CN113610167B - Equipment risk detection method based on metric learning and visual perception - Google Patents

Equipment risk detection method based on metric learning and visual perception Download PDF

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CN113610167B
CN113610167B CN202110915531.3A CN202110915531A CN113610167B CN 113610167 B CN113610167 B CN 113610167B CN 202110915531 A CN202110915531 A CN 202110915531A CN 113610167 B CN113610167 B CN 113610167B
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葛众望
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Suqian Wangchun Machinery Manufacturing Co ltd
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Abstract

The invention relates to the technical field of metric learning and visual perception, in particular to an equipment risk detection method based on metric learning and visual perception. Inputting a first image of equipment to be tested and a risk monitoring index thereof into a corresponding equipment risk detection network to obtain a corresponding risk prediction result; the loss function of the equipment risk detection network is the sum of a cross entropy loss function and a mean square error loss function of the associated total variation, wherein the associated total variation is the similarity of the probabilities of the predicted risk levels of a plurality of pieces of equipment in the equipment images acquired at the same time period in different data; the similarity is a weighted sum of probability ratios based on the predicted risk level between any two devices, the predicted risk probability, the risk fluctuation index, and the difference in risk level between the two devices. According to the invention, the risk prediction result of the equipment can be obtained in real time by utilizing the equipment image and the risk monitoring index to carry out network training, and the real-time performance and the accuracy of the equipment risk detection are improved.

Description

Equipment risk detection method based on metric learning and visual perception
Technical Field
The invention relates to the technical field of metric learning, in particular to an equipment risk detection method based on metric learning and visual perception.
Background
At present, the economic level development is in the process of advancing from the middle-low end to the high end, but the safety cognition level and the safety concept at the enterprise level are relatively lagged behind, so that the safety management level is still at the bottom level, the accident prevention is still at the passive stage, and most of enterprises still belong to extensive safety management. Therefore, the detection of the equipment risk is enhanced, a prevention mechanism is adopted in advance, and a risk classification implementation management and control system is constructed.
At present, the risk detection method usually adopts artificial risk investigation to find out the equipment risk. The risk detection method is low in accuracy, and mutual influence and mutual correlation among the devices are not considered.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a method for device risk detection based on metric learning and visual perception, which adopts the following technical solutions:
one embodiment of the invention provides a device risk detection method based on metric learning and visual perception, which comprises the following steps:
inputting a first image of equipment to be tested and a risk monitoring index thereof into a corresponding equipment risk detection network to obtain a corresponding risk prediction result;
the loss function of the equipment risk detection network is the sum of a cross entropy loss function and a mean square error loss function of the associated total variation, and the associated total variation is the difference value of the real-time associated total variation and the standard associated total variation; the standard association total variation is similarity according to probabilities of predicted risk levels of a plurality of devices in device images acquired at the same time period in historical data; the real-time correlation total variation is similarity according to probabilities of predicted risk levels of a plurality of devices in device images acquired at the same time period in real-time data; the similarity is a weighted sum of probability ratios of predicted risk levels between any two of the devices, and the probability of a predicted risk level is obtained from the predicted risk level of a device, the predicted risk probability, a risk fluctuation index, and a difference in risk level between the two devices.
Preferably, the method for acquiring a risk fluctuation index includes:
acquiring a risk level sequence corresponding to an equipment image of the equipment, and adopting Gaussian background modeling to acquire mixed Gaussian distribution and a mixed Gaussian distribution curve corresponding to the risk level sequence, wherein the abscissa of the mixed Gaussian distribution curve is a risk level, and the ordinate of the mixed Gaussian distribution curve is a risk probability;
averaging a plurality of mixed Gaussian distribution curves to obtain a final Gaussian distribution curve; and obtaining the risk fluctuation index of the equipment from the final Gaussian distribution curve.
Preferably, the method for acquiring the difference of the risk levels between the two devices includes:
calculating a distance value between the final Gaussian distribution curves corresponding to any two devices; the distance value is taken as the difference in risk level between the two devices.
Preferably, the obtaining the risk fluctuation index of the equipment from the final gaussian distribution comprises:
the risk fluctuation index calculation formula is as follows:
Figure BDA0003205383520000021
wherein, BiThe risk fluctuation index for the ith said device; simiGaussian distribution similarity for the ith of the devicesAnd (4) degree.
Preferably, the method for obtaining the gaussian distribution similarity of the ith device includes:
the calculation formula of the gaussian distribution similarity of the ith device is as follows:
Figure BDA0003205383520000022
wherein f isi(ii) is the final gaussian distribution for the ith said device; argmax (f)i) Is fiTaking the corresponding risk grade when the maximum value is obtained; n (argmax (f)i) 1) taking argmax (f) as the mean valuei) Normal distribution of (c).
