CN114238854A - Mining scene abnormity detection method based on graph regular increment nonnegative matrix decomposition - Google Patents

Mining scene abnormity detection method based on graph regular increment nonnegative matrix decomposition Download PDF

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CN114238854A
CN114238854A CN202111509423.2A CN202111509423A CN114238854A CN 114238854 A CN114238854 A CN 114238854A CN 202111509423 A CN202111509423 A CN 202111509423A CN 114238854 A CN114238854 A CN 114238854A
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陈自刚
肖琪
陈龙
张镇江
潘鼎
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Abstract

The invention provides a mining scene abnormity detection method based on graph regular increment nonnegative matrix decomposition, and belongs to the technical field of abnormity detection and diagnosis and the field of intelligent safety. The method comprises the steps that firstly, mining environment information under a normal state is collected by two sets of equipment, and data obtained by the two sets of equipment are processed as follows; preprocessing data to obtain a training set X'; then obtaining the optimal base matrix W through regular incremental nonnegative matrix decomposition of the graphnewSum coefficient matrix Hnew(ii) a Thereby establishing a monitoring statistic N2And SPE, calculating the control of the training sets of the two sets of equipmentLimiting; then, data (a test set X ") is collected again for detection, the statistic of the test set X" is calculated, and finally the statistic is compared with two sets of control limits, so that whether the mining scene is abnormal or not is judged; when the scene is abnormal, the maximum or larger contribution values are uploaded to the control interface as the abnormal reasons to be displayed. The method solves the problems that the traditional mining scene abnormity detection is not timely and accurate, and the like, and creates digital mining industry.

Description

Mining scene abnormity detection method based on graph regular increment nonnegative matrix decomposition
Technical Field
The invention relates to the technical field of anomaly detection and diagnosis and the field of intelligent safety, in particular to a mining scene anomaly detection method based on graph regular increment non-negative matrix decomposition.
Background
Mining safety issues have been a concern. At present, most mines, especially metal mines, adopt an underground mining mode, however, in the underground construction process, toxic gas, mine collapse and the like pose great threats to the safety of constructors; therefore, the method has important significance in risk detection and early warning of the underground construction environment. At present, a plurality of anomaly detection methods are provided, but the method is challenging work for ensuring timeliness and effective utilization of network resources, comprehensively analyzing multi-sensor (multi-factor) data to ensure accuracy and determining the root cause of scene anomaly.
A mining scene anomaly detection method based on graph regular increment nonnegative matrix decomposition is provided. The graph regular incremental nonnegative matrix decomposition can overcome the difficulty of the traditional nonnegative matrix decomposition in the online processing of a large data set, can online represent data content and maintain the geometric structure of data, and can also fully utilize the decomposition result of the previous step by combining incremental learning to avoid repeated calculation, thereby reducing the operation time; meanwhile, the dimension is obviously reduced, and the clustering precision is better; based on the operation of the point elements by the matrix points, the decomposition result of the graph regular increment non-negative matrix method is more likely to represent the local characteristics of the data; in addition, the nonnegative basis vectors obtained by the regular incremental nonnegative matrix decomposition of the graph have certain linear independence and sparsity, and large-scale high-dimensional data generated in an industrial process can be better described; finally, the regular incremental non-negative matrix method of the graph does not make a special ideal assumption on data distribution in the decomposition process, so that non-Gaussian data can be processed, and only a proper monitoring statistic needs to be designed by combining a density estimation method.
Disclosure of Invention
In view of the above, the invention provides a mining scene abnormity detection method based on graph regular increment non-negative matrix decomposition, which realizes dynamic data training and multi-sensor data comprehensive analysis, saves time and space cost, and can analyze the reason of abnormity if abnormity occurs. The method comprises the following steps:
step 1: collecting data; sampling environments at a plurality of normal working moments by using two sets of equipment; the data obtained by the two sets of equipment are processed in the same way as follows, and the operation performed by one set of equipment data will be described below, and will not be described repeatedly hereinafter.
