CN119629172A - A switch control method and system for Internet of Things based on edge computing - Google Patents
A switch control method and system for Internet of Things based on edge computing Download PDFInfo
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- H—ELECTRICITY
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
The invention provides an Internet of things switch control method and system based on edge calculation, relates to the technical field of Internet of things, and aims to realize data interaction with equipment by deploying an edge device at the network edge of target Internet of things switch equipment. The edge node is responsible for collecting state data of equipment and carrying out data processing and anomaly detection, the edge server collects communication state data of all edge computing nodes and analyzes connection relations among the nodes, a control instruction is generated according to a data detection result and is sent to target internet of things switch equipment in real time, meanwhile, the edge server carries out real-time updating and optimization according to feedback of data processing and anomaly detection, a network strategy is dynamically adjusted, an encryption technology and an access control strategy are introduced, and safety of data transmission and storage is protected. According to the method, the delay is reduced, the processing efficiency is improved, the safety and the reliability are enhanced, the method can effectively meet the requirements of modern intelligent application, and the overall performance of the Internet of things system is improved.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to an Internet of things switch control method and system based on edge calculation.
Background
With the rapid development of internet of things (IoT), the number of intelligent devices connected to a network has exploded. The traditional cloud computing mode needs to upload a large amount of data to the cloud for processing, and has the following problems:
(1) High delay, time is required for data transmission and processing, and real-time requirements cannot be met.
(2) High bandwidth consumption and large amounts of data uploading occupy network bandwidth, which may lead to network congestion.
(3) And safety risk that data may be intercepted and tampered in the transmission process, and potential safety hazard exists.
Edge computing is an emerging computing model that can effectively reduce latency, save bandwidth, and improve security by processing data at the network edge. However, most of the existing switch control methods of the internet of things do not fully utilize the advantages of edge calculation, and cannot meet the control requirements of high efficiency and safety.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide the Internet of things switch control method and system based on edge calculation, which can effectively improve the overall performance and user experience of the Internet of things system and adapt to the requirements of modern intelligent application by reducing delay, improving processing efficiency, enhancing safety and reliability and other advantages.
In order to achieve the above object, the present invention provides the following solutions:
An internet of things switch control method based on edge calculation comprises the following steps:
an edge server and a plurality of edge computing nodes are deployed at the network edge of the target internet of things switch device;
The control target internet of things switch equipment establishes connection with each edge node in a wireless or wired mode so as to perform data interaction;
Acquiring state data of the target internet of things switch equipment by utilizing an edge node, and performing data processing and anomaly detection on the state data to obtain a data detection result;
controlling the edge server to collect communication state data of all the edge computing nodes, and according to the communication state data, connecting relations among all the edge computing nodes;
Generating a control instruction according to the data detection result, and sending the control instruction to the target internet of things switch equipment through the edge node so as to realize real-time control;
According to the data processing and anomaly detection processes of the edge computing nodes, the edge server is updated and optimized in real time, the networking strategy is dynamically adjusted, and encryption technology and access control strategy are introduced into the edge server, the edge computing nodes and the target internet of things switch equipment so as to protect data.
Preferably, the acquiring, by using an edge node, state data of the target internet of things switch device, and performing data processing and anomaly detection on the state data to obtain a data detection result, including:
Acquiring state data of the target internet of things switch equipment in real time through the edge node based on a preset communication protocol, wherein the state data comprises a current signal, a voltage signal, an equipment state signal, a temperature signal, an environment signal where the equipment is located and a time sequence signal corresponding to each signal;
coarse granulating and mapping the time sequence signals to obtain simplified sequence signals;
Carrying out standardized processing on the current signal, the voltage signal, the equipment state signal, the temperature signal and the environmental signal where the equipment is located to obtain a standard signal;
denoising the standard signal to obtain a denoised signal;
Extracting features of the denoising signals and the corresponding simplified sequence signals to obtain abnormal features;
and inputting the abnormal characteristics into a trained equipment abnormal state detection model to obtain the data detection result.