Preferably, the standard associated total variation is similarity according to probabilities of predicted risk levels of a plurality of devices in device images acquired in the same period in historical data, and includes:
the calculation formula of the standard correlation total variation is as follows:
Figure BDA0003205383520000023
wherein L is0Correlating the total variation for the criteria; e is the set of all the devices; i, j belongs to the ith device and the jth device of the E set; w is ai，jA difference in the risk level for an ith device and a jth device; giAttention of the ith device; g is a radical of formulajAttention of the jth device; fiA risk probability sequence corresponding to a final Gaussian distribution function of the historical data of the ith device; fjA risk probability sequence corresponding to a final Gaussian distribution function of the history data of the jth device; max (F)i) The maximum risk probability in the risk probability sequence of the ith device; argmax (F)i) A risk level corresponding to the maximum risk probability on an ith device; max (F)j) The maximum risk probability in the risk probability sequence of the jth device; argmax (F)j) For the maximum risk profile on the jth deviceRate versus risk level.
Preferably, the method for acquiring the attention includes:
the calculation formula of the attention degree is as follows:
Figure BDA0003205383520000031
wherein, giThe attention of the ith said device; biThe risk fluctuation indicator for the ith said device; b isjThe risk fluctuation index for the jth of the devices; num is the number of all the devices.
Preferably, the real-time correlation total variation is a similarity of probabilities of predicted risk levels of a plurality of devices in a device image acquired at the same time interval in real-time data, and includes:
the calculation formula of the real-time total variation of the correlation diagram is as follows:
Figure BDA0003205383520000032
wherein L isuCorrelating the total variation for the criteria; e is the set of all the devices; i, j belongs to the ith device and the jth device of the E set; w is ai，jA difference in the risk level for an ith device and a jth device; giAttention of the ith device; gjAttention of the jth device; ziA real-time risk probability sequence corresponding to the real-time data of the ith device; z is a linear or branched memberjA real-time risk probability sequence corresponding to the real-time data of the jth device; max (Z)i) A real-time maximum risk probability in the real-time risk probability sequence for the ith device; argmax (Z)i) A real-time risk level corresponding to the real-time maximum risk probability on an ith device; max (Z)j) The real-time maximum risk probability in the real-time risk probability sequence of the jth device; argmax (Z)j) And the real-time risk level corresponding to the real-time maximum risk probability on the jth device.
Preferably, the loss function of the equipment risk detection network is the sum of a cross entropy loss function and a mean square error loss function of the associated total variation, and includes:
the construction formula of the loss function is as follows:
Figure BDA0003205383520000033
wherein M is the number of training data in a training batch of the equipment;
Figure BDA0003205383520000034
predicting risk probability that the u-th training data output by the risk detection network corresponding to the kth equipment belong to the class c risk level;
Figure BDA0003205383520000035
belonging to the class c risk level for the u training data in the kth device; l isuInputting the association graphs obtained by the device risk detection networks into the u training data of the kth device and the training data of other devices at the same position as the u training data to obtain real-time total variation of the association graphs; l is0The standard total variation is the correlation diagram.
The invention has the following beneficial effects:
according to the embodiment of the invention, based on metric learning and visual perception, various indexes such as risk fluctuation indexes, attention degrees, difference of risk levels, Gaussian mixture distribution, risk probability corresponding to the risk levels and the like are obtained according to an equipment image sequence, a risk monitoring index sequence and the corresponding risk levels, loss functions are constructed by the various indexes to train an equipment risk detection network, the equipment risk levels are predicted through the trained risk detection network, and artificial equipment risk level evaluation is not required; when the loss function is constructed, on one hand, the correlation degree between the devices is used as the difference of the risk levels between the nodes, and on the other hand, the accuracy of the risk level prediction of the devices to be tested is further improved through the influence of other devices on the devices to be tested; finally, the equipment image sequence and the risk monitoring index sequence are input into the equipment risk detection network to obtain the risk level of the equipment, artificial equipment risk level evaluation in fixed time is not needed, meanwhile, the fact that relevant relevance exists among the equipment in the same production scene is also considered, and accuracy and real-time performance of risk level prediction are further improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for detecting a device risk based on metric learning and visual perception according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a case of multiple training data packets according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for training a device risk detection network according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, structures, features and effects of an apparatus risk detection method based on metric learning and visual perception according to the present invention are provided with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment of the invention provides a specific implementation method of an equipment risk detection method based on metric learning and visual perception, the method is suitable for risk detection of formaldehyde filtering equipment in a chemical plant, a camera in the embodiment is a monitoring RGB camera, and the camera acquires equipment images in a neighborhood range of equipment to be detected; and a plurality of gas sensors are arranged around the equipment to be tested, and image information and gas sensor reading information of the equipment to be tested are acquired in real time. According to the embodiment of the invention, the network training is carried out by constructing the loss function, the risk grade of the equipment to be tested is obtained through the trained equipment risk detection network, the equipment does not need to be artificially labeled with the risk grade, the relevance among the equipment is considered, and the risk grade obtained by the equipment risk detection network is more accurate than the artificially evaluated risk grade. The method and the device achieve the purposes of obtaining the risk level of the equipment in real time and improving the accuracy and the real-time performance of risk level prediction through the relevance between the equipment.
