CN117195092A - Electromagnetic interference detection method and detection system in edge computing network - Google Patents
Electromagnetic interference detection method and detection system in edge computing network Download PDFInfo
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- CN117195092A CN117195092A CN202311155275.8A CN202311155275A CN117195092A CN 117195092 A CN117195092 A CN 117195092A CN 202311155275 A CN202311155275 A CN 202311155275A CN 117195092 A CN117195092 A CN 117195092A
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Abstract
The invention provides an electromagnetic interference detection method and a detection system in an edge computing network, wherein the detection method comprises the following steps: collecting flow signals of a plurality of devices in an edge computing network; analyzing the flow signal of each device to determine whether the device is subject to electromagnetic interference; acquiring a training data set, wherein the training data set comprises a plurality of training samples, and each training sample comprises a flow signal of equipment and a mark for judging whether weak electromagnetic interference is received; training an LSTM-AE model based on the training data set; and inputting the flow signal of the equipment to be tested into the trained LSTM-AE model, and obtaining the result of whether the equipment to be tested is subjected to weak electromagnetic interference or not, which is output by the model. By the method and the device, whether each device in the edge computing network is subjected to electromagnetic interference or not can be detected, and electromagnetic inhibition shielding is further carried out on the position, subjected to electromagnetic interference, in the edge computing network.
Description
Technical Field
The present invention relates to the field of electromagnetic interference detection, and more particularly, to an electromagnetic interference detection method and detection system in an edge computing network.
Background
With the development of big data, edge computing networks are becoming more and more widely used, and are interconnected by a number of devices.
However, during the process of data communication of the device, an attacker may perform electromagnetic interference on the device, and modify the data of the device by using the electromagnetic interference, so that the server makes an erroneous decision, and therefore, it is required to detect whether the device in the edge computing network is subjected to the electromagnetic interference.
Disclosure of Invention
The invention provides an electromagnetic interference detection method and a detection system in an edge computing network aiming at the technical problems existing in the prior art.
According to a first aspect of the present invention, there is provided a method for electromagnetic interference detection in an edge computing network, comprising:
collecting flow signals of a plurality of devices in an edge computing network;
analyzing the flow signal of each device to determine whether the device is subject to electromagnetic interference;
acquiring a training data set, wherein the training data set comprises a plurality of training samples, and each training sample comprises a flow signal of equipment and a mark for judging whether weak electromagnetic interference is received;
training an LSTM-AE model based on the training data set;
and inputting the flow signal of the equipment to be tested into the trained LSTM-AE model, and obtaining the result of whether the equipment to be tested is subjected to weak electromagnetic interference or not, which is output by the model.
On the basis of the technical scheme, the invention can also make the following improvements.
Optionally, the edge computing network includes a plurality of devices and a plurality of router gateways, each device accessing the router gateway, and detecting traffic signals of each device in real time through the router gateway.
Optionally, the analyzing the flow signal of each device to determine whether the device is subject to electromagnetic interference includes:
detecting the size and the flow rate of the flow signal of each device in real time, and judging whether the device is subjected to electromagnetic interference or not according to the change trend of the size and the flow rate of the flow signal of each device.
Optionally, the determining whether the device is subject to the electromagnetic interference according to the magnitude and the flow rate of the flow signal of each device includes:
if the magnitude and flow rate of the flow signal of the device are suddenly changed within a period of time, the device is judged to be subjected to weak electromagnetic interference.
Optionally, the acquiring a training data set includes:
and acquiring flow signals of a plurality of devices which are subjected to weak electromagnetic interference and flow signals of a plurality of devices which are not subjected to weak electromagnetic interference to form a training data set.
Optionally, inputting the flow signal of the device to be tested into the trained LSTM-AE model, and obtaining the result of whether the device to be tested output by the model is subjected to weak electromagnetic interference, and then further includes:
and acquiring the position of the tested equipment subjected to weak electromagnetic interference in the edge computing network, and performing electromagnetic interference suppression or shielding on the position of the edge computing network subjected to the electromagnetic interference by using an electromagnetic interference suppressor.
According to a second aspect of the present invention, there is provided an electromagnetic interference detection system in an edge computing network, comprising:
the acquisition module is used for acquiring flow signals of a plurality of devices in the edge computing network;
the judging module is used for analyzing the flow signal of each device and judging whether the device is subjected to electromagnetic interference;
a first acquisition module for acquiring a training data set, the training data set comprising a plurality of training samples, each training sample comprising a traffic signal of the device and a signature of whether the device is subject to weak electromagnetic interference;
the training module is used for training the LSTM-AE model based on the training data set;
the second acquisition module is used for inputting the flow signal of the equipment to be tested into the trained LSTM-AE model and acquiring the result of whether the equipment to be tested output by the model is subjected to weak electromagnetic interference or not.
