CN117034180A - Power communication equipment data anomaly detection method, system and storage medium - Google Patents
Power communication equipment data anomaly detection method, system and storage medium Download PDFInfo
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Abstract
The application relates to a method, a system and a storage medium for detecting data abnormality of power communication equipment, wherein the method comprises the following steps of 1) collecting historical data of various power communication equipment, wherein the historical data mainly comprises network performance data, equipment performance data, temperature, current and voltage and timestamp information; 2) Preprocessing various data collected in the step 1); 3) Converting the processed data into a time sequence format, and constructing an abnormality detection data set; 4) Inputting the anomaly detection data set obtained in the step 3) into an RNN (RNN recurrent neural network) for training, and generating a data anomaly detection model; 5) And (3) extracting real-time data of the power communication equipment, processing the data, inputting the processed data into the abnormality detection model generated in the step (4), and judging the state of the power communication equipment according to the model output result. The application can simultaneously detect the abnormality of the data of various power communication devices by using the time sequence and combining the neural network algorithm.
Description
Technical Field
The present application relates to the field of power system security, and in particular, to a method, a system, and a storage medium for detecting data anomalies in power communication devices.
Background
The power communication equipment is a core component in the power system and is used for realizing information transmission and remote control between the power equipment. These devices generate a large amount of data, including information on voltage, current, frequency, temperature, etc., which is critical to the detection, operation and safety of the power system. In order to ensure the normal operation of the power system, abnormal conditions in the data of the power communication equipment must be found and processed in time. Therefore, the data abnormality detection of the power communication equipment has important significance in a modern power system, and is helpful for ensuring the normal operation of the power equipment and improving the stability and reliability of the power system.
Because of the increasing variety and number of power communication devices, it is a difficult problem how to design a unified system to detect anomalies of multiple data at the same time. The data anomaly detection of the power communication equipment mainly has the following problems, namely the data generated by the power communication equipment is high-dimensional nowadays, and the traditional anomaly detection method is low in efficiency when processing the data. Secondly, in practical application, the labels of the abnormal data are usually missing, and the working condition of the power system can also change at any time, so the model needs to be robust. Thirdly, abnormal data is usually in a few classes, while normal data occupies most of data, so that the problem of data unbalance is caused.
Disclosure of Invention
The embodiment of the application aims to provide a method, a system and a storage medium for detecting data anomalies of power communication equipment, which solve the problem of low model robustness caused by complex and diversified data by combining a time sequence with a neural network algorithm, and can detect anomalies of data of various power communication equipment at the same time by constructing a deep learning model.
In order to achieve the above purpose, the present application provides the following technical solutions:
in a first aspect, an embodiment of the present application provides a method for detecting an abnormality of data in an electric power communication device, including the steps of:
1) Collecting historical data of various power communication devices, wherein the historical data mainly comprises network performance data, device performance data, temperature, current voltage and timestamp information;
2) Preprocessing various data collected in the step 1);
3) Converting the processed data into a time sequence format, and constructing an abnormality detection data set;
4) Inputting the anomaly detection data set obtained in the step 3) into an RNN (RNN recurrent neural network) for training, and generating a data anomaly detection model;
5) And (3) extracting real-time data of the power communication equipment, processing the data, inputting the processed data into the abnormality detection model generated in the step (4), and judging the state of the power communication equipment according to the model output result.
The network performance data in the step 1) comprises broadband utilization rate, packet loss rate, delay, throughput and the like; the equipment state data comprises the running state of equipment and the memory utilization rate; each data is secured with time stamp information to facilitate subsequent data processing.
In the step 2), various data collected in the step 1) need to be preprocessed, and the following processing modes are mainly performed:
processing the missing values: detecting missing values in the data, including deleting rows containing missing values, filling the missing values with a mean or median value, estimating the missing values using interpolation,
outlier processing: detecting and processing abnormal values, including identifying abnormal values by statistical methods or based on domain knowledge, and performing deletion or replacement processing;
timestamp processing: firstly, ensuring that the time stamps are ordered according to a time sequence so as to perform time sequence analysis, if the time stamps are not ordered, ordering the time stamps, calculating intervals among the time stamps, and ensuring that the time intervals among the data points are uniform;
and carrying out normalization processing on the data of different features, and shrinking the data to the same scale range.
In the step 3), the processed data is converted into a time sequence format, and an abnormal detection data set is constructed, and the time window sample sequence is created mainly by using a sliding window technology when the data is converted into the time sequence format.
