CN112148577B - Data anomaly detection method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to a data processing technology, and discloses a data anomaly detection method, which comprises the following steps: acquiring a standard training data set containing missing data and an anomaly detection model framework comprising a variable lower limit function; the missing data is utilized to adjust the variation lower limit function, and an optimized variation lower limit function is obtained; training an abnormality detection model framework by using the standard training data set to obtain an abnormality detection model; detecting the data set to be detected by using the anomaly detection model and obtaining the reconstruction probability of the data to be detected; if the target to-be-detected data with the reconstruction probability being greater than or equal to the reconstruction threshold value exists, determining that the target to-be-detected data is abnormal data. The invention also provides a data anomaly detection device, electronic equipment and a computer readable storage medium. Furthermore, the present invention relates to blockchain techniques, wherein the standard training data set is available from nodes of the blockchain. The invention can improve the efficiency and accuracy of KPI anomaly detection.
Description
Technical Field
The present invention relates to artificial intelligence technology, and in particular, to a method and apparatus for detecting data anomalies, an electronic device, and a computer-readable storage medium.
Background
KPI (key performance indicator) anomaly detection is a very important part of the intelligent operation and maintenance field. To ensure that the service is not interrupted, it is generally necessary to detect whether various KPIs (such as KPIs of an application program, KPIs of an operating system, etc.) are abnormal, so as to determine whether software or hardware of the system has faults, and to perform fault removal in time.
In the KPI anomaly detection method in the prior art, the robustness of the trained anomaly detection model is low, and the model stability is low, so that the problem of inaccurate detection results exists; meanwhile, a large number of labels can be generated in the detection method in the prior art, so that the detection efficiency is reduced while the computer resources are occupied.
Disclosure of Invention
The invention provides a data anomaly detection method, a data anomaly detection device and a computer readable storage medium, and mainly aims to improve the efficiency and the accuracy of KPI anomaly detection.
In order to achieve the above object, the present invention provides a data anomaly detection method, including:
Obtaining a standard training data set, wherein the standard training data set comprises abnormal detection data and missing data;
obtaining a pre-constructed abnormality detection model frame, wherein the abnormality detection model frame comprises a variable lower limit function;
the missing data is utilized to adjust the variation lower limit function, and an optimized variation lower limit function is obtained;
training the abnormality detection model frame containing the optimized variable lower limit function by using the standard training data set to obtain an abnormality detection model;
acquiring a data set to be detected, and detecting the data set to be detected by using the abnormal detection model to obtain the reconstruction probability of the data to be detected in the data set to be detected;
if the target to-be-detected data with the reconstruction probability being greater than or equal to the reconstruction threshold value exists, determining that the target to-be-detected data is abnormal data.
Optionally, the acquiring the standard training dataset includes:
acquiring an original training data set;
setting data of a preset proportion in the original training data set as missing data;
Normalizing the original training data set comprising the missing data through a preset normalization formula to obtain a normalized data set;
And inputting the normalized data set into a preset sliding window to obtain the standard training data set.
Optionally, the adjusting the variation lower limit function by using the missing data to obtain an optimized variation lower limit function includes:
calculating an optimized value based on the missing data;
And adding the optimized numerical value to the variation lower limit function to obtain the optimized variation lower limit function.
Optionally, the optimization variant lower limit function is:
Wherein W is the number of data in the standard training data set, x w is the W data in the standard training data set, a w is the missing coefficient of the W data, a w =1 when x w is missing data, a w =0 when x w is not missing data, β is an optimized value, and there is The z represents the hidden variable z in the standard training dataset.
Wherein,Representing a calculation expectation of the distribution of hidden variables z corresponding to x, logp θ (x|z) representing logarithm of p (x|z; θ), p θ (x|z) representing recovery of hidden variables z to x corresponding to the decoder, p θ (z) representing the distribution of hidden variables z under a standard training dataset, logp θ (z) representing logarithm of the p θ (z), logq φ (z|x) representing logarithm of the q φ (z|x), q φ (z|x) representing distribution of hidden variables z under a sample x corresponding to the encoder portion.
Optionally, the training the anomaly detection model framework including the optimized variation lower limit function by using the standard training dataset to obtain an anomaly detection model, including:
Step A: inputting the standard training data set into the abnormal detection model framework for calculation to obtain an output result;
and (B) step (B): calculating a loss value of the optimized variable lower limit function according to the output result;
Step C: and (C) if the loss value is greater than a preset loss threshold, adjusting parameters in the abnormal detection model frame, returning to the step (A), and stopping adjusting the parameters in the abnormal detection model frame until the loss value is less than or equal to the loss threshold to obtain the abnormal detection model.
Optionally, the inputting the standard training data set into the anomaly detection model framework for calculation to obtain an output result includes:
calculating hidden variable distribution parameters of data in the standard training data set by using an encoder in the anomaly detection model framework;
Sampling the hidden variable distribution parameters to obtain hidden variables;
And calculating the output result by using a decoder in the anomaly detection model framework and the hidden variable.
