Automatic equipment remote operation and maintenance updating and data analysis system

Abstract

The invention relates to the technical field of computer software, in particular to an automatic equipment remote operation and maintenance updating and data analysis system, which builds an efficient and stable remote management and control framework by means of MQTT proxy to realize accurate management of equipment software. On the information interaction level, software follows an MQTT protocol, and detailed state information and controllable functions of the software are published in a standard mode in a theme mode. A system resource manager (SRM) serves as a subscription end, and after subscribing to related themes, all state information of the equipment can be accurately obtained in real time. In a data transmission link, the system introduces an MQTT breakpoint resume mechanism, so that consumption of network bandwidth is effectively reduced, and continuity and high efficiency of data transmission can still be kept under the condition that the network is unstable. Meanwhile, by applying an incremental compression transmission mode, the size of a transmission file is remarkably reduced, the transmission efficiency is improved, and the updating time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of computer software, and more specifically, it relates to a system for remote operation and maintenance update and data analysis of automated equipment, which is used for version management, remote control and related data processing technologies of automated handling equipment. This technology is mainly applied to effectively manage the versions of the software of the three-dimensional storage warehouse under different usage scenarios, realize remote operation and maintenance, and perform operations such as data analysis and backup during the software operation process. Background Art

[0002] In the production operation of modern enterprises, Stocker (i.e., three-dimensional storage warehouse, which is an automatic storage subsystem of semiconductor factories and is designed specifically for semiconductor wafer factories) software is widely used in various semiconductor wafer factories and is responsible for industrial automation control, logistics scheduling management, etc. Currently, for the management of device software versions, most methods adopt manual on-site operations. When updating the software version, technicians need to go to the site of each device installed with the device software and manually perform the version upgrade operation. Since a large number of semiconductor devices are installed in different positions on different floors in the wafer factory, the efficiency of using the manual update method is low, consuming a large amount of manpower and time costs. Some also adopt remote update technologies. However, generally, industrial software has a large capacity, slow update speed, and lacks a unified remote management mechanism in software configuration management, resulting in difficult and slow synchronization of configuration changes to each device, and it is easy to have problems of inconsistent configurations.

[0003] In the current technical environment, for software version management, most technologies adopt the method of building a local version library to store and manage different versions of software. It is established within a single device or a local network environment, where the files of each version of the software are centrally stored and organized through a simple directory structure or management tool. When performing software version updates, the method of remotely updating the terminal device program is adopted. However, the existing remote update system is slow when updating larger software, which will affect the operation of the device, thereby resulting in a decline in user productivity.

[0004] In terms of log management, the existing methods usually simply record logs on the local device where the software runs. Various events and operation information generated during the software operation process will be recorded in the local log file in a certain format, and these log files may be stored on the hard disk or other storage media of the device. When it is necessary to analyze the software operation situation, technicians need to manually open these log files and find possible problems during the software operation process through manual viewing and analysis.

[0005] Low version management and transmission efficiency: The existing technology uses full - volume transmission, which results in long update time for large files, does not support resume - interrupted transfer. When network fluctuations occur, re - transmission is required, and the bandwidth utilization rate is insufficient. When updating large or numerous files, users often need to wait a long time for the software update to complete, seriously affecting the user experience and interfering with the device's production tasks. Additionally, in the file transmission link of version update, there is also a security - hazard problem that cannot be ignored, that is, the files to be updated are not effectively encrypted, which may cause data loss or tampering. The existing remote - update technology transmits program data in full volume, occupying a large amount of network bandwidth and not having the function of resume - interrupted transfer. Once the transmission is interrupted, re - transmission is needed, wasting time and resources.

[0006] Insufficient data - processing capacity: In terms of software - version file backup, the existing technology lacks a unified backup strategy and an automated backup mechanism. Different devices may adopt different backup methods. Some devices may rely on manual regular backup of version files, while some devices may not have any backup measures at all. This chaotic backup method is prone to data loss. At the same time, due to the lack of an automated backup mechanism, backup work is often not timely and cannot meet the demand for quickly restoring software operation.