In step 4), the anomaly detection data set obtained in step 3) is input into an RNN recurrent neural network for training, and a data anomaly detection model is generated, wherein the training based on the RNN recurrent neural network is obtained through the following process training:
forward propagation
Input data: feature sequences within a time windowWhere t represents a time step, initializing the hidden state h (0) to a zero vector or a random vector when time step t=0,
calculating a hidden state, wherein h (t) is the hidden state at time step t,is the hidden state at time step t-1, X (t) is the input at time step t, W and U are weight matrices, b is the bias term, and tanh is the hyperbolic tangent activation function:
,
calculating an output o (t), where o (t) is the model output at time step t,is a matrix of weights for the output layer,is the bias term for the output layer:
,
loss function: calculating the difference between the model output and the true value using the mean square error, where Loss (t) is the Loss at time step t, o (t) is the output of the model, y (t) is the true value,the method is quadratic:
,
counter-propagation
The gradient of the loss function with respect to the model parameters is calculated using the chain law, in order to update the weights and biases of the model,
calculating the gradient of the output layer weight and bias:
,
calculating gradient of hidden state:
,
calculating the parameter gradient of the RNN layer in the model:
,
parameter update, whereinIs learning rate, controls the step length of parameter updating:
,
、/>is a weight matrix after parameter update, +.>Is an offset term after the parameter is updated,is the weight matrix of the output layer after parameter update, < >>Is the bias term of the output layer after parameter updating, and the iteration is repeated until a certain iteration number is reached or the loss converges to a satisfactory level.
In a second aspect, an embodiment of the present application provides a system for detecting a data anomaly of a power communication device, including a data collection module, a data preprocessing module, a data set construction module, a detection model generation module, and a result output module,
the data collection module is used for collecting historical data of various power communication devices;
the data preprocessing module is used for preprocessing various data collected by the data collecting module;
the data set construction module converts the processed data into a time sequence format and constructs an abnormality detection data set;
the detection model generation module inputs the abnormal detection data set obtained by the data set construction module into the RNN circulating neural network for training to generate a data abnormal detection model;
the result output module extracts real-time data of the power communication equipment, processes the data, inputs the processed data into the abnormal detection model generated by the detection model generation module, and judges the state of the power communication equipment according to the model output result.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing program code that, when executed by a processor, implements the steps of the power communication device data anomaly detection method described above.
Compared with the prior art, the application has the beneficial effects that: the application collects the historical data of various power communication equipment, preprocesses all the data, converts the processed data into a time sequence format and constructs an abnormality detection data set. Inputting the data set into the RNN cyclic neural network for training, and generating a data abnormality detection model. Finally, real-time data of the power communication equipment are extracted and input into an anomaly detection model after being processed, and the state of the power communication equipment is judged according to the output result of the model. Compared with the existing method for detecting the data abnormality of the power communication equipment, the method solves the problem of low model robustness caused by complex and diversified data by combining the time sequence with the neural network algorithm, and can detect the abnormality of the data of various power communication equipment at the same time by constructing a deep learning model.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a graph of an RNN-based recurrent neural network model algorithm in the present application;
FIG. 2 is a flow chart of the method of the present application;
fig. 3 is a system block diagram of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The terms "first," "second," and the like, are used merely to distinguish one entity or action from another entity or action, and are not to be construed as indicating or implying any actual such relationship or order between such entities or actions.
As shown in fig. 1-2, the application provides a method for detecting data anomalies of power communication equipment based on deep learning, which comprises the following steps:
step 1: historical data of various power communication devices is collected, and the historical data mainly comprises network performance data, device performance data, temperature, current voltage and timestamp information. Wherein the network performance data comprises broadband utilization, packet loss, delay, throughput and the like; the equipment state data comprises the running state of equipment and the memory utilization rate; wherein each data is secured with time stamp information to facilitate subsequent data processing. The extraction of various data is mainly extracted from power communication equipment, and specifically comprises the following steps:
1) The data is obtained from the monitoring system of the power communication equipment, and the equipment has built-in monitoring functions.
2) Sensor devices are used to measure environmental parameters such as temperature, current, voltage, etc.
3) Network traffic data is captured using a network capture traffic tool, wireshark.
Step 2: preprocessing various data collected in the step 1, and mainly performing the following processing modes:
1) Processing the missing values: the missing values in the data are detected by a method comprising deleting the row containing the missing values, filling the missing values with the mean or median value, and estimating the missing values using an interpolation method.