Optionally, before the detecting the to-be-detected data set by using the anomaly detection model, the method further includes:
judging whether a missing value exists in the data set to be detected;
if the missing value exists in the data set to be detected, filling the missing value in the data set to be detected through a Monte Carlo interpolation method.
In order to solve the above problems, the present invention also provides a data anomaly detection apparatus, the apparatus comprising:
The data processing module is used for acquiring a standard training data set, wherein the standard training data set comprises abnormality detection data and missing data;
the model acquisition module is used for acquiring a pre-constructed abnormality detection model frame, wherein the abnormality detection model frame comprises a variable lower limit function;
the function adjusting module is used for adjusting the variation lower limit function by utilizing the missing data to obtain an optimized variation lower limit function;
The model training module is used for training the abnormality detection model frame containing the optimized variation lower limit function by utilizing the standard training data set to obtain an abnormality detection model;
The reconstruction probability acquisition module is used for acquiring a data set to be detected, detecting the data set to be detected by using the abnormal detection model, and obtaining the reconstruction probability of the data to be detected in the data set to be detected;
The anomaly detection module is used for determining that the target to-be-detected data is anomaly data if the target to-be-detected data with the reconstruction probability being greater than or equal to the reconstruction threshold value exists.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
A memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the data abnormality detection method.
In order to solve the above-described problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the above-described data anomaly detection method.
According to the embodiment of the invention, the variation lower limit function in the abnormal detection model frame is adjusted according to the missing data in the standard training data set, the variation lower limit function can be optimized, the model is trained by using the optimized variation lower limit function, the abnormal detection model with higher robustness can be obtained, the stability of the abnormal detection model is improved, the problem of inaccurate detection is avoided, and the accuracy rate of KPI abnormal detection is improved; meanwhile, in the embodiment of the invention, the detection is carried out according to the anomaly detection model obtained by optimizing the variable lower limit function, no label is generated in the process, the dependence on the label is reduced, excessive computer resources are prevented from being occupied, and the detection efficiency is improved. Therefore, the data anomaly detection method provided by the invention can improve the efficiency and accuracy of KPI anomaly detection.
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FIG. 1 is a flowchart illustrating a method for detecting data anomalies according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a data anomaly detection device according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device for implementing the data anomaly detection method according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The execution body of the data anomaly detection method provided by the embodiment of the application comprises at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the data anomaly detection method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a flow chart of a data anomaly detection method according to an embodiment of the invention is shown. In this embodiment, the data anomaly detection method includes:
s1, acquiring a standard training data set, wherein the standard training data set comprises abnormal detection data and missing data.
In an embodiment of the present invention, the standard training dataset may contain values for various KPIs (Key Performance Indicators ).
The KPI refers to monitoring indexes (such as delay, throughput and the like) of operation and maintenance objects such as services, systems and the like, and specifically, the standard training data set comprises numerical sequences formed by arranging the same or different KPIs according to the monitored time sequence.
In the embodiment of the invention, the missing data is data with a value of 0, and the anomaly detection data is KPI anomaly data.
For example, the standard training data set has CPU usage collected at different times, wherein the CPU usage at partial time is 0 or the CPU usage at partial time is abnormal; or the standard training data set has hardware resource consumption acquired at different times, wherein the hardware resource consumption at partial time is 0 or the hardware resource consumption at partial time is abnormal; or the number of online users is collected at different times in the standard training data set, wherein the number of online users at partial time is 0 or the number of online users at partial time is abnormal; or the number of the concurrent users collected at different times in the standard training data set, wherein the number of the concurrent users at part of time is 0 or the number of the concurrent users at part of time is abnormal.
Preferably, the standard training data set may be stored in a blockchain, and when implemented, the standard training data set is directly obtained from a node of the blockchain.
By storing the standard training data set in the blockchain, the privacy and security of KPI data can be improved.
Further, in an alternative embodiment of the present invention, the acquiring the standard training data set includes:
acquiring an original training data set;
setting data of a preset proportion in the original training data set as missing data;
Normalizing the original training data set comprising the missing data through a preset normalization formula to obtain a normalized data set;
And inputting the normalized data set into a preset sliding window to obtain the standard training data set.
In detail, the embodiment of the invention normalizes the original training data set by the following formula:
wherein n is the number of data in the original training data set, x i is the i-th data in the original training data set, y i is the i-th data in the normalized data set, and y i e [0,1].
In the embodiment of the invention, the normal data of lambda ratio (i.e. KPI data other than 0) is randomly set to 0 and is regarded as missing data, so that the effect of model training is enhanced.
In the embodiment of the invention, the original training data set is time sequence data, and the normalized data set is input into the sliding window, so that the sequence of the original training data set can be ensured, and the availability and consistency of the data are improved.
