Edge computing intelligent terminal based on Internet of Things technology

Abstract

The invention relates to the technical field of edge computing, and discloses an edge computing intelligent terminal based on the Internet of Things technology, which comprises a data acquisition module, a network state monitoring module and the like. The data acquisition module can dynamically adjust the acquisition frequency and precision according to network and equipment parameters; the network state monitoring and evaluation module can accurately judge the network risk and timely switch the network to guarantee data transmission; and the equipment operation monitoring and state evaluation module can effectively maintain the stability of the equipment. And the data processing decision module optimizes the data processing flow by using a self-adaptive algorithm. Meanwhile, the system performance is improved by adjusting the weight coefficient according to the scene, and the security encryption system protects the data security comprehensively. The edge computing intelligent terminal effectively solves the existing technical problems, has remarkable effects in the aspects of improving data processing efficiency, guaranteeing equipment stability, enhancing safety, adapting to different scenes and the like, and is of great significance in promoting development of the Internet of Things industry.

Description

Technical Field

[0001] The present invention relates to the field of edge computing technology, and specifically to an edge computing intelligent terminal based on Internet of Things technology. Background Art

[0002] With the rapid development of IoT technology, various IoT devices have exploded in number and are widely used in many fields such as industrial production, intelligent transportation, smart home, and environmental monitoring. These devices generate massive amounts of data during operation, and how to efficiently process and analyze this data has become a key issue. The traditional cloud computing model transmits data to a remote data center for processing. Although it can provide powerful computing power, it has many drawbacks when faced with scenarios with high real-time requirements, large amounts of data, and limited transmission bandwidth.

[0003] In the field of industrial production, a large number of sensors collect real-time data on equipment operating status, such as temperature, pressure, and speed. These data are crucial for monitoring the production process and early warning of faults, and need to be processed and analyzed in a timely manner. However, if the traditional cloud computing model is adopted, the process of transmitting data to the cloud and returning the processing results will produce a large delay, which cannot meet the needs of real-time monitoring and control, and may cause production accidents. For example, in chemical production, once the temperature of the reactor rises abnormally, if it cannot be responded and adjusted in time, it may cause serious safety accidents.

[0004] In intelligent transportation systems, vehicles frequently exchange data with roadside infrastructure and between vehicles, including information such as vehicle speed, location, and driving direction. Real-time processing of this data is critical for optimizing traffic flow and avoiding traffic accidents. However, transmission delays in traditional cloud computing models will cause traffic management decisions to lag behind, affecting traffic efficiency and safety. For example, in congested sections of road, if the duration of traffic lights cannot be adjusted in time according to real-time vehicle data, the congestion will worsen further.

[0005] In a smart home environment, users hope that their home devices can respond to commands in real time and achieve intelligent control. For example, if users control smart lights, air conditioners and other devices through mobile phone apps, if there is a delay in data processing, it will seriously affect the user experience and reduce the practicality of smart homes. In addition, the amount of data generated by IoT devices is huge, and transmitting all of it to the cloud will put great pressure on network bandwidth and may even cause network congestion. Especially in some remote areas with poor network conditions or areas with weak signal coverage, data transmission is unstable, which further exacerbates the limitations of traditional cloud computing models.

[0006] To solve the above problems, edge computing technology has emerged. Edge computing places data processing and analysis close to the data source or user, reducing data transmission delays and network bandwidth pressure. However, existing edge computing smart terminals still have many shortcomings. In terms of network status monitoring, most terminals can only simply obtain the network connection status, and cannot accurately evaluate the impact of network packet loss rate and bandwidth fluctuations on data transmission quality, resulting in poor data transmission reliability when the network is unstable. In terms of equipment operation monitoring and status evaluation, it is impossible to fully and accurately monitor key information such as the CPU computing load, memory read and write speed, and available space of storage devices, making it difficult to detect potential equipment failures in advance and affecting system stability. The data processing decision-making process also lacks intelligence and cannot be adaptively adjusted according to data characteristics, network conditions, and device status, resulting in low data processing efficiency. At the same time, with the continuous improvement of data security and privacy protection requirements in IoT application scenarios, existing edge computing smart terminals also have loopholes in security encryption and are vulnerable to security threats such as data leakage and malicious attacks. Summary of the invention