Step 2: preprocessing data; the measured data in the industrial process do not necessarily satisfy the non-negative condition, for example, the readings of the sensors such as temperature, pressure, etc. may be negative, and the unit can be adjusted to make the values non-negative; then preprocessing the data collected in the step 1 such as graying, vectorization, normalization and the like to obtain a training set X', and initially training a sample matrix
Figure BDA0003404694610000021
The matrix (k samples in the matrix) is composed of data samples in a certain time period in the training set X ', and the data samples at the later moment in the training set X' are sequentially used as the next new sample.
Initial training matrix
Figure BDA0003404694610000022
Wherein t is0≤ti≤…≤ti+k-1≤t1Is an initial period t0～t1Collected data;
Figure BDA0003404694610000023
represents data acquired by the ith device in the jth sample at tiCollected all the time; for example, i-1 indicates that the device is a camera, i-2 indicates that the device is a gas sensor,
Figure BDA0003404694610000024
data representing the gas sensor in a third sample at ti+2Time samplingCollected;
Figure BDA0003404694610000025
represents the first data sample, is at tiCollected all the time;
Figure BDA0003404694610000026
represents the (k + 1) th data sample, is at t2The samples are collected at the moment and are also used as the next newly added sample, and the rest is analogized by the newly added sample.
And step 3: a training stage; the initial training sample matrix obtained in the step 2
Figure BDA0003404694610000027
Carrying out graph regular increment non-negative matrix decomposition on the newly added samples to obtain an optimal base matrix WnewSum coefficient matrix Hnew(ii) a Therefore, the geometrical structure information of the sample can be kept in a low-dimensional space, the decomposition result of the previous step can be fully utilized by combining incremental learning, repeated calculation is avoided, and the operation time is reduced. The method comprises the following specific steps:
step 3.1: firstly, an initial training sample matrix is
Figure BDA0003404694610000028
SVD is carried out to obtain a singular value matrix sigma and a singular vector matrix U, V, and the singular value matrix and the singular vector matrix are respectively used for pairing
Figure BDA0003404694610000029
Initializing a base matrix and a coefficient matrix in the regular non-negative matrix decomposition of the graph, and updating and iterating the initialized base matrix and coefficient matrix until a target function tends to be stable to obtain Wk,Hk(ii) a Therefore, a better global optimal solution effect can be obtained, the input matrix does not need to be changed in any data structure, the data structure of the original data cannot be damaged, more detailed information can be reserved, and the decomposition effect is improved.
SVD decomposition formula:
Figure BDA0003404694610000031
Wk,Hkinitialization:
Figure BDA0003404694610000032
where | U | represents taking the absolute value of the matrix U, VΤRepresenting a transpose of the matrix V.
An objective function:
Figure BDA0003404694610000033
an iteration rule:
Figure BDA0003404694610000034
wherein R represents a weight matrix and D is a diagonal matrix
Figure BDA0003404694610000035
LkIs a Laplace matrix (L)kD-R), λ is a regularization parameter.
Step 3.2: when a new sample is added at the next moment, the optimal base matrix W is obtained by adopting the regular incremental nonnegative matrix decomposition of the graphnewSum coefficient matrix Hnew；
For example when
Figure BDA0003404694610000036
The objective function at the time of addition was:
Figure BDA0003404694610000037
iterative formula
Figure BDA0003404694610000038
Step 3.3: repeating the above operations on all newly added samples, and obtaining the optimal base matrix W after the updating is finishednewSum coefficient matrix Hnew。
And 4, step 4: calculating a control limit; w from step 3new、HnewCalculating a monitoring statistic N2And SPE, N2In order to monitor the change of the characteristic space, SPE is used for monitoring the change of the residual error space; then, probability density estimation is carried out on the process data by adopting a kernel density estimation (KED) method, and actual distribution information of the data is extracted, so that statistic control limits corresponding to two sets of equipment training samples are determined
Figure BDA0003404694610000041
SPE1'；
Figure BDA0003404694610000042
SPE'2。
And 5: a testing stage; the data is collected again (as a test set X ') for detection, the same processing is carried out on the test set X' at S2 and S3, corresponding statistic is obtained, and the monitoring statistic is compared with two sets of control limits, wherein the following three conditions can be adopted:
the first condition is as follows: when the two statistics are within the two sets of control limits, the scene is normal, and mining can be carried out.