Specifically, if the size of the sliding window is W, the number of data in the standard training data set is W, that is, the number of data in the standard training data set is: x W,…,x1.
In the embodiment of the invention, the original training data set is normalized, so that the data in the standard training data set can be standardized, and the time sequence of the data in the standard training data set is ensured by using the sliding window.
S2, acquiring a pre-constructed abnormality detection model frame, wherein the abnormality detection model frame comprises a variable lower limit function.
In the embodiment of the present invention, the pre-constructed anomaly detection model frame may be a VAE (Variational Autoencoders, variable self-encoder) anomaly detection model frame.
Specifically, the VAE includes an encoder, a decoder and a variation lower limit function, where the encoder calculates the hidden variable distribution parameters (mean and variance) in the standard training data set, samples the hidden variable to obtain hidden variables, and the decoder recovers the hidden variables to obtain an output result, and uses the output result and the variation lower limit function to calculate a reconstruction probability of the input KPI data, and determines whether the KPI data is abnormal according to the reconstruction probability.
And S3, adjusting the variation lower limit function by using the missing data to obtain an optimized variation lower limit function.
Specifically, the step of adjusting the variation lower limit function by using the missing data to obtain an optimized variation lower limit function includes:
calculating an optimized value based on the missing data;
And adding the optimized numerical value to the variation lower limit function to obtain the optimized variation lower limit function.
Further, an optimized value may be calculated based on the missing data by:
Calculating an optimized value beta according to the missing coefficient obtained by the missing data, specifically,
Wherein x w is the w data in the standard training dataset.
Further, the optimization variation lower limit function is:
Wherein W is the number of data in the standard training data set, x w is the W data in the standard training data set, a w is the missing coefficient of the W data, a w =1 when x w is missing data, a w =0 when x w is not missing data, β is an optimized value, and there is The z represents the hidden variable z in the standard training dataset.
Wherein,Representing a calculation expectation of the distribution of hidden variables z corresponding to x, logp θ (x|z) representing logarithm of p (x|z; θ), p θ (x|z) representing recovery of hidden variables z to x corresponding to the decoder, p θ (z) representing the distribution of hidden variables z under a standard training dataset, logp θ (z) representing logarithm of the p θ (z), logq φ (z|x) representing logarithm of the q φ (z|x), q φ (z|x) representing distribution of hidden variables z under a sample x corresponding to the encoder portion.
Further, in the embodiment of the present invention, the optimized variable lower limit function is adjusted according to the missing data, so that the missing data can be used to train the anomaly detection model frame, and stability of the anomaly detection model frame against the anomaly data is enhanced, thereby improving robustness of the model.
And S4, training the abnormality detection model frame containing the optimized variable lower limit function by using the standard training data set to obtain an abnormality detection model.
Preferably, the step S4 includes:
Step A: inputting the standard training data set into the abnormal detection model framework for calculation to obtain an output result;
and (B) step (B): calculating a loss value of the optimized variable lower limit function according to the output result;
Step C: and (C) if the loss value is greater than a preset loss threshold, adjusting parameters in the abnormal detection model frame, returning to the step (A), and stopping adjusting the parameters in the abnormal detection model frame until the loss value is less than or equal to the loss threshold to obtain the abnormal detection model.
Further, the inputting the standard training data set into the anomaly detection model framework for calculation to obtain an output result includes:
calculating hidden variable distribution parameters of data in the standard training data set by using an encoder in the anomaly detection model framework;
Sampling the hidden variable distribution parameters to obtain hidden variables;
And calculating the output result by using a decoder in the anomaly detection model framework and the hidden variable.
Specifically, the hidden variable distribution parameter is a hidden variable of all data in the standard training data set.
In the embodiment of the invention, the hidden variable is calculated by using the following formula:
z is the hidden variable and is used to determine the hidden variable, Mu (X), wherein sigma (X) is the mean and variance in the hidden variable distribution parameter, and mu (X) is the mean of the standard training data set.
In the embodiment of the invention, the output result is obtained by calculating by using the following formula:
p(x)＝∫p(x,z|θ)＝∫p(x|z；θ)p(z)dz
Wherein p (x) is the output result, Z is a point in the hidden variable space Z, p (Z) is the probability of taking the hidden variable Z, θ is a point in the parameter space Θ, and the range of the parameter space is a preset range.
p(x|z；θ)＝N(x|f(z；θ),σ2*I)
Wherein I represents an identity matrix, and sigma is a super parameter. f is a function mapping z, θ to X, i.e., f: xxΘ→x.
In the embodiment of the invention, the missing data is randomly selected before each training, so that the standard training data set can be repeatedly used for training the abnormality detection model frame, and the data utilization rate is improved.
S5, acquiring a data set to be detected, and detecting the data set to be detected by using the abnormal detection model to obtain the reconstruction probability of the data to be detected in the data set to be detected.
