Real-time monitoring system and method for material operation status

Abstract

The present invention provides a real-time monitoring system and method for material operation status. The method comprises: collecting multi-dimensional data of material operation, including operation parameter data stream, environmental status data stream and equipment interaction data stream; performing symmetric mapping preprocessing on the multi-dimensional data to obtain preprocessed data; performing first feature extraction on the preprocessed data based on a Transformer model to obtain a first feature vector sequence; performing predictive modeling on the first feature vector sequence by constructing a symmetry constraint and a multi-task prediction architecture to obtain a prediction model; performing second feature extraction on the output of the prediction model to obtain a second feature extraction result; calculating the state abnormality probability and issuing an early warning according to the prediction model and the second feature extraction result. The present invention significantly improves the accuracy and real-time performance of material state monitoring by constructing a symmetric data processing based on symmetry and a multi-task based modeling architecture.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial Internet of Things and artificial intelligence, and specifically relates to a method and device for real-time monitoring of material operation status. Background Art

[0002] In the process of implementing the present invention, the inventors found that the existing material operation status monitoring technology has the following problems:

[0003] First, the current mainstream material status monitoring system mainly relies on a simple threshold judgment mechanism, which cannot effectively cope with complex operation scenarios. Simple threshold judgments are difficult to capture subtle anomalies in the material operation process, and are prone to missed reports and false reports.

[0004] Second, the existing monitoring system is not capable of processing multi-source heterogeneous data. The operating status of materials is related to parameters in multiple dimensions, including operating parameters, environmental status, and interaction status with other equipment. These data come from different sources and have different structures. How to effectively integrate and utilize these data is an important challenge.

[0005] Third, the real-time performance of the condition monitoring system is insufficient. The traditional feature extraction and analysis methods have high computational complexity and are difficult to meet the needs of real-time monitoring, especially when the amount of data is large, the processing delay increases significantly.

[0006] Fourth, there is a lack of comprehensive analysis and prediction capabilities for the material operation status. Existing technologies mainly focus on judging the current status and lack the ability to predict possible abnormalities in the future, which is not conducive to timely discovery and prevention of potential problems.

[0007] A typical existing technology adopts a monitoring method based on fixed thresholds, which realizes status monitoring by setting early warning thresholds of various parameters. Although this method is simple and intuitive, it cannot adapt to complex and changeable operating environments and is easily disturbed by noise.

[0008] Another existing technology uses a single machine learning model for state classification. Although an intelligent algorithm is introduced, it is difficult to accurately grasp the material operation rules due to the lack of symmetry analysis and efficient feature extraction mechanism, and the monitoring effect is not ideal. Summary of the invention

[0009] In view of this, the present application provides a method and device for real-time monitoring of material operation status, which solves the problems of insufficient monitoring accuracy, poor real-time performance and limited prediction ability in the prior art.

[0010] The present application embodiment provides a method for real-time monitoring of material running status, comprising:

[0011] Collecting multi-dimensional data of material operation, wherein the multi-dimensional data includes an operation parameter data stream, an environmental status data stream, and an equipment interaction data stream;

[0012] Performing symmetric mapping preprocessing on the multidimensional data, including mapping the multidimensional data to a structure-preserving symmetric space and performing outlier processing to obtain preprocessed data;

[0013] Performing first feature extraction on the preprocessed data based on a Transformer model to obtain a first feature vector sequence;

[0014] Performing prediction modeling on the first feature vector sequence by constructing a symmetry constraint and a multi-task prediction architecture to obtain a prediction model;

[0015] Performing a second feature extraction on the output of the prediction model to obtain a second feature extraction result;

[0016] The abnormal probability of the current state is calculated according to the prediction model and the second feature extraction result, and an early warning is issued when the abnormal probability exceeds a preset threshold.

[0017] Optionally, the collecting of multi-dimensional data of material operation includes:

[0018] The operating parameter sensor collects the usage frequency, operating time and temperature data of the material to form the operating parameter data stream;

[0019] Collecting the ambient temperature, humidity and vibration data of the material through an ambient state sensor to form the ambient state data stream;

[0020] Collecting the interaction data between the material and other equipment through the interaction state sensor to form the equipment interaction data stream;

[0021] Data integrity verification and time stamping are performed on the operating parameter data stream, the environmental status data stream, and the device interaction data stream.

[0022] Optionally, the performing symmetric mapping preprocessing on the multidimensional data includes:

[0023] Mapping the multidimensional data to a symmetric space to achieve data dimensionality reduction by maintaining the topological structure of the data;

[0024] Determining the optimal dimension of the feature space based on a minimum boundary rank calculation, wherein the minimum boundary rank is obtained by iterative optimization;

[0025] Applying symmetric transformation to perform data denoising to eliminate noise and outliers in the multidimensional data;

[0026] The denoised data is normalized to make data of different dimensions comparable.

[0027] Optionally, the Transformer model includes a position encoding layer, a multi-head self-attention layer and a feedforward network layer, and the feature extraction of the preprocessed data based on the Transformer model includes:

[0028] In the position encoding layer, a position encoding matrix is ​​generated using a rotational position encoding mechanism;

[0029] In the multi-head self-attention layer, the input features are divided into multiple attention heads, and each head independently calculates the attention weight;

[0030] In the feedforward network layer, features are processed by two linear transformations and activation functions;

[0031] The outputs of each layer are combined through residual connection and layer normalization to obtain the first feature vector sequence.

[0032] Optionally, the performing predictive modeling on the first feature vector sequence by constructing a symmetry constraint and a multi-task prediction architecture includes:

[0033] Dividing the feature space into multiple local subspaces, and determining a sampling center and a sampling radius for each subspace according to the distribution of the first feature vector sequence, to obtain an initial sampling space;

[0034] Constructing a symmetric transformation matrix based on the initial sampling space, mapping the first feature vector sequence to the symmetric space, and realizing feature symmetry mapping;

[0035] According to the first feature vector sequence after the symmetry mapping, a multi-task prediction architecture including state classification, anomaly detection and trend prediction is designed;

[0036] In the training process of the multi-task prediction architecture, the feature density of each subspace is calculated and the sampling parameters are dynamically adjusted to achieve adaptive sampling;

[0037] Based on the result of the adaptive sampling, the prediction model is trained in combination with symmetry constraints and a multi-task loss function.

[0038] Optionally, the combining the symmetry constraint and the multi-task loss function to train the prediction model includes:

[0039] Calculating a symmetry loss, wherein the symmetry loss characterizes the consistency of the feature before and after the symmetry transformation;

[0040] Compute multi-task losses, including state classification loss, anomaly detection loss, and trend prediction loss;

[0041] Combining the symmetry loss and the multi-task loss to form a total loss function;

[0042] Adopt a progressive training strategy to gradually increase the size and complexity of training data;

[0043] Dynamically adjust the learning rate based on the validation set performance.

[0044] Optionally, performing a second feature extraction on the output of the prediction model includes:

[0045] Designing a seed encoding structure to achieve second feature compression by performing local sensitive hashing and random projection on the output of the prediction model;

[0046] Construct the second feature extraction pipeline to achieve parallel block processing of data;

[0047] Establish a second feature cache mechanism to cache frequently used second features;

[0048] An incremental update of the second feature is implemented to extract the updated second feature.

[0049] Optionally, the incremental update to achieve the second feature includes:

[0050] Calculate the change in the second feature brought about by the new data;

[0051] Quickly locate the second feature that needs to be updated based on the short seed;

[0052] Only the affected second feature is updated locally;

[0053] performing a second feature integrity check;

[0054] When the second feature is abnormal, the feature repair process is triggered.