The following describes a specific scheme of the equipment risk detection method based on metric learning and visual perception in detail with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for detecting a device risk based on metric learning and visual perception according to an embodiment of the present invention is shown,
and S100, inputting the first image of the equipment to be tested and the risk monitoring index thereof into the corresponding equipment risk detection network to obtain a corresponding risk prediction result.
The method comprises the steps of collecting RGB images of equipment to be detected by using an RGB monitoring camera, and extracting neighborhood images of the equipment to be detected in the RGB images to be used as first images. In the embodiment of the invention, a target monitoring network is utilized to obtain a central point coordinate and a width and height dimension of an enclosure frame of a device to be detected, wherein the central coordinate of the enclosure frame is (x, y), the width of the enclosure frame is w, and the height of the enclosure frame is h, the enclosure frame information is formed by the central coordinate and the width and height dimension, the device to be detected is arranged in the enclosure frame, and the enclosure frame is utilized to cut the RGB image to obtain a neighborhood image of the device to be detected as a first image.
As another embodiment, for the acquisition of the first image, an image of the device to be detected may be obtained by using edge detection, and then a neighborhood image of the device to be detected is obtained as the first image by performing a difference between the image of the device to be detected and the original RGB image.
And obtaining position information according to the coordinates of the central point of the surrounding frame of the equipment to be tested, determining the sensors deployed around the equipment to be tested, collecting all sensor data, and taking the sensor data as a risk monitoring index.
In an actual production scenario of a chemical plant, an administrator of an enterprise will perform maintenance on equipment at regular intervals of time t, and reset the safety risk of the equipment after the maintenance to 0. In order to facilitate the analysis of the risk fluctuation index of the subsequent equipment, a group of data segments are obtained when the acquisition time length is t during data acquisition.
In each group of data segments with the time length t, each elapsed time t0Obtaining a set of training data, that is, each set of data segment includes multiple sets of training data, each having a length of t0The training data of (2) comprises a group of first image sequences and risk monitoring index sequences thereof, and the groups of training data are grouped as shown in fig. 2.
Wherein, will be at t0And performing a concatemate operation on each frame of first image acquired in time to obtain a first image sequence. Will be at t0And (4) carrying out the concatenate operation on the risk monitoring indexes acquired by time to obtain a risk monitoring index sequence.
And inputting the first image sequence and the risk monitoring index sequence into a corresponding equipment risk detection network to obtain a corresponding risk prediction result. The risk prediction result is the corresponding risk grade and risk probability of the device to be tested. In an embodiment of the present invention, the risk classes are divided into 5 classes. The higher the grade is, the higher the risk of the device to be tested is, the more dangerous the device to be tested is, and the lower the grade is, the lower the risk of the device to be tested is, and the safer the device to be tested is.
Step S200, a loss function of the equipment risk detection network is the sum of a cross entropy loss function and a mean square error loss function of the associated total variation, and the associated total variation is the difference value of the real-time associated total variation and the standard associated total variation; the standard association total variation is similarity of probabilities of predicting risk levels of a plurality of devices in device images acquired at the same time period in historical data; the real-time correlation total variation is the similarity of the probabilities of the predicted risk levels of the plurality of devices in the device image acquired at the same time period in the real-time data; the similarity is a weighted sum of probability ratios of predicted risk levels between any two devices, and the probability of a predicted risk level is obtained from the predicted risk level of a device, the predicted risk probability, a risk fluctuation index, and the difference in risk levels between the two devices.
The equipment risk detection network training mainly comprises the following steps:
step S201, a risk fluctuation index obtaining manner.
And analyzing the training data of each device to obtain the risk fluctuation index of each device. The risk fluctuation index reflects the probability of the equipment risk level changing, and the risk fluctuation index of the equipment participates in the design of a subsequent loss function, so that the equipment risk detection network is more sensitive to equipment with a larger risk fluctuation index, and the accuracy of risk detection is improved.
Optionally selecting a device i as an example, and detailing the risk fluctuation index B of the device iiThe calculating method of (2):
(1) obtaining training data of a device i, for each data segment t1，t2，...，tnEach of length t0Training data of (2) is artificially labeled with risk level for each training data, i.e. each training data has a length of t0The equipment image sequence and the risk monitoring index sequence in the time period correspond to a risk level label.
Data segment t1Of multiple lengths t0The risk grades corresponding to the time period are arranged according to the sequence of time acquisition to obtain a data segment t1Corresponding risk rank sequence, denoted D1＝{d1，d2，d3，...，dm}. Wherein m is a data segment t1Number of risk classes involved, i.e. m-t1/t0。
(2) Adopting a mixed Gaussian background modeling mode according to the risk grade sequence D1Constructing a data segment t1Corresponding mixed Gaussian distribution f1And a Gaussian mixture profile.
1) First of all with the risk level d1Obtaining an initial mixed gaussian distribution:
Figure BDA0003205383520000061
wherein,
Figure BDA0003205383520000062
is a risk class d1The weight of (c);
Figure BDA0003205383520000063
as a risk class d1A corresponding gaussian distribution;
Figure BDA0003205383520000064
is a risk class d1Standard deviation of the data of (a); x is the current corresponding risk level;
Figure BDA0003205383520000071
is a risk class d1Mean of the data of (1).