2) Outlier processing: outliers are detected and processed using methods including statistical methods and domain-based knowledge to identify outliers and using appropriate processing means such as deletion, substitution.
3) Timestamp processing: first, the time stamps are ensured to be ordered in time sequence for time sequence analysis, and if the time stamps are not ordered, the ordering is performed, and the intervals between the time stamps are calculated, so that the time intervals between the data points are ensured to be uniform.
4) And carrying out normalization processing on the data of different features, and shrinking the data to the same scale range so as to avoid overlarge influence of certain features on the model.
Step 3: the processed data is converted into a time series format, and a different detection data set is constructed. Converting data to a time series format creates a sequence of time window samples, mainly using a sliding window technique. Defining a time set T to include time nodesDefine F as the feature set,> representing the individual features and defining-> Representing the respective characteristic values. Initially a time series of data is represented as follows:
TABLE 1 initial time series data
Converting the time sequence data into a time window, and storing the processed data into a data format suitable for model training, wherein the converted time window data is as follows:
TABLE 2 converted time window data
Step 4: inputting the anomaly detection data set obtained in the step 3 into an RNN (RNN-network-based) cyclic neural network for training, and generating a data anomaly detection model, wherein the training based on the RNN cyclic neural network is obtained through training by the following process:
1) Forward propagation
a. Input data: feature sequences within a time windowWhere t represents a time step. At time step t=0, the hidden state h (0) is initialized to a zero vector.
b. Calculating a hidden state, wherein h (t) is the hidden state at time step t, X (t) is the input at time step t, W and U are weight matrices, b is a bias term, and tanh is a hyperbolic tangent activation function:
,
c. calculating an output o (t), where o (t) is the model output at time step t,is the weight matrix of the output layer, +.>Is the bias term for the output layer:
,
d. loss function: calculating the difference between the model output and the true value using the mean square error, where Loss (t) is the Loss at time step t, o (t) is the output of the model, and y (t) is the true value:
,
2) Counter-propagation
Gradients of the loss function relative to the model parameters are calculated using the chain law to update the weights and biases of the model.
a. Calculating the gradient of the output layer weight and bias:
,
b. calculating gradient of hidden state:
,
c. calculating the parameter gradient of the RNN layer in the model:
,
d. parameter update, whereinIs learning rate, controls the step length of parameter updating:
,
、/>is a weight matrix after parameter update, +.>Is an offset term after the parameter is updated,is the weight matrix of the output layer after parameter update, < >>Is the bias term of the output layer after parameter updating,
3) And repeating the iteration until a certain iteration number or loss converges to a satisfactory level, setting a threshold value threshold according to the iteration training result, wherein the condition that the threshold value is lower indicates that the data is normal, and the condition that the data is higher indicates that the data is abnormal and needs to be further analyzed.
Step 5: and (4) extracting real-time data of the power communication equipment, processing the data, inputting the processed data into the abnormality detection model generated in the step (4), and judging the state of the power communication equipment according to the model output result. The output value is mainly compared with threshold, and the state of the power communication equipment corresponding to the abnormal data is further analyzed.
As shown in fig. 3, an embodiment of the present application provides a data anomaly detection system for power communication equipment, which includes a data collection module 1, a data preprocessing module 2, a data set construction module 3, a detection model generation module 4, a result output module 5,
the data collection module 1 collects historical data of various power communication devices;
the data preprocessing module 2 preprocesses various data collected by the data collecting module;
the data set construction module 3 converts the processed data into a time sequence format and constructs an abnormality detection data set;
the detection model generation module 4 inputs the abnormal detection data set obtained by the data set construction module into the RNN circulating neural network for training to generate a data abnormal detection model;
the result output module 5 extracts real-time data of the power communication equipment, processes the data, inputs the processed data into the abnormal detection model generated by the detection model generation module, and judges the state of the power communication equipment according to the model output result.
The method collects historical data of various power communication devices, preprocesses all the data, converts the processed data into a time sequence format and constructs an abnormality detection data set. Inputting the data set into the RNN cyclic neural network for training, and generating a data abnormality detection model. Finally, real-time data of the power communication equipment are extracted and input into an anomaly detection model after being processed, and the state of the power communication equipment is judged according to the output result of the model.
Compared with the existing method for detecting the data abnormality of the power communication equipment, the method solves the problem of low model robustness caused by complex and diversified data by combining the time sequence with the neural network algorithm, and can detect the abnormality of the data of various power communication equipment at the same time by constructing a deep learning model.
