Internet of Things switch control method and system based on edge computing

Abstract

The invention provides an internet of things switch control method and system based on edge computing, relates to the technical field of internet of things, and aims at deploying an edge device at the network edge of target internet of things switch equipment to realize data interaction with the 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 each edge computing node and analyzes a connection relationship between the nodes; generating a control instruction according to the data detection result and sending the control instruction to the target Internet of Things switch equipment in real time; meanwhile, the edge server performs real-time updating and optimization according to feedback of data processing and anomaly detection, and dynamically adjusts a network strategy; and an encryption technology and an access control strategy are introduced, so that the security of data transmission and storage is protected. According to the method, the delay is reduced, the processing efficiency is improved, and the safety and reliability are enhanced, so that the method can effectively adapt to the requirements of modern intelligent application, and the overall performance of an Internet of Things system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things technology, and in particular to an Internet of Things switch control method and system based on edge computing. Background Art

[0002] With the rapid development of the Internet of Things (IoT), the number of smart devices connected to the network has exploded. The traditional cloud computing model requires uploading a large amount of data to the cloud for processing, which has the following problems: (1) High latency: data transmission and processing take time and cannot meet real-time requirements.

[0003] (2) High bandwidth consumption: Large amounts of data uploaded occupy network bandwidth and may cause network congestion.

[0004] (3) Security risks: data may be intercepted or tampered with during transmission, posing a security risk.

[0005] As an emerging computing model, edge computing can effectively reduce latency, save bandwidth and improve security by processing data at the edge of the network. However, most existing IoT switch control methods do not fully utilize the advantages of edge computing and cannot meet the needs of efficient and secure control. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide an Internet of Things switch control method and system based on edge computing, which can effectively improve the overall performance and user experience of the Internet of Things system by reducing latency, improving processing efficiency, enhancing security and reliability, and adapting to the needs of modern intelligent applications.

[0007] To achieve the above object, the present invention provides the following solutions: An Internet of Things switch control method based on edge computing, comprising: Deploy an edge server and multiple edge computing nodes at the network edge of the target IoT switch device; Control the target IoT switch device to establish a connection with each edge node via wireless or wired means to interact with data; Using the edge node to collect the state data of the target IoT switch device, and performing data processing and anomaly detection on the state data to obtain a data detection result; Control the edge server to collect communication status data of each edge computing node, and determine the connection relationship between each edge computing node according to the communication status data; Generate a control instruction according to the data detection result, and send the control instruction to the target IoT switch device through the edge node to achieve real-time control; According to the data processing and anomaly detection process of the edge computing node, the edge server is updated and optimized in real time, and the networking strategy is dynamically adjusted. Encryption technology and access control strategy are introduced into the edge server, the edge computing node and the target Internet of Things switch device to protect data security.

[0008] Preferably, the state data of the target IoT switch device is collected by using an edge node, and data processing and abnormality detection are performed on the state data to obtain a data detection result, including: Based on a preset communication protocol, the state data of the target IoT switch device is collected in real time through the edge node; the state data includes: current signal, voltage signal, device state signal, temperature signal, device environment signal, and time series signal corresponding to each signal; Performing coarse-graining and mapping processing on the time series signal to obtain a simplified series signal; Standardizing the current signal, voltage signal, device status signal, temperature signal and device environment signal to obtain a standard signal; Performing denoising processing on the standard signal to obtain a denoised signal; Performing feature extraction on the denoised signal and the corresponding simplified sequence signal to obtain abnormal features; The abnormal features are input into a trained equipment abnormal state detection model to obtain the data detection result.

[0009] Preferably, the time series signal is subjected to coarse-graining and mapping processing to obtain a simplified series signal, including: Based on the formula The time series signal is coarse-grained to obtain a coarse-grained sequence; wherein, is the preset adaptive scale factor The i-th data of the coarse-grained sequence is: is the first b data points, is the length of the time series, is the number of data in the coarse-grained sequence, and the adaptive scale factor The calculation formula is: is the standard deviation of the time series, μ is the mean of the time series, k Preset empirical constants for target IoT switch devices; Assigning the coarse-grained sequence to an interval from 0 to 1 to obtain an assigned sequence; Map the allocated sequence to 1 to N+An integer between , to obtain the simplified sequence signal; N+ is an integer greater than 1.