In the embodiment of the invention, when the anomaly detection model is specifically utilized to detect the data set to be detected, a decoder in the anomaly detection model outputs mean and variance parameters for each piece of data to be detected in the data set to be detected. The encoder in the anomaly detection model uses the mean and variance parameters of the decoder output to calculate an average probability from the hidden variable distribution z that results in an approximation to the data to be detected, which is used as an anomaly score, called a reconstruction probability, which is used to evaluate the likelihood of anomalies in the data to be detected.
Preferably, in an embodiment of the present invention, before the detecting the to-be-detected data set using the anomaly detection model, the method further includes:
judging whether a missing value exists in the data set to be detected;
if the missing value exists in the data set to be detected, filling the missing value in the data set to be detected through a Monte Carlo interpolation method.
Specifically, the monte carlo interpolation method may be obtained from the prior art, and will not be described herein. In the embodiment of the invention, the missing values in the data set to be detected can cause deviation in the encoding process of the encoder in the anomaly detection model, thereby influencing the result of data anomaly detection, and the missing values in the data set to be detected are filled by the Monte Carlo interpolation method, so that the accuracy of data anomaly detection can be improved.
And S6, if target data to be detected with the reconstruction probability being greater than or equal to the reconstruction threshold value exist, determining the target data to be detected as abnormal data.
Specifically, the reconstruction threshold is preset.
In the embodiment of the invention, the data with high reconstruction probability is determined as the abnormal data.
Optionally, in an embodiment of the present invention, when it is determined that abnormal data exists in the data set to be detected, a warning message is sent to remind, where the warning message includes a running time corresponding to the abnormal data point. The warning message prompt contains the running time corresponding to the abnormal data point, so that the efficiency of operation and maintenance is improved.
In the embodiment of the invention, the data to be detected, of which the reconstruction probability is smaller than the reconstruction threshold value, is determined to be normal data.
Optionally, in the embodiment of the present invention, when it is determined that no abnormal data exists in the data set to be detected, monitoring is continuously performed, and the current abnormal detection result and the current detection time are recorded.
According to the embodiment of the invention, the variation lower limit function in the abnormal detection model frame is adjusted according to the missing data in the standard training data set, the variation lower limit function can be optimized, the model is trained by using the optimized variation lower limit function, the abnormal detection model with higher robustness can be obtained, the stability of the abnormal detection model is improved, the problem of inaccurate detection is avoided, and the accuracy rate of KPI abnormal detection is improved; meanwhile, in the embodiment of the invention, the detection is carried out according to the anomaly detection model obtained by optimizing the variable lower limit function, no label is generated in the process, the dependence on the label is reduced, excessive computer resources are prevented from being occupied, and the detection efficiency is improved. Therefore, the data anomaly detection method provided by the invention can improve the efficiency and accuracy of KPI anomaly detection.
Fig. 2 is a functional block diagram of a data anomaly detection device according to an embodiment of the present invention.
The data abnormality detection device 100 of the present invention may be mounted in an electronic apparatus. Depending on the implemented functions, the data anomaly detection device 100 may include a data processing module 101, a model acquisition module 102, a function adjustment module 103, a model training module 104, a reconstruction probability acquisition module 105, and an anomaly detection module 106. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The data processing module 101 is configured to obtain a standard training data set, where the standard training data set includes abnormality detection data and missing data.
In an embodiment of the present invention, the standard training dataset may contain values for various KPIs (Key Performance Indicators ).
The KPI refers to monitoring indexes (such as delay, throughput and the like) of operation and maintenance objects such as services, systems and the like, and specifically, the standard training data set comprises numerical sequences formed by arranging the same or different KPIs according to the monitored time sequence.
In the embodiment of the invention, the missing data is data with a value of 0, and the anomaly detection data is KPI anomaly data.
For example, the standard training data set has CPU usage collected at different times, wherein the CPU usage at partial time is 0 or the CPU usage at partial time is abnormal; or the standard training data set has hardware resource consumption acquired at different times, wherein the hardware resource consumption at partial time is 0 or the hardware resource consumption at partial time is abnormal; or the number of online users is collected at different times in the standard training data set, wherein the number of online users at partial time is 0 or the number of online users at partial time is abnormal; or the number of the concurrent users collected at different times in the standard training data set, wherein the number of the concurrent users at part of time is 0 or the number of the concurrent users at part of time is abnormal.
Preferably, the standard training data set may be stored in a blockchain, and when implemented, the standard training data set is directly obtained from a node of the blockchain.
By storing the standard training data set in the blockchain, the privacy and security of KPI data can be improved.
Further, the data processing module 101 is specifically configured to:
acquiring an original training data set;
setting data of a preset proportion in the original training data set as missing data;
Normalizing the original training data set comprising the missing data through a preset normalization formula to obtain a normalized data set;
And inputting the normalized data set into a preset sliding window to obtain the standard training data set.
In detail, the embodiment of the invention normalizes the original training data set by the following formula:
wherein n is the number of data in the original training data set, x i is the i-th data in the original training data set, y i is the i-th data in the normalized data set, and y i e [0,1].