[0007] The purpose of the present invention is to provide an edge computing intelligent terminal based on Internet of Things technology to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: an edge computing intelligent terminal based on Internet of Things technology, the system comprising: Data acquisition module, network status monitoring module, network data evaluation module, equipment operation monitoring module, equipment status evaluation module, data processing decision module and edge computing module; The data acquisition module is used to collect data from various IoT devices and dynamically adjust the acquisition frequency and accuracy according to the real-time network status and the device's own operating parameters; The network status monitoring module is responsible for obtaining network status information that affects the quality of data transmission, including network packet loss rate information and network bandwidth fluctuation information; The network data evaluation module compares the collected network status information with the preset threshold value. Compare; This module converts the collected network status information into a network evaluation index ;like , the system determines that the network is in a high-risk state, immediately interrupts the data transmission operation and tries to switch to the backup network; if , the network is identified as a low-risk state, and the system can continue to monitor the equipment operation status; The device operation monitoring module is used to monitor various operation status information inside the intelligent terminal device, including the computing load information of the central processor, the read and write speed information of the memory, and the available space information of the storage device; The device status assessment module analyzes the collected CPU computing load information, memory read and write speed information, and storage device available space information to generate a device status assessment index. ; Compare the index with the preset threshold Compare and judge whether the intelligent terminal equipment is suitable for data processing and collaborative work; if , the current device status is judged to be abnormal, the intelligent terminal device suspends data processing and collaborative processes, and starts the self-adjustment program; if , the device status is judged to be normal, and the intelligent terminal device continues to process data; The data processing decision module is used to perform preliminary processing and analysis on the collected data and decide whether the data needs to be transmitted to the edge computing module for in-depth processing; The edge computing module is used to calculate and analyze data that requires deep processing.

[0009] Preferably, the network packet loss rate information includes a packet loss frequency index; the network bandwidth fluctuation information includes a bandwidth variation amplitude index; The proposed packet loss frequency index is The packet loss frequency index is used to measure the frequency of network packet loss in a unit time. It is calculated in the time period Number of packet losses And the total number of packets sent during this time period Ratio of:

[0010] in Indicates the packet loss frequency index, reflecting the frequency of network packet loss; The proposed bandwidth variation index is The bandwidth variation index is used to evaluate the fluctuation of network bandwidth within a certain period of time and calculate the change of bandwidth per unit time. and the sum of the absolute values ​​of the bandwidth changes :

[0011] in represents the bandwidth variation index, is the average network bandwidth.

[0012] Preferably, the packet loss frequency index is calculated by and bandwidth variation index To evaluate the comprehensive impact of the network on the system, we can get the network evaluation index :

[0013] in It is the network evaluation index, which reflects the comprehensive impact of the network on the system; , are weight coefficients, representing the weights of the packet loss frequency index and the bandwidth variation index respectively; It is the packet loss frequency index, which reflects the frequency of network packet loss; It is the bandwidth variation index, which reflects the fluctuation range of network bandwidth.

[0014] Preferably, the weight coefficient , Adaptive adjustment according to different application scenarios: For application scenarios with high real-time requirements, increase the weight coefficient of the bandwidth change index ; For application scenarios that require high data accuracy, increase the weight coefficient of the packet loss frequency index .

[0015] Preferably, the CPU computing load information includes a computing task complexity index; the memory read / write speed information includes a read / write speed difference index; the storage device available space information includes a space occupancy ratio index; The proposed computational task complexity index is , calculate the average number of operations of instructions in a task and the average execution time of the task :

[0016] in It represents the computational task complexity index, reflecting the complexity of the tasks processed by the CPU; The proposed reading and writing speed difference index is , calculate the memory read speed and write speed The absolute value of the difference And the average reading and writing speed :

[0017] in Indicates the read / write speed difference index, reflecting the degree of difference in memory read / write speed; The proposed space occupancy ratio index is , calculate the used space of the storage device With total space Ratio of:

[0018] in Indicates the space occupancy ratio index, which reflects the space occupancy of the storage device.