[0010] Preferably, performing denoising processing on the standard signal to obtain a denoised signal comprises: Shifting the standard signal to obtain a shifted signal; Performing wavelet decomposition on the shifted signal to obtain a plurality of wavelet coefficients; Determine the filtering threshold according to the decomposition scale and length of the signal; Constructing a denoising function according to the filtering threshold; Using the denoising function to remove the noise of the translated signal to obtain a preliminary denoised signal; The preliminary denoised signal is smoothed by using a moving average method, and the denoising effect is evaluated by using indicators such as signal-to-noise ratio and mean square error to determine the final denoised signal.

[0011] Preferably, the calculation formula of the filtering threshold is: ;in, is the filtering threshold, j is the decomposition scale of the translated signal, d j The decomposition scale is j The wavelet coefficients of N represents the length of the shifted signal, median Represents the median operation.

[0012] Preferably, the abnormal characteristics include: time domain characteristics, frequency domain characteristics and timing characteristics of the signal.

[0013] Preferably, controlling the edge server to collect communication status data of each edge computing node, and determining the connection relationship between each edge computing node according to the communication status data includes: The edge server is used to periodically obtain the communication status information of each edge computing node and the edge computing nodes within a preset distance range around it; each edge computing node constitutes a topological network structure for communication interaction; Input the acquired communication status information into the pre-established node connection decision model to obtain real-time decision results; Based on the real-time decision results, the connection relationship between each edge computing node in the topological network structure and the surrounding edge computing nodes within a preset distance range is dynamically adjusted in real time to form an adaptive topological network structure; the real-time decision results include the edge computing nodes that need to be connected and disconnected by each edge computing node in the topological network structure.

[0014] Preferably, the communication status information includes at least one of signal strength, data transmission rate, packet loss rate, delay, energy consumption, moving speed and historical connection quality.

[0015] Preferably, encryption technology and access control strategies are introduced into the edge server, the edge computing node and the target IoT switch device to perform data security protection, including: End-to-end encryption technology and homomorphic encryption algorithm are used for data encryption, and blockchain technology is used to store encrypted data; Assign access rights based on user roles and provide multiple authentication methods for network access by combining biometrics and behavioral features; Combined with WiFi probe technology, performance indicators are collected on the edge server, the edge computing node and the target IoT switch device for performance monitoring.

[0016] An IoT switch control system based on edge computing, comprising: A deployment module, for deploying an edge server and multiple edge computing nodes at the network edge of the target IoT switch device; A connection module is used to control the target IoT switch device to establish a connection with each edge node via wireless or wired means for data interaction; A data processing module, used to collect the status data of the target IoT switch device using the edge node, and perform data processing and anomaly detection on the status data to obtain a data detection result; A computing module, used to control the edge server to collect communication status data of each edge computing node, and determine the connection relationship between each edge computing node according to the communication status data; A control module, used to generate a control instruction according to the data detection result, and send the control instruction to the target IoT switch device through the edge node to achieve real-time control; A protection module is used to update and optimize the edge server in real time according to the data processing and anomaly detection process of the edge computing node, and dynamically adjust the networking strategy, and introduce encryption technology and access control strategy into the edge server, the edge computing node and the target IoT switch device to protect data security.

[0017] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention provides an Internet of Things switch control method and system based on edge computing, the method comprising: deploying an edge server and multiple edge computing nodes at the network edge of the target Internet of Things switch device; controlling the target Internet of Things switch device to establish a connection with each edge node in a wireless or wired manner to interact with data; using the edge node to collect the state data of the target Internet of Things switch device, and performing data processing and abnormality detection on the state data to obtain a data detection result; controlling the edge server to collect the communication state data of each edge computing node, and according to the connection relationship between each edge computing node of the communication state data; generating a control instruction according to the data detection result, and sending the control instruction to the target Internet of Things switch device through the edge node to achieve real-time control; according to the data processing and abnormality detection process of the edge computing node, the edge server is updated and optimized in real time, and the networking strategy is dynamically adjusted, and encryption technology and access control strategy are introduced in the edge server, the edge computing node and the target Internet of Things switch device to perform data security protection. The present invention can effectively improve the overall performance and user experience of the Internet of Things system and meet the needs of modern intelligent applications by reducing delays, improving processing efficiency, enhancing security and reliability and other advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0019] Figure 1 A flow chart of a method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The purpose of the present invention is to provide an Internet of Things switch control method and system based on edge computing, which can effectively improve the overall performance and user experience of the Internet of Things system by reducing latency, improving processing efficiency, enhancing security and reliability, and adapting to the needs of modern intelligent applications.