[0055] Optionally, the calculating the abnormal probability of the current state according to the prediction model and the second feature extraction result, and issuing an early warning when the abnormal probability exceeds a preset threshold, includes:

[0056] Receive material status data stream in real time;

[0057] Calculating the probability of abnormal state based on the prediction model;

[0058] When the abnormal probability exceeds a first preset threshold, issuing a warning message;

[0059] When the abnormal probability exceeds a second preset threshold, an emergency processing process is triggered, wherein the second preset threshold is greater than the first preset threshold.

[0060] The present application also provides a real-time monitoring system for material operation status, including:

[0061] A collection unit, used to collect multi-dimensional data of material operation, wherein the multi-dimensional data includes an operation parameter data stream, an environmental status data stream, and an equipment interaction data stream;

[0062] A preprocessing unit, used for performing symmetric mapping preprocessing on the multidimensional data, including mapping the multidimensional data to a structure-preserving symmetric space and performing outlier processing to obtain preprocessed data;

[0063] A first feature extraction unit, configured to perform first feature extraction on the preprocessed data based on a Transformer model to obtain a first feature vector sequence;

[0064] A modeling unit, configured to perform predictive modeling on the first feature vector sequence by constructing a symmetry constraint and a multi-task prediction architecture to obtain a prediction model;

[0065] A second feature extraction unit, used to perform a second feature extraction on the output of the prediction model to obtain a second feature extraction result;

[0066] An early warning unit is used to calculate the abnormal probability of the current state according to the prediction model and the second feature extraction result, and to issue an early warning when the abnormal probability exceeds a preset threshold.

[0067] This application has the following technical effects:

[0068] The combination of symmetric mapping preprocessing and multi-task prediction architecture significantly improves the accuracy of material operation status monitoring. The feature extraction and modeling method based on symmetry constraints can better capture abnormal patterns in the material operation process and reduce false positives and negatives.

[0069] The Transformer model and efficient feature extraction mechanism are used to achieve effective processing and feature fusion of multi-source heterogeneous data, enhancing the system's adaptability to complex scenarios. The layered feature extraction strategy and parallel computing architecture ensure the system's real-time response capability.

[0070] Based on the multi-task prediction architecture, a comprehensive analysis and prediction of material status is achieved, which can not only detect current anomalies in time, but also predict potential problems and provide support for preventive maintenance. The design of a multi-level early warning mechanism further improves the reliability and practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the disclosed embodiments in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0072] Figure 1 It is a flow chart of the real-time monitoring method of material running status of the present invention;

[0073] Figure 2 This is a flow chart of data preprocessing based on symmetric mapping in the present invention;

[0074] Figure 3 This is a diagram of the first feature extraction structure based on Transformer in the present invention;

[0075] Figure 4 This is a modeling architecture diagram based on multi-task prediction in the present invention;

[0076] Figure 5 This is a second feature extraction flow chart of the present invention;

[0077] Figure 6 This is a flow chart for realizing the early warning mechanism in the present invention;

[0078] Figure 7 It is a structural block diagram of the real-time monitoring device for material running status of the present invention. DETAILED DESCRIPTION

[0079] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0080] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0081] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0082] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0083] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0084] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0085] like Figure 1 As shown, the present invention provides a flow chart of a method for real-time monitoring of material running status. The method comprises the following steps: step S101 to step S106.

[0086] Step S101: Collect multi-dimensional data of material operation.

[0087] like Figure 1 As shown, firstly, multi-dimensional data of material operation is collected through a data collection module (10), and the multi-dimensional data includes: an operation parameter data stream (11), an environmental status data stream (12) and an equipment interaction data stream (13).

[0088] Specifically, Figure 2 As shown, the material usage frequency, operation time and temperature data are collected through the operation parameter sensor (21) to form an operation parameter data stream (11); the environmental temperature, humidity and vibration data of the material are collected through the environmental state sensor (22) to form an environmental state data stream (12); and the interaction data between the material and other equipment are collected through the interaction state sensor (23) to form an equipment interaction data stream (13).

[0089] After data collection is completed, all data streams are processed by the integrity check module (24) and marked by the data integrity verification and time stamp marking module (25) to ensure the accuracy and time sequence of the data.

[0090] Step S102: performing symmetric mapping preprocessing on the multi-dimensional data.

[0091] like Figure 2 As shown, this step is completed by a pre-processing module (30), comprising the following sub-steps:

[0092] The multi-dimensional data is mapped to the symmetric space by a symmetric space mapping unit (31), and this process realizes data dimension reduction by maintaining the topological structure of the data;

[0093] By using a minimum boundary rank calculation unit (32), a minimum boundary rank is calculated based on an iterative optimization algorithm to determine the optimal dimension of the feature space;

[0094] Performing data noise reduction through a symmetric transformation unit (33) to eliminate noise and outliers in the multidimensional data;

[0095] The normalization processing unit (34) performs normalization processing on the noise-reduced data, so that data of different dimensions are comparable.

[0096] The specific description is as follows:

[0097] First, the symmetric mapping preprocessing maps the multidimensional data to the symmetric space through the symmetric space mapping unit. In this process, the system maintains the topological structure characteristics of the data and achieves effective dimensionality reduction of the data. Specifically, the mapping process is based on the intrinsic structural characteristics of the data to ensure that key data relationships and distribution characteristics are retained during the dimensionality reduction process.

[0098] Secondly, the system determines the optimal dimension of the feature space through the minimum boundary rank calculation unit. This process is achieved through an iterative optimization algorithm, which includes: initializing the estimated value of the boundary rank, repeatedly adjusting and optimizing the estimated value, until the optimal dimension that can best balance data expression ability and computational complexity is found. In this process, the system will continuously evaluate the data reconstruction error and computational overhead under different dimensions to find the most suitable feature space dimension.

[0099] Thirdly, the system uses the symmetric transformation unit to reduce the noise of the data. This step mainly uses the symmetry characteristics of the data to identify and eliminate noise and outliers in multidimensional data through symmetric transformation. The process of symmetric transformation includes: mapping the data, comparing the consistency before and after the transformation, and judging and processing outliers based on consistency measurement.

[0100] In addition, the system will also standardize the noise-reduced data through the normalization processing unit. The purpose of this step is to make data of different dimensions comparable and avoid affecting the subsequent analysis results due to inconsistent data scales. Normalization processing will consider the data distribution characteristics of each dimension and select an appropriate normalization method to ensure that the processed data maintains the original relative relationship and has good comparability.

[0101] Finally, the entire symmetric mapping preprocessing process is an organic and unified whole, with each step working together and closely related. Through this systematic preprocessing method, the data quality can be effectively improved, providing a reliable data basis for subsequent feature extraction and condition monitoring.

[0102] It should be noted that in actual applications, the system will dynamically adjust the parameters and processing strategies of each processing step according to the specific data characteristics and application requirements. For example, when the data noise is large, the noise reduction effect of the symmetric transformation may be strengthened; when the data dimension is particularly high, more attention may be paid to the selection and adjustment of the optimal dimension. This flexible adjustment mechanism ensures the effect and efficiency of preprocessing.