2) Grade the risk of d2And risk class d1Corresponding Gaussian distribution
Figure BDA0003205383520000072
And (6) matching. Judgment of
Figure BDA0003205383520000073
Figure BDA0003205383520000074
If yes, the risk level d is indicated2And Gaussian distribution
Figure BDA0003205383520000075
If the matching is successful, the risk grade d is used2For the Gaussian distribution of successful matching
Figure BDA0003205383520000076
Updating is carried out; the updated formula is as follows:
Figure BDA0003205383520000077
Figure BDA0003205383520000078
wherein,
Figure BDA0003205383520000079
for the risk level d after updating1The data mean of (2);
Figure BDA00032053835200000710
for the risk level d after updating1Standard deviation of the data of (a); ρ is a forgetting coefficient, i.e. the risk level d2Substituting gaussian distribution
Figure BDA00032053835200000711
Resulting updated Gaussian distribution
Figure BDA00032053835200000712
A value of (i), i.e
Figure BDA00032053835200000713
Then by the updated data mean
Figure BDA00032053835200000714
And standard deviation of data
Figure BDA00032053835200000715
For Gaussian distribution
Figure BDA00032053835200000716
Updating to obtain the updated Gaussian distribution
Figure BDA00032053835200000717
The Gaussian mixture distribution at this time is
Figure BDA00032053835200000718
3) If not, the risk grade d is indicated2And Gaussian distribution
Figure BDA00032053835200000719
If the matching fails, a new Gaussian distribution is obtained
Figure BDA00032053835200000720
Figure BDA00032053835200000721
Wherein,
Figure BDA00032053835200000722
is a risk class d2Standard deviation of the data of (a); x is the current corresponding risk level;
Figure BDA00032053835200000723
is a risk class d2Mean of the data of (1).
4) For multiple Gaussian distributions in mixed Gaussian distribution
Figure BDA00032053835200000724
Weights are assigned to obtain the final mixture gaussian distribution. The weight distribution method is as follows:
Figure BDA00032053835200000725
wherein,
Figure BDA00032053835200000726
representing a Gaussian distribution
Figure BDA00032053835200000727
Updating the previous weight;
Figure BDA00032053835200000728
representing a Gaussian distribution
Figure BDA00032053835200000729
Updating the previous weights due to previous Gaussian distribution
Figure BDA00032053835200000730
Without weight, then this time
Figure BDA00032053835200000731
Is 0;
Figure BDA00032053835200000732
indicates a risk rating d2And Gaussian distribution
Figure BDA00032053835200000733
The matching result of (1);
Figure BDA00032053835200000734
denotes d2And Gaussian distribution
Figure BDA00032053835200000735
The matching result of (1); and if the matching is successful, the matching result value is 1, and if the matching is failed, the matching result value is 0.
Finally, the weight is weighted
Figure BDA0003205383520000081
And weight
Figure BDA0003205383520000082
Normalizing to obtain final weight
Figure BDA0003205383520000083
The Gaussian mixture distribution at this time is
Figure BDA0003205383520000084
5) Rank the risk d3Matching each Gaussian distribution in the mixed Gaussian distributions according to the step 2). E.g. if the risk rating d2And Gaussian distribution
Figure BDA0003205383520000085
The matching is successful to obtain the updated Gaussian distribution
Figure BDA0003205383520000086
Then the risk level d3And Gaussian distribution
Figure BDA0003205383520000087
Matching, if matching is successful, the Gaussian distribution is conducted again
Figure BDA0003205383520000088
Updating to obtain updated Gaussian distribution
Figure BDA0003205383520000089
The mixture gaussian distribution at this time
Figure BDA00032053835200000810
If the risk grade d of the previous step2And Gaussian distribution
Figure BDA00032053835200000811
The matching is not successful, and a new Gaussian distribution is obtained
Figure BDA00032053835200000812
Then the risk level d3Respectively with a Gaussian distribution
Figure BDA00032053835200000813
And Gaussian distribution
Figure BDA00032053835200000814
Matching is performed and steps 2) -4) are repeated again.
If the risk class d3If the matching with each Gaussian distribution in the mixed Gaussian distribution fails, a new Gaussian distribution is obtained
Figure BDA00032053835200000815
Finally, weights are distributed to the Gaussian distributions to obtain a new mixed Gaussian distribution f1I.e. a Gaussian distribution
Figure BDA00032053835200000816
Gaussian distribution
Figure BDA00032053835200000817
And Gaussian distribution
Figure BDA00032053835200000818
Assigning weights to obtain a new Gaussian mixture distribution f1。
Repeat traversal D1All values of (1), i.e. d1，d2，d3，...，dmTo obtain the final data segment t1Corresponding mixed Gaussian distribution f1. And obtaining a Gaussian mixture distribution curve according to the Gaussian mixture distribution, wherein the abscissa of the Gaussian mixture distribution curve is the risk level, the ordinate of the Gaussian mixture distribution curve is the risk probability, and the risk probability is the probability of the corresponding risk level.