Case two: when any one or two statistics are outside the two sets of control limits, the situation is necessarily abnormal, a first-level alarm is immediately carried out, and mining cannot be carried out; and calculating and sequencing the contribution values, and uploading the largest or larger contribution values to a control interface as the abnormality reasons for displaying.
Case three: and when any one or two statistics are only outside one set of control limit, further confirming whether the equipment is in failure or not, if not, immediately carrying out secondary alarm, calculating contribution values and sequencing, and uploading the largest or larger contribution values to a control interface as abnormal reasons for display.
The invention has the beneficial effects that: the invention avoids the defects of the traditional detection, can carry out dynamic training, real-time tracking and prediction and ensures the timeliness; various factors such as toxic gas, water burst, mine collapse and the like can be comprehensively considered, and the data of the plurality of sensors are comprehensively analyzed to ensure the accuracy; if the scene is abnormal, the source of the scene abnormality can be found; and effective utilization of network resources can be ensured, and efficiency is improved.
The invention has the beneficial effects that: the method for carrying out abnormity detection by using the graph regular increment non-negative matrix decomposition can overcome the difficulty of the traditional non-negative matrix decomposition in the online processing of a large data set, can online represent the data content and maintain the geometric structure of the data, and simultaneously obviously reduces the dimension, reduces the operation time and has better clustering precision; based on the operation of the point elements by the matrix points, the decomposition result of the graph regular increment non-negative matrix method is more likely to represent the local characteristics of the data; meanwhile, the nonnegative basis vectors obtained by the regular incremental nonnegative matrix decomposition of the graph have certain linear independence and sparsity, and large-scale high-dimensional data generated in an industrial process can be better described; in addition, the graph regular increment non-negative matrix method does not make a special ideal assumption on data distribution in the decomposition process, so that non-Gaussian data can be processed, and only a proper monitoring statistic needs to be designed by combining a density estimation method.
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Fig. 1 is a general flowchart of a mining scene anomaly detection method based on graph regular incremental nonnegative matrix factorization according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of mining environment data collection provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of data structure provided by an embodiment of the present invention;
FIG. 4 is a specific flowchart of regular incremental non-negative matrix decomposition of a graph according to an embodiment of the present invention;
Detailed Description
The invention will be further described with reference to the accompanying drawings and specific embodiments.
The invention aims to provide a mining scene abnormity detection method based on graph regular increment nonnegative matrix decomposition, which can track and predict the safety of a mining scene in real time, achieve the effect of reducing the safety risk of mining personnel, feed back the reason of abnormity when the environment is abnormal, ensure the effective utilization of network resources and improve the efficiency.
As shown in fig. 1, a general flowchart of a mining scene anomaly detection method based on graph regular incremental non-negative matrix factorization is provided in the embodiment of the present invention. The method comprises the following steps:
step 1: collecting data; two sets of equipment are used (each set of equipment comprises a camera and a plurality of sensors (a gas sensor and a CO sensor)2Sensors, etc.) in a single quantity) samples the environment at multiple times of normal operation.
Fig. 2 is a schematic diagram of mining environment data acquisition. And data acquired by the two sets of equipment are transmitted to the nodes and then uploaded to the cloud server for processing. The equipment is not limited to the above three types and may be tailored to the particular mining environment.
The data obtained by the two sets of equipment are respectively processed in the following way, and the operation performed by the data collected by the set of equipment will be described below, and is respectively described if necessary, and the repeated description is not repeated hereinafter.
Step 2: preprocessing data; carrying out graying, vectorization, normalization and other processing on the data acquired in the step 1 to obtain a training set X', and obtaining an initial training sample matrix
Figure BDA0003404694610000051
The matrix (k samples in the matrix) is composed of data samples in a certain time period in the training set X ', and the data samples at the later moment in the training set X' are sequentially used as the next new sample.