[0103] For example, the following code (part of the code) is the implementation of the above function:

[0104] import numpy as np

[0105] from sklearn.preprocessing import StandardScaler

[0106] from scipy.linalg import svd

[0107] from scipy.spatial.distance import cdist

[0108] class SymmetricMappingPreprocessor:

[0109] def __init__(self, min_rank_iterations=100, noise_threshold=0.01):

[0110] self.min_rank_iterations = min_rank_iterations

[0111] self.noise_threshold = noise_threshold

[0112] self.scaler = StandardScaler()

[0113] def fit_transform(self, data):

[0114] """Main process flow"""

[0115] # 1. Mapping to symmetric space

[0116] symmetric_data = self._map_to_symmetric_space(data)

[0117] # 2. Calculate the minimum boundary rank

[0118] optimal_dim = self._compute_minimal_boundary_rank(symmetric_data)

[0119] # 3. Apply symmetric transformation for noise reduction

[0120] denoised_data = self._symmetric_denoising(symmetric_data, optimal_dim)

[0121] # 4. Normalization

[0122] normalized_data = self._normalize_data(denoised_data)

[0123] return normalized_data

[0124] def _map_to_symmetric_space(self, data):

[0125] """Map data into a symmetric space, preserving topological structure"""

[0126] # Calculate the distance matrix

[0127] dist_matrix = cdist(data, data)

[0128] # Construct a symmetric kernel matrix

[0129] n = data.shape[0]

[0130] H = np.eye(n) np.ones((n, n)) / n

[0131] K = -0.5 * H @ (dist_matrix ** 2) @ H

[0132] # Eigenvalue decomposition

[0133] eigenvalues, eigenvectors = np.linalg.eigh(K)

[0134] # Select the eigenvector corresponding to the positive eigenvalue

[0135] positive_indices = eigenvalues ​​> 0

[0136] symmetric_data = eigenvectors[:, positive_indices] @ np.diag(np.sqrt(eigenvalues[positive_indices]))

[0137] return symmetric_data

[0138] def _compute_minimal_boundary_rank(self, data):

[0139] """Determine the optimal dimension of the feature space through iterative optimization"""

[0140] n_samples, n_features = data.shape

[0141] # Initialize optimal dimension estimate

[0142] optimal_dim = n_features

[0143] # Iterative optimization process

[0144] for _ in range(self.min_rank_iterations):

[0145] # Calculate singular value decomposition

[0146] U, s, Vt = svd(data, full_matrices=False)

[0147] # Calculate the cumulative energy ratio

[0148] energy_ratio = np.cumsum(s) / np.sum(s)

[0149] # Determine the dimension based on the energy ratio

[0150] dim_candidate = np.sum(energy_ratio < 0.99) # retain 99% of the information

[0151] # Update optimal dimension

[0152] optimal_dim = min(optimal_dim, dim_candidate)

[0153] # If the dimension changes very little, end the iteration early

[0154] if abs(optimal_dim dim_candidate) < 1e-6:

[0155] break

[0156] return optimal_dim

[0157] def _symmetric_denoising(self, data, optimal_dim):

[0158] """Data denoising using symmetric transformation"""

[0159] # SVD decomposition

[0160] U, s, Vt = svd(data, full_matrices=False)

[0161] # Keep important singular values ​​and corresponding vectors

[0162] U_reduced = U[:, :optimal_dim]

[0163] s_reduced = s[:optimal_dim]

[0164] Vt_reduced = Vt[:optimal_dim, :]

[0165] # Reconstructing data

[0166] denoised = U_reduced @ np.diag(s_reduced) @ Vt_reduced

[0167] # Noise processing

[0168] noise_mask = np.abs(data denoised) < self.noise_threshold

[0169] denoised[noise_mask] = data[noise_mask]

[0170] return denoised

[0171] def _normalize_data(self, data):

[0172] """Normalization makes data of different dimensions comparable"""

[0173] return self.scaler.fit_transform(data)

[0174] # Example

[0175] if __name__ == "__main__":

[0176] # Generate sample data

[0177] np.random.seed(42)

[0178] n_samples = 1000

[0179] n_features = 10

[0180] # Multidimensional data of material operation simulation

[0181] operating_params = np.random.randn(n_samples, 3) # operating parameters

[0182] environmental_params = np.random.randn(n_samples, 4) # Environmental status

[0183] interaction_params = np.random.randn(n_samples, 3) # Device interaction

[0184] # Combine all parameters

[0185] raw_data = np.hstack([operating_params, environmental_params,interaction_params])

[0186] # Add noise

[0187] noisy_data = raw_data + np.random.normal(0, 0.1, raw_data.shape)

[0188] # Create a preprocessor instance

[0189] preprocessor = SymmetricMappingPreprocessor()

[0190] # Perform preprocessing

[0191] preprocessed_data = preprocessor.fit_transform(noisy_data)

[0192] # Output result dimensions and basic statistics

[0193] print(f"Original data dimension: {raw_data.shape}")

[0194] print(f"Preprocessed data dimension: {preprocessed_data.shape}")

[0195] print(f"Preprocessed data mean: {np.mean(preprocessed_data, axis=0)[:3]}")# Display the first three dimensions

[0196] print(f"Standard deviation of preprocessed data: {np.std(preprocessed_data, axis=0)[:3]}")# Display the first three dimensions

[0197] The following is a detailed step-by-step description of the sample code:

[0198] Step 1: Class initialization design

[0199] 1. Create the SymmetricMappingPreprocessor class and set two key parameters:

[0200] min_rank_iterations: used to control the number of iterations for calculating the minimum boundary rank, the default is 100;

[0201] noise_threshold: used to control the noise reduction threshold, the default value is 0.01;

[0202] Initialize the StandardScaler object for subsequent data normalization;

[0203] Step 2: Main processing flow (fit_transform method);

[0204] 1. Call _map_to_symmetric_space to perform symmetric space mapping;

[0205] 2. Call _compute_minimal_boundary_rank to calculate the optimal dimension;

[0206] 3. Use _symmetric_denoising to perform symmetric transformation noise reduction;

[0207] 4. Finally, normalize the data through _normalize_data;

[0208] 5. Return the preprocessed data;

[0209] Step 3: Symmetric space mapping implementation (_map_to_symmetric_space method)

[0210] 1. Calculate the distance matrix between samples: use scipy.spatial.distance.cdist to calculate the Euclidean distance;

[0211] 2. Construct a symmetric kernel matrix:

[0212] Generate a centered matrix H = I 1 / n * ones(n,n);

[0213] Calculate the kernel matrix K = -0.5 * H * (D^2) * H, where D is the distance matrix;

[0214] 3. Perform eigenvalue decomposition:

[0215] Calculate the eigenvalues ​​and eigenvectors of the kernel matrix;

[0216] Select the eigenvector corresponding to the positive eigenvalue;

[0217] Scaling by the square root of the eigenvalue;

[0218] 4. Return the mapped data;

[0219] Step 4: Minimum boundary rank calculation (_compute_minimal_boundary_rank method);

[0220] 1. Get the initial dimension of the data;

[0221] 2. Determine the optimal dimension through an iterative optimization process:

[0222] Perform singular value decomposition (SVD) on the data;

[0223] Calculate the cumulative energy ratio (variance contribution ratio);

[0224] Determine candidate dimensions based on 99% energy retention rate;

[0225] Update the optimal dimension;

[0226] End the iteration early when the dimension changes very little;

[0227] 3. Return the calculated optimal dimension;

[0228] Step 5: Symmetric transformation noise reduction (_symmetric_denoising method);

[0229] 1. Perform SVD decomposition on the data to obtain U, s, and Vt;

[0230] 2. Based on the optimal dimension calculated previously, extract the important components:

[0231] Keep the first optimal_dim singular values ​​and their corresponding vectors;

[0232] Reconstruct the data using the truncated matrix;

[0233] 3. Noise processing:

[0234] Calculate the difference between the original data and the reconstructed data;

[0235] Keep the original value for differences smaller than the noise threshold;

[0236] Use the reconstructed values ​​for differences larger than the noise threshold;

[0237] 4. Return the denoised data

[0238] Step 6: Normalization (_normalize_data method);

[0239] 1. Use StandardScaler for standardization:

[0240] Calculate the mean and standard deviation of each feature;

[0241] Centralize and scale data;

[0242] 2. Return normalized data;

[0243] Step 7: Example usage process

[0244] 1. Generate simulated data:

[0245] Create operating parameter data (3D);

[0246] Create environmental state data (4D);

[0247] Create device interaction data (3D);

[0248] Combine all parameters to form raw data;

[0249] 2. Add random noise: simulate data noise in real scenes

[0250] 3. Create a preprocessor instance and perform preprocessing:

[0251] Instantiate SymmetricMappingPreprocessor;

[0252] Call the fit_transform method for processing;

[0253] 4. Output processing results:

[0254] Display raw and processed data dimensions;

[0255] Display basic statistics of processed data (mean and standard deviation);

[0256] Step S103: performing first feature extraction based on the Transformer model.