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides an Internet of Things switch control method based on edge computing, comprising: Step 100: deploy an edge server and multiple edge computing nodes at the network edge of the target IoT switch device; Step 200: Control the target IoT switch device to establish a connection with each edge node via wireless or wired means to perform data interaction; Step 300: using the edge node to collect the state data of the target IoT switch device, and performing data processing and anomaly detection on the state data to obtain a data detection result; Step 400: Control the edge server to collect communication status data of each edge computing node, and determine the connection relationship between each edge computing node according to the communication status data; Step 500: Generate a control instruction according to the data detection result, and send the control instruction to the target IoT switch device through the edge node to achieve real-time control; Step 600: According to the data processing and anomaly detection process of the edge computing node, the edge server is updated and optimized in real time, and the networking strategy is dynamically adjusted. Encryption technology and access control strategies are introduced in the edge server, edge computing node and target IoT switch device to protect data security.

[0024] Preferably, the state data of the target IoT switch device is collected by using an edge node, and data processing and abnormality detection are performed on the state data to obtain a data detection result, including: Based on a preset communication protocol, the state data of the target IoT switch device is collected in real time through the edge node; the state data includes: current signal, voltage signal, device state signal, temperature signal, device environment signal, and time series signal corresponding to each signal; Performing coarse-graining and mapping processing on the time series signal to obtain a simplified series signal; The current signal, voltage signal, device status signal, temperature signal and device environment signal are standardized to obtain a standard signal; illustratively, the standardization process of this embodiment is to standardize data of different dimensions for subsequent processing.

[0025] Performing denoising processing on the standard signal to obtain a denoised signal; Performing feature extraction on the denoised signal and the corresponding simplified sequence signal to obtain abnormal features; The abnormal features are input into a trained equipment abnormal state detection model to obtain the data detection result.

[0026] Preferably, the time series signal is subjected to coarse-graining and mapping processing to obtain a simplified series signal, including: Based on the formula The time series signal is coarse-grained to obtain a coarse-grained sequence; wherein, is the preset adaptive scale factor The i-th data of the coarse-grained sequence is: is the first b data points, is the length of the time series, is the number of data in the coarse-grained sequence, and the adaptive scale factor The calculation formula is: is the standard deviation of the time series, μ is the mean of the time series, k Preset empirical constants for target IoT switch devices; Assigning the coarse-grained sequence to an interval from 0 to 1 to obtain an assigned sequence; Map the allocated sequence to 1 to N+ An integer between , to obtain the simplified sequence signal; N+ is an integer greater than 1.

[0027] Furthermore, this embodiment determines a suitable k value by analyzing historical data and observing the signal fluctuations when the device is running under specific conditions. This embodiment statistically analyzes the standard deviation and mean of the device under normal working conditions to select a k value that allows the coarse-grained signal to still effectively reflect the device state; in addition, this embodiment can also analyze the distribution characteristics of the time series signal (such as normal distribution, skewed distribution, etc.) and select a k value suitable for the distribution to ensure the representativeness of the signal after coarse-graining. Exemplarily, in a high-noise environment, this embodiment recommends setting the value of k to 1.5 to 2.5 to ensure that the noise can be effectively filtered out during the coarse-graining process; for switch devices in industrial environments, the value of k can be between 1.0 and 3.0. Industrial equipment is usually faced with greater load and environmental changes, so a larger k value is required to adapt to signal fluctuations. For smart switch devices in home environments, the value of k is usually between 0.5 and 1.5. This is because the signal fluctuations of home devices are relatively small, and selecting a smaller k value can better capture changes in device status.