Fig. 3 is a schematic diagram of data composition. Preprocessing the acquired data, graying the image data acquired by the camera to obtain an image matrix V belonging to a multiplied by b.
Vectorizing the obtained image matrix V, first extracting each column and recombining into a column vector as shown in FIG. 3
Figure BDA0003404694610000061
Then, the data collected by other sensors in a corresponding set of equipment are combined into a column vector in sequence as shown in FIG. 3
Figure BDA0003404694610000062
Finally, the set of equipment is put at tiThe data collected at the moment are normalized to be between 0 and 1 and combined into a column vector
Figure BDA0003404694610000063
Indicating the first sample data, is at tiAcquired at a moment of time, wherein
Figure BDA0003404694610000064
Data representing the second plant gas sensor in the first sample, also at tiAnd (4) acquiring at any moment.
Continuously collecting data at multiple moments, performing repeated processing according to the above manner, and finally obtaining an initial training sample matrix
Figure BDA0003404694610000065
The data collected at different later times are sequentially used as the added samples.
In this embodiment, 200 groups of normal data samples are collected within 5 hours as samples in an initial training sample matrix, 40 groups are collected every hour, 10 groups are collected every hour after 5 hours as added samples, and 100 groups are added as newly added samples.
And step 3: a training stage; the initial training sample matrix obtained in the step 2
Figure BDA0003404694610000066
Carrying out graph regular increment non-negative matrix decomposition on the newly added samples to obtain an optimal base matrix WnewSum coefficient matrix Hnew。
As shown in fig. 4, a specific process of regular incremental non-negative matrix factorization is provided for the graph according to the embodiment of the present invention. Firstly, an initial training sample matrix is
Figure BDA0003404694610000067
SVD is performed to obtain a singular value matrix sigma and a singular vector matrix U, V.
SVD decomposition:
Figure BDA0003404694610000068
using singular value matrix and singular vector matrix respectively
Figure BDA0003404694610000069
And initializing a base matrix and a coefficient matrix in the regular non-negative matrix decomposition of the graph.
Wk,HkInitialization:
Figure BDA00034046946100000610
where | U | represents taking the absolute value of the matrix U, VΤIs the transpose of matrix V.
Updating and iterating the initialized base matrix and coefficient matrix until the objective function tends to be stable to obtain Wk,Hk。
An objective function:
Figure BDA0003404694610000071
an iteration rule:
Figure BDA0003404694610000072
wherein R represents a weight matrix and D is a diagonal matrix
Figure BDA0003404694610000073
LkIs a Laplace matrix (L)kD-R), λ is a regularization parameter.
Sequentially increasing samples
Figure BDA0003404694610000074
Performing graph regular increment non-negative matrix decomposition once every adding one sample, and iteratively updating until the target function meets the convergence condition to obtain the optimal base matrix and coefficient matrix Wnew,Hnew。
For example when
Figure BDA0003404694610000075
The objective function at the time of addition was:
Figure BDA0003404694610000076
Wk+1and Hk+1Respectively representing sample sets Xk+1Base matrix and coefficient matrix L obtained by performing regular nonnegative matrix decomposition on graphk+1Is a sample set Xk+1The laplacian matrix of. When the number of training samples is large enough, the influence of adding a new training sample on the base matrix and the coefficient matrix is small, so that it is assumed that the coefficient matrix H is added when a new sample is addedk+1Is approximately equal to HkColumn vector of (i.e. H)k+1＝[Hk,hk+1]At this time, the objective function Fk+1Rewritable in the form:
Figure BDA0003404694610000077
obtain an objective function Fk+1After the incremental expression is obtained, a corresponding iterative updating formula can be deduced by using a gradient descent method:
Figure BDA0003404694610000081
repeating the above operations on all newly added samples, and obtaining the optimal base matrix W after the updating is finishednewSum coefficient matrix Hnew。
And 4, step 4: calculating a control limit; obtaining corresponding W from two sets of training setsnew、HnewCalculating a corresponding monitoring statistic N2And SPE.