[0257] like Figure 3 As shown, the Transformer model (40) includes a position encoding layer (41), a multi-head self-attention layer (42) and a feedforward network layer (43). The specific processing process is:

[0258] In the position encoding layer (41), a position encoding matrix (411) is generated using a rotational position encoding mechanism;

[0259] In the multi-head self-attention layer (42), the input features are divided into multiple attention heads (421), and each head independently calculates the attention weight (422);

[0260] In the feed-forward network layer (43), the features are processed by a first linear transformation unit (431), an activation function unit (432), and a second linear transformation unit (433);

[0261] The outputs of each layer are combined through a residual connection module (44) and a layer normalization module (45) to obtain a first feature vector sequence.

[0262] Specifically,

[0263] First, the system uses a position encoding layer based on the RoPE (Rotary Position Embedding) mechanism to process the input data. Specifically, the position encoding layer is implemented by generating a rotational position encoding matrix R, where the matrix element R_i,j = exp(iθ_j), and θ_j represents the position angle of the jth dimension. This encoding mechanism maintains the relative position information in the sequence through complex domain mapping, ensuring that the position dependency can be accurately captured in subsequent attention calculations. When applying the RoPE transformation to the input sequence X, it is implemented in the form of X' = X⊗R, where ⊗ represents an element-by-element complex multiplication operation.

[0264] Secondly, the system implements a parallel feature processing mechanism in the multi-head self-attention layer. Specifically, the system divides the input features into h attention heads, each of which has a dimension of d_k = d_model / h, where d_model is the total dimension of the model. Each attention head independently calculates the query matrix Q, key matrix K, and value matrix V, that is, Q = XW_Q, K = XW_K, V = XW_V, where W_Q, W_K, and W_V are learnable parameter matrices. The attention score is calculated using the formula Attention(Q,K,V)= softmax(QK^T / √d_k)V, and finally the outputs of multiple heads are merged through the concat operation and linearly transformed.

[0265] Again, the system uses a combination of two linear transformations and activation functions in the feedforward network layer. The processing flow of this layer is: first, feature mapping is performed through the first linear transformation unit, then nonlinear transformation is introduced through the activation function unit (such as ReLU), and finally the feature dimension is adjusted through the second linear transformation unit. Specifically, it can be expressed as: FFN(x)= max(0, xW_1 + b_1)W_2 + b_2, where W_1 and W_2 are weight matrices, and b_1 and b_2 are bias terms.

[0266] In addition, the system implements a feature attention extraction mechanism to dynamically calculate the attention weight according to the importance of different material parameters. This process includes: first calculating the attention score w_i = softmax(f(x_i)), where f is the attention score function; then fusing the attention representations of different features F = Σ(w_i * x_i); finally, controlling the information flow through the feature gating mechanism g = sigmoid(W_g * F + b_g), and obtaining the final feature representation F' = g * F + (1-g) * X.

[0267] At the same time, the system has designed a special temporal dependency modeling module. This module ensures that the current prediction depends only on historical information by building a causal attention mechanism. The specific implementation includes generating a causal mask matrix: mask =generate_causal_mask(seq_len), and then applying it to the attention score: causal_attention =attention_scores * mask. In order to improve computational efficiency, the system also implements a sliding window attention mechanism, which balances computational complexity and information capture capabilities by setting an appropriate window size.

[0268] Finally, the system adopts an optimized model training strategy, including using a progressive learning strategy, starting with a shorter sequence and gradually increasing the sequence length to adapt to longer time dependencies; implementing a learning rate warm-up and decay mechanism: lr = min(step_num^(-0.5), step_num * warmup_steps^(-1.5)); adding gradient clipping to prevent gradient explosion: when the gradient norm exceeds the threshold, the corresponding scaling is performed.

[0269] It should be noted that during the entire feature extraction process, the system stabilizes the training process through residual connections and layer normalization. Specifically, residual connections and layer normalization operations are added after each sublayer: LayerNorm(x +Sublayer(x)), where Sublayer represents the transformation function of the sublayer. This design not only helps to stabilize the propagation of gradients, but also improves the expressiveness of the model.

[0270] The following is a specific example:

[0271] A.1: Constructing a position coding layer based on the RoPE mechanism:

[0272] Generate a rotation position encoding matrix R, where R_i,j = exp(iθ_j), θ_j is the position angle of the jth dimension;

[0273] Apply the RoPE transform to the input sequence X: X' = X⊗R, where ⊗ represents element-wise complex multiplication;

[0274] Maintain relative position information through complex domain mapping: The feature vector of each position is encoded through complex rotation so that the relative position relationship is maintained in the attention calculation.

[0275] A.2: Implementing multi-head self-attention mechanism:

[0276] Divide the input features into h heads, each of which has a dimension of d_k = d_model / h, where d_model is the model dimension;

[0277] For each head, calculate the query matrix Q, key matrix K and value matrix V:

[0278] Q = XW_Q, K = XW_K, V = XW_V, where W_Q, W_K, W_V are learnable parameter matrices;

[0279] Calculate the attention score: Attention(Q,K,V) = softmax(QK^T / √d_k)V;

[0280] Combine the outputs of multiple heads and perform linear transformation: MultiHead(X) = Concat(head_1,...,head_h)W_O.

[0281] A.3: Design encoder layer architecture:

[0282] Construct an encoder consisting of a self-attention sublayer and a feed-forward network sublayer:

[0283] Self-attention sublayer: receives the output of the previous layer and processes the sequence information through a multi-head self-attention mechanism;

[0284] Feedforward network sublayer: contains two linear transformations and a ReLU activation function:

[0285] FFN(x) = max(0, xW_1 + b_1)W_2 + b_2;

[0286] Add residual connections and layer normalization after each sublayer:

[0287] LayerNorm(x + Sublayer(x)), where Sublayer represents the transformation function of the sublayer.

[0288] A.4: Implement feature attention extraction:

[0289] According to the importance of different parameters of the material, the attention weight is dynamically calculated:

[0290] w_i = softmax(f(x_i)), where f is the attention score function;

[0291] Fusion of attention representations of different features:

[0292] F = Σ(w_i * x_i), get the weighted feature representation;

[0293] Add feature gating mechanism to control the flow of information:

[0294] g = sigmoid(W_g * F + b_g),

[0295] F' = g * F + (1-g) * X.

[0296] A.5: Design timing dependency modeling module:

[0297] Construct a causal attention mechanism to ensure that the current moment only depends on past information:

[0298] mask = generate_causal_mask(seq_len);

[0299] causal_attention = attention_scores * mask;

[0300] Implement sliding window attention to reduce computational complexity:

[0301] window_size = min(seq_len, max_window_size);

[0302] sliding_attention = compute_local_attention(Q, K, V, window_size).