(3) And (3) repeatedly obtaining the Gaussian mixture distribution of each data segment and the corresponding Gaussian mixture distribution curve according to the method in the step (2), and marking the Gaussian mixture distribution as f1，f2，...，fn。
In order to avoid the influence of errors in the mixed Gaussian distribution obtained by a single data segment on subsequent processing, the embodiment of the invention calculates the average value of n risk probability values corresponding to the same abscissa in n mixed Gaussian distribution curves to obtain the final Gaussian distribution curve corresponding to the device i, and the final Gaussian distribution curve is marked as fi。
(4) As known from the priori knowledge, when the risk level of the equipment does not change, namely the risk level of the equipment is stable, namely the safety degree of the equipment is stable, the final Gaussian distribution curve fiThe normal distribution should be conformed, i.e. the more similar the final gaussian distribution curve is to the normal distribution curve, the more stable the risk level, and the larger the gaussian distribution similarity value, the more stable the risk level. The idea of metric-based learning consists of a final Gaussian distribution fiObtaining the similarity Sim with the normal distributioni。
Gaussian distribution similarity Sim of ith deviceiThe calculation formula of (2) is as follows:
Figure BDA00032053835200000819
wherein f isiThe final gaussian distribution for the ith device; argmax (f)i) Is fiTaking the corresponding risk grade when the maximum value is obtained; n (argmax (f)i) 1) taking argmax (f) as the mean valuei) Normal distribution of (2) avoiding the Gaussian distribution curve fiAnd calculating the influence of translation on the similarity. It should be noted that the DTW algorithm is a known technique for calculating the similarity of curves, and is not described in detail.
Known from the prior knowledge, when the risk level of the equipment does not change, the final Gaussian distribution curve fiThe risk fluctuation index of the equipment can be measured by using the similarity with the normal distribution, namely, the closer the final Gaussian distribution curve corresponding to the equipment is to the normal distribution, the greater the similarity is, the smaller the fluctuation degree of the wind wave grade of the equipment is, and the more stable the risk grade is. And calculating the risk fluctuation index of the equipment according to the similarity obtained by the final Gaussian distribution.
Risk fluctuation index BiThe calculation formula of (2) is as follows:
Figure BDA0003205383520000091
wherein, BiRisk wave for ith deviceDynamic indexes; simiIs the gaussian distribution similarity of the ith device. It should be noted that the more stable the risk level of the equipment is, the higher the safety factor of the equipment is, and the risk fluctuation index BiThe smaller.
And step S202, carrying out equipment risk correlation analysis and constructing an equipment risk correlation diagram.
The production procedures of enterprises are fixed and unchangeable, so that certain risk relevance exists among all the devices, the risk relevance can provide auxiliary supervision information for the training of the subsequent device risk detection network, and the output accuracy of the device risk detection network is improved. And performing equipment risk correlation analysis according to the final Gaussian distribution curve of each equipment to construct an equipment risk correlation diagram, wherein the equipment risk correlation diagram can reflect the risk correlation existing between the equipment.
According to step S201, a final gaussian distribution curve for each device is obtained. And regarding each device as a node, storing the final Gaussian distribution curve corresponding to the device in each node, calculating a distance value between the final Gaussian distribution curves corresponding to any two devices by using a DTW algorithm, and taking the distance value as an edge weight value between the two devices to obtain a device risk association graph, wherein the edge weight value is also the difference of risk levels. The edge weight reflects the deviation between the gaussian distribution curves of the two devices, namely reflects the difference of the risk levels of the two devices, and each node has a connection relation with all other nodes in the device risk association diagram.
Because the risk fluctuation indexes of the devices are different, in order to ensure the safe operation of enterprise production, the device with the higher risk fluctuation index needs to be assigned with a higher attention, and the device with the higher risk fluctuation index is the device with the low risk level unstable safety degree. Therefore, the calculation of the attention of each device is obtained by fitting the risk fluctuation indexes corresponding to each device, and each device and each node correspond to one attention.
Degree of attention giThe calculation formula of (2) is as follows:
Figure BDA0003205383520000092
wherein, giAttention of the ith device; b isiA risk fluctuation index for the ith device; b isjA risk fluctuation index of the jth equipment; num is the number of all devices.
In step S203, the standard association total variation is a similarity of probabilities of predicted risk levels of the plurality of devices in the device image acquired from the same period in the history data. The similarity is a weighted sum of probability ratios of predicted risk levels between any two devices, and the probability of a predicted risk level is obtained from the predicted risk level of a device, the predicted risk probability, a risk fluctuation index, and the difference in risk levels between the two devices.
The degree of association between devices is characterized by the attention, risk level and risk probability between devices. Because the relationship between the parameters is complex, the standard association total variation between the devices is obtained by fitting the attention degree, the difference of the risk level, the risk level and the risk level probability between the devices in a mathematical modeling mode.
Wherein the training data of the device is historical training data.