Statistic N for monitoring feature spatial variation2：N2(i)＝XΤ(i)WWΤX(i)。
Statistic SPE for degree of deviation of reaction data:
Figure BDA0003404694610000082
wherein
Figure BDA0003404694610000083
The reconstructed value representing the ith sample vector is calculated as:
Figure BDA0003404694610000084
the control limits for two statistics are calculated: probability density estimation is carried out on the two statistics by using a nuclear density estimation method, the actual distribution condition of the statistics is extracted, and the control limits of the statistics of the two sets of equipment training samples are respectively calculated by setting the significance level alpha
Figure BDA0003404694610000085
SPE1'；
Figure BDA0003404694610000086
SPE2'。
And 5: a testing stage; the data is collected again (as a test set X ') for detection, the same processing as the step 2 and the step 3 is carried out on the test set X', and the corresponding statistic N is obtained2And SPE, comparing the monitoring statistic with two sets of control limits.
When in use
Figure BDA0003404694610000087
When the two statistics are within the control limit trained by the two equipment training sets, the scene is normal, and mining can be performed.
When in use
Figure BDA0003404694610000088
When any one or two statistics are beyond the control limit trained by the two sets of equipment training sets, the situation is necessarily abnormal, a first-level alarm is immediately carried out, and mining cannot be carried out; calculating and sequencing the contribution values, uploading several maximum or larger contribution values serving as abnormal reasons to a control interface to be displayedShown in the figure.
Calculating the contribution value
Figure BDA0003404694610000091
The subscript j represents the label of the variable, abs indicates the absolute value; and deltajIs the jth column of the n × n identity matrix; suppose there are four devices, δ for the 2 nd device (variable) gas sensor2＝[0 1 0 0]Τ。
And when any one or two statistics are only larger than the control limit trained by one set of equipment training set, further confirming whether the equipment is in failure or not, if not, immediately carrying out secondary alarm, calculating contribution values and sequencing, and uploading the largest or larger contribution values serving as abnormal reasons to a control interface for display.

Claims (4)

1. The mining scene abnormity detection method based on graph regular increment nonnegative matrix decomposition is characterized by comprising the following steps of:
s1: collecting data; two sets of equipment are used (each set of equipment comprises a camera and a plurality of sensors (a gas sensor and a CO sensor)2Sensors, etc.), the number of which is one) samples the environment at a plurality of normal working moments; the data obtained by the two sets of equipment are processed in the same way as follows, and the operation performed by one set of equipment data will be described below, and will not be described repeatedly hereinafter.
S2: preprocessing data; preprocessing the data collected in S1 such as graying, vectorization, normalization and the like to obtain a training set X', and acquiring an initial training sample matrix
Figure FDA0003404694600000011
The matrix (k samples in the matrix) is composed of data samples in a certain time period in the training set X ', and the data samples at the later moment in the training set X' are sequentially used as the next new sample.
S3: a training stage; using the initial training sample matrix obtained in S2
Figure FDA0003404694600000012
Carrying out graph regular increment non-negative matrix decomposition on the newly added samples to obtain an optimal base matrix WnewSum coefficient matrix Hnew(ii) a In which use is made of
Figure FDA0003404694600000013
The singular value matrix and the singular vector matrix obtained by SVD are respectively paired
Figure FDA0003404694600000014
Initializing a base matrix and a coefficient matrix in the regular nonnegative matrix decomposition of the graph; therefore, the data structure of the original data cannot be damaged, the geometric structure information of the sample can be kept in a low-dimensional space, the decomposition result of the previous step can be fully utilized by combining incremental learning, repeated calculation is avoided, and the operation time is shortened.