[0303] A.6: Optimize the model training process:

[0304] Adopt an incremental learning strategy:

[0305] First train the model on shorter sequences;

[0306] Gradually increase the sequence length to accommodate longer time dependencies;

[0307] Implement learning rate warmup and decay:

[0308] lr = min(step_num^(-0.5), step_num * warmup_steps^(-1.5));

[0309] Add gradient clipping to prevent gradient explosion:

[0310] if ‖g‖ > max_grad_norm:

[0311] g = (max_grad_norm / ‖g‖) * g.

[0312] A.7: Output feature representation:

[0313] Merge the representation of time series and feature dimensions:

[0314] output = concat([temporal_features, attention_features]);

[0315] Adjust feature dimensions through linear mapping:

[0316] final_features = linear_projection(output);

[0317] Generates a sequence of feature vectors for downstream tasks.

[0318] Through the above steps, the model can:

[0319] 1. Effectively capture the temporal dependencies of material operation status;

[0320] 2. Accurately extract the correlation between different parameters;

[0321] 3. Adaptively adjust the importance weights of features;

[0322] 4. Achieve efficient sequence modeling and feature extraction.

[0323] These features will be used as inputs to the prediction model in the next step for subsequent status assessment and early warning. The design of the entire step fully utilizes the position encoding advantages of RoPE and the parallel computing capabilities of Transformer, providing powerful feature representation capabilities for material operation status monitoring.

[0324] Step S104: Modeling based on symmetry constraints and multi-task prediction.

[0325] like Figure 4 As shown, this step is completed by the modeling module (50), which specifically includes:

[0326] 1. The feature space division unit (51) divides the feature space into a plurality of local subspaces (511), and determines the sampling center and sampling radius for each subspace through the sampling parameter calculation module (512) according to the distribution of the first feature vector sequence, so as to obtain an initial sampling space;

[0327] 2. A symmetric transformation matrix construction unit (52) constructs a symmetric transformation matrix based on the initial sampling space to achieve symmetric space mapping of the first eigenvector sequence;

[0328] 3. The multi-task prediction architecture design unit (53) designs a multi-task prediction architecture including a state classification module (531), an anomaly detection module (532) and a trend prediction module (533) according to the mapped feature vector sequence;

[0329] 4. The adaptive sampling unit (54) dynamically adjusts the sampling parameters by calculating the feature density of each subspace;

[0330] 5. The model training unit (55) trains the prediction model based on the adaptive sampling results, combined with the symmetry constraint (551) and the multi-task loss function (552).

[0331] Among them, the training process of symmetry constraints and multi-task loss functions includes:

[0332] The symmetry loss calculation module (56) calculates the consistency loss of the feature before and after the symmetry transformation;

[0333] The multi-task loss calculation module (57) calculates the multi-task loss including the state classification loss (571), the anomaly detection loss (572) and the trend prediction loss (573);

[0334] The loss function combination module (58) combines the symmetry loss and the multi-task loss to form a total loss function;

[0335] The progressive training module (59) adopts a progressive strategy for training and dynamically adjusts the learning rate.

[0336] Specifically:

[0337] First, the system divides the feature space into multiple local subspaces through the feature space division unit. In this process, the system determines the appropriate sampling center and sampling radius for each subspace and constructs the initial sampling space. The specific implementation includes: defining the local sampling space S and dividing it into multiple subspaces {S1, S2, ..., Sk}; determining the sampling center ci and sampling radius ri for each subspace Si; constructing the sampling probability density function p(x) = Σ wi * N(x; ci, ri), where wi is the weight coefficient. At the same time, the system implements the uniformity constraint by calculating the feature density density(Si) =count(xi ∈ Si) / volume(Si) of the local space, designing the density balance factor balance(Si) = mean_density / density(Si), and adjusting the sampling weight wi = wi * balance(Si) accordingly.

[0338] Secondly, the system constructs a symmetric transformation matrix to achieve symmetric spatial mapping of features. Specifically, the system designs a set of symmetric transformation matrices T = {T1, T2, ..., Tm}, where each Ti represents a specific symmetric transformation. For the input feature x, the system realizes feature mapping by applying the symmetric transformation x' = Ti(x) and calculates the symmetry loss Lsym =Σ ||f(x) f(Ti(x))||2. The final optimization objective function is L = Ltask + λ * Lsym, where λ is a balancing factor used to adjust the relative importance of task loss and symmetry loss.

[0339] Thirdly, the system designed a multi-layer prediction network architecture, including: an input layer for receiving feature vector sequences, a symmetric convolution layer using symmetric convolution kernels for feature extraction, a local attention layer for calculating attention weights based on local sampling space, and a prediction layer containing a state classification head and anomaly detection head. This hierarchical design can gradually extract and integrate feature information and improve the accuracy of prediction.

[0340] In addition, the system implements a local adaptive sampling strategy to improve the model training efficiency by dynamically adjusting the sampling parameters. The specific implementation includes: calculating the feature importance score score(xi) = ||gradient(xi)||2, updating the sampling probability p(xi) ∝ exp(score(xi)), and implementing adaptive sampling based on this information. This strategy can dynamically adjust the sampling density according to the importance of the feature and improve the sampling efficiency.

[0341] At the same time, the system builds a multi-task learning framework, including multiple prediction tasks: the state classification task is used to predict the current operating state of the material, the anomaly detection task is used to identify potential abnormal patterns, and the trend prediction task is used to predict the trend of key indicators. Each task has its own specific loss function: classification loss Lcls = CrossEntropy(y_true, y_pred), anomaly detection loss Lano = FocalLoss(anomaly_true, anomaly_pred), trend prediction loss Ltrend = MSE(trend_true, trend_pred). These loss functions are finally combined into the overall loss: Ltotal = α * Lcls + β * Lano + γ * Ltrend + λ * Lsym.

[0342] Finally, the system adopts a progressive model training strategy. It starts training from a small-scale local space, gradually expands the sampling space and increases the number of sampling points, and dynamically adjusts the weights of symmetry constraints. The learning rate scheduling optimizes the training process through warm-up and decay mechanisms to ensure that the model converges to a better solution.

[0343] It should be noted that the system realizes the generation of multi-dimensional prediction results in the prediction result output link, including: current state classification result state_pred = classifier(features), anomaly probability estimation anomaly_prob = anomaly_detector(features), and trend prediction result trend_pred = trend_predictor(features). These prediction results will be integrated into a complete prediction output to provide comprehensive information support for subsequent decision-making.

[0344] This detailed technical description not only clearly demonstrates the specific implementation method of modeling based on symmetry constraints and multi-task prediction, but also clarifies the collaborative working mechanism between various modules, which helps to better understand and implement this technology. This design fully considers the characteristics of material operation status monitoring and can provide accurate and reliable prediction results.

[0345] The following is a specific example:

[0346] B1: Construct a local sampleable uniform distribution framework:

[0347] Define the local sampling space S:

[0348] Divide the feature space into multiple local subspaces {S1, S2, ..., Sk};

[0349] Determine the sampling center ci and sampling radius ri for each subspace Si;

[0350] Construct the sampling probability density function: p(x) = Σ wi * N(x; ci, ri), where wi is the weight coefficient;

[0351] Implementing uniformity constraints:

[0352] Calculate the feature density of the local space: density(Si) = count(xi ∈ Si) / volume(Si);

[0353] Design density balance factor: balance(Si) = mean_density / density(Si);

[0354] Adjust sampling weight: wi = wi * balance(Si).

[0355] B2: Constructing symmetry constraint layer:

[0356] Design the symmetric transformation matrix T:

[0357] T = {T1, T2, ..., Tm}, where Ti represents the i-th symmetry transformation;

[0358] Implement feature symmetry mapping:

[0359] Apply a symmetric transformation to the input feature x: x' = Ti(x);

[0360] Calculate the symmetry loss: Lsym = Σ ||f(x) f(Ti(x))||2;

[0361] Optimization objective function: L = Ltask + λ * Lsym, where λ is the balance factor.

