Abnormal wear monitoring method for disc cutter of shield tunneling machine

Abstract

According to the abnormal wear monitoring method for the disc cutter of the shield tunneling machine, tunneling parameters closely related to the wear state of the disc cutter are screened according to the abnormal wear monitoring requirement of the disc cutter in the tunneling process of the shield tunneling machine, and data missing caused by the collection problem is filled up. According to the long and short term memory self-encoder model based on the abnormal attention mechanism, prior correlation in an original sequence is obtained through a learnable Gaussian kernel, the distinguishability between normal and abnormal conditions is improved by obtaining correlation differences of the sequence, and accurate and reliable abnormal wear monitoring is conducted on the shield tunneling machine disc cutter in combination with the extreme value theory. According to the method, the tunneling parameters closely related to the running state of the disc cutter are identified more accurately, and the model training precision and the accuracy of abnormal wear monitoring of the disc cutter are improved. The method is of great significance to the application of abnormal wear monitoring of the disk cutter of the shield tunneling machine.

Description

[Technical field]

[0001] The invention relates to the field of tool wear monitoring, and in particular to a method for monitoring abnormal wear of a disc cutter of a shield machine. [Background technology]

[0002] The shield method refers to the method of using a shield machine to excavate a tunnel. Due to its advantages such as high degree of automation, fast construction speed and high safety, it is widely used in tunnel excavation. As a key component of the shield machine, the shield cutter is responsible for the main rock breaking work in the actual shield excavation project. Abnormal wear of the cutter will increase the load on other cutters on the cutter disc, and in severe cases, it may even cause the entire disc of cutters to be scrapped. Researching the intelligent monitoring method of abnormal wear of shield machine cutters has important practical significance for guiding the replacement of cutters and shortening the construction period.

[0003] The disc cutter is installed on the cutterhead of the shield machine and undertakes the main excavation task in shield tunneling. During the excavation process, the shield cutterhead is pressed against the excavation surface and rotates slowly along the normal of the excavation surface, thereby driving the disc cutter to rotate around its own roller and revolve around the center bearing of the cutterhead to break the soil on the excavation surface and peel it off. The wear state of the disc cutter can be divided into normal wear and abnormal wear. In order to improve construction efficiency, real-time cutter state monitoring technology has emerged. Usually, this method directly collects the wear condition of the cutter through sensors such as magnetoresistance effect, ultrasonic monitoring, eddy current monitoring, and acoustic emission monitoring to help practitioners determine the accurate time to change the cutter. However, the sensors installed on the cutterhead are affected by harsh working environments such as high temperature, high humidity, and strong vibration, resulting in low reliability of the sensor direct monitoring method.

[0004] In recent years, data-driven disc cutter wear monitoring methods have received widespread attention. In particular, deep learning unsupervised anomaly monitoring models driven by normal data have been widely used. For example, the combination of attention mechanism and long short-term memory neural network (LSTM) algorithm has a certain promoting effect on the abnormal wear monitoring of disc cutters. However, in actual engineering problems, due to the scarcity of abnormalities in time series, it is extremely difficult to associate abnormal points with the entire sequence. The existing shield machine disc cutter abnormal wear monitoring method can improve the performance of abnormal monitoring to a certain extent, but due to the scarcity of disc cutter abnormalities, it is extremely difficult to associate abnormal points with the entire sequence, resulting in the existing attention mechanism not effectively improving the distinguishability of abnormal states.

[0005] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. [Summary of the invention]

[0006] To solve the above problems, the present invention proposes a method for monitoring abnormal wear of disc cutters of shield machines, performs data screening and missing value filling on the excavation parameters during the excavation process of the shield machine, obtains the optimized excavation parameter data for model training, constructs a long short-term memory autoencoder network model based on the abnormal attention mechanism, and combines the extreme value theory for abnormal monitoring. The method of the present invention improves the accuracy and reliability of abnormal wear monitoring of disc cutters of shield machines.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for monitoring abnormal wear of a shield machine disc cutter, characterized by comprising:

[0009] Step S1: Collect various excavation parameter data during shield machine excavation, and determine whether it is overall missing data or overall unchanged data through its data length and data variance, and delete the parameters of overall missing data and / or overall unchanged data; in this way, the excavation parameter data can be optimized, and the accuracy of model training and the accuracy of abnormal cutter wear monitoring can be improved.