[0007] Patent reference document CN201410214813.0 discloses a method for remotely updating the program of a terminal device. First, the version to be updated of the terminal device is obtained from a data server through a communication server. Then the terminal device feeds back the current version information and generates confirmation version information, and finally confirms the program version to be upgraded based on the above information. Then the data of this program version is sent to the terminal device, and the terminal device receives and flashes the program to complete the process of remotely updating the program of the terminal device. This solution uses full - volume transmission. When the transmitted file is large, there will be a problem of long update time, and it does not support resume - interrupted transfer. When network fluctuations occur, re - transmission is required, and the bandwidth utilization rate is insufficient. When updating large or numerous files, users often need to wait a long time for the software update to complete. Additionally, in the file - transmission link of version update, there are also security - hazard problems. Summary of the Invention

[0008] Therefore, a system for remote operation and maintenance update and data analysis of automated equipment is required. The technical solution of the present invention constructs an efficient remote management and control system by introducing MQTT proxy technology. It adopts incremental compression transmission (only transmits differential files), combines with the MQTT resume mechanism to reduce network bandwidth consumption. At the same time, it creates an encrypted update chain for the updated data. Each update package is attached with an encrypted hash value, which is associated with the hash value of the previous update package. During the update process, the device sequentially verifies the hash value of each update package to ensure the integrity of the updated data and the reliability of the source, preventing data from being tampered with, thus improving the problems of long time consumption for transmitting large files and security during transmission. By using machine learning classification algorithms to classify and store previous version files and automatically back them up, and automatically perform version rollback when the update fails, avoiding equipment downtime caused by update failures. And using an anomaly detection model with Local Outlier Factor (LOF) for model training, using a stream processing framework (Apache Kafka) to process the real-time collected log data, and passing the log data to the trained machine learning model for anomaly detection in real time. The log data is classified by detecting the log file to determine whether it is abnormal, so as to quickly locate the problem point.

[0009] The present invention proposes a method and system for remotely operating, managing, and updating software versions of STK and analyzing, backing up, and extracting log files. The system builds an efficient and stable remote management and control architecture with the help of an MQTT proxy to achieve precise management of device software. At the information interaction level, the software follows the MQTT protocol and publishes its detailed status information and controllable functions in a standardized form as topics. The System Resource Manager (SRM), as the subscriber, can obtain all the status information of the device in real time and accurately after subscribing to relevant topics. In the data transmission link, the system introduces the MQTT resume mechanism, effectively reducing the consumption of network bandwidth and ensuring the continuity and efficiency of data transmission in the case of unstable network. At the same time, the incremental compression transmission method is used to significantly reduce the size of the transmitted file, improve the transmission efficiency, and shorten the update time. To ensure the security of the updated data during transmission, the updated data is encrypted to prevent data from being tampered with or stolen during transmission, ensuring the integrity and confidentiality of the data.

[0010] This system uses machine learning algorithms to classify and backup historical version files. When the update operation fails, the system can quickly and accurately perform version rollback based on the backup version files, restoring the device to its previous stable operating state, avoiding device downtime caused by update failures, and ensuring business continuity. In addition, this system uses an analysis algorithm to monitor the log files in real time. Through in-depth analysis and mining of the log data, problems that occur during the system operation can be located in a timely and accurate manner, facilitating quick troubleshooting and resolution of faults, reducing the impact of system failures on the business, and improving the reliability and availability of the system.

[0011] To achieve the above objectives, the present invention adopts the following technical solutions:

[0012] A system for remote operation and maintenance update and data analysis of automated equipment, comprising:

[0013] Build an MQTT communication architecture: Install an MQTT broker on the main device. The Stocker software publishes topics regarding its status and controllable functions through the MQTT protocol. The SRM subscribes to these topics to obtain the real-time status of the device and achieve remote control;

[0014] Version management process: Select the new version compressed file on the SRM. The system automatically checks the status of the devices that need to be updated, generates and sends an update schedule. When the device is ready for update, the SRM performs a pause operation, kills the stack process, compares the current version file with the main system file, only updates the changed files, saves them to the backup folder through encrypted compression, downloads the new version file, modifies the files after decompression and decryption, and restarts the device software process;

[0015] Data processing mechanism: Regularly compress and backup the previous and current version files, storing them in the specified backup folder. At the same time, establish a remote log collection, analysis, and backup system to collect and analyze the device software operation logs in real time, promptly detect abnormal situations and perform backups;

[0016] Intelligent update scheduling: Optimize the generation algorithm of the update schedule according to factors such as the device's load condition and network status to ensure that the software update is carried out at the most appropriate time, reducing the impact on the business;

[0017] Log analysis optimization: Adopt machine learning algorithms to deeply analyze the log data, improve the detection accuracy of abnormal logs, be able to automatically identify potential software fault risks, and issue early warnings in a timely manner.