The calculation method of the standard correlation total variation is as follows:
Figure BDA0003205383520000101
wherein L is0The standard correlation total variation; e is the set of all devices; i, j belongs to the ith device and the jth device of the E set; w is ai，jIs the difference in risk level for the ith device and the jth device; giAttention of the ith device; g is a radical of formulajAttention of the jth device; fiA risk probability sequence corresponding to a final Gaussian distribution function of the historical data of the ith device; fjA risk probability sequence corresponding to a final Gaussian distribution function of the history data of the jth device; max (F)i) For the ith apparatusThe maximum risk probability in the risk probability sequence; argmax (F)i) A risk level corresponding to the maximum risk probability on the ith device; max (F)j) The maximum risk probability in the risk probability sequence of the jth device; argmax (F)j) The risk level corresponding to the maximum risk probability on the jth device.
It should be noted that i and j respectively represent the numbers of the nodes of the device, and are manually specified according to a certain rule. In order to distinguish different nodes when calculating the total variation of the association, the number corresponding to the node of each device is different,
step S204, the real-time correlation total variation is the similarity of the probabilities of the predicted risk levels of the plurality of devices in the device image acquired in the same time period in the real-time data. The similarity is a weighted sum of probability ratios of predicted risk levels between any two devices, and the probability of a predicted risk level is obtained from the predicted risk level of a device, the predicted risk probability, a risk fluctuation index, and the difference in risk levels between the two devices.
The degree of association between devices is characterized by the attention, risk level and risk probability between devices. Due to the fact that the relation among the parameters is complex, the real-time correlation total variation among the devices is obtained by means of mathematical modeling through the attention degree, the difference of the risk levels, the risk levels and the risk level probability among the devices.
Wherein the training data of the device is real-time training data.
The calculation formula of the real-time total variation of the correlation diagram is as follows:
Figure BDA0003205383520000111
wherein L isuThe standard correlation total variation; e is the set of all devices; i, j belongs to the ith device and the jth device of the E set; w is ai，jIs the difference in risk level for the ith device and the jth device; giAttention of the ith device; gjAttention of the jth device; z is a linear or branched memberiA real-time risk probability sequence corresponding to the real-time data of the ith device; zjA real-time risk probability sequence corresponding to the real-time data of the jth device; max (Z)i) The real-time maximum risk probability in the real-time risk probability sequence of the ith device; argmax (Z)i) A real-time risk level corresponding to the real-time maximum risk probability on the ith device; max (Z)j) The real-time maximum risk probability in the real-time risk probability sequence of the jth equipment; argmax (Z)j) The real-time risk level corresponding to the real-time maximum risk probability on the jth device.
And S205, constructing a loss function of the equipment risk detection network, and training the equipment risk detection network.
Each device corresponds to a device risk detection network. And training the network corresponding to each device, and taking the trained network as a device risk detection network. The equipment risk detection network inputs training data of each equipment and outputs the training data as risk levels, and the training data are equipment images and risk monitoring indexes.
Firstly, a loss function of the equipment risk detection network is established, the loss function needs to be minimized as much as possible when the loss function is established, and the standard associated total variation and the real-time associated total variation are differentiated to enable the real-time associated total variable to be close to the standard total variation as much as possible. Due to the fact that the comprehensive relation among the standard correlation total variation, the real-time correlation total variation and the risk levels and risk probabilities corresponding to the training data of different time periods of each device is complex, the functional relation among the standard correlation total variation, the real-time correlation total variation and the risk levels and risk probabilities corresponding to the training data of different time periods of each device is fitted through a mathematical modeling method, and the loss function of the device risk detection network is obtained.
The loss function is constructed by the formula:
Figure BDA0003205383520000112
wherein M is the number of training data in a training batch of equipment;
Figure BDA0003205383520000113
predicting risk probability that the u-th training data output by the risk detection network corresponding to the k-th equipment belong to the c-type risk level;
Figure BDA0003205383520000114
belonging to the class c risk level for the u training data in the k equipment; l isuInputting the association graphs obtained by the respective device risk detection networks for the u-th training data of the kth device and the training data of other devices at the same position as the u-th training data to obtain real-time total variation; l is0Is the correlation graph standard total variation.
The loss function provides supervision information by using the relevance of risks among the devices, and the accuracy of network output is ensured.
In summary, in the embodiments of the present invention, metric learning and visual perception are utilized, a risk fluctuation index, an attention degree, a mixed gaussian distribution and a risk probability corresponding to a risk level are obtained by utilizing an apparatus image sequence, a risk monitoring index sequence and a corresponding risk level, a difference in risk level between any two apparatuses is obtained according to the mixed gaussian distribution, a standard associated total variation and a real-time associated total variation are obtained by utilizing the mixed gaussian distribution, the risk level, the risk probability and the attention degree, a loss function is constructed by utilizing a sum of the standard associated total variation, the real-time associated total variation and a difference in risk level between any two apparatuses, and an apparatus risk detection network is trained by utilizing the loss function. Finally, the equipment image sequence and the risk monitoring index sequence are input into the equipment risk detection network to obtain the risk level of the equipment, the equipment risk level evaluation is not needed manually, meanwhile, the correlation among the equipment in the same production scene is considered, and the accuracy and the real-time performance of risk level prediction are further improved.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And that specific embodiments have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.