[0010] Step S2: using a screening strategy to screen the excavation parameters that are closely related to the operating state of the disc cutter;

[0011] Preferably, the screening strategy is a strategy combining Pearson and Spearman correlation coefficients for screening, and the screening strategy is as follows:

[0012]

[0013]

[0014]

[0015] In the formula, f represents the number of model training and case test sets used, are Pearson correlation coefficient and Spearman correlation coefficient, respectively. Represents the standard deviation of two random variables a and b, cov(a,b) is the covariance matrix of variables a and b, Y i It represents the difference in the rank value of the i-th data pair, N is the total number of observed samples of this type of tunneling parameters, ε, η are weight coefficients, 0≤ε, η≤1, the sum of ε, η is equal to 1, and correlation is the total correlation coefficient.

[0016] Through the screening strategy combining two correlation coefficients, the trend characteristics of the data are retained, and the weight coefficient is adjusted according to the actual working conditions of the disc cutter of the shield machine, which can more accurately identify the excavation parameters closely related to the operating status of the disc cutter.

[0017] Step S3: Normalize the screened excavation parameter data, and use the mean of the previous and next rows of data to fill the missing data row, so as to fill the data gaps and improve the data quality. The original sequence sample x of size [U×C] is intercepted through the sliding window. By filling the missing data rows, the integrity and continuity of the data are maintained, and the accurate training and prediction effects of the network model are improved.

[0018] Fill in the empty value data through data filling. The data filling method is as follows:

[0019]

[0020] Among them, x Nan is the missing value, The data before the missing data row is not empty in the first five rows. They are the 5 rows of data that are not null values ​​after the row with missing data, and avg means finding the average value of the data.

[0021] The filled data is sampled through a sliding window and divided into model training set and case test set.

[0022] Step S4: Constructing a long short-term memory autoencoder network model with improved abnormal attention mechanism Set the hyperparameters of the LSTM autoencoder network with improved abnormal attention mechanism, abnormal attention mechanism layer E, and LSTM autoencoder network layer D;

[0023] Long short-term memory autoencoder network layer with improved abnormal attention mechanism The hyperparameters include the number of network layers, the number of hidden layer units, and the number of neurons in the fully connected layer.

[0024] Step S5: Randomly initialize the network parameters, input the sample x in step S3 into the abnormal attention mechanism layer, output the abnormal attention mechanism hidden layer feature h, and calculate the abnormal attention mechanism associated difference loss L association , input the hidden layer feature h of the abnormal attention mechanism into the long short-term memory autoencoder network layer to output a reconstructed sample with the same size as x Calculate x and The root mean square error reconstruction loss L RMSE ;

[0025] The abnormal attention mechanism layer in step S5 includes the prior association of adjacent time periods at each time point and the sequence association with the entire time series. The prior association uses a learnable Gaussian kernel to represent the inductive bias of the adjacent set of each time point, while the sequence association corresponds to the self-attention weights learned from the original sequence, and the calculation formula is as follows:

[0026] Q,K,V,σ=x l-1 w l Q ,x l-1 w l K ,x l-1 w l V ,x l-1 w l σ

[0027]

[0028]

[0029] h=S l V

[0030] Where Q, K, V∈R U×r are the query, key, and value vectors of attention, σ∈R U×g is the learnable Gaussian kernel scale parameter, w l Q ,w l K ,w l V ∈R r×r , w l σ ∈R r×1 are the parameter matrices of Q, K, V, and σ at the lth layer, respectively, and x l-1 is the sample data of the input l-1 layer, P l is the prior association, i and j represent the i-th time and j-th time step respectively, σ i is the scale corresponding to the i-th time step learned, and Rescale(·) is a discretization operation by removing rows. S l ,S l ∈R U×U is the serial correlation, K T is the transpose of the attention key-value vector, r is the number of model channels, l is the number of model layers, Softmax(·) is the attention map normalization operation, h,h∈R U×r is the hidden layer representation of the abnormal attention mechanism. The conventional parameters can be set according to actual needs.

[0031] The abnormal attention mechanism associated difference loss is as follows:

[0032]

[0033]

[0034] Where KL(·) is the KL divergence operation between distributions. is the rounding up operation, max(.) is the maximum value operation, U is the sliding window interception size window length, and g is the number of multi-head attention.