[0019] Preferably, the task complexity index is calculated by weighted combination , Reading and Writing Speed ​​Difference Index and space occupancy ratio index To evaluate the health status of the smart terminal device, get the device status evaluation index :

[0020] in Indicates the device status evaluation index, which is used to reflect the internal operating health status of the smart terminal device; , , are weight coefficients, representing the weights of the computing task complexity index, the read / write speed difference index, and the space occupancy ratio index respectively; Indicates the complexity index of the computational task; Indicates the reading and writing speed difference index; Indicates the space occupancy ratio index.

[0021] Preferably, the weight coefficient , , Real-time adjustment is made according to the different operation stages and business needs of smart terminal devices. The specific adjustment method is: when the smart terminal is at the peak of data collection, the weight coefficient of the calculation task complexity index is increased. ; When performing large amounts of data read and write operations, increase the weight coefficient of the read and write speed difference index ; When the storage device is close to saturation, increase the weight coefficient of the space occupancy ratio index .

[0022] Preferably, the data processing decision module adopts an adaptive decision algorithm, which is based on the type, size and network evaluation index of the data. and Equipment Condition Assessment Index , calculate a decision weight , the calculation formula is:

[0023] in, , , , is the weight coefficient, which is pre-set according to different data types and application scenarios; is the data size; The real-time requirement level of the data; When the decision weight When the data is greater than the preset threshold, the data is directly processed in the edge computing module; when the decision weight When it is less than or equal to the preset threshold, the data is first cached locally and processed after the network environment and device status improve.

[0024] Preferably, the present invention further comprises a security encryption system; the security encryption system comprises a data encryption module, a key management module, an intrusion prevention module and a security audit module; The data encryption module is used to encrypt the collected and processed data; The key management module is responsible for generating, storing and updating the keys required for encryption; The intrusion prevention module identifies and blocks potential intrusions by monitoring network traffic and device behavior in real time; The security audit module records and reviews the operations and data access of smart terminal devices.

[0025] Compared with the prior art, the present invention has the following beneficial effects: In the data collection phase, the data collection module dynamically adjusts the collection frequency and accuracy based on the real-time network status and the device's own operating parameters, effectively avoiding network congestion and equipment performance degradation. Reducing the collection frequency when the network is unstable or the device load is too high can not only ensure the stability of data collection, but also reduce invalid data transmission and save network bandwidth resources; while increasing the collection frequency when the network is stable and the device is running well can obtain richer data, provide more sufficient information for subsequent analysis and decision-making, and improve the accuracy and reliability of data processing.

[0026] The design of network status monitoring and evaluation has great advantages. The network status monitoring module accurately obtains network packet loss rate information and network bandwidth fluctuation information, and the network data evaluation module converts this information into a network evaluation index. and with the threshold Compare. When a fault occurs, the system quickly interrupts data transmission and switches to the backup network, effectively preventing data loss or transmission errors and ensuring the security and stability of data transmission. For application scenarios with extremely high network real-time requirements, such as remote medical surgery data transmission and communication between autonomous driving vehicles, this function ensures the reliable transmission of key data and avoids serious consequences caused by network problems.

[0027] The equipment operation monitoring and status evaluation functions provide a strong guarantee for the stable operation of intelligent terminals. The equipment operation monitoring module comprehensively monitors the CPU computing load, memory read and write speed, storage device available space and other information. The equipment status evaluation module calculates the equipment status evaluation index. With threshold By comparing the data, abnormal status of the equipment can be discovered in time. When the device is in a state of failure, it will suspend data processing and start the self-adjustment program to avoid damage to the device due to overload or other abnormal conditions, extend the service life of the device, and improve the overall stability of the system. For industrial control equipment that runs for a long time, this function can provide early warning of potential failures, reduce downtime, and reduce maintenance costs.