Preferably, the coarsening and mapping process is performed on the time-series signal to obtain a simplified-series signal, including:
Based on the formula Coarse-graining the time-series signal to obtain a coarse-grained sequence, wherein,For a preset adaptive scale factorThe ith data of the lower coarsening sequence,For the b-th data point in the time series signal,For the length of the time series,For coarse-grained sequence data number, adaptive scale factorThe calculation formula of (2) is as follows: the standard deviation of the time sequence is that mu is the average value of the time sequence, and k is the preset experience constant of the target internet of things switch equipment;
distributing the coarse-grained sequence to the interval from 0 to 1 to obtain a distributed sequence;
Mapping the allocated sequence to an integer between 1 and N+ to obtain the simplified sequence signal, wherein N+ is an integer greater than 1.
Preferably, denoising the standard signal to obtain a denoised signal, including:
Translating the standard signal to obtain a translated signal;
Performing wavelet decomposition on the translated signal to obtain a plurality of wavelet coefficients;
determining a filtering threshold according to the decomposition scale and the length of the signal;
Constructing a denoising function according to the filtering threshold;
removing noise of the translated signal by using the denoising function to obtain a preliminary denoising signal;
and carrying out signal smoothing on the preliminary denoising signals by adopting a moving average method, and evaluating the denoising effect through indexes of signal-to-noise ratio and mean square error to determine the final denoising signals.
Preferably, the calculation formula of the filtering threshold is: Wherein, the method comprises the steps of, For the filtering threshold, j is the decomposition scale of the translated signal, d j is the wavelet coefficient of the decomposition scale j, N represents the length of the translated signal, and mean represents the median operation.
Preferably, the anomaly characteristics include time domain characteristics, frequency domain characteristics and time sequence characteristics of the signal.
Preferably, the controlling the edge server to collect communication status data of each edge computing node, and according to the communication status data, the connecting relationship between each edge computing node includes:
Periodically acquiring communication state information of each edge computing node and the edge computing nodes in a preset distance range around the edge computing node by using an edge server, wherein each edge computing node forms a topological network structure for communication interaction;
Inputting the acquired communication state information into a pre-established node connection decision model to obtain a real-time decision result;
And dynamically adjusting the connection relation between each edge computing node in the topological network structure and the edge computing nodes in the surrounding preset distance range in real time based on a real-time decision result to form a self-adaptive topological network structure, wherein the real-time decision result comprises edge computing nodes which need to be connected and disconnected in the topological network structure.
Preferably, the communication status information includes at least one of signal strength, data transmission rate, packet loss rate, delay, power consumption, moving speed, and historical connection quality.
Preferably, encryption technology and access control policy are introduced in the edge server, the edge computing node and the target internet of things switch device to secure data, including:
Encrypting data by adopting an end-to-end encryption technology and a homomorphic encryption algorithm, and storing the encrypted data by adopting a blockchain technology;
distributing access rights according to the roles of the users, and providing various authentication modes of network access by combining biological characteristics and behavior characteristics;
And acquiring performance indexes on the edge server, the edge computing node and the target internet of things switch equipment to perform performance monitoring by combining with a WiFi probe technology.
An internet of things switch control system based on edge computation, comprising:
the deployment module is used for deploying an edge server and a plurality of edge computing nodes on the network edge of the target internet of things switch device;
the connection module is used for controlling the target internet of things switch equipment to establish connection with each edge node in a wireless or wired mode so as to perform data interaction;
the data processing module is used for acquiring the state data of the target internet of things switch equipment by utilizing an edge node, and carrying out data processing and anomaly detection on the state data to obtain a data detection result;
The computing module is used for controlling the edge server to collect communication state data of all the edge computing nodes and controlling the connection relation among all the edge computing nodes according to the communication state data;
the control module is used for generating a control instruction according to the data detection result and sending the control instruction to the target internet of things switch equipment through the edge node so as to realize real-time control;
and the protection module is used for carrying out real-time updating and optimization of the edge server according to the data processing and abnormality detection processes of the edge computing node, carrying out dynamic adjustment of a networking strategy, and introducing an encryption technology and an access control strategy into the edge server, the edge computing node and the target internet of things switch equipment so as to carry out safety protection of data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