[0028] Specifically, the coarse-grained processing of time series in this embodiment has multiple benefits, including the following aspects: 1. Reduce the amount of data. Coarse-graining can significantly reduce the storage and processing requirements of data, especially when processing large-scale time series data. By reducing the number of data points, storage space and computing resources can be saved.

[0029] 2. Improve computing efficiency. Processing fewer data points usually means faster computing speed. This is very important for real-time data processing and analysis, especially in edge computing or resource-constrained environments.

[0030] Preferably, performing denoising processing on the standard signal to obtain a denoised signal comprises: Shifting the standard signal to obtain a shifted signal; Performing wavelet decomposition on the shifted signal to obtain a plurality of wavelet coefficients; Determine the filtering threshold according to the decomposition scale and length of the signal; Constructing a denoising function according to the filtering threshold; Using the denoising function to remove the noise of the translated signal to obtain a preliminary denoised signal; The preliminary denoised signal is smoothed by using a moving average method, and the denoising effect is evaluated by using indicators such as signal-to-noise ratio and mean square error to determine the final denoised signal.

[0031] Preferably, the calculation formula of the filtering threshold is: ;in, is the filtering threshold, j is the decomposition scale of the translated signal, d j The decomposition scale is j The wavelet coefficients of N represents the length of the shifted signal, median Represents the median operation.

[0032] Preferably, the abnormal characteristics include: time domain characteristics, frequency domain characteristics and timing characteristics of the signal.

[0033] Specifically, the abnormal features of this embodiment mainly include: 1. Time domain characteristics (1) Mean: The average value of the signal within a certain time window, reflecting the overall level of the signal.

[0034] (2) Standard Deviation: The degree of signal fluctuation. A larger standard deviation may indicate abnormal signal fluctuations.

[0035] (3) Maximum and minimum values ​​(Max / Min): The maximum and minimum values ​​of the signal. Abnormal conditions may correspond to extreme values ​​that exceed the normal range.

[0036] (4) Kurtosis: It describes the sharpness of the signal distribution. A higher kurtosis may indicate abnormal changes in the signal.

[0037] (5) Skewness: describes the symmetry of the signal distribution. Changes in skewness may indicate abnormal behavior of the signal.

[0038] 2. Frequency domain characteristics (1) Spectral characteristics: The spectrum obtained by Fourier transform analyzes the frequency components of the signal. Abnormal frequency components may indicate equipment failure.

[0039] (2) Dominant Frequency: The most significant frequency in the signal, usually related to the normal operating frequency of the device. Deviations from the normal frequency may indicate an abnormality.

[0040] (3) Band Energy: The energy distribution within a specific frequency band. Abnormal energy concentration may indicate an abnormal state of the device.

[0041] 3. Timing characteristics (1) Rate of Change: The rate at which a signal changes per unit time. Abnormally rapid changes may indicate a fault.

[0042] (2) Anomaly Points Detection: Anomalies or mutations detected in a time series, usually identified through statistical methods or machine learning models.

[0043] Preferably, controlling the edge server to collect communication status data of each edge computing node, and determining the connection relationship between each edge computing node according to the communication status data includes: The edge server is used to periodically obtain the communication status information of each edge computing node and the edge computing nodes within a preset distance range around it; each edge computing node constitutes a topological network structure for communication interaction; Input the acquired communication status information into the pre-established node connection decision model to obtain real-time decision results; Based on the real-time decision results, the connection relationship between each edge computing node in the topological network structure and the surrounding edge computing nodes within a preset distance range is dynamically adjusted in real time to form an adaptive topological network structure; the real-time decision results include the edge computing nodes that need to be connected and disconnected by each edge computing node in the topological network structure.

[0044] Preferably, the communication status information includes at least one of signal strength, data transmission rate, packet loss rate, delay, energy consumption, moving speed and historical connection quality.