[0028] The adaptive decision algorithm used in the data processing decision module significantly improves data processing efficiency. The algorithm comprehensively considers data type, size, network evaluation index, and Equipment Condition Assessment Index Calculating decision weights , which determines the data processing method. For data with high real-time requirements, it is directly processed in the edge computing module when the network and device status allow, meeting the real-time requirements; for data with high accuracy requirements but relatively low real-time requirements, it is cached first when the network or device status is poor, and processed after the conditions improve to ensure the quality of data processing. In the video surveillance system, real-time video stream data can be processed and analyzed in a timely manner, while historical video data can be deeply processed when the system is idle, optimizing the utilization of system resources.

[0029] In different application scenarios, the intelligent terminal of the present invention shows good adaptability. By adaptively adjusting the weight coefficient according to the application scenario, the weight coefficient of the bandwidth change amplitude index is increased in scenarios with high real-time requirements. , increase the weight coefficient of the packet loss frequency index in scenarios with high requirements for data accuracy , and adjust the weight coefficient of the device status evaluation index in real time according to the different operation stages and business needs of smart terminal devices , , , making network evaluation and equipment status evaluation more in line with actual conditions, and further improving system performance and data processing effects.

[0030] The security encryption system provides all-round data security protection for smart terminals. The data encryption module encrypts the collected and processed data, the key management module securely generates, stores and updates keys, the intrusion prevention module monitors and blocks potential intrusions in real time, and the security audit module records and reviews device operations and data access, effectively preventing security threats such as data leakage and malicious attacks, ensuring data security and privacy, and meeting the strict data security requirements of IoT applications. It is of great significance in areas such as finance and medical care that are highly sensitive to data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a working principle diagram of the edge computing intelligent terminal described in the present invention; Figure 2 A step diagram of how to obtain and calculate network packet loss rate information and network bandwidth fluctuation information; Figure 3 A diagram showing the steps for obtaining and calculating the internal status information of a smart terminal device. DETAILED DESCRIPTION

[0032] 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.

[0033] See also Figure 1-3 , the present invention provides a technical solution: an edge computing intelligent terminal based on Internet of Things technology, the system comprising: The data collection module collects data from various IoT devices and dynamically adjusts the collection frequency and accuracy based on the real-time network status and the device's own operating parameters. For example, when the network is stable and the device is running well, the collection frequency is increased to obtain richer data; when the network fluctuates or the device load is high, the collection frequency is appropriately reduced to ensure the stability of data collection and avoid network congestion or device performance degradation due to excessive data volume.

[0034] The network status monitoring module is responsible for obtaining network status information that affects the quality of data transmission, including network packet loss rate information and network bandwidth fluctuation information. This module collects this information in real time by interacting with network devices or using specific network monitoring technologies, providing a data basis for subsequent network evaluation.

[0035] The network data evaluation module converts the collected network status information into a network evaluation index and with a pre-set threshold If , the system determines that the network is in a high-risk state, immediately interrupts the data transmission operation and tries to switch to the backup network to prevent data loss or transmission errors; if , the network is identified as a low-risk state, and the system can continue to monitor the equipment operation status.

[0036] The device operation monitoring module monitors various operating status information inside the intelligent terminal device, including the computing load information of the central processor, the read and write speed information of the memory, and the available space information of the storage device. By monitoring this information, the operating status of the device can be understood in real time, providing a basis for device status evaluation.

[0037] The device status assessment module analyzes the collected CPU computing load information, memory read and write speed information, and storage device available space information to generate a device status assessment index. and with the preset threshold For comparison. , the current device status is judged to be abnormal, the intelligent terminal device suspends data processing and collaborative processes, and starts the self-adjustment program; if , the device status is judged to be normal, and the intelligent terminal device continues to process data.

[0038] The data processing decision module performs preliminary processing and analysis on the collected data to determine whether the data needs to be transmitted to the edge computing module for in-depth processing. This module comprehensively determines the data processing method based on factors such as data characteristics, network conditions, and device status, and optimizes the data processing process. The edge computing module calculates and analyzes data that requires in-depth processing. In the edge computing module, efficient computing algorithms and processing technologies are used to process data in real time, reduce data transmission delays, and improve the response speed of the system.

