulations. The three types of feedback signals are processed by a normalization and weighted fusion layer, outputting an updated dynamic weight vector: [energy consumption target weight]. Processing efficiency target weight Drug consumption target weight This re-drives the multi-objective optimization function, enabling adaptive focusing of the optimal solution region.

[0155] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0156] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0157] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. Intelligent optimization method for sludge deep dewatering process parameters, specifically including: S1: Real-time acquisition of sludge moisture content, organic matter content, pH value, temperature and equipment load status parameters in the sludge deep dewatering system, and construction of near-time sequence feature sequence in the form of sliding time window to form a multi-dimensional dynamic operating condition dataset; S2: Perform normalization and outlier filtering on the near-time series feature sequence, use the moving average algorithm to eliminate sensor noise interference, and generate a standardized time series feature matrix; S3: Input the standardized time-series feature matrix into the CNN-GRU hybrid model. The CNN-GRU hybrid model extracts local fluctuation features through convolutional layers, captures time dependencies through GRU layers, and outputs a working condition evolution prediction vector. S4: Based on the working condition evolution prediction vector, construct a three-layer feedback channel of trend guidance feedback, preference evolution feedback, and stability feedback to output three types of feedback signals; S5: Normalize the three types of feedback signals respectively and input them into the weighted fusion layer. The weighted fusion layer controls the contribution ratio of each feedback channel through trainable attention weight parameters and outputs a dynamic weight vector. S6: Construct a multi-objective optimization function based on dynamic weight vectors, use the NSGA-II algorithm to search for Pareto optimal solution set in the solution space, generate candidate process parameter combinations, calculate the trend alignment index of each solution, and screen the optimal solution that is highly correlated with the predicted evolution direction of the operating conditions.

2. The intelligent optimization method for sludge deep dewatering process parameters according to claim 1, characterized in that, Step S6 is followed by: S7: Send the selected optimal combination of process parameters to the control system, perform parameter adjustment operations, and continuously monitor the deviation rate between the actual operating status and the optimization target. When the deviation rate exceeds the preset threshold, trigger the weight feedback mechanism to recalculate the dynamic weight.

3. The intelligent optimization method for sludge deep dewatering process parameters according to claim 1, characterized in that, The trend-guided feedback channel in step S4 specifically includes: the trend-guided feedback maps the predicted trend to the target priority offset based on the fuzzy rule engine.

4. The intelligent optimization method for sludge deep dewatering process parameters according to claim 3, characterized in that, The trend-guided feedback channel in step S4 specifically includes: the preference evolution feedback channel specifically includes: preference evolution feedback captures operational control tendencies through an online Bayesian update mechanism.

5. The intelligent optimization method for sludge deep dewatering process parameters according to claim 4, characterized in that, The stability feedback channel in step S4 specifically includes: the stability feedback uses the rolling Horizon simulation mechanism to evaluate the multi-step performance consistency of candidate solutions.

6. The intelligent optimization method for sludge deep dewatering process parameters according to claim 1, characterized in that, Step S1 employs an industrial Internet of Things (IoT) architecture to deploy a multi-channel sensor network, synchronously collecting key parameters such as sludge moisture content, organic matter content, pH value, temperature, and equipment load status in the sludge deep dewatering system.

7. The intelligent optimization method for sludge deep dewatering process parameters according to claim 1, characterized in that, Step S3 specifically includes: Based on the standardized temporal feature matrix, a one-dimensional convolutional neural network (CNN) is used to perform local fluctuation feature extraction processing on the matrix to obtain a local fluctuation feature vector. The ReLU activation function and max pooling operation are performed on the local fluctuation feature vector to generate a compressed local feature representation; the compressed local feature representation is input into the gated recurrent unit (GRU) layer, and time dependency modeling is performed based on the serialized features to generate a time-dependent feature sequence; The time-dependent feature sequence is subjected to attention-based weighted fusion processing to output a weighted time evolution feature vector; based on the weighted time evolution feature vector, nonlinear mapping and linear regression processing are performed through a fully connected layer to output a working condition evolution prediction vector.

8. The system as described in claim 1, characterized in that: The CNN-GRU hybrid model in the time series modeling and trend prediction module uses a multi-scale one-dimensional convolutional channel with a kernel size of 7 for the CNN and 256 hidden units for the GRU layer.

9. The intelligent optimization method for sludge deep dewatering process parameters according to claim 5, characterized in that, Step S5 specifically includes: The trend-guided feedback signal, preference evolution feedback signal, and stability feedback signal are normalized to eliminate the differences in the dimensions and amplitude scales of each feedback signal, thus obtaining three types of standardized feedback signals. A weighted fusion layer is constructed based on the three types of normalized feedback signals. The normalized trend-guided feedback signal is input into the weighted fusion layer, and the trend-guided feedback signal is weighted using the attention weight parameter to output the trend-guided weighted feedback component. The normalized preference evolution feedback signal is input into the weighted fusion layer, and the preference evolution feedback signal is weighted using the attention weight parameter to output the preference evolution weighted feedback component. The normalized stability feedback signal is input into the weighted fusion layer, and the stability feedback signal is weighted using the attention weight parameter to output the stability weighted feedback component. The trend-guided weighted feedback component, the preference evolution weighted feedback component, and the stability weighted feedback component are summed and fused to output the fused dynamic weight vector.

10. The intelligent optimization method for sludge deep dewatering process parameters according to claim 9, characterized in that, The weighted fusion layer consists of a set of trainable attention weight parameters, which are optimized using a backpropagation algorithm.

Images