Claims (6)

1. The equipment risk detection method based on metric learning and visual perception is characterized by comprising the following steps of:
inputting a first image of equipment to be tested and a risk monitoring index thereof into a corresponding equipment risk detection network to obtain a corresponding risk prediction result;
the loss function of the equipment risk detection network is the sum of a cross entropy loss function and a mean square error loss function of the associated total variation, and the associated total variation is the difference value of the real-time associated total variation and the standard associated total variation; the standard association total variation is similarity according to probabilities of predicted risk levels of a plurality of devices in device images acquired at the same time period in historical data; the real-time correlation total variation is similarity according to probabilities of predicted risk levels of a plurality of devices in device images acquired at the same time period in real-time data; the similarity is a weighted sum of probability ratios of predicted risk levels between any two devices, and the probability of the predicted risk level is obtained according to the predicted risk level, the predicted risk probability, a risk fluctuation index and the difference of the risk levels between the two devices of the devices;
wherein the first image is: collecting an RGB image of the equipment to be tested by using an RGB monitoring camera, and extracting a neighborhood image of the equipment to be tested in the RGB image;
wherein the risk monitoring indicators are: sensor data of sensors deployed around a device to be tested;
the risk fluctuation index is obtained in the following mode: acquiring a risk level sequence corresponding to an equipment image of the equipment, and adopting Gaussian background modeling to acquire mixed Gaussian distribution and a mixed Gaussian distribution curve corresponding to the risk level sequence, wherein the abscissa of the mixed Gaussian distribution curve is a risk level, and the ordinate of the mixed Gaussian distribution curve is a risk probability; averaging a plurality of the mixed Gaussian distribution curves to obtain a final Gaussian distribution curve; obtaining a risk fluctuation index of the equipment according to the final Gaussian distribution curve;
obtaining a risk fluctuation index of the equipment from the final Gaussian distribution curve by the following steps: the risk fluctuation index calculation formula is as follows:
Figure FDA0003646402230000011
wherein, BiThe risk fluctuation index for the ith said device; simi(ii) a gaussian distribution similarity for the ith said device;
wherein the difference in risk level between the two devices is obtained in the following manner: calculating a distance value between the final Gaussian distribution curves corresponding to any two devices; taking the distance value as a difference in risk level between two of the devices;
the obtaining process of the multiple Gaussian mixture distribution curves comprises the following steps:
when data are collected, a group of data segments are obtained when the collection time length is t; in each group of data segments with the time length t, each elapsed time t0Obtaining a group of training data, wherein each group of data segment comprises a plurality of groups of training data, and each group of data segment has a length of t0The training data comprises a set of first image sequences anda risk monitoring index sequence thereof; (1) obtaining training data of a device i, for each data segment t1，t2，...，tnEach of length t0Training data of (2) is artificially labeled with risk level for each training data, i.e. each training data has a length of t0The equipment image sequence and the risk monitoring index sequence in the time period both correspond to a risk level label; data segment t1Of multiple lengths t0The risk levels corresponding to the time periods are arranged according to the time acquisition sequence to obtain data segments t1Corresponding risk rank sequence, denoted D1＝{d1，d2，d3，...，dm}; where m is the data segment t1Number of risk classes involved, i.e. m-t1/t0；
(2) Constructing Gaussian distribution corresponding to the data segment and a Gaussian mixture distribution curve according to the risk level sequence by adopting a Gaussian mixture background modeling mode;
1) first of all with the risk level d1Obtaining an initial mixed gaussian distribution:
Figure FDA0003646402230000021
wherein,
Figure FDA0003646402230000022
is a risk class d1The weight of (c);
Figure FDA0003646402230000023
is a risk class d1A corresponding gaussian distribution;
Figure FDA0003646402230000024
as a risk class d1The standard deviation of the data of (a); x is the current corresponding risk level;
Figure FDA0003646402230000025
as a risk class d1The data mean of (2);
2) rank the risk d2And risk class d1Corresponding Gaussian distribution
Figure FDA0003646402230000026
Matching is carried out; judgment of
Figure FDA0003646402230000027
Figure FDA0003646402230000028
If yes, the risk level d is indicated2And Gaussian distribution
Figure FDA0003646402230000029
If the matching is successful, the risk grade d is utilized2For the Gaussian distribution with successful matching
Figure FDA00036464022300000210
Updating is carried out; the updated formula is as follows:
Figure FDA00036464022300000211
Figure FDA00036464022300000212
wherein,
Figure FDA00036464022300000213
for the risk level d after updating1The data mean of (2);
Figure FDA00036464022300000214
for the risk level d after updating1Standard deviation of the data of (a); ρ is a forgetting coefficient, i.e. a risk level d2Substituting gaussian distribution
Figure FDA00036464022300000215
Resulting updated Gaussian distribution
Figure FDA00036464022300000216
A value of (i), i.e
Figure FDA00036464022300000217
Then by the updated data mean
Figure FDA00036464022300000218
And standard deviation of data
Figure FDA00036464022300000219
For Gaussian distribution
Figure FDA00036464022300000220
Updating to obtain updated Gaussian distribution
Figure FDA00036464022300000221
The Gaussian mixture distribution at this time is
Figure FDA00036464022300000222
3) If not, the risk grade d is indicated2And Gaussian distribution
Figure FDA00036464022300000223
If the matching fails, a new Gaussian distribution is obtained
Figure FDA00036464022300000224
Figure FDA0003646402230000031
Wherein,
Figure FDA0003646402230000032
is a risk class d2Standard deviation of the data of (a); x is the current corresponding risk level;
Figure FDA0003646402230000033
is a risk class d2The data mean of (2);
4) for multiple Gaussian distributions in mixed Gaussian distribution
Figure FDA0003646402230000034
Distributing weight to obtain final mixed Gaussian distribution;
5) rank the risk d3Matching with each of the mixed gaussian distributions;
repeat traversal D1All values of (1), i.e. d1，d2，d3，...，dmTo obtain the final data segment t1Corresponding mixed Gaussian distribution f1；
(3) And (3) repeatedly obtaining the mixed Gaussian distribution of each data segment and the corresponding mixed Gaussian distribution curve according to the method in the step (2).
