CN117354066A - Abnormal data processing system for power communication flow prediction - Google Patents
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Abstract
The invention discloses an abnormal data processing system for power communication flow prediction, which can efficiently process abnormal data in a power communication network by introducing an advanced data processing algorithm and model, conduct flow prediction and analysis, improve the stability and reliability of the system, improve the processing speed and accuracy of the abnormal data and effectively reduce the fault risk of the power communication system; based on flow prediction analysis, an important reference basis is provided for power communication network planning and operation decision-making; advanced machine learning and data mining technologies are adopted, large data resources are fully utilized, the system performance is superior, and the application range is wide.
Description
Technical Field
The invention relates to the technical field of power communication data processing, in particular to an abnormal data processing system for power communication flow prediction.
Background
With the scale expansion of the power communication network and the increase of service demands, the scale of network data traffic is larger and larger, so that the problems of abnormal data processing, traffic prediction and the like are increasingly complicated. In the power communication system, abnormal data such as network attacks, equipment failures, etc. may have serious influence on the system, so that development of a special system for processing such abnormal data and performing traffic prediction and analysis is urgently required.
The abnormal data processing system must be capable of rapidly and accurately identifying and processing various types of abnormal data, including but not limited to data interference, abnormal traffic, malicious attacks, and the like, in the face of large-scale data. At the same time, the system should also have the ability to accurately predict and analyze the flow of the power communication network to meet the demands for network performance and stability.
Therefore, in the field of power communication, an abnormal data processing system integrating advanced algorithms and technologies is developed, so that abnormal data can be efficiently processed, flow prediction is performed, system decision is supported, and the safety, the running efficiency and the reliability of a network are deeply influenced.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems with the existing power communication systems.
Therefore, the technical problems solved by the invention are as follows: the method solves the problem that the problems of abnormal data processing, flow prediction and the like of the existing power communication system are increasingly complicated.
In order to solve the technical problems, the invention provides the following technical scheme: the abnormal data processing system for power communication flow prediction comprises the following components, a data acquisition and preprocessing unit, a data processing unit and a data processing unit, wherein the data acquisition and preprocessing unit is used for networking big data and acquiring and preprocessing power communication data; the analysis unit is in data connection with the data acquisition and preprocessing unit, a flow characteristic model is constructed, and a first flow abnormality index of each terminal is calculated according to the preprocessed network flow data packet; according to the first flow anomaly index, based on K-fold cross validation, a non-greedy teaching and learning optimization algorithm is adopted to determine a network flow anomaly detection model; the network anomaly detection model takes network state data, protocol analysis data and service operation state data as input and takes network flow state as output; and determining the network traffic state of the communication network by adopting the network anomaly detection model according to the network state data, the protocol analysis data and the service operation state data of the converged communication network to be detected.
As a preferable mode of the anomaly data processing system for power communication traffic prediction according to the present invention, wherein: the data acquisition and preprocessing unit specifically comprises the following steps of acquiring network communication topology of a plurality of terminals and a master station in a networking way, performing bypass extraction on network traffic of the plurality of terminals, and acquiring network traffic data packets of the plurality of terminals after standardized processing; compressing a plurality of network traffic data packets; to the analysis unit.
As a preferable mode of the anomaly data processing system for power communication traffic prediction according to the present invention, wherein: the analysis unit further comprises training of the flow characteristic model after constructing the flow characteristic model;
the training process specifically comprises the following steps: for each of a plurality of terminals, collecting a cluster of network traffic according to a preset period; based on the selected statistical features, counting the cluster of network flows according to the statistical features, and generating statistical feature data comprising a plurality of statistical features; calculating the information entropy of the statistical feature data to generate flow training data, including:
the information entropy calculation formula of the statistical characteristic data is as follows:
wherein: h (X) is the information entropy of the statistical feature data, X represents the N states of the feature, X= { X i |i＝1 ,2 ,… ,N}，n i Is the i-th state x i The number of occurrences, S, represents the total number of occurrences of the N states of the statistical feature, N i S represents the ith state x i Probability of occurrence, H (X) ∈ [0, log 2 N]。
As a preferable mode of the anomaly data processing system for power communication traffic prediction according to the present invention, wherein: the analysis unit trains the flow characteristic model through the flow training data to generate a flow characteristic model of each of a plurality of terminals; when the first flow abnormality index is larger than a set first threshold value, judging that the flow of the terminal is abnormal; or when the first flow abnormality index is smaller than a set first threshold value, analyzing the network flow data packets of the plurality of terminals, and calculating a second flow abnormality index of each terminal according to the data packet characteristic model and the analysis data of the network flow data packets of the plurality of terminals; calculating a flow anomaly composite index based on the first flow anomaly index and the second flow anomaly index according to the determined weights of the first flow anomaly index and the second flow anomaly index; and when the flow abnormality comprehensive index is larger than a set second threshold value, judging that the flow of the terminal is abnormal.