[0018] For further optimization of this technical solution, the MQTT communication is used for device communication. Each device will install the SRM software. The SRM on the master device uploads the files that need to be updated. The master device will communicate with each device, and by monitoring the running status of each device, it will automatically perform the update when the device is idle.

[0019] For further optimization of this technical solution, the MQTT communication method is as follows: In the MQTT message transmission, a unique identifier and a transmission status flag are set for each message. When the transmission is interrupted, the current transmission progress is recorded and stored in the local database or a temporary file. After the network is restored, the client sends a resume transmission request to the proxy server, including the transmission progress information. The proxy server verifies and negotiates and then continues to send the unfinished messages from the breakpoint. During the resume transmission process, the transmission status is monitored in real time, and a retransmission mechanism is adopted to automatically adjust the transmission rate and retry strategy to optimize the resume transmission efficiency.

[0020] For further optimization of this technical solution, the version management uses a machine learning classification algorithm for classified storage, automatic backup, and rollback in case of update failure:

[0021] Collect the feature data of historical version files, train and construct a classification model using a machine learning classification algorithm, and input the new files into the model to be automatically classified and stored in different areas;

[0022] Set up a regular backup task. According to the classification results, only the changed file categories are backed up. An incremental backup and a full backup are combined. The backed-up files are stored in a remote location and are redundantly stored and encrypted;

[0023] When the update fails, the system monitors and records the detailed information in real time. According to the classified storage and backup information, it quickly locates and automatically restores to the state before the update, analyzes the reasons to generate an error report, and reduces the impact on the system operation.

[0024] For further optimization of this technical solution, the specific steps of the version process management are as follows: First, upload the files that need to be updated to the SRM software on the master device. The new files will be compared and analyzed with the old files, and only the changed files will be transmitted. Send a request update instruction to each device through the MQTT communication protocol, judge whether the update conditions are met according to the feedback status information, and transmit the new files to the SRM software of each device that meets the update conditions. During the file transmission process, a breakpoint resume transmission mechanism is adopted, and the files will be automatically encrypted to ensure security. The devices that receive the files will perform automatic updates, and a machine learning algorithm will be used to perform a system backup on the updated files.

[0025] For further optimization of this technical solution, the file transfer adopts incremental compression transfer. Before the software version is updated, the system first uses the file hash algorithm to generate unique hash values for the current and target version files, and accurately identifies the different files by combining the byte-by-byte comparison technology of the file content. For the identified different files, an efficient incremental compression algorithm is used for compression, and appropriate compression parameters are automatically selected according to the file type and change characteristics. When transmitting the compressed different files, the block transfer and flow control technology are adopted, and the file verification mechanism is used to ensure data integrity.

[0026] For further optimization of this technical solution, the encryption adopts a combination of advanced symmetric encryption algorithm and asymmetric encryption algorithm to encrypt the updated data. The symmetric encryption algorithm is used for encrypting a large amount of data, and the asymmetric encryption algorithm is used for securely exchanging the encryption key of the symmetric encryption algorithm. Before the updated data is transmitted, a unique digital signature is generated for each update data packet, and the receiver verifies it with the sender's public key. The update data packets are numbered and encrypted to form an encrypted update chain. Each data packet contains the encrypted information of the previous data packet to prevent the data from being maliciously tampered with. At the same time, a perfect key management system is established, the key is updated regularly, stored in a secure hardware device and protected by multiple encryptions, and the key backup and recovery mechanism is adopted.

[0027] For further optimization of this technical solution, the machine learning algorithm is adopted to allocate an update schedule for the device.

[0028] For further optimization of this technical solution, the log data adopts the anomaly detection model of local outlier factor and the stream processing framework. A large amount of log data of the normal operation of the STK software is collected for preprocessing, key features are extracted, and the anomaly detection model is trained and constructed by the local outlier factor algorithm, and the parameters are adjusted to optimize the performance. The Apache Kafka stream processing framework is deployed to send the log data to the Kafka cluster topic in real time for partitioning and storage. The real-time processing program is written using the Kafka Streams API to consume the log data from the topic and pass it to the local outlier factor model for analysis, calculate the local outlier factor value, and determine it as abnormal data if it exceeds the threshold, and an alarm is issued in time and stored in a dedicated database.