[0039] The present invention will be further described below in conjunction with Examples 1 to 5: Example 1: This example describes in detail how to obtain and calculate network packet loss rate information (packet loss frequency index) and network bandwidth fluctuation information (bandwidth variation index), providing accurate data support for the network data evaluation module to more accurately evaluate the network status and ensure the stability of data transmission.

[0040] Network packet loss rate information includes packet loss frequency index , which is used to measure the frequency of network packet loss in a unit of time. In actual applications, network monitoring tools or the built-in network statistics function of the device are used to count the number of packets lost in a time period. Number of packet losses And the total number of packets sent during this time period For example, in a network monitoring cycle Number of packet losses detected within seconds times, total number of packets sent times. According to the formula , the packet loss frequency index can be calculated .

[0041] Network bandwidth fluctuation information includes bandwidth change index , which is used to evaluate the fluctuation of network bandwidth within a certain period of time. To obtain the bandwidth change , the network bandwidth data can be collected regularly through the network monitoring equipment, for example, once every 1 second. The bandwidth data collected at consecutive time points are , then the bandwidth changes are , , , . Calculate the sum of the absolute values ​​of the bandwidth changes . Assuming the average network bandwidth , according to the formula , we can get the bandwidth variation index .

[0042] Through the above precise calculation method, the accurate packet loss frequency index and bandwidth change index are obtained, which provides a reliable data basis for the subsequent comprehensive evaluation of the impact of the network on the system.

[0043] Example 2: This example focuses on how to calculate the network evaluation index by comprehensively considering the packet loss frequency index and the bandwidth variation index, and adaptively adjust the weight coefficient according to different application scenarios, so that the network evaluation is more in line with actual needs, ensuring that the system can make reasonable network decisions in different application environments.

[0044] Comprehensive packet loss frequency index and bandwidth variation index To evaluate the comprehensive impact of the network on the system, we can get the network evaluation index , the calculation formula is .

[0045] In the application scenario of live video, which has high real-time requirements, the stability of network bandwidth is crucial to the smooth playback of video. At this time, in order to highlight the impact of bandwidth changes on network status, the weight coefficient of bandwidth change index is increased. Assume that in a live video broadcast scenario, the packet loss frequency index is , bandwidth variation index , initial weight coefficient , The network evaluation index If Increased to 0.8, Adjust accordingly to 0.2 and recalculate the network evaluation index It can be seen that improving After that, the network evaluation index can better reflect the impact of bandwidth fluctuations on the network status. In this scenario, the system can respond to bandwidth problems more promptly, such as adjusting the video bit rate to ensure the smoothness of live broadcast.

[0046] In the case of file transfer, which requires high data accuracy, packet loss may cause file transfer errors or incompleteness. Therefore, the weight coefficient of the packet loss frequency index is increased. . Assume that in a file transfer scenario, , , initial weight coefficient , , Network Evaluation Index If Increased to 0.7, Adjust to 0.3 and recalculate the network evaluation index After this adjustment, the system can pay more attention to packet loss and take timely measures such as retransmission when the packet loss frequency is high to ensure the accuracy of file transmission.

[0047] Example 3: This example describes in detail the acquisition and calculation method of the central processing unit computing load information (computation task complexity index), memory read and write speed information (read and write speed difference index) and storage device available space information (space occupancy ratio index) inside the smart terminal device, providing key data for accurately evaluating the device status so that the system can promptly detect potential problems with the device and take corresponding measures.

[0048] CPU computing load information including computing task complexity index In actual computing, relevant data is obtained by monitoring the number of instructions and execution time when the processor executes a task. For example, when processing a task, the average number of operations of the instructions in the task is counted. The average execution time of the task Seconds. According to the formula , the computational task complexity index can be calculated .

[0049] Memory read and write speed information including read and write speed difference index . Obtain memory read speed through memory performance monitoring tools and write speed . Calculate the average reading and writing speed , the absolute value of the difference , according to the formula , we can get the reading and writing speed difference index .