An embodiment of the present application provides a computer-readable storage medium storing program code which, when executed by a processor, implements the steps of the power communication device data anomaly detection method described above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (7)
1. A method for detecting anomalies in data of an electrical communication device, comprising the steps of:
1) Collecting historical data of various power communication devices, including network performance data, device performance data, temperature, current voltage and timestamp information;
2) Preprocessing various data collected in the step 1);
3) Converting the processed data into a time sequence format, and constructing an abnormality detection data set;
4) Inputting the anomaly detection data set obtained in the step 3) into an RNN (RNN recurrent neural network) for training, and generating a data anomaly detection model;
5) And (3) extracting real-time data of the power communication equipment, processing the data, inputting the processed data into the abnormality detection model generated in the step (4), and judging the state of the power communication equipment according to the model output result.
2. The method for detecting abnormal data of a power communication apparatus according to claim 1, wherein the network performance data in step 1) includes a broadband utilization rate, a packet loss rate, a delay and a throughput; the equipment state data comprises the running state of equipment and the memory utilization rate; each data is secured with time stamp information to facilitate subsequent data processing.
3. The method for detecting abnormal data of power communication equipment according to claim 1, wherein in the step 2), various data collected in the step 1) need to be preprocessed, and the following processing methods are performed:
processing the missing values: detecting missing values in the data, including deleting rows containing missing values, filling the missing values with a mean or median value, estimating the missing values using interpolation,
outlier processing: detecting and processing abnormal values, including identifying abnormal values by statistical methods or based on domain knowledge, and performing deletion or replacement processing;
timestamp processing: firstly, ensuring that the time stamps are ordered according to a time sequence so as to perform time sequence analysis, if the time stamps are not ordered, ordering the time stamps, calculating intervals among the time stamps, and ensuring that the time intervals among the data points are uniform;
and carrying out normalization processing on the data of different features, and shrinking the data to the same scale range.
4. The method according to claim 1, wherein in the step 3), the processed data is converted into a time-series format, and the anomaly detection data set is constructed, and the sliding window technique is used to create the time-window sample sequence.
5. The method for detecting data anomalies of a power communication device according to claim 1, wherein in step 4), the anomaly detection data set obtained in step 3) is input into an RNN recurrent neural network for training, and a data anomaly detection model is generated, wherein training based on the RNN recurrent neural network is obtained by training through the following processes:
input data: feature sequences within a time windowWhere t represents a time step, initializing the hidden state h (0) to a zero vector or a random vector when time step t=0,
calculating a hidden state, wherein h (t) is the hidden state at time step t,is the hidden state at time step t-1, X (t) is the input at time step t, W and U are weight matrices, b is the bias term, and tanh is the hyperbolic tangent activation function:
,
calculating an output o (t), where o (t) is the model output at time step t,is the weight matrix of the output layer, +.>Is the bias term for the output layer:
,
loss function: calculating the difference between the model output and the true value using the mean square error, where Loss (t) is the Loss at time step t, o (t) is the output of the model, y (t) is the true value,the method is quadratic:
,
the gradient of the loss function with respect to the model parameters is calculated using the chain law, in order to update the weights and biases of the model,
calculating the gradient of the output layer weight and bias:
,
calculating gradient of hidden state:
,
calculating the parameter gradient of the RNN layer in the model:
,
parameter update, whereinIs learning rate, controls the step length of parameter updating:
,
、/>is a weight matrix after parameter update, +.>Is an offset item after parameter update, +.>Is the weight matrix of the output layer after parameter update, < >>Is the bias term of the output layer after parameter updating, and the iteration is repeated until a certain iteration number is reached or the loss converges to a satisfactory level.
6. The system for detecting the data abnormality of the power communication equipment is characterized by comprising a data collection module, a data preprocessing module, a data set construction module, a detection model generation module and a result output module,
the data collection module is used for collecting historical data of various power communication devices;
the data preprocessing module is used for preprocessing various data collected by the data collecting module;
the data set construction module converts the processed data into a time sequence format and constructs an abnormality detection data set;
the detection model generation module inputs the abnormal detection data set obtained by the data set construction module into the RNN circulating neural network for training to generate a data abnormal detection model;
the result output module extracts real-time data of the power communication equipment, processes the data, inputs the processed data into the abnormal detection model generated by the detection model generation module, and judges the state of the power communication equipment according to the model output result.
7. A computer-readable storage medium storing program code which, when executed by a processor, implements the steps of the power communication device data anomaly detection method of any one of claims 1 to 5.
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