In the embodiment of the invention, the normal data of lambda ratio (i.e. KPI data other than 0) is randomly set to 0 and is regarded as missing data, so that the effect of model training is enhanced.
In the embodiment of the invention, the original training data set is time sequence data, and the normalized data set is input into the sliding window, so that the sequence of the original training data set can be ensured, and the availability and consistency of the data are improved.
Specifically, if the size of the sliding window is W, the number of data in the standard training data set is W, that is, the number of data in the standard training data set is: x W,…,x1.
In the embodiment of the invention, the original training data set is normalized, so that the data in the standard training data set can be standardized, and the time sequence of the data in the standard training data set is ensured by using the sliding window.
The model obtaining module 102 is configured to obtain a pre-constructed anomaly detection model framework, where the anomaly detection model framework includes a variable lower-limit function.
In the embodiment of the present invention, the pre-constructed anomaly detection model frame may be a VAE (Variational Autoencoders, variable self-encoder) anomaly detection model frame.
Specifically, the VAE includes an encoder, a decoder and a variation lower limit function, where the encoder calculates the hidden variable distribution parameters (mean and variance) in the standard training data set, samples the hidden variable to obtain hidden variables, and the decoder recovers the hidden variables to obtain an output result, and uses the output result and the variation lower limit function to calculate a reconstruction probability of the input KPI data, and determines whether the KPI data is abnormal according to the reconstruction probability.
The function adjustment module 103 is configured to adjust the variation lower limit function by using the missing data, so as to obtain an optimized variation lower limit function.
Specifically, the function adjustment module 103 is specifically configured to:
calculating an optimized value based on the missing data;
And adding the optimized numerical value to the variation lower limit function to obtain the optimized variation lower limit function.
Further, an optimized value may be calculated based on the missing data by:
Calculating an optimized value beta according to the missing coefficient obtained by the missing data, specifically,
Wherein x w is the w data in the standard training dataset.
Further, the optimization variation lower limit function is:
Wherein W is the number of data in the standard training data set, x w is the W data in the standard training data set, a w is the missing coefficient of the W data, a w =1 when x w is missing data, a w =0 when x w is not missing data, β is an optimized value, and there is The z represents the hidden variable z in the standard training dataset.
Wherein,Representing a calculation expectation of the distribution of hidden variables z corresponding to x, logp θ (x|z) representing logarithm of p (x|z; θ), p θ (x|z) representing recovery of hidden variables z to x corresponding to the decoder, p θ (z) representing the distribution of hidden variables z under a standard training dataset, logp θ (z) representing logarithm of the p θ (z), logq φ (z|x) representing logarithm of the q φ (z|x), q φ (z|x) representing distribution of hidden variables z under a sample x corresponding to the encoder portion.
Further, in the embodiment of the present invention, the optimized variable lower limit function is adjusted according to the missing data, so that the missing data can be used to train the anomaly detection model frame, and stability of the anomaly detection model frame against the anomaly data is enhanced, thereby improving robustness of the model.
The model training module 104 is configured to train the anomaly detection model framework including the optimized variation lower limit function by using the standard training data set, so as to obtain an anomaly detection model.
Preferably, the model training module 104 includes:
The first calculation unit is used for inputting the standard training data set into the anomaly detection model framework for calculation to obtain an output result;
A second calculation unit for calculating a loss value of the optimization variation lower limit function according to the output result;
And the model acquisition and adjustment unit is used for adjusting parameters in the abnormal detection model frame if the loss value is larger than a preset loss threshold value, and the starting first calculation unit inputs the standard training data set into the abnormal detection model frame for calculation to obtain an output result, and the adjustment of the parameters in the abnormal detection model frame is stopped until the loss value is smaller than or equal to the loss threshold value to obtain the abnormal detection model.
Further, the first computing unit is specifically configured to:
calculating hidden variable distribution parameters of data in the standard training data set by using an encoder in the anomaly detection model framework;
Sampling the hidden variable distribution parameters to obtain hidden variables;
And calculating the output result by using a decoder in the anomaly detection model framework and the hidden variable.
Specifically, the hidden variable distribution parameter is a hidden variable of all data in the standard training data set.
In the embodiment of the invention, the hidden variable is calculated by using the following formula:
z is the hidden variable and is used to determine the hidden variable, Mu (X), wherein sigma (X) is the mean and variance in the hidden variable distribution parameter, and mu (X) is the mean of the standard training data set.
In the embodiment of the invention, the output result is obtained by calculating by using the following formula:
p(x)＝∫p(x,z|θ)＝∫p(x|z；θ)p(z)dz
Wherein p (x) is the output result, Z is a point in the hidden variable space Z, p (Z) is the probability of taking the hidden variable Z, θ is a point in the parameter space Θ, and the range of the parameter space is a preset range.
p(x|z；θ)＝N(x|f(z；θ),σ2*I)
Wherein I represents an identity matrix, and sigma is a super parameter. f is a function mapping z, θ to X, i.e., f: xxΘ→x.