[0029] Different from the prior art, the above technical solution has the following beneficial effects:

[0030] 1. Improve management efficiency: By adopting the MQTT proxy technology, a set of efficient remote management and control systems are constructed, which increases the transmission efficiency of the updated files and also increases the security of the transmission process.

[0031] 2. Enhance system stability: Real-time monitor the software status of the device, promptly detect and resolve problems during software operation, and adjust and maintain the software through the remote control function, effectively enhancing the stability and reliability of the software and reducing the impact of software failures on business operations.

[0032] 3. Optimize data management: Standardize the compression, backup, and analysis of software version files and operation logs to ensure data security and traceability. When problems occur in the software, it is possible to quickly locate the root cause of the problem, and by viewing historical version files and log information, quickly restore the software to normal operation and reduce operation and maintenance costs. Description of the Drawings

[0033] Figure 1 is a schematic diagram of the network architecture;

[0034] Figure 2 is a flowchart of the device software update. Detailed Implementation Manner

[0035] To elaborate in detail on the technical content, structural features, achieved objectives, and effects of the technical solution, the following will be described in detail in conjunction with specific embodiments and with reference to the accompanying drawings.

[0036] The present invention proposes a system for remote operation and maintenance update and data analysis of automated devices, including:

[0037] Build an MQTT communication architecture: Install an MQTT broker on the main device. Stocker publishes topics regarding its status and controllable functions through the MQTT protocol. SRM subscribes to these topics to obtain the real-time status of the device and achieve remote control. Each device will install the SRM software, and the SRM on the main device uploads the files that need to be updated. The main device communicates with each device, and by monitoring the operation status of each device, it selects to automatically update when the device is idle.

[0038] Version management process: Select a new version compressed file on SRM. The system automatically checks the status of the devices that need to be updated, generates and sends an update schedule. When the device is ready for update, SRM performs a pause operation, kills the stack process, compares the current version file with the main system file, only updates the changed files, saves them to the backup folder through encrypted compression, downloads the new version file, decompresses and decrypts it, modifies the files, and restarts the device software process.

[0039] The software update process is as follows: Update process: First, upload the files to be updated to the SRM software of the master device. The new files will be compared and analyzed with the old files, and only the changed files will be transmitted. Send an instruction to request an update to each device via the MQTT communication protocol, and judge whether the update conditions are met based on the feedback status information, and transmit the new files to the SRM software of each device that meets the update conditions. The breakpoint resumption mechanism is adopted during the file transmission process, and the files will be automatically encrypted to ensure security. The devices that receive the files will perform automatic updates and use machine learning algorithms to back up the updated files for the system.

[0040] Data processing mechanism: Regularly compress and back up the previous and current version files and store them in the specified backup folder. At the same time, establish a remote log collection, analysis, and backup system to collect and analyze the device software operation logs in real time, promptly detect abnormal situations and perform backups.

[0041] Intelligent update scheduling: Optimize the algorithm for generating the update schedule based on factors such as the device load and network conditions to ensure that the software update is carried out at the most appropriate time and reduce the impact on the business.

[0042] Log analysis optimization: Adopt machine learning algorithms to deeply analyze the log data, improve the detection accuracy of abnormal logs, be able to automatically identify potential software failure risks, and issue early warnings in a timely manner.

[0043] Build an MQTT broker server on the master device. Build a network architecture as Figure 1 shown, adopt port multiplexing and dynamic allocation technologies to ensure that different types of communication requests can be efficiently processed in parallel. At the same time, combine firewall policies to strictly control access to ports and prevent illegal intrusion. Integrate a high-performance MQTT client in the SRM system to achieve efficient communication with the STK software and the MQTT broker server. According to the management requirements of the system, the client adopts dynamic subscription and filtering technologies, and flexibly adjusts the scope of relevant topics published by the subscribed device software according to different business scenarios and user permissions to reduce unnecessary data transmission and processing overhead; receive the messages forwarded by the MQTT broker server in real time, adopt an efficient data parsing algorithm and protocol stack to quickly convert binary messages into readable text information, and verify the legality and integrity of the messages to ensure that the received information is accurate; at the same time, according to the management requirements, send control instructions to the STK software through the client to achieve remote management and control of the STK software. During the instruction sending process, encryption and authentication technologies are adopted to ensure the security and reliability of the instructions.