The invention provides an Internet of things switch control method and system based on edge calculation, wherein the method comprises the steps of deploying an edge server and a plurality of edge calculation nodes on the network edge of target Internet of things switch equipment, controlling the target Internet of things switch equipment to be connected with each edge node in a wireless or wired mode so as to conduct data interaction, collecting state data of the target Internet of things switch equipment by the edge nodes, conducting data processing and anomaly detection on the state data to obtain a data detection result, controlling the edge server to collect communication state data of each edge calculation node, generating a control instruction according to the connection relation between each edge calculation node of the communication state data, sending the control instruction to the target Internet of things switch equipment by the edge nodes so as to achieve real-time control, conducting real-time updating and optimization of networking strategies according to the data processing and anomaly detection process of the edge calculation nodes, conducting dynamic adjustment of the networking strategies, introducing encryption strategies into the edge server, the edge calculation nodes and the target Internet of things switch equipment so as to conduct security access technology data protection. The invention can effectively improve the overall performance and user experience of the Internet of things system by reducing the delay, improving the processing efficiency, enhancing the safety and the reliability and other advantages, and is suitable for the requirements of modern intelligent application.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
The invention aims to provide an Internet of things switch control method and system based on edge calculation, which can effectively improve the overall performance and user experience of an Internet of things system and adapt to the requirements of modern intelligent application by reducing delay, improving processing efficiency, enhancing safety, reliability and other advantages.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a flowchart of a method provided by an embodiment of the present invention, and as shown in fig. 1, the present invention provides a method for controlling an internet of things switch based on edge calculation, including:
step 100, deploying an edge server and a plurality of edge computing nodes on the network edge of the target internet of things switch device;
Step 200, controlling the target internet of things switch equipment to establish connection with each edge node in a wireless or wired mode so as to perform data interaction;
step 300, acquiring state data of the target internet of things switch equipment by utilizing an edge node, and performing data processing and anomaly detection on the state data to obtain a data detection result;
step 400, controlling an edge server to collect communication state data of all edge computing nodes, and according to the communication state data, connecting relations among all edge computing nodes;
step 500, generating a control instruction according to the data detection result, and sending the control instruction to the target internet of things switch equipment through the edge node so as to realize real-time control;
And 600, according to the data processing and anomaly detection processes of the edge computing nodes, carrying out real-time updating and optimization of the edge servers, carrying out dynamic adjustment of networking strategies, and introducing encryption technology and access control strategies into the edge servers, the edge computing nodes and the target internet of things switch equipment so as to carry out safety protection of the data.
Preferably, the acquiring, by using an edge node, state data of the target internet of things switch device, and performing data processing and anomaly detection on the state data to obtain a data detection result, including:
Acquiring state data of the target internet of things switch equipment in real time through the edge node based on a preset communication protocol, wherein the state data comprises a current signal, a voltage signal, an equipment state signal, a temperature signal, an environment signal where the equipment is located and a time sequence signal corresponding to each signal;
coarse granulating and mapping the time sequence signals to obtain simplified sequence signals;
the current signal, the voltage signal, the equipment state signal, the temperature signal and the environmental signal of the equipment are subjected to standardized processing to obtain a standard signal, and the standardized processing process of the embodiment is exemplified by standardized data with different dimensions so as to facilitate subsequent processing.
Denoising the standard signal to obtain a denoised signal;
Extracting features of the denoising signals and the corresponding simplified sequence signals to obtain abnormal features;
and inputting the abnormal characteristics into a trained equipment abnormal state detection model to obtain the data detection result.
Preferably, the coarsening and mapping process is performed on the time-series signal to obtain a simplified-series signal, including:
Based on the formula Coarse-graining the time-series signal to obtain a coarse-grained sequence, wherein,For a preset adaptive scale factorThe ith data of the lower coarsening sequence,For the b-th data point in the time series signal,For the length of the time series,For coarse-grained sequence data number, adaptive scale factorThe calculation formula of (2) is as follows: the standard deviation of the time sequence is that mu is the average value of the time sequence, and k is the preset experience constant of the target internet of things switch equipment;
distributing the coarse-grained sequence to the interval from 0 to 1 to obtain a distributed sequence;
Mapping the allocated sequence to an integer between 1 and N+ to obtain the simplified sequence signal, wherein N+ is an integer greater than 1.
Further, the present embodiment determines an appropriate k value by analyzing the history data and observing the signal fluctuation when the apparatus is operating under specific conditions. In addition, the embodiment can analyze the distribution characteristics (such as normal distribution, bias distribution and the like) of the time series signals and select the k value suitable for the distribution so as to ensure the representativeness of the signals after coarse graining. Illustratively, in a high noise environment, the present embodiment suggests setting the value of k to 1.5 to 2.5 to ensure that noise can be effectively filtered out during coarse graining, and for switching devices in an industrial environment, the value of k can be between 1.0 and 3.0. Industrial equipment is typically faced with greater load and environmental changes, and therefore greater k values are required to accommodate signal fluctuations. For intelligent switching devices in a home environment, the value of k is typically between 0.5 and 1.5. This is because the signal fluctuations of the home device are relatively small, and selecting a smaller k value may better capture the change in device state.