According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor for implementing the steps of the electromagnetic interference detection method in an edge computing network when executing a computer management class program stored in the memory.
According to a fourth aspect of the present invention there is provided a computer readable storage medium having stored thereon a computer management class program which when executed by a processor implements the steps of a method of electromagnetic interference detection in an edge computing network.
The electromagnetic interference detection method and the detection system in the edge computing network can detect whether each device in the edge computing network is subjected to electromagnetic interference or not, and further electromagnetic inhibition and shielding are carried out on the position, subjected to electromagnetic interference, in the edge computing network.
Drawings
Fig. 1 is a flowchart of an electromagnetic interference detection method in an edge computing network according to the present invention;
fig. 2 is a schematic structural diagram of an electromagnetic interference detection system in an edge computing network according to the present invention;
fig. 3 is a schematic hardware structure of one possible electronic device according to the present invention;
fig. 4 is a schematic hardware structure of a possible computer readable storage medium according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. In addition, the technical features of each embodiment or the single embodiment provided by the invention can be combined with each other at will to form a feasible technical scheme, and the combination is not limited by the sequence of steps and/or the structural composition mode, but is necessarily based on the fact that a person of ordinary skill in the art can realize the combination, and when the technical scheme is contradictory or can not realize, the combination of the technical scheme is not considered to exist and is not within the protection scope of the invention claimed.
Fig. 1 is a flowchart of a method for detecting electromagnetic interference in an edge computing network according to the present invention, where, as shown in fig. 1, the method includes:
and step 1, collecting flow signals of a plurality of devices in an edge computing network.
The edge computing network includes a plurality of devices and a plurality of router gateways, each device accesses the router gateway, and during the process of surfing the internet through the router gateway, the device may be affected by electromagnetic interference, so it is necessary to detect whether the device is affected by electromagnetic interference. The traffic signal of each device is detected in real time by the router gateway.
And 2, analyzing the flow signal of each device to determine whether the device is subjected to electromagnetic interference.
As an embodiment, the analyzing the flow signal of each device to determine whether the device is subject to electromagnetic interference includes: detecting the size and the flow rate of the flow signal of each device in real time, and judging whether the device is subjected to electromagnetic interference or not according to the change trend of the size and the flow rate of the flow signal of each device.
Wherein, according to the magnitude and the flow rate of the flow signal of each device, the judging whether the device is subject to the electromagnetic interference or not comprises: if the magnitude and flow rate of the flow signal of the device are suddenly changed within a period of time, the device is judged to be subjected to weak electromagnetic interference.
It can be understood that the flow signal of each device flowing through the router gateway is collected, and if the flow of the device suddenly changes, for example, the device is determined to be affected by weak electromagnetic interference, and otherwise, the device is not affected by weak electromagnetic interference.
And step 3, acquiring a training data set, wherein the training data set comprises a plurality of training samples, and each training sample comprises a flow signal of the equipment and a mark whether the equipment is subjected to weak electromagnetic interference or not.
As an embodiment, the acquiring a training data set includes: and acquiring flow signals of a plurality of devices which are subjected to weak electromagnetic interference and flow signals of a plurality of devices which are not subjected to weak electromagnetic interference to form a training data set.
It can be appreciated that according to step 2, it can be determined whether each device in the edge computing network is affected by weak electromagnetic interference, and a training data set is formed by collecting traffic signals of a plurality of devices that are affected by electromagnetic interference and traffic signals of devices that are not affected by electromagnetic interference.
And 4, training an LSTM-AE model based on the training data set.
And 5, inputting the flow signal of the equipment to be tested into the trained LSTM-AE model, and obtaining the result of whether the equipment to be tested is subjected to weak electromagnetic interference or not, which is output by the model.
It is appreciated that training an LSTM-AE network based on a training data set may be used to detect whether devices in an edge computing network are subject to weak electromagnetic interference. During detection, the flow signal of the equipment to be detected is input into a trained LSTM-AE model, and the result of weak electromagnetic interference is output by the model.
If the device to be tested is subjected to weak electromagnetic interference, the position of the device to be tested in the edge computing network is obtained, and the electromagnetic interference inhibitor is utilized to inhibit or shield the position of the edge computing network subjected to electromagnetic interference.
Referring to fig. 2, an electromagnetic interference detection system in an edge computing network provided by the present invention includes an acquisition module 201, a determination module 202, a first acquisition module 203, a training module 204, and a second acquisition module 205, where:
the acquisition module 201 is configured to acquire flow signals of a plurality of devices in an edge computing network;
a determining module 202, configured to analyze the flow signal of each device and determine whether the device is subject to electromagnetic interference;
a first obtaining module 203, configured to obtain a training data set, where the training data set includes a plurality of training samples, and each training sample includes a traffic signal of a device and a flag that whether the traffic signal is subjected to weak electromagnetic interference;
a training module 204 for training the LSTM-AE model based on the training dataset;
the second obtaining module 205 is configured to input the flow signal of the device under test to the trained LSTM-AE model, and obtain a result of whether the device under test output by the model is subjected to weak electromagnetic interference.