As a preferable mode of the anomaly data processing system for power communication traffic prediction according to the present invention, wherein: based on K-fold cross validation, the network traffic abnormality detection model is determined by adopting a non-greedy teaching and learning optimization algorithm according to the first traffic abnormality index, and the method specifically comprises the following steps: acquiring a machine learning algorithm; the machine learning algorithm includes: support vector machines, decision trees, and neural networks; based on K-fold cross validation, adopting a non-greedy teaching and learning optimization algorithm to optimize parameters of the machine learning algorithm; the parameters comprise penalty coefficients and kernel widths; and determining a network flow abnormality detection model according to the flow data and the optimized parameters of the machine learning algorithm.
As a preferable mode of the anomaly data processing system for power communication traffic prediction according to the present invention, wherein: preprocessing includes standard format conversion, normalization processing, and invalid data purging.
As a preferable mode of the anomaly data processing system for power communication traffic prediction according to the present invention, wherein: parameters that optimize the machine learning algorithm include in particular,
acquiring a first training data set, wherein the first training data set comprises real electric quantity change data of any two power grid nodes which are mutually communicated in the power grid system in N continuous time periods;
and training the electric quantity change prediction model by using the first training data set, and inputting real electric quantity change data corresponding to an ith time period in the N continuous time periods into the electric quantity change prediction model during training to obtain predicted electric quantity change data corresponding to an (i+1) th time period, and updating parameters of the electric quantity change prediction model based on errors of the predicted electric quantity change data corresponding to the (i+1) th time period and the real electric quantity change data of the (i+1) th time period until the errors of the predicted electric quantity change data and the corresponding real electric quantity change data meet a first preset condition, so as to obtain the trained electric quantity change prediction model, wherein N is a positive integer greater than or equal to 2, and i is sequentially 1 to N-1.
The invention has the beneficial effects that: the invention provides an abnormal data processing system for power communication flow prediction, which can efficiently process abnormal data in a power communication network by introducing an advanced data processing algorithm and model, and conduct flow prediction and analysis, thereby improving the stability and reliability of the system, and has the following advantages:
1. the processing speed and accuracy of the abnormal data are improved, and the fault risk of the power communication system is effectively reduced;
2. based on flow prediction analysis, an important reference basis is provided for power communication network planning and operation decision-making;
3. advanced machine learning and data mining technologies are adopted, large data resources are fully utilized, the system performance is superior, and the application range is wide. Therefore, the invention has higher practical and popularization value and has positive significance for improving the operation efficiency and stability of the power communication network.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
With the scale expansion of the power communication network and the increase of service demands, the scale of network data traffic is larger and larger, so that the problems of abnormal data processing, traffic prediction and the like are increasingly complicated. In the power communication system, abnormal data such as network attacks, equipment failures, etc. may have serious influence on the system, so that development of a special system for processing such abnormal data and performing traffic prediction and analysis is urgently required.
Accordingly, the present invention provides an anomaly data processing system for power communication traffic prediction, comprising the following components,
the data acquisition and preprocessing unit is used for networking big data and acquiring and preprocessing power communication data;
the analysis unit is in data connection with the data acquisition and preprocessing unit, a flow characteristic model is constructed, and a first flow abnormality index of each terminal is calculated according to the preprocessed network flow data packet;
according to the first flow anomaly index, based on K-fold cross validation, a non-greedy teaching and learning optimization algorithm is adopted to determine a network flow anomaly detection model; the network anomaly detection model takes network state data, protocol analysis data and service operation state data as input and takes network flow state as output;
and determining the network traffic state of the communication network by adopting the network anomaly detection model according to the network state data, the protocol analysis data and the service operation state data of the converged communication network to be detected.
Further, the data acquisition and preprocessing unit specifically comprises the following steps when acquiring and preprocessing the power communication data,
networking to obtain network communication topology of a plurality of terminals and a master station, carrying out bypass extraction on network traffic of the plurality of terminals, and obtaining network traffic data packets of the plurality of terminals after standardized processing;
compressing a plurality of network traffic data packets;
to the analysis unit.