In particular, the present embodiment provides various benefits for coarsening a time series, including the following:
1. Reducing the amount of data, coarsening can significantly reduce the storage and processing requirements of the data, especially when processing large-scale time-series data. By reducing the number of data points, memory space and computing resources can be saved.
2. Increasing computational efficiency, processing fewer data points generally means faster computational speed. This is important for real-time data processing and analysis, especially in environments where edge computing or resources are limited.
Preferably, denoising the standard signal to obtain a denoised signal, including:
Translating the standard signal to obtain a translated signal;
Performing wavelet decomposition on the translated signal to obtain a plurality of wavelet coefficients;
determining a filtering threshold according to the decomposition scale and the length of the signal;
Constructing a denoising function according to the filtering threshold;
removing noise of the translated signal by using the denoising function to obtain a preliminary denoising signal;
and carrying out signal smoothing on the preliminary denoising signals by adopting a moving average method, and evaluating the denoising effect through indexes of signal-to-noise ratio and mean square error to determine the final denoising signals.
Preferably, the calculation formula of the filtering threshold is: Wherein, the method comprises the steps of, For the filtering threshold, j is the decomposition scale of the translated signal, d j is the wavelet coefficient of the decomposition scale j, N represents the length of the translated signal, and mean represents the median operation.
Preferably, the anomaly characteristics include time domain characteristics, frequency domain characteristics and time sequence characteristics of the signal.
Specifically, the abnormal features of the present embodiment mainly include:
1. time domain features
(1) Mean, the average of the signal over a time window, reflects the overall level of the signal.
(2) Standard deviation (Standard Deviation), the degree of fluctuation of the signal, a larger standard deviation may indicate that there is abnormal fluctuation of the signal.
(3) Maximum and minimum (Max/Min), maximum and minimum of signal, abnormal state may correspond to extremum out of normal range.
(4) Kurtosis (Kurtosis), describes the sharpness of the signal distribution, with higher Kurtosis possibly indicating the presence of abnormal changes in the signal.
(5) Degree of bias (Skewness), describing symmetry of the signal distribution, changes in the degree of bias may indicate abnormal behavior of the signal.
2. Frequency domain features
(1) Spectral features, the frequency content of the signal is analyzed by means of a frequency spectrum obtained by fourier transformation, and abnormal frequency content may indicate equipment failure.
(2) The dominant frequency (Dominant Frequency), the most significant frequency in the signal, typically associated with the normal operating frequency of the device, may represent an anomaly.
(3) Band Energy (Band Energy), the distribution of Energy within a particular Band, the abnormal Energy concentration may be indicative of an abnormal state of the device.
3. Timing characteristics
(1) Rate of Change (Rate of Change) of signal, rate of Change of signal per unit time, rapid changes in anomaly may indicate a fault.
(2) Outlier detection (Anomaly Points), outliers or mutations detected in the time series are typically identified by statistical methods or machine learning models.
Preferably, the controlling the edge server to collect communication status data of each edge computing node, and according to the communication status data, the connecting relationship between each edge computing node includes:
Periodically acquiring communication state information of each edge computing node and the edge computing nodes in a preset distance range around the edge computing node by using an edge server, wherein each edge computing node forms a topological network structure for communication interaction;
Inputting the acquired communication state information into a pre-established node connection decision model to obtain a real-time decision result;
And dynamically adjusting the connection relation between each edge computing node in the topological network structure and the edge computing nodes in the surrounding preset distance range in real time based on a real-time decision result to form a self-adaptive topological network structure, wherein the real-time decision result comprises edge computing nodes which need to be connected and disconnected in the topological network structure.
Preferably, the communication status information includes at least one of signal strength, data transmission rate, packet loss rate, delay, power consumption, moving speed, and historical connection quality.
Preferably, encryption technology and access control policy are introduced in the edge server, the edge computing node and the target internet of things switch device to secure data, including:
Encrypting data by adopting an end-to-end encryption technology and a homomorphic encryption algorithm, and storing the encrypted data by adopting a blockchain technology;
distributing access rights according to the roles of the users, and providing various authentication modes of network access by combining biological characteristics and behavior characteristics;
And acquiring performance indexes on the edge server, the edge computing node and the target internet of things switch equipment to perform performance monitoring by combining with a WiFi probe technology.