2. The method for detecting device risk based on metric learning and visual perception according to claim 1, wherein the method for obtaining the gaussian distribution similarity of the ith device comprises:
the calculation formula of the gaussian distribution similarity of the ith device is as follows:
Figure FDA0003646402230000035
wherein f isi(ii) is the final gaussian distribution for the ith said device; argmax (f)i) Is fiTaking the corresponding risk grade when the maximum value is obtained; n (argmax (f)i) 1) taking argmax (f) as the mean valuei) Normal distribution of (2); DTW () is the DTW distance.
3. The method of claim 1, wherein the standard association total variation is similarity of probabilities of predicted risk levels of a plurality of devices in device images acquired from a same time period in historical data, comprising:
the calculation formula of the standard correlation total variation is as follows:
Figure FDA0003646402230000036
wherein L is0Correlating the total variation for the criteria; e is the set of all the devices; i, j belongs to E and belongs to the ith equipment and the jth equipment of the E set; w is ai，jA difference in the risk level for an ith device and a jth device; giAttention of the ith device; gjAttention of the jth device; fiA risk probability sequence corresponding to a final Gaussian distribution function of the historical data of the ith device; fjA risk probability sequence corresponding to a final Gaussian distribution function of the history data of the jth device; max (F)i) The maximum risk probability in the risk probability sequence of the ith device; argmax (F)i) A risk level corresponding to the maximum risk probability on an ith device; max (F)j) The maximum risk probability in the risk probability sequence of the jth device; argmax (F)j) A risk level corresponding to the maximum risk probability on the jth device.
4. The method of claim 1, wherein the real-time associated total variation is a similarity of probabilities of predicted risk levels for a plurality of devices in a device image captured from a same time period in real-time data, comprising:
the calculation formula of the real-time correlation total variation is as follows:
Figure FDA0003646402230000041
wherein L isuA total variation for the real-time correlation; e is the set of all the devices; i, j belongs to the ith device and the jth device of the E set; w is ai，jA difference in the risk level for an ith device and a jth device; giAttention of the ith device; g is a radical of formulajAttention of the jth device; ziA real-time risk probability sequence corresponding to the real-time data of the ith device; zjA real-time risk probability sequence corresponding to the real-time data of the jth device; max (Z)i) A real-time maximum risk probability in the real-time risk probability sequence for the ith device; argmax (Z)i) A real-time risk level corresponding to the real-time maximum risk probability on an ith device; max (Z)j) The real-time maximum risk probability in the real-time risk probability sequence of the jth device; argmax (Z)j) And the real-time risk level corresponding to the real-time maximum risk probability on the jth device.
5. The device risk detection method based on metric learning and visual perception according to claim 3 or 4, wherein the attention degree obtaining method comprises:
the calculation formula of the attention degree is as follows:
Figure FDA0003646402230000042
wherein, giThe attention of the ith said device; b isiThe risk fluctuation indicator for the ith said device; b isjThe risk fluctuation index for the jth of the devices; num is the number of all the devices.
6. The apparatus risk detection method based on metric learning and visual perception according to claim 1, wherein the loss function of the apparatus risk detection network is a sum of a cross entropy loss function and a mean square error loss function of an associated total variation, comprising:
the construction formula of the loss function is as follows:
Figure FDA0003646402230000051
wherein M is the number of training data in a training batch of the equipment;
Figure FDA0003646402230000052
predicting risk probability that the u-th training data output by the risk detection network corresponding to the kth equipment belong to the class c risk level;
Figure FDA0003646402230000053
belonging to the class c risk level for the u training data in the kth device; l isuInputting the real-time associated total variation obtained by the device risk detection network for the u-th training data of the kth device and the training data of other devices at the same position as the u-th training data; l is0A total variation is associated with the criterion.
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