[0362] B3: Design the prediction model architecture:

[0363] Build a multi-layer prediction network:

[0364] Input layer: receives the feature vector sequence from step 3;

[0365] Symmetric convolution layer: uses symmetric convolution kernels for feature extraction;

[0366] Local attention layer: calculates attention weights based on local sampling space;

[0367] Prediction head: includes state classification head and anomaly detection head.

[0368] B4: Implementing local adaptive sampling strategy:

[0369] Dynamically adjust sampling parameters:

[0370] Calculate the feature importance score: score(xi) = ||gradient(xi)||2;

[0371] Update sampling probability: p(xi) ∝ exp(score(xi));

[0372] Implement the adaptive sampling algorithm:

[0373] def adaptive_sampling(features, sample_size):

[0374] importance_scores = compute_importance(features)

[0375] sampling_prob = softmax(importance_scores)

[0376] sampled_indices = weighted_sample(indices, sampling_prob, sample_size)

[0377] return features[sampled_indices]

[0378] B5: Building a multi-task learning framework:

[0379] Define multiple prediction tasks:

[0380] Status classification task: predict the current operating status of the material;

[0381] Anomaly detection tasks: identifying potentially abnormal patterns;

[0382] Trend forecasting tasks: predict the changing trends of key indicators;

[0383] Design task-specific loss functions:

[0384] Classification loss: Lcls = CrossEntropy(y_true, y_pred);

[0385] Anomaly detection loss: Lano = FocalLoss(anomaly_true, anomaly_pred);

[0386] Trend prediction loss: Ltrend = MSE(trend_true, trend_pred);

[0387] The overall loss of the combination is:

[0388] Ltotal = α * Lcls + β * Lano + γ * Ltrend + λ * Lsym.

[0389] B6: Implementing symmetry-constrained model training:

[0390] Training process design:

[0391] ```Python

[0392] def train_step(batch_data):

[0393] # Forward pass

[0394] features = feature_extractor(batch_data)

[0395] # Apply symmetry transformation

[0396] sym_features = apply_symmetry_transform(features)

[0397] # Calculate the prediction results

[0398] predictions = model(features)

[0399] sym_predictions = model(sym_features)

[0400] # Calculate loss

[0401] task_loss = compute_task_loss(predictions, labels)

[0402] sym_loss = compute_symmetry_loss(predictions, sym_predictions)

[0403] # Total loss

[0404] total_loss = task_loss + lambda_sym * sym_loss

[0405] # Backward Propagation

[0406] optimize(total_loss)

[0407] B7: Design model optimization strategy:

[0408] Implementing progressive training:

[0409] Start training in a small, local space;

[0410] Gradually expand the sampling space and increase the number of sampling points;

[0411] Dynamically adjust the weights of symmetry constraints;

[0412] Apply a learning rate schedule:

[0413] ```Python

[0414] def lr_schedule(epoch):

[0415] if epoch < warmup_epochs:

[0416] return initial_lr * epoch / warmup_epochs

[0417] else:

[0418] return initial_lr * (decay_rate ** (epoch warmup_epochs))

[0419] B8: Implement prediction result output:

[0420] Generate multi-dimensional prediction results:

[0421] Current state classification result: state_pred = classifier(features);

[0422] Anomaly probability estimation: anomaly_prob = anomaly_detector(features);

[0423] Trend prediction results: trend_pred = trend_predictor(features);

[0424] Integrate the prediction output:

[0425] ```Python

[0426] def generate_prediction(features):

[0427] results = {

[0428] 'state': state_classifier(features),

[0429] 'anomaly_prob': anomaly_detector(features),

[0430] 'trend': trend_predictor(features),

[0431] 'confidence': compute_confidence(features)

[0432] }

[0433] return results

[0434] This step achieves efficient prediction model construction through the principle of local sampleable uniform and symmetric distribution.

[0435] Step S105: performing a second feature extraction on the output of the prediction model.

[0436] like Figure 5 As shown, this step is completed by the second feature extraction module (60), which specifically includes:

[0437] The seed coding structure design unit (61) realizes second feature compression through a local sensitive hash module (611) and a random projection module (612);

[0438] The feature extraction pipeline unit (62) realizes block processing of data through a parallel processing module (621);

[0439] The feature cache mechanism unit (63) performs cache management on the frequently used second feature;

[0440] The incremental update unit (64) implements the incremental update of the feature, specifically including:

[0441] The feature change calculation module (641) calculates the second feature change amount brought by the new data;

[0442] The feature positioning module (642) quickly locates the second feature that needs to be updated according to the short seed;

[0443] The local update module (643) locally updates the affected second feature;

[0444] The integrity check module (644) performs a second feature integrity check;

[0445] The feature repair module (645) triggers a repair process when the second feature is abnormal.

[0446] Specifically, on the one hand, the system realizes feature compression through the seed coding structure design unit. This process mainly includes two key steps: using local sensitive hashing (LSH) for initial compression, that is, calculating hash_value = hash_function(feature_vector); then applying random projection for further compression, that is, projection = random_projection_matrix * feature_vector. Finally, the system combines the two compression results through a specific combination function to obtain compressed_feature = combine_compression(hash_value,projection). This design can significantly reduce the feature storage overhead while maintaining the distinguishability of the features.

[0447] Secondly, the system builds a feature extraction pipeline to achieve parallel block processing of data. The specific implementation steps include: first determine the optimal data block size block_size = optimize_block_size(data_size, available_memory), which is dynamically calculated based on the data size and available memory; then divide the data into multiple blocks blocks = create_data_blocks(data, block_size); finally, process each data block through the parallel processing module. This parallel processing mechanism significantly improves the efficiency of feature extraction.

[0448] Thirdly, the system implements a feature cache mechanism, which manages the cache of frequently used second features through the LRU (least recently used) cache strategy. The core functions of the cache mechanism include: the feature fast retrieval function get_cached_feature(seed), which directly returns when the requested feature exists in the cache; the cache update function update_cache(seed, feature), which is used to maintain and update the feature information in the cache. At the same time, the system also implements a feature reuse strategy, including building a feature dependency graph dependency_graph = build_feature_dependency_graph(), identifying reusable features reusable_features = find_reusable_features(dependency_graph), and optimizing the computation order computation_order = optimize_computation_sequence(dependency_graph).

[0449] In addition, the system has designed an incremental feature update mechanism to improve the efficiency of feature updates. The specific implementation includes: first, calculate the feature changes brought by the new data delta_features = compute_feature_delta(new_data); then quickly locate the features that need to be updated based on the short seed affected_features = locate_affected_features(delta_features); finally, only partially update the affected features updated_features = update_features(existing_features, affected_features). This incremental update strategy avoids the computational overhead caused by a full update.

[0450] At the same time, the system has established a complete feature quality control mechanism. This includes the feature verification process: check feature completeness = check_feature_completeness(extracted_features); assess feature quality quality_score = assess_feature_quality(extracted_features); when the quality score is lower than the threshold, trigger the feature repair process. The feature repair strategy includes: identify anomalies = detect_feature_anomalies(features); generate repair plans repair_plan = generate_repair_plan(anomalies); execute feature repair repaired_features = execute_repair(features, repair_plan).

[0451] It should be noted that the system implements a memory management optimization mechanism. This includes feature memory mapping, allocating memory space through memory_block = find_available_block(memory_map, feature_size), and releasing memory space through mark_as_available(memory_map, memory_block). At the same time, the system also implements a memory recycling strategy: tracking feature usage frequency usage_stats = track_feature_usage(); identifying releasable memory releasable_memory = identify_releasable_memory(usage_stats); performing memory recycling reclaim_memory(releasable_memory).