Specifically, first, the data encryption of the embodiment adopts an end-to-end encryption technology, so that the data is always in an encrypted state in the transmission process, and man-in-the-middle attack is prevented. Specifically, the data is encrypted by the source device using a symmetric encryption algorithm (such as AES) before transmission, and only the target device holds the decryption key, ensuring confidentiality of the data. At the same time, the homomorphic encryption algorithm (such as Paillier or BGN) is combined, so that calculation on encrypted data is allowed without decryption, thereby protecting data privacy and supporting edge computing nodes to perform necessary data processing. Secondly, the data storage adopts a blockchain technology, and the encrypted data is stored in a distributed account book in the form of blocks. Each data block contains a time stamp, encrypted data and a hash value of the previous block, ensuring the non-tamper-ability and traceability of the data. Through the smart contracts, the system can automatically manage the access rights and authentication process for the data. In terms of access control, the system allocates different access rights according to user roles (such as administrator, general user, visitor, etc.), ensuring that only authorized users can access specific data. Meanwhile, multiple authentication modes are provided by combining biological characteristics (such as fingerprints and facial recognition) and behavioral characteristics (such as operation habits and login time of a user) so as to enhance safety. When a user accesses the system, the user needs to carry out identity verification through multi-factor authentication (MFA), so that only legal users can access sensitive data. Finally, by combining with the WiFi probe technology, performance indexes (such as network delay, data transmission rate, equipment load and the like) are collected on an edge server, an edge computing node and target Internet of things switch equipment in real time, and the system performance is monitored through a data analysis tool. These performance metrics will be used to optimize network configuration and resource allocation, ensuring that the system will still operate efficiently under high load conditions.
Through the steps, the method and the device can realize the safety protection, access control and performance monitoring of the data, and ensure the data safety and the equipment reliability in the environment of the Internet of things.
As an optional implementation manner, in the smart home environment, the internet of things switch control method of the invention is deployed, and the steps are as follows:
1. edge node deployment, edge computing nodes are deployed at a home gateway, and an intelligent control module is built in.
2. And equipment connection, and intelligent lighting, air conditioning and other switching equipment are connected to the edge nodes.
3. And the local data processing and control, when the sensor detects that a person enters the room, the edge node immediately controls the light to be turned on, and the switch and the temperature setting of the air conditioner are automatically adjusted according to the indoor temperature and the preset condition.
4. And secure communication is realized, and all data transmission adopts an AES encryption algorithm, so that the communication security is ensured.
5. The cloud cooperation enables a user to connect with a cloud server through a mobile phone App and remotely check equipment states and control logs.
As another optional implementation mode, in the industrial Internet of things, the method is applied to control production line equipment, and the method specifically comprises the following steps:
1. Edge node deployment, and high-performance edge computing nodes are deployed in a production workshop.
2. The equipment connections, machine equipment and sensors within the plant are connected to the edge nodes.
3. And (3) processing and controlling local data, and monitoring the running state of equipment in real time, such as parameters of temperature, vibration and the like. When an abnormal condition is detected, the edge node immediately sends out an instruction, and the operation of the equipment is stopped, so that accidents are prevented.
4. Secure communication, establishing a secure communication channel using VPN and SSL protocols.
5. And the cloud end is cooperated, and the production data and the equipment log are uploaded to the cloud end regularly, so that big data analysis and decision making are supported.
Corresponding to the above method, the embodiment further provides an internet of things switch control system based on edge calculation, which comprises:
the deployment module is used for deploying an edge server and a plurality of edge computing nodes on the network edge of the target internet of things switch device;
the connection module is used for controlling the target internet of things switch equipment to establish connection with each edge node in a wireless or wired mode so as to perform data interaction;
the data processing module is used for acquiring the state data of the target internet of things switch equipment by utilizing an edge node, and carrying out data processing and anomaly detection on the state data to obtain a data detection result;
The computing module is used for controlling the edge server to collect communication state data of all the edge computing nodes and controlling the connection relation among all the edge computing nodes according to the communication state data;
the control module is used for generating a control instruction according to the data detection result and sending the control instruction to the target internet of things switch equipment through the edge node so as to realize real-time control;
and the protection module is used for carrying out real-time updating and optimization of the edge server according to the data processing and abnormality detection processes of the edge computing node, carrying out dynamic adjustment of a networking strategy, and introducing an encryption technology and an access control strategy into the edge server, the edge computing node and the target internet of things switch equipment so as to carry out safety protection of data.