Further, the analysis unit further comprises training of the flow characteristic model after constructing the flow characteristic model;
the training process specifically comprises the following steps:
for each of a plurality of terminals, collecting a cluster of network traffic according to a preset period;
based on the selected statistical features, counting the cluster of network flows according to the statistical features, and generating statistical feature data comprising a plurality of statistical features;
calculating the information entropy of the statistical feature data to generate flow training data, including:
the information entropy calculation formula of the statistical characteristic data is as follows:
wherein: h (X) is the information entropy of the statistical feature data, X represents the N states of the feature, X= { X i |i＝1 ,2 ,… ,N}，n i Is the i-th state x i The number of occurrences, S, represents the total number of occurrences of the N states of the statistical feature, N i S represents the ith state x i Probability of occurrence, H (X) ∈ [0, log 2 N]。
Further, the analysis unit trains the flow characteristic model through the flow training data to generate a flow characteristic model of each of a plurality of terminals;
when the first flow abnormality index is larger than a set first threshold value, judging that the flow of the terminal is abnormal;
or when the first flow abnormality index is smaller than a set first threshold value, analyzing the network flow data packets of the plurality of terminals, and calculating a second flow abnormality index of each terminal according to the data packet characteristic model and the analysis data of the network flow data packets of the plurality of terminals;
calculating a flow anomaly composite index based on the first flow anomaly index and the second flow anomaly index according to the determined weights of the first flow anomaly index and the second flow anomaly index;
and when the flow abnormality comprehensive index is larger than a set second threshold value, judging that the flow of the terminal is abnormal.
Further, the determining the network traffic anomaly detection model based on the first traffic anomaly index and based on K-fold cross validation by adopting a non-greedy teaching and learning optimization algorithm specifically includes:
acquiring a machine learning algorithm; the machine learning algorithm includes: support vector machines, decision trees, and neural networks;
based on K-fold cross validation, adopting a non-greedy teaching and learning optimization algorithm to optimize parameters of the machine learning algorithm; the parameters comprise penalty coefficients and kernel widths;
and determining a network flow abnormality detection model according to the flow data and the optimized parameters of the machine learning algorithm.
It should be noted that preprocessing includes standard format conversion, normalization processing, and invalid data clearing.
Additionally, the parameters that optimize the machine learning algorithm include, in particular,
acquiring a first training data set, wherein the first training data set comprises real electric quantity change data of any two power grid nodes which are mutually communicated in the power grid system in N continuous time periods;
and training the electric quantity change prediction model by using the first training data set, and inputting real electric quantity change data corresponding to an ith time period in the N continuous time periods into the electric quantity change prediction model during training to obtain predicted electric quantity change data corresponding to an (i+1) th time period, and updating parameters of the electric quantity change prediction model based on errors of the predicted electric quantity change data corresponding to the (i+1) th time period and the real electric quantity change data of the (i+1) th time period until the errors of the predicted electric quantity change data and the corresponding real electric quantity change data meet a first preset condition, so as to obtain the trained electric quantity change prediction model, wherein N is a positive integer greater than or equal to 2, and i is sequentially 1 to N-1.
The invention provides an abnormal data processing system for power communication flow prediction, which can efficiently process abnormal data in a power communication network by introducing an advanced data processing algorithm and model, and conduct flow prediction and analysis, thereby improving the stability and reliability of the system, and has the following advantages:
1. the processing speed and accuracy of the abnormal data are improved, and the fault risk of the power communication system is effectively reduced;
2. based on flow prediction analysis, an important reference basis is provided for power communication network planning and operation decision-making;
3. advanced machine learning and data mining technologies are adopted, large data resources are fully utilized, the system performance is superior, and the application range is wide. Therefore, the invention has higher practical and popularization value and has positive significance for improving the operation efficiency and stability of the power communication network.
It should be noted that the above embodiments are only 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 the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (7)
1. An exception data processing system for power communication traffic prediction, characterized by: comprising the following components of the device,
the data acquisition and preprocessing unit is used for networking big data and acquiring and preprocessing power communication data;
the analysis unit is in data connection with the data acquisition and preprocessing unit, a flow characteristic model is constructed, and a first flow abnormality index of each terminal is calculated according to the preprocessed network flow data packet;
according to the first flow anomaly index, based on K-fold cross validation, a non-greedy teaching and learning optimization algorithm is adopted to determine a network flow anomaly detection model; the network anomaly detection model takes network state data, protocol analysis data and service operation state data as input and takes network flow state as output;
and determining the network traffic state of the communication network by adopting the network anomaly detection model according to the network state data, the protocol analysis data and the service operation state data of the converged communication network to be detected.
2. The anomaly data processing system for power communication traffic prediction of claim 1, wherein: the data acquisition and preprocessing unit specifically comprises the following steps when acquiring and preprocessing the power communication data,
networking to obtain network communication topology of a plurality of terminals and a master station, carrying out bypass extraction on network traffic of the plurality of terminals, and obtaining network traffic data packets of the plurality of terminals after standardized processing;
compressing a plurality of network traffic data packets;
to the analysis unit.