It can be understood that the electromagnetic interference detection system in the edge computing network provided by the present invention corresponds to the electromagnetic interference detection method in the edge computing network provided in the foregoing embodiments, and the relevant technical features of the electromagnetic interference detection system in the edge computing network may refer to the relevant technical features of the electromagnetic interference detection method in the edge computing network, which are not described herein again.
Referring to fig. 3, fig. 3 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 3, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and capable of running on the processor 320, where the processor 320 executes the computer program 311 to implement steps of a method for detecting electromagnetic interference in an edge computing network.
Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of a computer readable storage medium according to the present invention. As shown in fig. 4, the present embodiment provides a computer readable storage medium 400 having stored thereon a computer program 411, which computer program 411 when executed by a processor implements the steps of a method for electromagnetic interference detection in an edge computing network.
The electromagnetic interference detection method and the detection system in the edge computing network can detect whether each device in the edge computing network is subjected to electromagnetic interference or not, and then electromagnetic inhibition shielding is carried out on the position of the edge computing network subjected to electromagnetic interference.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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 computer, 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A method for electromagnetic interference detection in an edge computing network, comprising:
collecting flow signals of a plurality of devices in an edge computing network;
analyzing the flow signal of each device to determine whether the device is subject to electromagnetic interference;
acquiring a training data set, wherein the training data set comprises a plurality of training samples, and each training sample comprises a flow signal of equipment and a mark for judging whether weak electromagnetic interference is received;
training an LSTM-AE model based on the training data set;
and inputting the flow signal of the equipment to be tested into the trained LSTM-AE model, and obtaining the result of whether the equipment to be tested is subjected to weak electromagnetic interference or not, which is output by the model.
2. The electromagnetic interference detection method of claim 1, wherein the edge computing network comprises a plurality of devices and a plurality of router gateways, each device accessing the router gateway through which the traffic signal of each device is detected in real-time.
3. The method of claim 1, wherein analyzing the traffic signal of each device to determine whether the device is subject to the electromagnetic interference comprises:
detecting the size and the flow rate of the flow signal of each device in real time, and judging whether the device is subjected to electromagnetic interference or not according to the change trend of the size and the flow rate of the flow signal of each device.
4. The method of claim 3, wherein determining whether each device is subject to the electromagnetic interference based on the magnitude and flow rate of the traffic signal of the device comprises:
if the magnitude and flow rate of the flow signal of the device are suddenly changed within a period of time, the device is judged to be subjected to weak electromagnetic interference.
5. The electromagnetic interference detection method of claim 1, wherein the acquiring a training data set comprises:
and acquiring flow signals of a plurality of devices which are subjected to weak electromagnetic interference and flow signals of a plurality of devices which are not subjected to weak electromagnetic interference to form a training data set.
6. The method for detecting electromagnetic interference according to claim 1, wherein the inputting the flow signal of the device under test into the trained LSTM-AE model, obtaining the result of whether the device under test output by the model is subjected to weak electromagnetic interference, and then further comprises:
and acquiring the position of the tested equipment subjected to weak electromagnetic interference in the edge computing network, and performing electromagnetic interference suppression or shielding on the position of the edge computing network subjected to the electromagnetic interference by using an electromagnetic interference suppressor.
7. An electromagnetic interference detection system in an edge computing network, comprising:
the acquisition module is used for acquiring flow signals of a plurality of devices in the edge computing network;
the judging module is used for analyzing the flow signal of each device and judging whether the device is subjected to electromagnetic interference;
a first acquisition module for acquiring a training data set, the training data set comprising a plurality of training samples, each training sample comprising a traffic signal of the device and a signature of whether the device is subject to weak electromagnetic interference;
the training module is used for training the LSTM-AE model based on the training data set;
the second acquisition module is used for inputting the flow signal of the equipment to be tested into the trained LSTM-AE model and acquiring the result of whether the equipment to be tested output by the model is subjected to weak electromagnetic interference or not.
8. An electronic device comprising a memory, a processor for implementing the steps of the electromagnetic interference detection method in an edge computing network according to any of claims 1-6 when executing a computer management class program stored in the memory.
9. A computer readable storage medium, having stored thereon a computer management class program which when executed by a processor implements the steps of the electromagnetic interference detection method in an edge computing network according to any of claims 1-6.
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| CN202311155275.8A CN117195092A (en) | 2023-09-07 | 2023-09-07 | Electromagnetic interference detection method and detection system in edge computing network |
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| CN202311155275.8A CN117195092A (en) | 2023-09-07 | 2023-09-07 | Electromagnetic interference detection method and detection system in edge computing network |
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