In the embodiment of the invention, the missing data is randomly selected before each training, so that the standard training data set can be repeatedly used for training the abnormality detection model frame, and the data utilization rate is improved.
The reconstruction probability obtaining module 105 is configured to obtain a data set to be detected, and detect the data set to be detected by using the anomaly detection model, so as to obtain a reconstruction probability of data to be detected in the data set to be detected.
In the embodiment of the invention, when the anomaly detection model is specifically utilized to detect the data set to be detected, a decoder in the anomaly detection model outputs mean and variance parameters for each piece of data to be detected in the data set to be detected. The encoder in the anomaly detection model uses the mean and variance parameters of the decoder output to calculate an average probability from the hidden variable distribution z that results in an approximation to the data to be detected, which is used as an anomaly score, called a reconstruction probability, which is used to evaluate the likelihood of anomalies in the data to be detected.
Preferably, in an embodiment of the present invention, the apparatus further includes a judging module, where the judging module is configured to:
before the anomaly detection model is used for detecting the data set to be detected, judging whether a missing value exists in the data set to be detected;
if the missing value exists in the data set to be detected, filling the missing value in the data set to be detected through a Monte Carlo interpolation method.
Specifically, the monte carlo interpolation method may be obtained from the prior art, and will not be described herein.
In the embodiment of the invention, the missing values in the data set to be detected can cause deviation in the encoding process of the encoder in the anomaly detection model, so that the result of data anomaly detection is affected, the missing values in the data set to be detected are filled by the Monte Carlo interpolation method, the accuracy of data anomaly detection can be improved, and meanwhile, the reconstruction probability is output by utilizing the anomaly detection model, so that the speed of data anomaly detection is greatly improved.
The anomaly detection module 106 is configured to determine that the target to-be-detected data is anomaly data if there is target to-be-detected data with a reconstruction probability greater than or equal to a reconstruction threshold.
Specifically, the reconstruction threshold is preset.
In the embodiment of the invention, the data with high reconstruction probability is determined as the abnormal data.
Optionally, in an embodiment of the present invention, when it is determined that abnormal data exists in the data set to be detected, a warning message is sent to remind, where the warning message includes a running time corresponding to the abnormal data point. The warning message prompt contains the running time corresponding to the abnormal data point, so that the efficiency of operation and maintenance is improved.
In the embodiment of the invention, the data to be detected, of which the reconstruction probability is smaller than the reconstruction threshold value, is determined to be normal data.
Optionally, in the embodiment of the present invention, when it is determined that no abnormal data exists in the data set to be detected, monitoring is continuously performed, and the current abnormal detection result and the current detection time are recorded.
According to the embodiment of the invention, the variation lower limit function in the abnormal detection model frame is adjusted according to the missing data in the standard training data set, the variation lower limit function can be optimized, the model is trained by using the optimized variation lower limit function, the abnormal detection model with higher robustness can be obtained, the stability of the abnormal detection model is improved, the problem of inaccurate detection is avoided, and the accuracy rate of KPI abnormal detection is improved; meanwhile, in the embodiment of the invention, the detection is carried out according to the anomaly detection model obtained by optimizing the variable lower limit function, no label is generated in the process, the dependence on the label is reduced, excessive computer resources are prevented from being occupied, and the detection efficiency is improved. Therefore, the data anomaly detection device provided by the invention can improve the efficiency and accuracy of KPI anomaly detection.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a data anomaly detection method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a data anomaly detection program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the data abnormality detection program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (e.g., a data abnormality detection program, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The data abnormality detection program 12 stored in the memory 11 in the electronic device 1 is a combination of a plurality of instructions, and when executed in the processor 10, can realize:
Obtaining a standard training data set, wherein the standard training data set comprises abnormal detection data and missing data;
obtaining a pre-constructed abnormality detection model frame, wherein the abnormality detection model frame comprises a variable lower limit function;
the missing data is utilized to adjust the variation lower limit function, and an optimized variation lower limit function is obtained;
training the abnormality detection model frame containing the optimized variable lower limit function by using the standard training data set to obtain an abnormality detection model;
acquiring a data set to be detected, and detecting the data set to be detected by using the abnormal detection model to obtain the reconstruction probability of the data to be detected in the data set to be detected;
if the target to-be-detected data with the reconstruction probability being greater than or equal to the reconstruction threshold value exists, determining that the target to-be-detected data is abnormal data.