[0035] The root mean square error loss formula is as follows:

[0036]

[0037] In the formula, is the reconstructed sample. ||·||2 is the 2-norm;

[0038] Step S6: Define the loss function L total , through L association With L RMSE Calculate L total , back propagation calculates the gradient of each network layer, and uses the optimizer to update each network parameter;

[0039] Optionally, the optimizer is an Adam optimizer.

[0040] The loss function L in step S6 total The calculation formula is as follows:

[0041] L total =L RMSE -λL association

[0042] Where, L RMSE is the root mean square error reconstruction loss, L association is the abnormal attention mechanism associated difference loss, and λ is the penalty term weight.

[0043] Step S7: repeating steps S5 to S6, and when the training reaches a preset number of rounds M, saving the trained abnormal attention mechanism improved long short-term memory autoencoder model network parameters;

[0044] Step S8: Input sample x into the trained The reconstruction error is obtained and the anomaly score is calculated; the formula for obtaining the sample reconstruction error and calculating the anomaly score in step S8 is as follows:

[0045] e rec =L RMSE

[0046] Score = Softmax(L association )⊙e rec

[0047] Where e rec is the sample reconstruction error, Score is the anomaly score, and ⊙ is the dot product operation.

[0048] Step S9: Use extreme value theory to define an abnormal threshold for each case test sample and output the abnormal wear monitoring result of the shield machine disc cutter.

[0049] In step S9, the extreme value theory is used to calculate the abnormal threshold value for each case test sample as follows:

[0050]

[0051] In the formula, thr F is the final threshold, thr is the initial threshold of the model error, q is the probability of abnormal data in the entire sequence, N is the number of observations, N thr is the number of times the abnormal score Score>thr, is the parameter in the generalized Pareto distribution.

[0052] The beneficial effects of the present invention are as follows: the shield machine disc cutter abnormal wear monitoring method of the present invention performs data screening and missing value filling for the excavation parameters during the shield machine excavation process, optimizes the excavation parameter data, and screens the excavation parameters closely related to the disc cutter operation status through a strategy combining the Pearson and Spearman correlation coefficients, retaining the trend characteristics of the data. The long short-term memory autoencoder based on the abnormal attention mechanism uses a learnable Gaussian kernel to obtain the prior association in the original sequence, obtains the sequence association from the attention weight learned from the original sequence, improves the distinguishability between normal and abnormal by obtaining the correlation difference of the sequence, and combines the extreme value theory to accurately and reliably monitor the abnormal wear of the shield machine disc cutter. The method of the present invention more accurately identifies the excavation parameters closely related to the operation status of the disc cutter, and improves the accuracy of model training and the accuracy of abnormal wear monitoring of the cutter.

Brief Description of the Drawings

[0053] Figure 1 It is a schematic diagram of the abnormal wear monitoring method of the shield machine disc cutter in the present invention;

[0054] Figure 2 : is a Pearson and Spearman correlation coefficient screening effect diagram in an embodiment of the present invention;

[0055] Figure 3 This is a diagram showing the effect of filling in part of the cutter head torque data of Case 4 in an embodiment of the present invention;

[0056] Figure 4 1 is a diagram showing the effect of monitoring abnormal wear of a hob in an embodiment of the present invention.

[0057] Figure 5 It is a comparison chart of the monitoring effect of abnormal wear of hobs by the method of the present invention and the conventional method. [Specific implementation method]

[0058] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with embodiments and drawings. It should be understood that the exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined in any way as long as they do not conflict with each other.

[0059] See also Figure 1-5 The present invention provides a method for monitoring abnormal wear of a shield machine disc cutter, comprising:

[0060] Step S1: Collect the tunneling parameter data of the shield machine during tunneling, and determine whether it is overall missing data or overall unchanged data based on its data length and data variance, and delete the parameters of overall missing data and overall unchanged data; the tunneling parameter data can be optimized to improve the accuracy of model training.

[0061] Preferably, if the data length does not reach 60% of other normal data lengths and / or the data variance is less than 0.01, it is judged as overall missing data or overall unchanged data, and the overall missing data or overall unchanged data is deleted.

[0062] Optionally, the wear state of the disc type hob can be divided into normal wear and abnormal wear, wherein normal wear refers to a small amount of uniform wear produced along the circumference of the cutter ring of the disc type hob, and abnormal wear mainly includes excessive wear, eccentric wear of the cutter ring, etc.

[0063] In one embodiment, through the data preprocessing in step S1, the optimized categories of various tunneling parameters of the shield machine during tunneling are obtained as shown in Table 1.