[0452] It should be noted that the system also implements an adaptive seed update mechanism. This mechanism evaluates the performance of the current seed seed_performance = evaluate_seed_performance(current_seed) and determines whether it needs to be updated based on the performance indicators. If an update is required, the system generates a new optimized seed new_seed = generate_optimized_seed(current_seed, performance_metrics) and updates it after verifying the performance of the new seed validate_new_seed(new_seed).

[0453] This detailed technical description not only clarifies the specific implementation method of the second feature extraction, but also explains the collaborative working mechanism between various modules. This design not only ensures the efficiency of feature extraction, but also ensures the quality of extracted features, providing a reliable data basis for subsequent status monitoring. The entire design fully considers the real-time requirements and can meet the needs of material operation status monitoring.

[0454] The following is a specific example (including pseudo code and sample code):

[0455] C1: Build a short seed generator:

[0456] Design seed encoding structure:

[0457] ```Python

[0458] class SeedEncoder:

[0459] def __init__(self, input_dim, seed_dim):

[0460] self.seed_dim = seed_dim

[0461] self.compression_matrix = initialize_compression_matrix(input_dim,seed_dim)

[0462] self.hash_functions = generate_hash_functions(seed_dim)

[0463] def encode(self, feature_vector):

[0464] # Compress feature vector

[0465] compressed = self.compress_features(feature_vector)

[0466] # Generate short seed

[0467] seed = self.generate_seed(compressed)

[0468] return seed

[0469] Implement feature compression mechanism:

[0470] Initial compression using Locality Sensitive Hashing (LSH):

[0471] hash_value = hash_function(feature_vector);

[0472] Apply random projection compression:

[0473] projection = random_projection_matrix * feature_vector;

[0474] Merge and compress results:

[0475] compressed_feature = combine_compression(hash_value, projection).

[0476] C2: Achieving near-linear time feature extraction:

[0477] Build the feature extraction pipeline:

[0478] ```Python

[0479] def feature_extraction_pipeline(input_data):

[0480] # Processing data chunks in parallel

[0481] data_blocks = split_into_blocks(input_data)

[0482] extracted_features = parallel_process(data_blocks, extract_block_features)

[0483] # Merge features

[0484] merged_features = merge_features(extracted_features)

[0485] # Apply short seed optimization

[0486] optimized_features = apply_seed_optimization(merged_features)

[0487] return optimized_features

[0488] Design a block processing strategy:

[0489] Determine the optimal block size: block_size = optimize_block_size(data_size, available_memory);

[0490] Implement data blocking: blocks = create_data_blocks(data, block_size);

[0491] Process each data block in parallel.

[0492] C3: Optimizing feature extraction efficiency:

[0493] Implement feature caching mechanism:

[0494] ```Python

[0495] class FeatureCache:

[0496] def __init__(self, cache_size):

[0497] self.cache = LRUCache(cache_size)

[0498] def get_cached_feature(self, seed):

[0499] if seed in self.cache:

[0500] return self.cache[seed]

[0501] return None

[0502] def update_cache(self, seed, feature):

[0503] self.cache.put(seed, feature)

[0504] Design feature reuse strategy:

[0505] Build feature dependency graph: dependency_graph = build_feature_dependency_graph();

[0506] Identify reusable features: reusable_features = find_reusable_features(dependency_graph);

[0507] Optimize the computation order: computation_order = optimize_computation_sequence(dependency_graph).

[0508] C4: Implementing incremental feature updates:

[0509] ```Python

[0510] def incremental_update(existing_features, new_data):

[0511] # Calculate the feature changes of new data

[0512] delta_features = compute_feature_delta(new_data)

[0513] # Quickly locate features that need to be updated based on short seeds

[0514] affected_features = locate_affected_features(delta_features)

[0515] # Incremental update features

[0516] updated_features = update_features(existing_features, affected_features)

[0517] return updated_features

[0518] C5: Build feature quality control mechanism:

[0519] Implementing feature validation:

[0520] ```Python

[0521] def validate_features(extracted_features):

[0522] # Check feature integrity

[0523] completeness = check_feature_completeness(extracted_features)

[0524] # Verify feature quality

[0525] quality_score = assess_feature_quality(extracted_features)

[0526] # Handling abnormal features

[0527] if quality_score < threshold:

[0528] extracted_features = repair_features(extracted_features)

[0529] return extracted_features

[0530] Design feature repair strategy:

[0531] Identify anomaly features: anomalies = detect_feature_anomalies(features);

[0532] Generate repair plan: repair_plan = generate_repair_plan(anomalies);

[0533] Execute feature repair: repaired_features = execute_repair(features, repair_plan).

[0534] C6: Implementing adaptive seed updates:

[0535] ```Python

[0536] def adaptive_seed_update(current_seed, performance_metrics):

[0537] # Evaluate current seed performance

[0538] seed_performance = evaluate_seed_performance(current_seed)

[0539] # Determine whether an update is needed

[0540] if requires_update(seed_performance, performance_metrics):

[0541] # Generate new seeds

[0542] new_seed = generate_optimized_seed(current_seed, performance_metrics)

[0543] # Verify new seed performance

[0544] if validate_new_seed(new_seed):

[0545] return new_seed

[0546] return current_seed

[0547] C7: Optimized memory management:

[0548] Implementing feature memory mapping:

[0549] ```Python

[0550] class FeatureMemoryManager:

[0551] def __init__(self, memory_limit):

[0552] self.memory_map = initialize_memory_map(memory_limit)

[0553] def allocate_memory(self, feature_size):

[0554] memory_block = find_available_block(self.memory_map, feature_size)

[0555] return memory_block

[0556] def release_memory(self, memory_block):

[0557] mark_as_available(self.memory_map, memory_block)

[0558] Design memory recycling strategy:

[0559] Track feature usage frequency: usage_stats = track_feature_usage();

[0560] Identify releasable memory: releasable_memory = identify_releasable_memory(usage_stats);

[0561] Perform memory reclaim: reclaim_memory(releasable_memory).

[0562] Step S106: Calculate the abnormal probability and issue an early warning.

[0563] like Figure 6 As shown, this step is completed by the early warning module (70), which specifically includes:

[0564] The data receiving unit (71) receives the material status data stream in real time;

[0565] The abnormality probability calculation unit (72) calculates the state abnormality probability based on the prediction model;

[0566] The early warning trigger unit (73) comprises:

[0567] First-level warning module (731): when the abnormal probability exceeds a first preset threshold, a warning message is issued;

[0568] Second-level warning module (732): When the abnormal probability exceeds a second preset threshold, an emergency processing process is triggered, wherein the second preset threshold is greater than the first preset threshold.

[0569] like Figure 7 As shown, the present invention also provides a real-time monitoring device for material running status, comprising:

[0570] A collection unit (81), used for collecting multi-dimensional data of material operation;

[0571] A preprocessing unit (82), used for performing symmetric mapping preprocessing on the multi-dimensional data;

[0572] A first feature extraction unit (83), used for performing first feature extraction based on a Transformer model;

[0573] A modeling unit (84) for modeling through symmetry constraints and multi-task prediction architecture;

[0574] A second feature extraction unit (85), used for performing a second feature extraction on the output of the prediction model;

[0575] The early warning unit (86) is used to calculate the abnormal probability and issue an early warning.

[0576] The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the essence and spirit of the present invention should be included in the protection scope of the present invention.