The beneficial effects of the invention are as follows:
(1) According to the invention, by deploying the edge server and the computing node at the network edge, the generation of the data processing and control instructions can be completed locally, and the delay of data transmission is obviously reduced. This is particularly important for application scenarios requiring fast response (e.g. smart home, industrial automation, etc.).
(2) The edge node can perform real-time data processing and anomaly detection at the data generation site, so that the dependence on the cloud is reduced, the occupation of network bandwidth is reduced, and the data processing efficiency is improved.
(3) The edge computing architecture of the invention can continue to work under the condition of unstable network or network disconnection, and ensures the reliability and stability of the system. The edge node can perform local control and monitoring even if the connection to the cloud is broken.
(4) According to the invention, by carrying out data processing and filtering on the edge node, only important and processed data can be uploaded to the cloud, so that the data transmission quantity is reduced, and the bandwidth and the related cost are saved.
(6) According to the invention, an encryption technology and an access control strategy are introduced into the edge server and the computing node, so that the security of data can be effectively protected, and data leakage and unauthorized access are prevented. This is critical to the security of the internet of things device.
(7) The edge server can update and optimize in real time according to the data processing and abnormality detection results of each edge computing node, and dynamically adjust the network strategy to adapt to the continuously changing environment and requirements.
(8) The invention can be widely applied to a plurality of fields such as intelligent home, industrial automation, intelligent cities and the like, has good flexibility and expandability, and can be adjusted and optimized according to different application requirements.
(9) By means of real-time monitoring and control, a user can obtain more intelligent experience, for example, light, temperature and the like can be automatically adjusted, and comfort level of life and work is improved.
(10) By means of real-time analysis and anomaly detection of the state data, the system can provide more accurate decision support, and help users to find and solve potential problems in time.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, which are intended to facilitate an understanding of the principles and concepts of the invention and are to be varied in scope and detail by persons of ordinary skill in the art based on the teachings herein. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. The Internet of things switch control method based on edge calculation is characterized by comprising the following steps of:
an edge server and a plurality of edge computing nodes are deployed at the network edge of the target internet of things switch device;
The control target internet of things switch equipment establishes connection with each edge node in a wireless or wired mode so as to perform data interaction;
Acquiring state data of the target internet of things switch equipment by utilizing an edge node, and performing data processing and anomaly detection on the state data to obtain a data detection result;
controlling the edge server to collect communication state data of all the edge computing nodes, and according to the communication state data, connecting relations among all the edge computing nodes;
Generating a control instruction according to the data detection result, and sending the control instruction to the target internet of things switch equipment through the edge node so as to realize real-time control;
According to the data processing and anomaly detection processes of the edge computing nodes, the edge server is updated and optimized in real time, the networking strategy is dynamically adjusted, and encryption technology and access control strategy are introduced into the edge server, the edge computing nodes and the target internet of things switch equipment so as to protect data.
2. The internet of things switch control method based on edge computing according to claim 1, wherein collecting state data of the target internet of things switch device by using an edge node, and performing data processing and anomaly detection on the state data to obtain a data detection result, comprises:
Acquiring state data of the target internet of things switch equipment in real time through the edge node based on a preset communication protocol, wherein the state data comprises a current signal, a voltage signal, an equipment state signal, a temperature signal, an environment signal where the equipment is located and a time sequence signal corresponding to each signal;
coarse granulating and mapping the time sequence signals to obtain simplified sequence signals;
Carrying out standardized processing on the current signal, the voltage signal, the equipment state signal, the temperature signal and the environmental signal where the equipment is located to obtain a standard signal;
denoising the standard signal to obtain a denoised signal;
Extracting features of the denoising signals and the corresponding simplified sequence signals to obtain abnormal features;
and inputting the abnormal characteristics into a trained equipment abnormal state detection model to obtain the data detection result.