[0050] Storage device available space information including space occupancy ratio index Assume that the total storage space , used space According to the formula , the space occupancy ratio index can be calculated .

[0051] By accurately calculating these indexes, the system can fully understand the internal operating status of smart terminal devices, provide accurate data support for device status assessment, and thus ensure the stable operation of the equipment.

[0052] Embodiment 4: This embodiment introduces in detail how to evaluate the internal health status of smart terminal devices by weighted combination of operation task complexity index, read / write speed difference index and space occupancy ratio index, calculate the device status evaluation index, and adjust the weight coefficient in real time according to different operation stages and business needs of the device, so as to make the device status evaluation more targeted and accurate.

[0053] By weighted combination of computational task complexity index , read and write speed difference index and space occupancy ratio index To evaluate the health status of the smart terminal device, get the device status evaluation index , the calculation formula is .

[0054] When the intelligent terminal is at the peak of data collection, the complexity of the computing task increases. At this time, the weight coefficient of the computing task complexity index is increased. . Assume that during the peak period of data collection, , initial weight coefficient Equipment Condition Assessment Index If Increased to 0.6, and Adjust to 0.2 respectively and recalculate the equipment status assessment index After such adjustment, the system can better highlight the impact of the complexity of computing tasks on the device status and promptly discover the problem of excessive device load caused by data collection.

[0055] When performing large amounts of data read and write operations, the stability of memory read and write speed has a greater impact on device performance. Increase the weight coefficient of the read and write speed difference index. . Assume that when a large amount of data is read and written, , , , initial weight coefficient , , , Equipment Condition Assessment Index If Increase to 0.5, Adjust to 0.3, Adjust to 0.2 and recalculate the equipment status assessment index By improving , the system can pay more attention to the impact of memory read and write speed differences on device status and optimize memory performance in a timely manner.

[0056] When the storage device is close to saturation, the space occupancy ratio becomes a key factor, and the weight coefficient of the space occupancy ratio index is increased. . Assuming that the storage device is close to saturation, , , , initial weight coefficient , , , Equipment Condition Assessment Index If Increase to 0.5, Adjust to 0.3, Adjust to 0.2 and recalculate the equipment status assessment index This will enable the system to pay more attention to the space usage of storage devices and take timely measures such as clearing space or expanding storage.

[0057] Example 5: This example describes in detail the adaptive decision algorithm used by the data processing decision module, determines the data processing method based on multiple factors, and improves data processing efficiency. At the same time, it introduces the specific working methods of each module of the security encryption system to ensure the data security of the intelligent terminal device and the stable operation of the system.

[0058] The data processing decision module uses an adaptive decision algorithm to determine the data type, size, and network evaluation index. and Equipment Condition Assessment Index , calculate a decision weight , the calculation formula is .

[0059] For example, for video surveillance data with high real-time requirements and small data volume, assuming that the pre-set weight coefficient , , , , data size , real-time requirement level (The higher the level, the higher the real-time requirement), network evaluation index , Equipment Condition Assessment Index The decision weight If the preset threshold is 1, due to , the video surveillance data is directly processed in the edge computing module to meet the real-time requirements.

[0060] For file data with high data accuracy requirements but relatively low real-time requirements, suppose , , , , data size , real-time requirement level , , . Calculate decision weights If the preset threshold is 1.5, because , the file data is first cached locally and processed after the network environment and device status improve to ensure the accuracy of data processing.

[0061] The security encryption system includes a data encryption module, a key management module, an intrusion prevention module and a security audit module.

[0062] The data encryption module is used to encrypt the collected and processed data. In the data collection stage, when the data is transmitted from the IoT device to the smart terminal, the data encryption module uses the Advanced Encryption Standard (AES) algorithm to encrypt the data. For example, for environmental data such as temperature and humidity collected by the sensor, the AES-256-bit encryption algorithm is used before transmission to convert the plaintext data into ciphertext to ensure the security of the data during transmission and prevent the data from being stolen or tampered with. During the data processing process, the data stored locally in the smart terminal and the data processed by the edge computing module are also encrypted to ensure the security of the data at all stages.