3. The anomaly data processing system for power communication traffic prediction of claim 2, wherein: the analysis unit further comprises training of the flow characteristic model after constructing the flow characteristic model;
the training process specifically comprises the following steps:
for each of a plurality of terminals, collecting a cluster of network traffic according to a preset period;
based on the selected statistical features, counting the cluster of network flows according to the statistical features, and generating statistical feature data comprising a plurality of statistical features;
calculating the information entropy of the statistical feature data to generate flow training data, including:
the information entropy calculation formula of the statistical characteristic data is as follows:
；
wherein: h (X) is the information entropy of the statistical feature data, X represents the N states of the feature, X= { X i |i＝1 ,2 ,… ,N}，n i Is the i-th state x i The number of occurrences, S, represents the total number of occurrences of the N states of the statistical feature, N i S represents the ith state x i Probability of occurrence, H (X) ∈ [0, log 2 N]。
4. An anomaly data processing system for power communication traffic prediction according to claim 3, wherein: the analysis unit trains the flow characteristic model through the flow training data to generate a flow characteristic model of each of a plurality of terminals;
when the first flow abnormality index is larger than a set first threshold value, judging that the flow of the terminal is abnormal;
or when the first flow abnormality index is smaller than a set first threshold value, analyzing the network flow data packets of the plurality of terminals, and calculating a second flow abnormality index of each terminal according to the data packet characteristic model and the analysis data of the network flow data packets of the plurality of terminals;
calculating a flow anomaly composite index based on the first flow anomaly index and the second flow anomaly index according to the determined weights of the first flow anomaly index and the second flow anomaly index;
and when the flow abnormality comprehensive index is larger than a set second threshold value, judging that the flow of the terminal is abnormal.
5. The anomaly data processing system for power communication traffic prediction of claim 4, wherein: based on K-fold cross validation, the network traffic abnormality detection model is determined by adopting a non-greedy teaching and learning optimization algorithm according to the first traffic abnormality index, and the method specifically comprises the following steps:
acquiring a machine learning algorithm; the machine learning algorithm includes: support vector machines, decision trees, and neural networks;
based on K-fold cross validation, adopting a non-greedy teaching and learning optimization algorithm to optimize parameters of the machine learning algorithm; the parameters comprise penalty coefficients and kernel widths;
and determining a network flow abnormality detection model according to the flow data and the optimized parameters of the machine learning algorithm.
6. The anomaly data processing system for power communication traffic prediction of claim 5, wherein: preprocessing includes standard format conversion, normalization processing, and invalid data purging.
7. The anomaly data processing system for power communication traffic prediction of claim 6, wherein: parameters that optimize the machine learning algorithm include in particular,
acquiring a first training data set, wherein the first training data set comprises real electric quantity change data of any two power grid nodes which are mutually communicated in the power grid system in N continuous time periods;
and training the electric quantity change prediction model by using the first training data set, and inputting real electric quantity change data corresponding to an ith time period in the N continuous time periods into the electric quantity change prediction model during training to obtain predicted electric quantity change data corresponding to an (i+1) th time period, and updating parameters of the electric quantity change prediction model based on errors of the predicted electric quantity change data corresponding to the (i+1) th time period and the real electric quantity change data of the (i+1) th time period until the errors of the predicted electric quantity change data and the corresponding real electric quantity change data meet a first preset condition, so as to obtain the trained electric quantity change prediction model, wherein N is a positive integer greater than or equal to 2, and i is sequentially 1 to N-1.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111092862A (en) * | 2019-11-29 | 2020-05-01 | 中国电力科学研究院有限公司 | Method and system for detecting abnormal communication flow of power grid terminal |
| CN112396135A (en) * | 2021-01-21 | 2021-02-23 | 北京电信易通信息技术股份有限公司 | Method and system for detecting abnormal traffic of converged communication network |
| CN114157486A (en) * | 2021-12-03 | 2022-03-08 | 上海斗象信息科技有限公司 | Communication flow data abnormity detection method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111092862A (en) * | 2019-11-29 | 2020-05-01 | 中国电力科学研究院有限公司 | Method and system for detecting abnormal communication flow of power grid terminal |
| CN112396135A (en) * | 2021-01-21 | 2021-02-23 | 北京电信易通信息技术股份有限公司 | Method and system for detecting abnormal traffic of converged communication network |
| CN114157486A (en) * | 2021-12-03 | 2022-03-08 | 上海斗象信息科技有限公司 | Communication flow data abnormity detection method and device, electronic equipment and storage medium |
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