[0064] Table 1 Optimized data parameters after deleting missing data and unchanged data

[0065]

[0066]

[0067] Step S2: using a screening strategy to screen the excavation parameters that are closely related to the operating state of the disc cutter;

[0068] Preferably, the screening strategy is a strategy combining Pearson and Spearman correlation coefficients for screening, and the screening strategy is as follows:

[0069]

[0070]

[0071]

[0072] In the formula, are the Pearson correlation coefficient and the Spearman correlation coefficient respectively, and f represents the number of model training and case test sets used. Represents the standard deviation of two random variables a and b, cov(a,b) is the covariance matrix of variables a and b, Y i It represents the difference in the rank value of the i-th data pair, N is the total number of observation samples of this type of tunneling parameters, ε, η are weight coefficients, and correlation is the total correlation coefficient.

[0073] Optionally, ε and η are 0.4 and 0.6 respectively.

[0074] The ε,η are dynamically adjusted according to the actual working requirements of the shield machine or the excavation distance of the shield machine. For example, the ε,η change linearly with the excavation distance of the shield machine, and the sum of ε,η is kept equal to 1. When the total correlation coefficient is greater than the set threshold, the variable is a excavation parameter closely related to the operating state of the disc cutter. Optionally, the threshold is set to 0.4.

[0075] The strategy of combining two correlation coefficients is used to screen the excavation parameters closely related to the operating status of the disc cutter, retain the trend characteristics of the data, and adjust the weight coefficient according to the actual working conditions of the shield machine disc cutter, so as to more accurately identify the excavation parameters closely related to the operating status of the disc cutter.

[0076] Step S3: Normalize the screened excavation parameter data, and use the mean of the previous and next rows of data to fill the missing data row, and intercept the sample x of size [U×C] through the sliding window; by filling the missing data row, the data quality, accurate training of the network model and prediction effect are improved.

[0077] Fill the empty value data by data filling, the data filling method is as follows:

[0078]

[0079] Among them, x Nan is the missing value, The data before the missing data row is not empty in the first five rows. The 5 rows after the missing row are not empty values, and avg indicates the average value of the data. Of course, those skilled in the art know that the number of non-empty values ​​before and after the missing value can be selected to fill the empty value data according to the actual situation.

[0080] The filled data is sampled through a sliding window and divided into model training samples and case test samples. The sliding window capture size is U, the window length, and C, the number of variables input to the model or the number of variables screened out, preferably 8. In one embodiment, the window length is set to 10, the sliding step is 3, and the model training samples and case test samples are set as shown in Table 2.

[0081] Table 2 Model training sample and case test sample collection settings

[0082]

[0083] In the table, "ring" is an engineering definition. According to the actual engineering situation, the tunnel is divided into a "ring" of a certain length for construction and management. The number of collected samples can be adjusted according to the specific model training.

[0084] Step S4: Constructing a long short-term memory autoencoder network model with improved abnormal attention mechanism Including setting an improved long short-term memory autoencoder network with an unusual attention mechanism Hyperparameters of

[0085] The long short-term memory autoencoder network layer improved by the abnormal attention mechanism in step S4 The hyperparameters include the number of network layers, the number of hidden layer units, and the number of neurons in the fully connected layer.

[0086] Step S5: Randomly initialize the network parameters, input the sample x in step S3 into the abnormal attention mechanism layer according to the batch size, output the abnormal attention mechanism hidden layer feature h of size [U×r], and calculate the abnormal attention mechanism associated difference loss L association , the abnormal attention mechanism hidden layer feature h is input into the long short-term memory autoencoder network layer Output the reconstructed samples with the same size as x x and The size is [U×C], calculate x and The root mean square error reconstruction loss L RMSE ;

[0087] The abnormal attention mechanism layer in step S5 includes modeling the prior association of adjacent time periods at each time point and the serial association with the entire time series. The prior association uses a learnable Gaussian kernel to represent the inductive bias of the neighboring set at each time point, while the serial association corresponds to the self-attention weights learned from the original sequence. The calculation formula of the hidden layer feature h of the abnormal attention mechanism is as follows:

[0088] Q,K,V,σ=x l-1 w l Q ,xl-1 w l K ,x l-1 w l V ,x l-1 w l σ

[0089]

[0090]