[0063] The key management module is responsible for generating, storing, and updating the keys required for encryption. Asymmetric encryption algorithms (such as RSA) are used to generate key pairs, with public keys used to encrypt data and private keys used to decrypt data. The key management module periodically updates the keys, such as generating a new set of key pairs every 7 days. When storing keys, a secure key storage method is used, such as storing the keys in a hardware-protected security chip to prevent the keys from being illegally obtained. When the data encryption module needs to encrypt or decrypt data, the key management module securely provides the required keys based on the corresponding encryption algorithm and operation requirements.

[0064] The intrusion prevention module identifies and blocks potential intrusions by monitoring network traffic and device behavior in real time. It uses intrusion detection system (IDS) and intrusion prevention system (IPS) technology to perform deep packet inspection on network traffic. For example, it analyzes information such as the source IP address, destination IP address, port number, protocol type, and packet content of network data packets. If a certain IP address is found to frequently initiate a large number of abnormal connection requests in a short period of time, the intrusion prevention module will determine it as a potential attack behavior and automatically block the connection of the IP address to prevent malware from invading smart terminal devices and protect the normal operation of the system and data security. At the same time, the system calls and process behaviors of the device are monitored in real time. Once abnormal process activities are found, such as a process attempting to illegally access sensitive files or modify key system configurations, the intrusion prevention module immediately takes measures to terminate the process to prevent the device from being maliciously controlled.

[0065] The security audit module records and reviews the operations and data access of smart terminal devices. It records all data collection operations, key steps in the data processing process, data transmission paths, and user operations on the device. For example, it records when and from which IoT device the data was collected, what processing operations the data underwent in the edge computing module, and to which external devices or systems the data was transmitted. The security audit module regularly reviews these records to check for abnormal operations or potential security risks. If a user is found to frequently access sensitive data during non-working hours, the security audit module will generate an alarm message to notify the system administrator to conduct further investigation so that security issues can be discovered and handled in a timely manner to ensure the security of equipment and data.

[0066] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0067] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An edge computing intelligent terminal based on Internet of Things technology, characterized by: It includes data acquisition module, network status monitoring module, network data evaluation module, equipment operation monitoring module, equipment status evaluation module, data processing decision module and edge computing module; The data acquisition module is used to collect data from various IoT devices and dynamically adjust the acquisition frequency and accuracy according to the real-time network status and the device's own operating parameters; The network status monitoring module is responsible for obtaining network status information that affects the quality of data transmission, including network packet loss rate information and network bandwidth fluctuation information; The network data evaluation module compares the collected network status information with the preset threshold value. Compare; This module converts the collected network status information into a network evaluation index ;like , the system determines that the network is in a high-risk state, immediately interrupts the data transmission operation and tries to switch to the backup network; if , the network is identified as a low-risk state, and the system can continue to monitor the equipment operation status; The device operation monitoring module is used to monitor various operation status information inside the intelligent terminal device, including the computing load information of the central processor, the read and write speed information of the memory, and the available space information of the storage device; The device status assessment module analyzes the collected CPU computing load information, memory read and write speed information, and storage device available space information to generate a device status assessment index. ; Compare the index with the preset threshold Compare and judge whether the intelligent terminal equipment is suitable for data processing and collaborative work; if , the current device status is judged to be abnormal, the intelligent terminal device suspends data processing and collaborative processes, and starts the self-adjustment program; if , the device status is judged to be normal, and the intelligent terminal device continues to process data; The data processing decision module is used to perform preliminary processing and analysis on the collected data and decide whether the data needs to be transmitted to the edge computing module for in-depth processing; The edge computing module is used to calculate and analyze data that requires deep processing.

2. The edge computing intelligent terminal according to claim 1, characterized in that: The network packet loss rate information includes a packet loss frequency index; the network bandwidth fluctuation information includes a bandwidth variation index; The proposed packet loss frequency index is The packet loss frequency index is used to measure the frequency of network packet loss in a unit time. It is calculated in the time period Number of packet losses And the total number of packets sent during this time period Ratio of: ； in Indicates the packet loss frequency index, reflecting the frequency of network packet loss; The proposed bandwidth variation index is The bandwidth variation index is used to evaluate the fluctuation of network bandwidth within a certain period of time and calculate the change of bandwidth per unit time. and the sum of the absolute values ​​of the bandwidth changes : ； in represents the bandwidth variation index, is the average network bandwidth.