[0577] The system of the embodiments of the present disclosure can execute the method provided by the embodiments of the present disclosure, and the implementation principles are similar. The actions performed by each module in the system of each embodiment of the present disclosure correspond to the steps in the method of each embodiment of the present disclosure. For the detailed functional description of each module of the system, please refer to the description in the corresponding method shown in the previous text, which will not be repeated here.

[0578] The above is only an optional implementation method for some implementation scenarios of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the scheme of the present disclosure, other similar implementation methods based on the technical ideas of the present disclosure are also within the protection scope of the embodiments of the present disclosure.

Claims

1. A method for real-time monitoring of material running status, characterized in that: include: Collecting multi-dimensional data of material operation, wherein the multi-dimensional data includes an operation parameter data stream, an environmental status data stream, and an equipment interaction data stream; Performing symmetric mapping preprocessing on the multidimensional data, including mapping the multidimensional data to a structure-preserving symmetric space and performing outlier processing to obtain preprocessed data; Performing first feature extraction on the preprocessed data based on a Transformer model to obtain a first feature vector sequence; Performing prediction modeling on the first feature vector sequence by constructing a symmetry constraint and a multi-task prediction architecture to obtain a prediction model; Performing a second feature extraction on the output of the prediction model to obtain a second feature extraction result; Calculate the abnormal probability of the current state according to the prediction model and the second feature extraction result, and issue an early warning when the abnormal probability exceeds a preset threshold; The step of performing predictive modeling on the first feature vector sequence by constructing a symmetry constraint and a multi-task prediction architecture includes: Dividing the feature space into multiple local subspaces, and determining a sampling center and a sampling radius for each subspace according to the distribution of the first feature vector sequence, to obtain an initial sampling space; Constructing a symmetric transformation matrix based on the initial sampling space, mapping the first feature vector sequence to the symmetric space, and realizing feature symmetry mapping; According to the first feature vector sequence after the symmetry mapping, a multi-task prediction architecture including state classification, anomaly detection and trend prediction is designed; In the training process of the multi-task prediction architecture, the feature density of each subspace is calculated and the sampling parameters are dynamically adjusted to achieve adaptive sampling; Based on the result of the adaptive sampling, the prediction model is trained in combination with symmetry constraints and a multi-task loss function; The step of training the prediction model by combining symmetry constraints and a multi-task loss function includes: Calculating a symmetry loss, wherein the symmetry loss characterizes the consistency of the feature before and after the symmetry transformation; Compute multi-task losses, including state classification loss, anomaly detection loss, and trend prediction loss; Combining the symmetry loss and the multi-task loss to form a total loss function; Adopt a progressive training strategy to gradually increase the size and complexity of training data; Dynamically adjust the learning rate based on the validation set performance.

2. The method according to claim 1, characterized in that The collecting of multi-dimensional data of material operation includes: The operating parameter sensor collects the usage frequency, operating time and temperature data of the material to form the operating parameter data stream; Collecting the ambient temperature, humidity and vibration data of the material through an ambient state sensor to form the ambient state data stream; Collecting the interaction data between the material and other equipment through the interaction state sensor to form the equipment interaction data stream; Data integrity verification and time stamping are performed on the operating parameter data stream, the environmental status data stream, and the device interaction data stream.

3. The method according to claim 1, characterized in that The symmetric mapping preprocessing of the multi-dimensional data includes: Mapping the multidimensional data to a symmetric space to achieve data dimensionality reduction by maintaining the topological structure of the data; Determining the optimal dimension of the feature space based on a minimum boundary rank calculation, wherein the minimum boundary rank is obtained by iterative optimization; Applying symmetric transformation to perform data denoising to eliminate noise and outliers in the multidimensional data; The denoised data is normalized to make data of different dimensions comparable.

4. The method according to claim 1, characterized in that The Transformer model includes a position encoding layer, a multi-head self-attention layer and a feedforward network layer, and the first feature extraction of the preprocessed data based on the Transformer model includes: In the position encoding layer, a position encoding matrix is ​​generated using a rotational position encoding mechanism; In the multi-head self-attention layer, the input features are divided into multiple attention heads, and each head independently calculates the attention weight; In the feedforward network layer, features are processed by two linear transformations and activation functions; The outputs of each layer are combined through residual connection and layer normalization to obtain the first feature vector sequence.

5. The method according to claim 1, characterized in that Performing a second feature extraction on the output of the prediction model, comprising: Designing a seed encoding structure to achieve second feature compression by performing local sensitive hashing and random projection on the output of the prediction model; Construct the second feature extraction pipeline to achieve parallel block processing of data; Establish a second feature cache mechanism to cache frequently used second features; An incremental update of the second feature is implemented to extract the updated second feature.

6. The method according to claim 5, characterized in that The incremental update for realizing the second feature includes: Calculate the change in the second feature brought about by the new data; Quickly locate the second feature that needs to be updated based on the short seed; Only the affected second feature is updated locally; performing a second feature integrity check; When the second feature is abnormal, the feature repair process is triggered.

7. The method according to claim 1, characterized in that The calculating the abnormal probability of the current state according to the prediction model and the second feature extraction result, and issuing an early warning when the abnormal probability exceeds a preset threshold, includes: Receive material status data stream in real time; Calculating the probability of abnormal state based on the prediction model; When the abnormal probability exceeds a first preset threshold, issuing a warning message; When the abnormal probability exceeds a second preset threshold, an emergency processing process is triggered, wherein the second preset threshold is greater than the first preset threshold.

8. A real-time monitoring system for material operation status, characterized in that: include: A collection unit, used to collect multi-dimensional data of material operation, wherein the multi-dimensional data includes an operation parameter data stream, an environmental status data stream, and an equipment interaction data stream; A preprocessing unit, used for performing symmetric mapping preprocessing on the multidimensional data, including mapping the multidimensional data to a structure-preserving symmetric space and performing outlier processing to obtain preprocessed data; A first feature extraction unit, configured to perform first feature extraction on the preprocessed data based on a Transformer model to obtain a first feature vector sequence; A modeling unit, configured to perform predictive modeling on the first feature vector sequence by constructing a symmetry constraint and a multi-task prediction architecture to obtain a prediction model; A second feature extraction unit, used to perform a second feature extraction on the output of the prediction model to obtain a second feature extraction result; An early warning unit, configured to calculate an abnormal probability of a current state according to the prediction model and the second feature extraction result, and issue an early warning when the abnormal probability exceeds a preset threshold; The modeling unit is used to perform predictive modeling on the first feature vector sequence by constructing a symmetry constraint and a multi-task prediction architecture, including: Dividing the feature space into multiple local subspaces, and determining a sampling center and a sampling radius for each subspace according to the distribution of the first feature vector sequence, to obtain an initial sampling space; Constructing a symmetric transformation matrix based on the initial sampling space, mapping the first feature vector sequence to the symmetric space, and realizing feature symmetry mapping; According to the first feature vector sequence after the symmetry mapping, a multi-task prediction architecture including state classification, anomaly detection and trend prediction is designed; In the training process of the multi-task prediction architecture, the feature density of each subspace is calculated and the sampling parameters are dynamically adjusted to achieve adaptive sampling; Based on the result of the adaptive sampling, the prediction model is trained in combination with symmetry constraints and a multi-task loss function; The step of training the prediction model by combining symmetry constraints and a multi-task loss function includes: Calculating a symmetry loss, wherein the symmetry loss characterizes the consistency of the feature before and after the symmetry transformation; Compute multi-task losses, including state classification loss, anomaly detection loss, and trend prediction loss; Combining the symmetry loss and the multi-task loss to form a total loss function; Adopt a progressive training strategy to gradually increase the size and complexity of training data; Dynamically adjust the learning rate based on the validation set performance.

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