[0091] h=S l V

[0092] Where Q, K, V∈R U×r are the query, key, and value vectors of attention, σ∈R U×g is the learnable Gaussian kernel scale parameter, w l Q ,w l K ,w l V ∈R r×r , w l σ ∈R r×1 are the parameter matrices of Q, K, V, and σ at the lth layer, respectively. l is the prior association, i and j represent the i-th time and j-th time step respectively, σ i is the scale corresponding to the i-th time step learned, Rescale(·) is the discretization operation by removing rows, S l ,S l ∈R U×U is the serial correlation, K T is the transpose of the attention key-value vector, r is the number of model channels, l is the number of model layers, Softmax(·) is the attention map normalization operation, h,h∈R U×r is the hidden layer representation of the abnormal attention mechanism.

[0093] The abnormal attention mechanism associated difference loss is as follows:

[0094]

[0095]

[0096] Where KL(·) is the KL divergence operation between distributions. is a round-up operation, max(.) is a maximum value operation, U is the window length, g is the number of multi-head attention, and g is preferably 8.

[0097] The root mean square error loss formula is as follows:

[0098]

[0099] In the formula, is the reconstructed sample. ·2 is the 2-norm;

[0100] Step S6: Define the loss function L total , through L association With L RMSE Calculate L total , back propagation calculates the gradient of each network layer and uses the optimizer to update the network parameters;

[0101] Optionally, the optimizer is an Adam optimizer.

[0102] The loss function L in step S6 total The calculation formula is as follows:

[0103] L total =L RMSE -λL association

[0104] Where, L RMSE is the root mean square error reconstruction loss, L association is the abnormal attention mechanism associated difference loss, λ is the penalty term weight, which is set according to the model training requirements, and λ is preferably 0.003;

[0105] Step S7: Repeat steps S5 to S6. When the training reaches the preset number of rounds M, save the trained abnormal attention mechanism improved long short-term memory autoencoder model. Network parameters;

[0106] Optionally, the preset number of rounds M=200.

[0107] Step S8: Input the case test sample into the trained The reconstruction error of the case test sample is obtained and the anomaly score is calculated;

[0108] The formula for calculating the abnormality score in step S8 is as follows:

[0109] Score = Softmax(L association )⊙L RMSE

[0110] Where L RMSE is the root mean square error loss, Score is the anomaly score, Softmax is the activation function, and ⊙ is the dot product operation.

[0111] Step S9: Use extreme value theory to define an abnormal threshold for each case test sample and output the abnormal wear monitoring result of the shield machine disc cutter.

[0112] In step S9, the extreme value theory is used to calculate the abnormal threshold value for each case test sample as follows:

[0113]

[0114] In the formula, thr F is the abnormal threshold, thr is the initial threshold of the model error, q is the probability of abnormal data in the entire sequence, N is the number of observations, N thr is the number of times Score>thr, is the parameter in the generalized Pareto distribution.

[0115] When the abnormality score is greater than a threshold, the disc cutter is determined to be abnormally worn.

[0116] Optionally, in order to ensure reliable and effective collection of excavation parameter data, the present invention may be provided with a step of manually or automatically detecting tool wear after each excavation interval is completed, so as to ensure that the excavation quality is not affected by tool wear during the excavation process.

[0117] Optionally, in the step of automatically detecting tool wear, a sensor for detecting tool wear is provided, and the sensor may be any sensor in the prior art, such as a visual sensor, and if the tool wear is detected to exceed a set wear threshold, a manual or automatic tool replacement procedure is executed. At the same time, the tool wear detected by the sensor may be compared and analyzed with the results of the abnormal wear monitoring method of the present invention, and model training may be performed to further improve the accuracy and reliability of the method of the present invention.

[0118] The present invention provides a model building embodiment.

[0119] The model is built based on the Pytorch deep learning framework. The experimental equipment configuration: Windows 10 64-bit operating system, CPU is Intel Xeon Silver 4210R @ 2.4 GHz, GPU is NVIDIA GeForce RTX 3090, and the specific parameters of the network structure are shown in Table 3.

[0120] Table 3 Specific parameter settings of the network structure of the method of the embodiment of the present invention

[0121]

[0122] Among them, after the mask layer of the abnormal attention mechanism, the LeakyRelu activation function is sequentially connected (the negative activation slope is set to 0.2); the Sigmoid activation function is sequentially connected to the fully connected layer of the long short-term memory autoencoder.