3. The edge computing intelligent terminal according to claim 2, characterized in that: Comprehensive packet loss frequency index and bandwidth variation index To evaluate the comprehensive impact of the network on the system, we can get the network evaluation index : ； in It is the network evaluation index, which reflects the comprehensive impact of the network on the system; , are weight coefficients, representing the weights of the packet loss frequency index and the bandwidth variation index respectively; It is the packet loss frequency index, which reflects the frequency of network packet loss; It is the bandwidth variation index, which reflects the fluctuation range of network bandwidth.

4. The edge computing intelligent terminal according to claim 3, characterized in that: The weight coefficient , Adaptive adjustment according to different application scenarios: For application scenarios with high real-time requirements, increase the weight coefficient of the bandwidth change index ; For application scenarios that require high data accuracy, increase the weight coefficient of the packet loss frequency index .

5. The edge computing intelligent terminal according to claim 1, characterized in that: The CPU computing load information includes a computing task complexity index; the memory read / write speed information includes a read / write speed difference index; the storage device available space information includes a space occupancy ratio index; The proposed computational task complexity index is , calculate the average number of operations of instructions in a task and the average execution time of the task : ； in It represents the computational task complexity index, reflecting the complexity of the tasks processed by the CPU; The proposed reading and writing speed difference index is , calculate the memory read speed and write speed The absolute value of the difference And the average reading and writing speed : ； in Indicates the read / write speed difference index, reflecting the degree of difference in memory read / write speed; The proposed space occupancy ratio index is , calculate the used space of the storage device With total space Ratio of: ； in Indicates the space occupancy ratio index, which reflects the space occupancy of the storage device.

6. The edge computing intelligent terminal according to claim 5, characterized in that: By weighted combination of computational task complexity index , Reading and Writing Speed ​​Difference Index and space occupancy ratio index To evaluate the health status of the smart terminal device, get the device status evaluation index : ； in Indicates the device status evaluation index, which is used to reflect the internal operating health status of the smart terminal device; , , are weight coefficients, representing the weights of the computing task complexity index, the read / write speed difference index, and the space occupancy ratio index respectively; Indicates the complexity index of the computational task; Indicates the reading and writing speed difference index; Indicates the space occupancy ratio index.

7. The edge computing intelligent terminal according to claim 6, characterized in that: The weight coefficient , , Real-time adjustment is made according to the different operation stages and business needs of smart terminal devices. The specific adjustment method is: when the smart terminal is at the peak of data collection, the weight coefficient of the calculation task complexity index is increased. ; When performing large amounts of data read and write operations, increase the weight coefficient of the read and write speed difference index ; When the storage device is close to saturation, increase the weight coefficient of the space occupancy ratio index .

8. The edge computing intelligent terminal according to claim 1, characterized in that: The data processing decision module adopts an adaptive decision algorithm, which is based on the type, size and network evaluation index of the data. and Equipment Condition Assessment Index , calculate a decision weight , the calculation formula is: ； in, , , , is the weight coefficient, which is pre-set according to different data types and application scenarios; is the data size; The real-time requirement level of the data; When the decision weight When the data is greater than the preset threshold, the data is directly processed in the edge computing module; when the decision weight When it is less than or equal to the preset threshold, the data is first cached locally and processed after the network environment and device status improve.

9. The edge computing intelligent terminal according to claim 1, characterized in that: It also includes a security encryption system; the security encryption system includes a data encryption module, a key management module, an intrusion prevention module and a security audit module; The data encryption module is used to encrypt the collected and processed data; The key management module is responsible for generating, storing and updating the keys required for encryption; The intrusion prevention module identifies and blocks potential intrusions by monitoring network traffic and device behavior in real time; The security audit module records and reviews the operations and data access of smart terminal devices.

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