[0045] Preferably, encryption technology and access control strategies are introduced into the edge server, the edge computing node and the target IoT switch device to perform data security protection, including: End-to-end encryption technology and homomorphic encryption algorithm are used for data encryption, and blockchain technology is used to store encrypted data; Assign access rights based on user roles and provide multiple authentication methods for network access by combining biometrics and behavioral features; Combined with WiFi probe technology, performance indicators are collected on the edge server, the edge computing node and the target IoT switch device for performance monitoring.

[0046] Specifically, first, the data encryption of this embodiment adopts end-to-end encryption technology to ensure that the data is always encrypted during transmission to prevent man-in-the-middle attacks. Specifically, the data is encrypted by the source device using a symmetric encryption algorithm (such as AES) before sending, and only the target device holds the decryption key to ensure the confidentiality of the data. At the same time, combined with homomorphic encryption algorithms (such as Paillier or BGN), it allows calculations on encrypted data without decryption, thereby protecting data privacy and supporting edge computing nodes to perform necessary data processing. Secondly, data storage adopts blockchain technology to store encrypted data in the form of blocks in a distributed ledger. Each data block contains a timestamp, encrypted data, and a hash value of the previous block to ensure the immutability and traceability of the data. Through smart contracts, the system can automatically manage the access rights and verification process of data. In terms of access control, the system assigns different access rights according to user roles (such as administrators, ordinary users, visitors, etc.) to ensure that only authorized users can access specific data. At the same time, combined with biometrics (such as fingerprints, facial recognition) and behavioral characteristics (such as user operating habits, login time, etc.), multiple authentication methods are provided to enhance security. When users access the system, they need to authenticate through multi-factor authentication (MFA) to ensure that only legitimate users can access sensitive data. Finally, combined with WiFi probe technology, performance indicators (such as network latency, data transmission rate, device load, etc.) are collected in real time on edge servers, edge computing nodes, and target IoT switch devices, and system performance is monitored through data analysis tools. These performance indicators will be used to optimize network configuration and resource allocation to ensure that the system can still operate efficiently under high load conditions.

[0047] Through the above steps, this embodiment can achieve data security protection, access control and performance monitoring, and ensure data security and device reliability in the Internet of Things environment.

[0048] As an optional implementation, in a smart home environment, the IoT switch control method of the present invention is deployed, and the specific steps are as follows: 1. Edge node deployment: deploy edge computing nodes at the home gateway with built-in intelligent control modules.

[0049] 2. Device connection: smart lighting, air conditioning and other switch devices are connected to the edge node.

[0050] 3. Local data processing and control: When the sensor detects someone entering the room, the edge node immediately controls the lights to turn on; according to the indoor temperature and preset conditions, the air conditioner switch and temperature settings are automatically adjusted.

[0051] 4. Secure communication: all data transmission uses AES encryption algorithm to ensure communication security.

[0052] 5. Cloud collaboration: users can connect to the cloud server through the mobile app to remotely view device status and control logs.

[0053] As another optional implementation, in the industrial Internet of Things, the method of the present invention is applied to control production line equipment, and the specific steps are as follows: 1. Edge node deployment: deploy high-performance edge computing nodes in the production workshop.

[0054] 2. Equipment connection: the machines and sensors in the workshop are connected to the edge nodes.

[0055] 3. Local data processing and control, real-time monitoring of equipment operating status, such as temperature, vibration and other parameters. When an abnormal situation is detected, the edge node immediately issues a command to stop the equipment operation to prevent accidents.

[0056] 4. Secure communication: Use VPN and SSL protocols to establish a secure communication channel.

[0057] 5. Cloud collaboration: production data and equipment logs are uploaded to the cloud regularly to support big data analysis and decision-making.

[0058] Corresponding to the above method, this embodiment also provides an IoT switch control system based on edge computing, including: A deployment module, for deploying an edge server and multiple edge computing nodes at the network edge of the target IoT switch device; A connection module is used to control the target IoT switch device to establish a connection with each edge node via wireless or wired means for data interaction; A data processing module, used to collect the status data of the target IoT switch device using the edge node, and perform data processing and anomaly detection on the status data to obtain a data detection result; A computing module, used to control the edge server to collect communication status data of each edge computing node, and determine the connection relationship between each edge computing node according to the communication status data; A control module, used to generate a control instruction according to the data detection result, and send the control instruction to the target IoT switch device through the edge node to achieve real-time control; A protection module is used to update and optimize the edge server in real time according to the data processing and anomaly detection process of the edge computing node, and dynamically adjust the networking strategy, and introduce encryption technology and access control strategy into the edge server, the edge computing node and the target IoT switch device to protect data security.