Specifically, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1 may be stored in a non-volatile computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A method for detecting data anomalies, the method comprising:
Obtaining a standard training data set, wherein the standard training data set comprises abnormal detection data and missing data;
obtaining a pre-constructed abnormality detection model frame, wherein the abnormality detection model frame comprises a variable lower limit function;
And adjusting the variation lower limit function by using the missing data to obtain an optimized variation lower limit function, wherein the method comprises the following steps: calculating an optimized value based on the missing data, and adding the optimized value to the variation lower limit function to obtain an optimized variation lower limit function;
Training the anomaly detection model framework containing the optimized variation lower limit function by using the standard training data set to obtain an anomaly detection model, wherein the method comprises the following steps: calculating hidden variable distribution parameters in the standard training data set by using an encoder of the anomaly detection model framework, sampling the hidden variable distribution parameters to obtain hidden variables, recovering the hidden variables by using a decoder of the anomaly detection model framework to obtain an output result, calculating a loss value of the optimized variable lower limit function by using the output result, and obtaining the anomaly detection model framework if the loss value is smaller than or equal to a loss threshold value;
acquiring a data set to be detected, and detecting the data set to be detected by using the abnormal detection model to obtain the reconstruction probability of the data to be detected in the data set to be detected;
if the target to-be-detected data with the reconstruction probability being greater than or equal to the reconstruction threshold value exists, determining that the target to-be-detected data is abnormal data.
2. The method for detecting data anomalies according to claim 1, wherein said obtaining a standard training data set includes:
acquiring an original training data set;
setting data of a preset proportion in the original training data set as missing data;
Normalizing the original training data set comprising the missing data through a preset normalization formula to obtain a normalized data set;
And inputting the normalized data set into a preset sliding window to obtain the standard training data set.
3. The data anomaly detection method according to any one of claims 1 to 2, wherein the optimization variation lower limit function is:
Wherein W is the number of data in the standard training data set, x ww is the W data in the standard training data set, a w is the missing coefficient of the W data, a w =1 when x w is missing data, a w =0 when x w is not missing data, β is an optimized value, and there is The z represents the hidden variable z in the standard training dataset.
Wherein,Representing a calculation expectation of the distribution of hidden variables z corresponding to x, logp θ (x|z) representing logarithm of p (x|z; θ), p θ (x|z) representing recovery of hidden variables z to x corresponding to the decoder, p θ (z) representing the distribution of hidden variables z under a standard training dataset, logp θ (z) representing logarithm of the p θ (z), logq φ (z|x) representing logarithm of the q φ (z|x), q φ (z|x) representing distribution of hidden variables z under a sample x corresponding to the encoder portion.
4. The data anomaly detection method of claim 1, wherein prior to detecting the set of data to be detected using the anomaly detection model, the method further comprises:
judging whether a missing value exists in the data set to be detected;
if the missing value exists in the data set to be detected, filling the missing value in the data set to be detected through a Monte Carlo interpolation method.
5. A data anomaly detection device, the device comprising:
The data processing module is used for acquiring a standard training data set, wherein the standard training data set comprises abnormality detection data and missing data;
the model acquisition module is used for acquiring a pre-constructed abnormality detection model frame, wherein the abnormality detection model frame comprises a variable lower limit function;
the function adjustment module is used for calculating an optimized value based on the missing data, and adding the optimized value to the variation lower limit function to obtain an optimized variation lower limit function;
The model training module is configured to train the anomaly detection model framework including the optimization variation lower limit function by using the standard training data set to obtain an anomaly detection model, and includes: calculating hidden variable distribution parameters in the standard training data set by using an encoder of the anomaly detection model framework, sampling the hidden variable distribution parameters to obtain hidden variables, recovering the hidden variables by using a decoder of the anomaly detection model framework to obtain an output result, calculating a loss value of the optimized variable lower limit function by using the output result, and stopping adjusting the anomaly detection model framework if the loss value is smaller than or equal to a loss threshold value;
The reconstruction probability acquisition module is used for acquiring a data set to be detected, detecting the data set to be detected by using the abnormal detection model, and obtaining the reconstruction probability of the data to be detected in the data set to be detected;
The anomaly detection module is used for determining that the target to-be-detected data is anomaly data if the target to-be-detected data with the reconstruction probability being greater than or equal to the reconstruction threshold value exists.
6. An electronic device, the electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the data anomaly detection method of any one of claims 1 to 4.
7. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the data anomaly detection method according to any one of claims 1 to 4.