[0044] such as Figure 2As shown, when the device software needs to be updated, just place the update file on the main device. Select the files that need to be updated through comparison and analysis of the files, and then issue an update command through the remote system. At this time, the slave device feeds back the current status, and assigns an update schedule for it through a machine learning algorithm, and performs the update when it is idle, avoiding occupying production time caused by updating during device operation. When the device meets the update conditions, it will automatically close the current program for backup and version management, then perform software update, and automatically start the program for use after completion.

[0045] To reduce the transmission volume, incremental compression transmission is adopted. Before the software version is updated, the system first uses the file hash algorithm (SHA-256) to generate unique hash values for the current and target version files, and accurately identifies the different files by combining the per-byte comparison technology of the file content. For the identified different files, efficient incremental compression algorithms (xdelta, bsdiff) are used for compression, and appropriate compression parameters are automatically selected according to the file type and change characteristics. When transmitting the compressed different files, block transmission and flow control technologies are adopted, and the file verification mechanism (CRC verification) is used to ensure data integrity.

[0046] Combined with the MQTT breakpoint resumption mechanism to reduce network bandwidth consumption. In the MQTT message transmission, a unique identifier and a transmission status flag are set for each message. When the transmission is interrupted, the current transmission progress is recorded and stored in the local database or temporary file. After the network is restored, the client sends a resumption request to the proxy server, including the transmission progress information. The proxy server verifies and negotiates and then continues to send the unfinished messages from the breakpoint. During the resumption process, the transmission status is monitored in real time, and the retransmission mechanism is adopted to automatically adjust the transmission rate and retry strategy to optimize the resumption efficiency.

[0047] To increase the transmission security, an encrypted update chain is created for the update data. An advanced symmetric encryption algorithm (AES algorithm) and an asymmetric encryption algorithm (RSA algorithm) are combined to encrypt the update data. AES is used for encrypting a large amount of data, and RSA is used for securely exchanging the AES encryption key. Before the update data is transmitted, a unique digital signature is generated for each update data packet, and the receiver verifies it with the sender's public key. The update data packets are numbered and encrypted to form an encrypted update chain. Each data packet contains the encrypted information of the previous data packet to prevent the data from being maliciously tampered with. At the same time, a perfect key management system is established, the key is updated regularly, stored in a secure hardware device and protected by multiple encryptions, and a key backup and recovery mechanism is adopted.

[0048] In terms of version file management, a machine learning classification algorithm is adopted for classified storage, automatic backup, and rollback in case of update failure. Historical version file feature data (such as file size, modification time, file type, functional module, etc.) is collected, and a classification model is trained and constructed using a machine learning classification algorithm (decision tree). New files are input into the model for automatic classification and storage in different regions. Regular backup tasks are set, and only the changed file categories are backed up according to the classification results. An incremental backup and full backup combination method is adopted, and the backup files are stored in a remote location with redundant storage and encryption. When an update fails, the system monitors and records detailed information in real time, quickly locates based on the classified storage and backup information, and automatically restores to the state before the update. The reason is analyzed to generate an error report, reducing the impact on system operation.

[0049] For log data processing, an outlier detection model based on the Local Outlier Factor (LOF) and a stream processing framework are adopted. A large amount of log data during the normal operation of the STK software is collected for preprocessing, and key features (such as timestamp, operation type, error code, resource usage, etc.) are extracted. An outlier detection model is trained and constructed using the LOF (Local Outlier Factor) algorithm, and the parameters are adjusted to optimize the performance. The Apache Kafka stream processing framework is deployed to send log data to the Kafka cluster topic for partitioning and storage in real time. A real-time processing program is written using the Kafka Streams API to consume log data from the topic and pass it to the LOF model for analysis, calculate the local outlier factor value, and determine abnormal data when it exceeds the threshold, and an alarm is sent in a timely manner and stored in a dedicated database.