[0059] The beneficial effects of the present invention are as follows: (1) By deploying edge servers and computing nodes at the edge of the network, the present invention can complete data processing and control instruction generation locally, significantly reducing the delay of data transmission. This is particularly important for application scenarios that require fast response (such as smart homes, industrial automation, etc.).

[0060] (2) The edge nodes of the present invention can perform real-time data processing and anomaly detection at the location where the data is generated, reducing dependence on the cloud, reducing network bandwidth usage, and improving data processing efficiency.

[0061] (3) The edge computing architecture of the present invention can continue to work even when the network is unstable or disconnected, ensuring the reliability and stability of the system. Even if the connection with the cloud is interrupted, the edge node can still perform local control and monitoring.

[0062] (4) The present invention processes and filters data at the edge nodes, so that only important and processed data can be uploaded to the cloud, reducing the amount of data transmission, thereby saving bandwidth and related costs.

[0063] (6) The present invention introduces encryption technology and access control strategies in edge servers and computing nodes, which can effectively protect data security and prevent data leakage and unauthorized access. This is crucial to the security of IoT devices.

[0064] (7) The edge server of the present invention can perform real-time updates and optimizations based on the data processing and anomaly detection results of each edge computing node, and dynamically adjust network strategies to adapt to the ever-changing environment and needs.

[0065] (8) The present invention can be widely used in smart homes, industrial automation, smart cities and other fields. It has good flexibility and scalability and can be adjusted and optimized according to different application requirements.

[0066] (9) Through real-time monitoring and control, the present invention enables users to obtain a more intelligent experience, such as automatic adjustment of lighting and temperature, thereby improving the comfort of life and work.

[0067] (10) Through real-time analysis and anomaly detection of status data, the system can provide more accurate decision support and help users discover and solve potential problems in a timely manner.

[0068] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred 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 parts can be referred to the method part.

[0069] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An IoT switch control method based on edge computing, characterized in that: include: Deploy an edge server and multiple edge computing nodes at the network edge of the target IoT switch device; Control the target IoT switch device to establish a connection with each edge node via wireless or wired means to interact with data; Using the edge node to collect the state data of the target IoT switch device, and performing data processing and anomaly detection on the state data to obtain a data detection result; Control the edge server to collect communication status data of each edge computing node, and determine the connection relationship between each edge computing node according to the communication status data; Generate a control instruction according to the data detection result, and send the control instruction to the target IoT switch device through the edge node to achieve real-time control; According to the data processing and anomaly detection process of the edge computing node, the edge server is updated and optimized in real time, and the networking strategy is dynamically adjusted. Encryption technology and access control strategy are introduced into the edge server, the edge computing node and the target Internet of Things switch device to protect data security.

2. The IoT switch control method based on edge computing according to claim 1 is characterized in that: The state data of the target IoT switch device is collected by using the edge node, and the state data is processed and anomaly detected to obtain data detection results, including: Based on a preset communication protocol, the state data of the target IoT switch device is collected in real time through the edge node; the state data includes: current signal, voltage signal, device state signal, temperature signal, device environment signal, and time series signal corresponding to each signal; Performing coarse-graining and mapping processing on the time series signal to obtain a simplified series signal; Standardizing the current signal, voltage signal, device status signal, temperature signal and device environment signal to obtain a standard signal; Performing denoising processing on the standard signal to obtain a denoised signal; Performing feature extraction on the denoised signal and the corresponding simplified sequence signal to obtain abnormal features; The abnormal features are input into a trained equipment abnormal state detection model to obtain the data detection result.