S4: calculating a control limit; w from S3new、HnewCalculating a monitoring statistic N2And SPE, N2In order to monitor the change of the characteristic space, SPE is used for monitoring the change of the residual error space; then, probability density estimation is carried out on the process data by adopting a kernel density estimation (KED) method, and actual distribution information of the data is extracted, so that the statistic control limit corresponding to each set of equipment training sample is determined
Figure FDA0003404694600000015
SPE′1；
Figure FDA0003404694600000016
SPE′2。
S5: a testing stage; re-collecting data (as a test set X ") for detection, performing the same processing of S2 and S3 on the test set X', calculating corresponding statistic, and comparing the monitoring statistic with two sets of control limits; if the statistic is within the control limit of the two sets of equipment training sets, the normal state is represented; if any one or two statistic values are outside the control limits of the two sets of equipment training sets, indicating an abnormal state, and immediately carrying out primary alarm; if any one or two statistics are only outside the control limit of one set of equipment training set, further checking whether the statistics are abnormal equipment, if not, judging that the statistics are abnormal environment, and performing secondary alarm; and when the scene is abnormal, calculating the contribution values, sequencing the contribution values, and uploading the largest or larger contribution values serving as abnormal reasons to a control interface for displaying.
2. The graph-canonical increment non-negative matrix factorization-based mining scene anomaly detection method according to claim 1, wherein: the specific parameters of the data sample in step S2 are as follows:
initial training sample matrix
Figure FDA0003404694600000021
Wherein t is0≤ti≤…≤ti+k-1≤t1Is an initial period t0～t1Collected data;
Figure FDA0003404694600000022
represents data acquired by the ith device in the jth sample at tiCollected all the time; for example, i-1 indicates that the device is a camera, i-2 indicates that the device is a gas sensor,
Figure FDA0003404694600000023
data representing the gas sensor in a third sample at ti+2Collected all the time;
Figure FDA0003404694600000024
represents the first data sample, is at tiCollected all the time;
Figure FDA0003404694600000025
represents the (k + 1) th data sample, is at t2The samples are collected at the moment and are also used as the next newly added sample, and the rest is analogized by the newly added sample.
3. The graph-canonical increment non-negative matrix factorization-based mining scene anomaly detection method according to claim 1, wherein: the training phase in step S3 includes the following steps:
s3.1: firstly, an initial training sample matrix is
Figure FDA0003404694600000026
SVD is carried out to obtain a singular value matrix sigma and a singular vector matrix U, V, and the singular value matrix and the singular vector matrix are respectively used for pairing
Figure FDA0003404694600000027
Initializing a base matrix and a coefficient matrix in the regular non-negative matrix decomposition of the graph, and updating and iterating the initialized base matrix and coefficient matrix until a target function tends to be stable to obtain Wk,Hk(ii) a Therefore, a better global optimal solution effect can be obtained, any data structure change processing is not needed for the input matrix, the data structure of the original data is not damaged, more detailed information can be reserved, and the decomposition effect is improved.
SVD decomposition formula:
Figure FDA0003404694600000028
Wk,Hkinitialization:
Figure FDA0003404694600000031
an objective function:
Figure FDA0003404694600000032
an iteration rule:
Figure FDA0003404694600000033
note: wherein R represents a weight matrix and D is a diagonal matrix
Figure FDA0003404694600000034
LkIs a Laplace matrix (L)kD-R), λ is a regularization parameter.
S3.2: and when a new sample at the next moment is added, obtaining the optimal base matrix and coefficient matrix by adopting regular incremental nonnegative matrix decomposition of the graph.
For example when
Figure FDA0003404694600000035
The objective function at the time of addition was:
Figure FDA0003404694600000036
iteration rule
Figure FDA0003404694600000037
S3.3: repeating the above operations on all newly added samples, and obtaining the optimal base matrix W after the updating is finishednewSum coefficient matrix Hnew。
4. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer instructions for causing a computer to perform the mining scenario anomaly detection method of any one of claims 1-3.
CN202111509423.2A 2021-12-10 2021-12-10 Mining scene abnormity detection method based on graph regular increment nonnegative matrix decomposition Pending CN114238854A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117576568A (en) * 2023-12-08 2024-02-20 成都理工大学 Depth robust non-negative matrix factorization method based on incremental learning

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117576568A (en) * 2023-12-08 2024-02-20 成都理工大学 Depth robust non-negative matrix factorization method based on incremental learning
CN117576568B (en) * 2023-12-08 2024-07-05 成都理工大学 Depth robust non-negative matrix factorization method based on incremental learning

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