[0050] The objectives of the present invention are:

[0051] 1. Improve the efficiency of version management

[0052] The Stocker software version management and remote operation and maintenance system proposed by the present invention can significantly improve the efficiency of device software version management. Although the remote update system of the prior art can remotely update the device, it will take a lot of time when the updated files are large and frequent. With the help of this system, through MQTT communication, the system establishes a powerful remote operation and maintenance ability, and only updates the changed files in an incremental compression transmission manner. And the breakpoint resume mechanism can be realized, reducing the consumption of network bandwidth. At the same time, a security verification mechanism is added to ensure the security of the files. And when the update fails, the version is automatically rolled back, avoiding device downtime caused by update failure. At the same time, an advanced compression algorithm is adopted to process the version files, reducing the storage volume of the files while ensuring the integrity of the file content and saving storage space.

[0053] 2. Optimize data management

[0054] The system has comprehensively optimized the data management of device software. For operation logs, the system has realized remote collection, analysis, and backup. By adopting an anomaly detection model with Local Outlier Factor (LOF) for model training, using a stream processing framework (Apache Kafka) to process the real-time collected log data, transmitting the log data in real time to the trained machine learning model for anomaly detection, deeply mining and analyzing the log data, it can quickly locate the root cause of software faults. At the same time, detailed log records also provide valuable references for software performance optimization and function improvement, thus overall enhancing the software operation and maintenance level.

[0055] MQTT-based remote management architecture: By introducing MQTT proxy technology, a set of efficient remote management and control systems has been constructed. Incremental compression transmission (only transmitting differential files) is adopted to reduce file capacity, combined with the MQTT resume mechanism to reduce network bandwidth consumption. At the same time, an encrypted update chain is created for updated data to ensure the integrity and reliability of the source of the updated data, prevent data from being tampered with, and increase the security during the transmission process.

[0056] Data management innovation: Unified compression, backup, and analysis management are carried out on software version files and operation logs. Especially, machine learning algorithms are used to optimize log analysis, which can more accurately find the software problem points, ensure the stable operation of the software, and enhance the level and value of data management.

[0057] In summary, compared with the prior art, the present invention has obvious advantages in aspects such as software update and maintenance, data backup, and intelligent analysis of log files. These advantages can reduce the labor cost during equipment maintenance and improve the software update efficiency, help users better analyze log files to find problems, and improve work efficiency and decision-making accuracy.

[0058] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, elements defined by the statement "including..." or "comprising..." do not exclude the existence of additional elements in the process, method, article or terminal device including the said elements. In addition, in this article, "greater than", "less than", "exceeding" are understood not to include the present number; "above", "below", "within" are understood to include the present number.

[0059] Although the above-described embodiments have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the above are only the embodiments of the present invention and do not limit the patent protection scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, is equally included in the patent protection scope of the present invention.

Claims

1. A system for remote operation and maintenance update and data analysis of an automated device, characterized in that, Including: Build an MQTT communication architecture: Install an MQTT broker on the master device. The Stocker software publishes topics regarding its status and controllable functions through the MQTT protocol. The SRM subscribes to these topics to obtain the real-time status of the device and achieve remote control; Version management process: Select a new version compressed file on the SRM. The system automatically checks the status of the devices that need to be updated, generates and sends an update schedule. When the device is ready for update, the SRM performs a pause operation, kills the stack process, compares the current version file with the main system file, only updates the changed files, saves them to the backup folder after encryption and compression, downloads the new version file, modifies the file after decompression and decryption, and restarts the device software process; Data processing mechanism: Regularly compress and back up the previous and current version files, and store them in the specified backup folder. At the same time, establish a remote log collection, analysis, and backup system to collect and analyze the device software operation logs in real time, detect abnormal situations in a timely manner, and perform backups; Intelligent update scheduling: Optimize the generation algorithm of the update schedule according to factors such as the device's load situation and network conditions to ensure that software updates are carried out at the most appropriate time and reduce the impact on the business; Log analysis optimization: Adopt machine learning algorithms to deeply analyze the log data, improve the detection accuracy of abnormal logs, be able to automatically identify potential software failure risks, and issue early warnings in a timely manner.

2. The system for remote operation and maintenance update and data analysis of an automated device according to claim 1, wherein, The MQTT communication is used for device communication. The SRM software is installed on each device. The SRM on the master device uploads the files that need to be updated. The master device communicates with each device, and automatically performs updates when the device is idle by monitoring the running status of each device.