[0123] The network model uses the Adam optimizer for parameter optimization, where the learning rate is 0.001, the batch size is set to 50, and a total of 200 rounds of training are performed. The embodiment of the present invention selects the sequence encoder anomaly monitoring model (Tranad) based on the self-attention mechanism, the anomaly monitoring model (MemAE) with a memory query module and the time series feature extraction anomaly monitoring model (LSTM-AE), and the inference generative anomaly monitoring model (VAE) as comparison methods for comparison. Load the parameters of the long short-term memory autoencoder model based on the abnormal attention mechanism, input the case test sample in the data sample into the trained model to obtain the anomaly score, and combine the extreme value theory to define the threshold and output the anomaly monitoring result.

[0124] Attached Figure 2 The excavation parameters closely related to the disc cutter operating status were screened out by combining the Pearson and Spearman correlation coefficients. Among them, the total correlation coefficient was greater than 0.4, indicating that it was strongly correlated with the disc cutter operating status.

[0125] Attached Figure 3 This is the data filling diagram of the cutter head torque (N·m) of the shield machine. The data is filled by the 5 rows of data before and after the missing data row, which can better fill the data gaps and retain the trend characteristics of the data.

[0126] The monitoring results of the test samples in each case in Table 2 are shown in the attached Figure 4 After each excavation interval is completed, since the tool wear exceeds the abnormal threshold, the tool replacement procedure is executed manually or by program. Therefore, there is a situation where the number of samples exceeding the abnormal threshold in the case of a longer excavation distance is less than the number of samples exceeding the abnormal threshold in the case of a shorter excavation distance.

[0127] The comparison between the method of the present invention and conventional monitoring methods (TranAD, Memae, LSTM-AE, VAE) is shown in the attached figure. Figure 5 As shown in the figure, since all models in the test sample of case 1 had false reports, the test sample of case 1 was not included in the early warning quantity comparison. Among them, "*, #" indicates the existence of false reports and missed reports. The abnormal wear monitoring method of the shield machine disc cutter of the present invention has no false reports and missed reports, and can achieve effective early warning.

[0128] The shield machine disc cutter abnormal wear monitoring method of the present invention performs data screening and missing value filling on the excavation parameters during the shield machine excavation process, optimizes the excavation parameter data, and screens the excavation parameters closely related to the disc cutter operation status through a strategy combining the Pearson and Spearman correlation coefficients, retaining the trend characteristics of the data. The long short-term memory autoencoder based on the abnormal attention mechanism uses a learnable Gaussian kernel to obtain the prior association in the original sequence, obtains the sequence association from the attention weight learned from the original sequence, and improves the distinguishability between normal and abnormal by obtaining the correlation difference of the sequence. Combined with the extreme value theory, the shield machine disc cutter is accurately and reliably monitored for abnormal wear. The method of the present invention more accurately identifies the excavation parameters closely related to the disc cutter operation status, and improves the model training accuracy and the accuracy of the abnormal wear monitoring of the cutter.

[0129] The above embodiments are intended to illustrate the present invention rather than to limit the present invention. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be included in the protection scope of the present invention.

Claims

1. A method for monitoring abnormal wear of a shield machine disc cutter, characterized in that: include: Step S1: collecting tunneling parameter data of the shield machine during tunneling, judging whether it is overall missing data or overall unchanged data by data length and data variance, and deleting overall missing data and overall unchanged data; Step S2: using a screening strategy to screen the excavation parameters closely related to the operation status of the disc cutter, wherein the screening strategy is a combination of Pearson and Spearman correlation coefficients; Step S3: normalize the selected excavation parameter data, fill the missing data row with the mean of the data of the rows before and after the missing data row, and intercept the sample x through the sliding window; Step S4: Constructing a long short-term memory autoencoder network model with improved abnormal attention mechanism set up Hyperparameters of Step S5: Randomly initialize the network parameters, input the sample x in step S3 into the abnormal attention mechanism layer, output the abnormal attention mechanism hidden layer feature h, and calculate the abnormal attention mechanism associated difference loss L association , input the hidden layer feature h of the abnormal attention mechanism into the long short-term memory autoencoder network layer, and output a reconstructed sample with the same size as the sample x Calculate x and The root mean square error reconstruction loss L RMSE ; Step S6: Define the loss function L total , through L association With L RMSE Calculate L total , back propagate to calculate the gradient of each network layer and update it with the optimizer Network parameters; Loss function L total The calculation formula is as follows: THE total =L RMSE -λL association Where, L RMSE is the root mean square error reconstruction loss, L association is the abnormal attention mechanism associated difference loss, λ is the penalty term weight; Step S7: Repeat steps S5 to S6, train for a preset number of rounds M, and save the trained abnormal attention mechanism improved long short-term memory autoencoder model Network parameters; Step S8: Input the case test sample into the trained The reconstruction error of the case test sample is obtained and the anomaly score is calculated; Step S9: Use extreme value theory to define an abnormal threshold for each case test sample and output the abnormal wear monitoring result of the shield machine disc cutter.