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Families Citing this family (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11909482B2 (en) * | 2020-08-18 | 2024-02-20 | Qualcomm Incorporated | Federated learning for client-specific neural network parameter generation for wireless communication |
| CN112463646B (en) * | 2021-01-25 | 2021-05-11 | 北京工业大数据创新中心有限公司 | Sensor abnormity detection method and device |
| CN113114529B (en) * | 2021-03-25 | 2022-05-24 | 清华大学 | KPI (Key Performance indicator) anomaly detection method and device based on condition variation automatic encoder and computer storage medium |
| CN113204569B (en) * | 2021-03-30 | 2024-06-18 | 联想(北京)有限公司 | Information processing method and device |
| CN113592019B (en) * | 2021-08-10 | 2023-09-15 | 平安银行股份有限公司 | Fault detection method, device, equipment and medium based on multi-model fusion |
| CN113705684B (en) * | 2021-08-30 | 2023-11-24 | 平安科技（深圳）有限公司 | Reverse iteration anomaly detection method and device, electronic equipment and medium |
| CN113971513A (en) * | 2021-10-22 | 2022-01-25 | 河南鑫安利安全科技股份有限公司 | Data storage and optimization method of enterprise security risk management platform |
| CN113988687A (en) * | 2021-11-05 | 2022-01-28 | 哈尔滨工程大学 | Nuclear power device state monitoring method and system |
| CN114185881B (en) * | 2021-12-14 | 2024-06-04 | 中国平安财产保险股份有限公司 | Automatic abnormal data repairing method, device, equipment and storage medium |
| CN114190897B (en) * | 2021-12-15 | 2024-04-05 | 中国科学院空天信息创新研究院 | Training method of sleep stage model, sleep stage method and device |
| CN114493291B (en) * | 2022-01-28 | 2022-11-01 | 中铁北京工程局集团有限公司 | Intelligent detection method and system for high fill quality |
| CN114722061B (en) * | 2022-04-08 | 2023-11-14 | 中国电信股份有限公司 | Data processing method and device, equipment and computer readable storage medium |
| CN115034286B (en) * | 2022-04-24 | 2024-07-02 | 国家计算机网络与信息安全管理中心 | Abnormal user identification method and device based on self-adaptive loss function |
| CN114881157A (en) * | 2022-05-17 | 2022-08-09 | 中国南方电网有限责任公司超高压输电公司广州局 | Method, device and equipment for detecting working state of converter valve and storage medium |
| CN114880384B (en) * | 2022-07-11 | 2022-09-23 | 杭州宇谷科技有限公司 | Unsupervised two-wheeled electric vehicle charging time sequence abnormity detection method and system |
| CN116049157B (en) * | 2023-01-04 | 2024-05-07 | 北京京航计算通讯研究所 | Quality data analysis method and system |
| CN116956637B (en) * | 2023-09-06 | 2024-03-05 | 湖南光华防务科技集团有限公司 | Method for detecting robustness of coverage surface of fire extinguishing bomb |
| CN117041018B (en) * | 2023-10-09 | 2024-01-02 | 中电科大数据研究院有限公司 | Remote intelligent operation and maintenance management method for data center and related equipment |
| CN117849700B (en) * | 2024-03-07 | 2024-05-24 | 南京国网电瑞电力科技有限责任公司 | Modular electric energy metering system capable of controlling measurement |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978379A (en) * | 2019-03-28 | 2019-07-05 | 北京百度网讯科技有限公司 | Time series data method for detecting abnormality, device, computer equipment and storage medium |
| CN110581834A (en) * | 2018-06-11 | 2019-12-17 | 中国移动通信集团浙江有限公司 | communication capability opening abnormity detection method and device |
| CN110851338A (en) * | 2019-09-23 | 2020-02-28 | 平安科技（深圳）有限公司 | Abnormality detection method, electronic device, and storage medium |
| CN115903741A (en) * | 2022-11-18 | 2023-04-04 | 南京信息工程大学 | Data anomaly detection method for industrial control system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10999247B2 (en) * | 2017-10-24 | 2021-05-04 | Nec Corporation | Density estimation network for unsupervised anomaly detection |
| EP3748545A1 (en) * | 2019-06-07 | 2020-12-09 | Tata Consultancy Services Limited | Sparsity constraints and knowledge distillation based learning of sparser and compressed neural networks |
| CN111562996B (en) * | 2020-04-11 | 2021-11-23 | 北京交通大学 | Method and system for detecting time sequence abnormality of key performance index data |
| CN111652278B (en) * | 2020-04-30 | 2024-04-30 | 中国平安财产保险股份有限公司 | User behavior detection method, device, electronic equipment and medium |
| CN111598881B (en) * | 2020-05-19 | 2022-07-12 | 西安电子科技大学 | Image anomaly detection method based on variational self-encoder |
-
2020
- 2020-10-09 CN CN202011074730.8A patent/CN112148577B/en active Active
- 2020-11-27 WO PCT/CN2020/131984 patent/WO2021189904A1/en active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110581834A (en) * | 2018-06-11 | 2019-12-17 | 中国移动通信集团浙江有限公司 | communication capability opening abnormity detection method and device |
| CN109978379A (en) * | 2019-03-28 | 2019-07-05 | 北京百度网讯科技有限公司 | Time series data method for detecting abnormality, device, computer equipment and storage medium |
| CN110851338A (en) * | 2019-09-23 | 2020-02-28 | 平安科技（深圳）有限公司 | Abnormality detection method, electronic device, and storage medium |
| CN115903741A (en) * | 2022-11-18 | 2023-04-04 | 南京信息工程大学 | Data anomaly detection method for industrial control system |
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