3. The internet of things switch control method based on edge calculation according to claim 2, wherein the performing coarse-grain and mapping processing on the time-series signal to obtain a simplified-series signal includes:
Based on the formula Coarse-graining the time-series signal to obtain a coarse-grained sequence, wherein,For a preset adaptive scale factorThe ith data of the lower coarsening sequence,For the b-th data point in the time series signal,For the length of the time series,For coarse-grained sequence data number, adaptive scale factorThe calculation formula of (2) is as follows: the standard deviation of the time sequence is that mu is the average value of the time sequence, and k is the preset experience constant of the target internet of things switch equipment;
distributing the coarse-grained sequence to the interval from 0 to 1 to obtain a distributed sequence;
Mapping the allocated sequence to an integer between 1 and N+ to obtain the simplified sequence signal, wherein N+ is an integer greater than 1.
4. The internet of things switch control method based on edge calculation according to claim 2, wherein denoising the standard signal to obtain a denoised signal comprises:
Translating the standard signal to obtain a translated signal;
Performing wavelet decomposition on the translated signal to obtain a plurality of wavelet coefficients;
determining a filtering threshold according to the decomposition scale and the length of the signal;
Constructing a denoising function according to the filtering threshold;
removing noise of the translated signal by using the denoising function to obtain a preliminary denoising signal;
and carrying out signal smoothing on the preliminary denoising signals by adopting a moving average method, and evaluating the denoising effect through indexes of signal-to-noise ratio and mean square error to determine the final denoising signals.
5. The internet of things switch control method based on edge calculation according to claim 4, wherein the calculation formula of the filtering threshold is: Wherein, the method comprises the steps of, For the filtering threshold, j is the decomposition scale of the translated signal, d j is the wavelet coefficient of the decomposition scale j, N represents the length of the translated signal, and mean represents the median operation.
6. The method for controlling the switch of the Internet of things based on edge calculation of claim 2, wherein the abnormal features comprise a time domain feature, a frequency domain feature and a time sequence feature of the signal.
7. The internet of things switch control method based on edge computing according to claim 1, wherein controlling the edge server to collect communication state data of each edge computing node and according to the communication state data, the connection relationship between each edge computing node includes:
Periodically acquiring communication state information of each edge computing node and the edge computing nodes in a preset distance range around the edge computing node by using an edge server, wherein each edge computing node forms a topological network structure for communication interaction;
Inputting the acquired communication state information into a pre-established node connection decision model to obtain a real-time decision result;
And dynamically adjusting the connection relation between each edge computing node in the topological network structure and the edge computing nodes in the surrounding preset distance range in real time based on a real-time decision result to form a self-adaptive topological network structure, wherein the real-time decision result comprises edge computing nodes which need to be connected and disconnected in the topological network structure.
8. The internet of things switch control method based on edge calculation according to claim 1, wherein the communication status information includes at least one of signal strength, data transmission rate, packet loss rate, delay, power consumption, moving speed, and historical connection quality.
9. The internet of things switch control method based on edge computing according to claim 1, wherein an encryption technology and an access control policy are introduced in the edge server, the edge computing node and the target internet of things switch device to perform security protection of data, comprising:
Encrypting data by adopting an end-to-end encryption technology and a homomorphic encryption algorithm, and storing the encrypted data by adopting a blockchain technology;
distributing access rights according to the roles of the users, and providing various authentication modes of network access by combining biological characteristics and behavior characteristics;
And acquiring performance indexes on the edge server, the edge computing node and the target internet of things switch equipment to perform performance monitoring by combining with a WiFi probe technology.
10. An internet of things switch control system based on edge calculation, which is characterized by comprising:
the deployment module is used for deploying an edge server and a plurality of edge computing nodes on the network edge of the target internet of things switch device;
the connection module is used for controlling the target internet of things switch equipment to establish connection with each edge node in a wireless or wired mode so as to perform data interaction;
the data processing module is used for acquiring the state data of the target internet of things switch equipment by utilizing an edge node, and carrying out data processing and anomaly detection on the state data to obtain a data detection result;
The computing module is used for controlling the edge server to collect communication state data of all the edge computing nodes and controlling the connection relation among all the edge computing nodes according to the communication state data;
the control module is used for generating a control instruction according to the data detection result and sending the control instruction to the target internet of things switch equipment through the edge node so as to realize real-time control;
and the protection module is used for carrying out real-time updating and optimization of the edge server according to the data processing and abnormality detection processes of the edge computing node, carrying out dynamic adjustment of a networking strategy, and introducing an encryption technology and an access control strategy into the edge server, the edge computing node and the target internet of things switch equipment so as to carry out safety protection of data.
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