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Power grid data anomaly detection method and device based on ensemble learning
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An integrated learning and anomaly detection technology, applied in the field of data processing, can solve the problems of less data, inapplicable burst type anomalies, and inability to detect abnormal patterns
Pending Publication Date: 2021-11-09
SHENZHEN COMTOP INFORMATION TECH
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Due to the limitation of traditional anomaly detection technology, the anomaly detection problem that can be detected is closely related to the time factor, that is, the anomaly can be reflected in the data set when it is formed in the early stage, and the development of the anomaly becomes more and more obvious with the accumulation of time It is not suitable for the detection of sudden type abnormalities: such as abnormal equipment function or damage caused by sudden environmental factors or human factors, etc.
At the same time, in the field of business operations and economic activities, that is, in the field of macro-indicator detection, there are few anomaly detection algorithms for power grid operation indicators
However, there is a big difference between the abnormal patterns of enterprise operation and production activities and the field of operation and maintenance monitoring, and most of the field of operation and maintenance monitoring adopts supervised methods, which are not suitable for the characteristics of enterprise operation and production activities with few data and labels, so it is impossible to analyze the abnormal patterns of enterprises. Appropriate and comprehensive detection of various abnormal patterns in operational activities
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Embodiment 1
[0063] see figure 1 , figure 1 It is a schematicflowchart of an integrated learning-based power grid data anomaly detection method disclosed in an embodiment of the present invention. in, figure 1 The described method can be applied to a corresponding detection terminal, detection device or detection server, and the server can be a local server or a cloud server. Such as figure 1 As shown, the grid data anomaly detection method based on integrated learning may include the following operations:
[0064] 101. Obtain the target grid index data, and preprocess the target grid index data;
[0065] 102. Extract the time series data features in the target power grid index data;
[0066] 103. Based on the preset ensemble learning anomaly detection model, anomaly detection is performed on the time series data features in the target power grid index data, and an anomaly detection result is obtained.
[0067] In the embodiment of the present invention, the basic model of the ensem...
Embodiment 2
[0106] see figure 2 , figure 2 It is a schematic structural diagram of an integrated learning-based grid data anomaly detection device disclosed in an embodiment of the present invention. in, figure 2 The described apparatus may be applied to a corresponding detection terminal, detection device or detection server, and the server may be a local server or a cloud server, which is not limited in this embodiment of the present invention. Such as figure 2 As shown, the device may include:
[0107] An acquisition processing module 201, configured to acquire target power grid index data, and preprocess the target power grid index data;
[0108] A feature extraction module 202, configured to extract time series data features in the target grid index data;
[0109] The anomaly detection module 203 is configured to perform anomaly detection on time-series data features in target power grid index data based on a preset ensemble learning anomaly detection model, and obtain an an...
Embodiment 3
[0147] see image 3 , image 3 It is a schematic structural diagram of another power grid data anomaly detection device based on integrated learning disclosed in the embodiment of the present invention. Such as image 3 As shown, the device may include:
[0148] A memory 301 storing executable program codes;
[0149] a processor 302 coupled to the memory 301;
[0150] The processor 302 invokes the executableprogram code stored in the memory 301 to execute some or all of the steps in the method for detecting anomalies in grid data based on integrated learning disclosed in Embodiment 1 of the present invention.
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Abstract
The invention discloses a power grid data anomaly detection method and device based on ensemble learning, and the method comprises the steps: obtaining target power grid index data, and carrying out the preprocessing of the target power grid index data; extracting time sequence data characteristics in the target power grid index data; on the basis of a preset ensemble learninganomaly detection model, carrying out anomaly detection on time sequence data features in the target power grid index data, and obtaining an anomaly detection result, wherein the basic model of the integrated learning anomaly detection model comprises at least one of a statistics anomaly detection model, a data distance anomaly detection model and a clustering anomaly detection model. Therefore, unsupervised anomaly detection of the power grid index data can be realized in combination with an integration algorithm, so that anomaly monitoring of various types of power grid index data on a macroscopic level can be realized, and appropriate and comprehensive detection of various anomaly modes in operation activities of a power grid enterprise is realized.
Description
technical field [0001] The invention relates to the technical field of data processing, in particular to a method and device for detecting anomalies in power grid data based on integrated learning. Background technique [0002] With the improvement of power grid informatization, the amount of power grid data is also increasing, and the task of anomaly analysis for power grid data is becoming more and more heavy. At present, the anomaly detection for the power grid is mainly concentrated in the field of operation and maintenance monitoring, such as abnormal network traffic, high or low temperature and other common abnormal phenomena or faults in the process of power grid information operation and maintenance. Due to the limitation of traditional anomaly detection technology, the anomaly detection problem that can be detected is closely related to the time factor, that is, the anomaly can be reflected in the data set when it is formed in the early stage, and the development of...
Claims
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Application Information
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