3. The system for remote operation and maintenance update and data analysis of the automated device according to claim 1, characterized in that, The MQTT communication method is as follows: In the MQTT message transmission, set a unique identifier and a transmission status flag for each message. When the transmission is interrupted, record the current transmission progress and store it in the local database or temporary file; after the network is restored, the client sends a resume transmission request to the broker server, including the transmission progress information. The broker server verifies and negotiates and then continues to send the unfinished message from the breakpoint. During the resume transmission process, monitor the transmission status in real time, adopt a retransmission mechanism, and automatically adjust the transmission rate and retry strategy to optimize the resume transmission efficiency.

4. The system for remote operation and maintenance update and data analysis of an automated device as described in claim 1, wherein The version management adopts machine learning classification algorithms for classified storage, automatic backup, and update failure backtracking: Collect the feature data of historical version files, train and construct a classification model with machine learning classification algorithms, and automatically classify and store the new files into different regions by inputting them into the model; Set regular backup tasks, only back up the changed file categories according to the classification results, adopt a combination of incremental backup and full backup methods, store the backup files in a remote location and perform redundant storage and encryption; When the update fails, the system monitors and records detailed information in real time, quickly locates and automatically restores to the state before the update according to the classification storage and backup information, analyzes the reasons to generate an error report, and reduces the impact on the system operation.

5. The system for remote operation and maintenance update and data analysis of an automated device according to claim 1, characterized in that, The specific steps of the version process management are as follows: First, upload the files to be updated to the SRM software of the master device. The new files will be compared and analyzed with the old files, and only the changed files will be transmitted. Send a request update instruction to each device through the MQTT communication protocol, judge whether the update conditions are met according to the feedback status information, and transmit the new files to the SRM software of each device that meets the update conditions. During the file transmission process, the breakpoint resumption mechanism is adopted, and the files will be automatically encrypted to ensure security. The devices that receive the files will be automatically updated, and the machine learning algorithm will be used to perform system backup on the updated files.

6. The system for remote operation and maintenance update and data analysis of the automated device according to claim 1, wherein The file transmission adopts incremental compression transmission. Before the software version is updated, the system first uses the file hash algorithm to generate unique hash values for the current and target version files, and accurately identifies the different files by combining the byte-by-byte comparison technology of the file content. For the identified different files, an efficient incremental compression algorithm is used for compression, and appropriate compression parameters are automatically selected according to the file type and change characteristics. When transmitting the compressed different files, the block transmission and flow control technology are adopted, and the file verification mechanism is used to ensure data integrity.

7. The system for remote operation and maintenance update and data analysis of an automated device according to claim 1, wherein, The encryption adopts a combination of advanced symmetric encryption algorithm and asymmetric encryption algorithm to encrypt the update data. The symmetric encryption algorithm is used for encrypting a large amount of data, and the asymmetric encryption algorithm is used for securely exchanging the encryption key of the symmetric encryption algorithm. Before the update data is transmitted, a unique digital signature is generated for each update data packet, and the receiver verifies it with the sender's public key. The update data packets are numbered and encrypted to form an encrypted update chain. Each data packet contains the encrypted information of the previous data packet to prevent the data from being maliciously tampered with. At the same time, a perfect key management system is established, the key is updated regularly, stored in a secure hardware device and protected by multiple encryptions, and a key backup and recovery mechanism is adopted.

8. The system for remote operation and maintenance update and data analysis of an automated device according to claim 1, characterized in that, The machine learning algorithm is used to allocate an update schedule for the device.

9. The system for remote operation and maintenance update and data analysis of an automated device according to claim 1, wherein The log data adopts the anomaly detection model of the local outlier factor and the stream processing framework. A large amount of log data of the normal operation of the STK software is collected for preprocessing, key features are extracted, and an anomaly detection model is trained and constructed by the local outlier factor algorithm, and the parameters are adjusted to optimize the performance. Deploy the Apache Kafka stream processing framework, and send the log data to the Kafka cluster topic in real time for partitioning and storage. Use the Kafka Streams API to write a real-time processing program, consume the log data from the topic and pass it to the local outlier factor model for analysis, calculate the local outlier factor value, and determine it as abnormal data if it exceeds the threshold, and issue an alarm in time and store it in a dedicated database.

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