2. A shield machine disc cutter abnormal wear monitoring method as claimed in claim 1, wherein the strategy of combining the Pearson and Spearman correlation coefficients is: In the formula, ρ a,b , are the Pearson correlation coefficient and the Spearman correlation coefficient respectively, and f represents the number of model training and case test sets used. Represents the standard deviation of two random variables a and b, cov(a,b) is the covariance matrix of variables a and b, Y i It represents the difference in the rank value of the i-th data pair, N is the total number of observation samples of this type of tunneling parameters, ε, η are weight coefficients, and correlation is the total correlation coefficient.

3. A shield machine disc cutter abnormal wear monitoring method as claimed in claim 2, wherein the data filling method in step S3 is: in, x Nan is the missing value, The data before the missing data row is not empty in the first five rows. They are the 5 rows of data that are not null values ​​after the row with missing data, and avg means finding the average value of the data.

4. A method for monitoring abnormal wear of a shield machine disc cutter as described in claim 1, wherein the hyperparameters of the long short-term memory autoencoder network layer of the improved abnormal attention mechanism in step S4 include the number of network layers, the number of hidden layer units, and the number of neurons in the fully connected layer.

5. A shield machine disc cutter abnormal wear monitoring method as described in claim 3, wherein the abnormal attention mechanism layer in step S5 includes modeling the prior association of adjacent time periods at each time point and the sequence association with the entire time series, the prior association uses a learnable Gaussian kernel to represent the adjacency set inductive bias of each time point, the sequence association corresponds to the self-attention weight learned from the original sequence, and the calculation formula of the hidden layer feature h of the abnormal attention mechanism is as follows: Q,K,V,σ=x l-1 w l Q ,x l-1 w l K ,x l-1 w l V ,x l-1 w l σ h=S l V Where Q, K, V∈R U×r are the query, key, and value vectors of attention, σ∈R U×g is the learnable Gaussian kernel scale parameter, w l Q ,w l K ,w l V ∈R r×r , w l σ ∈R r×1 are the parameter matrices of Q, K, V, and σ at the lth layer, respectively. l is the prior association, i and j represent the i-th time and j-th time step respectively, σ i is the scale corresponding to the i-th time step learned, Rescale(·) is the discretization operation by removing rows, S l ,S l ∈R U×U is the serial correlation, K T is the transpose of the attention key-value vector, r is the number of model channels, l is the number of model layers, Softmax(·) is the attention map normalization operation, h,h∈R U×r is the hidden layer representation of the abnormal attention mechanism.

6. According to a shield machine disc cutter abnormal wear monitoring method as claimed in claim 4, the abnormal attention mechanism associated difference loss is as follows: Where KL(·) is the KL divergence operation between distributions. is the rounding up operation, max(.) is the maximum value operation, U is the window length, and g is the number of multi-head attention.

7. A method for monitoring abnormal wear of a shield machine disc cutter as claimed in claim 6, The root mean square error loss formula is as follows: In the formula, is the reconstructed sample, and ||·||2 is the 2-norm.

8. A shield machine disc cutter abnormal wear monitoring method as claimed in claim 7, characterized in that: The formula for calculating the abnormality score in step S8 is as follows: Score=Softmax(L association )⊙L RMSE Where L RMSE is the root mean square error loss, Score is the anomaly score, Softmax is the activation function, and ⊙ is the dot product operation.

9. A shield machine disc cutter abnormal wear monitoring method as claimed in claim 8, characterized in that: In step S9, the extreme value theory is used to calculate the abnormal threshold value for each case test sample as follows: In the formula, thr F is the final threshold, thr is the initial threshold of the model error, q is the probability of abnormal data in the entire sequence, N is the number of observations, N thr is the number of times the abnormal score Score>thr, is the parameter in the generalized Pareto distribution.

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