3. The IoT switch control method based on edge computing according to claim 2 is characterized in that: The time series signal is subjected to coarse-graining and mapping processing to obtain a simplified series signal, including: Based on the formula The time series signal is coarse-grained to obtain a coarse-grained sequence; wherein, is the preset adaptive scale factor The i-th data of the coarse-grained sequence is: is the first b data points, is the length of the time series, is the number of data in the coarse-grained sequence, and the adaptive scale factor The calculation formula is: is the standard deviation of the time series, μ is the mean of the time series, k Preset empirical constants for target IoT switch devices; Assigning the coarse-grained sequence to an interval from 0 to 1 to obtain an assigned sequence; Map the allocated sequence to 1 to N+ An integer between , to obtain the simplified sequence signal; N+ is an integer greater than 1.

4. The IoT switch control method based on edge computing according to claim 2 is characterized in that: Performing denoising processing on the standard signal to obtain a denoised signal includes: Shifting the standard signal to obtain a shifted signal; Performing wavelet decomposition on the shifted signal to obtain a plurality of wavelet coefficients; Determine the filtering threshold according to the decomposition scale and length of the signal; Constructing a denoising function according to the filtering threshold; Using the denoising function to remove the noise of the translated signal to obtain a preliminary denoised signal; The preliminary denoised signal is smoothed by using a moving average method, and the denoising effect is evaluated by using indicators such as signal-to-noise ratio and mean square error to determine the final denoised signal.

5. The IoT switch control method based on edge computing according to claim 4 is characterized in that: The calculation formula of the filtering threshold is: ;in, is the filtering threshold, j is the decomposition scale of the translated signal, d j The decomposition scale is j The wavelet coefficients of N represents the length of the shifted signal, median Represents the median operation.

6. The IoT switch control method based on edge computing according to claim 2 is characterized in that: The abnormal features include: time domain features, frequency domain features and timing features of the signal.

7. The IoT switch control method based on edge computing according to claim 1, characterized in that: Controlling the edge server to collect communication status data of each edge computing node, and determining the connection relationship between each edge computing node according to the communication status data, including: The edge server is used to periodically obtain the communication status information of each edge computing node and the edge computing nodes within a preset distance range around it; each edge computing node constitutes a topological network structure for communication interaction; Input the acquired communication status information into the pre-established node connection decision model to obtain real-time decision results; Based on the real-time decision results, the connection relationship between each edge computing node in the topological network structure and the surrounding edge computing nodes within a preset distance range is dynamically adjusted in real time to form an adaptive topological network structure; the real-time decision results include the edge computing nodes that need to be connected and disconnected by each edge computing node in the topological network structure.

8. The IoT switch control method based on edge computing according to claim 1, characterized in that: The communication status information includes at least one of signal strength, data transmission rate, packet loss rate, delay, energy consumption, moving speed and historical connection quality.

9. The IoT switch control method based on edge computing according to claim 1, characterized in that: Introducing encryption technology and access control strategies into the edge server, the edge computing node, and the target IoT switch device to perform data security protection, including: End-to-end encryption technology and homomorphic encryption algorithm are used for data encryption, and blockchain technology is used to store encrypted data; Assign access rights based on user roles and provide multiple authentication methods for network access by combining biometrics and behavioral features; Combined with WiFi probe technology, performance indicators are collected on the edge server, the edge computing node and the target IoT switch device for performance monitoring.

10. An IoT switch control system based on edge computing, characterized in that: include: A deployment module, for deploying an edge server and multiple edge computing nodes at the network edge of the target IoT switch device; A connection module is used to control the target IoT switch device to establish a connection with each edge node via wireless or wired means for data interaction; A data processing module, used to collect the status data of the target IoT switch device using the edge node, and perform data processing and anomaly detection on the status data to obtain a data detection result; A computing module, used to control the edge server to collect communication status data of each edge computing node, and determine the connection relationship between each edge computing node according to the communication status data; A control module, used to generate a control instruction according to the data detection result, and send the control instruction to the target IoT switch device through the edge node to achieve real-time control; A protection module is used to update and optimize the edge server in real time according to the data processing and anomaly detection process of the edge computing node, and dynamically adjust the networking strategy, and introduce encryption technology and access control strategy into the edge server, the edge computing node and the target IoT switch device to protect data security.

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