Novel self-diagnosis device of chain transmission system

Abstract

The invention relates to the technical field of detection equipment, and discloses a novel chain transmission system self-diagnosis device which comprises a detection box, the front side of the detection box is provided with a plurality of control knobs, and the control knobs are used for manually controlling the detection state of the detection box; the centralized controller is mounted at the top of the detection box and is used for displaying the diagnosis progress and result; the protection pipes are mounted on two sides of the integrated controller and are used for protecting cables of the integrated controller for controlling the detection box; the chain is arranged in the detection box in a penetrating manner and is used for carrying out transmission operation; the warning light bars are installed on the upper side and the lower side of the control knob, connected with the detection box and used for conducting flickering warning when the chain breaks down and is in a self-diagnosis state; and the PLC module is arranged in the centralized controller. Through the combination of the dual-system redundancy design and the distributed database, the data availability and the fault-tolerant capability of the system are improved, so that the shutdown risk is reduced, the continuity and the stability of services are ensured, and a reliable operation environment is provided for a user.

Description

Technical Field

[0001] The invention relates to the technical field of detection equipment, in particular to a novel self-diagnosis device for a chain transmission system. Background Art

[0002] The chain drive system is a transmission method widely used in the field of mechanical transmission. It mainly transmits power from one place to another through the interaction between the chain and the gear. The chain drive system consists of components such as chains, sprockets (or gears), and tensioning devices. Its working principle is that when the motor or other power source drives the sprocket to rotate, the chain rotates around the sprocket, thereby transmitting power to the load. The chain drive system has many advantages, including simple structure, high transmission efficiency, strong load-bearing capacity, and wear resistance. Therefore, it has been widely used in automated production lines, conveying equipment, agricultural machinery, and industrial equipment. In the use of the chain drive system, in order to be able to identify potential problems in time and issue alarms to ensure the safety and stable operation of the system, the corresponding self-diagnosis device is often used. It integrates multiple sensors, such as vibration sensors, temperature sensors, and displacement sensors, to collect the operation status of the chain in real time. These sensors monitor the vibration of the chain, temperature changes, and key parameters such as relaxation and displacement, and transmit the monitoring data to the control system for analysis through the data acquisition module.

[0003] However, the existing chain drive system self-diagnosis device still has some problems in actual use. First, many chain drive systems still rely on a single control system, which makes the system prone to performance bottlenecks when facing high concurrent requests. At the same time, if the main system fails, it often causes the entire production line to shut down, causing economic losses. The lack of effective redundancy design makes the system's fault tolerance low when facing unexpected failures, affecting business continuity and stability.

[0004] Secondly, in terms of data management, traditional storage methods usually store all data in the same type of media, failing to effectively distinguish the access frequency and importance of data. This leads to slow access to hot data and high storage costs. Infrequently used data is mixed in high-performance storage, wasting storage resources and reducing the overall data management efficiency of the system.

[0005] Finally, in a distributed environment, ensuring data consistency between the primary and backup systems is a major challenge. Traditional systems often lack an effective consensus mechanism, resulting in the risk of inconsistency when data is updated. This inconsistency may cause serious failures and data errors, affecting the reliability of the system. In addition, the lack of a regular verification mechanism makes it difficult to detect and correct potential data problems in a timely manner. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a novel self-diagnosis device for a chain transmission system, which solves the problem of low fault tolerance of the device when facing unexpected failures.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A new self-diagnosis device for a chain transmission system, comprising:

[0008] A detection box, a plurality of control knobs are installed on the front side of the detection box, and the control knobs are used to manually control the detection state of the detection box;

[0009] A centralized controller, which is mounted on the top of the test box and is used to display the diagnosis progress and results;

[0010] Protection tubes, which are installed on both sides of the centralized controller and are used to protect the cables of the centralized controller controlling the detection box;

[0011] A chain is arranged throughout the interior of the detection box for transmission operation;

[0012] Warning light bar, which is installed on the upper and lower sides of the control knob and connected to the detection box, is used to flash and warn when the chain fails and is in the self-diagnosis state;

[0013] The PLC module is installed inside the centralized controller and is used to control the self-diagnosis operation of the detection box.

[0014] Preferably, the PLC module includes a sensor module, a data acquisition module, a data processing module, a storage module, an alarm system module, a dual system redundancy module, a data archiving module and a data consistency assurance module;

[0015] The sensor module is responsible for real-time monitoring of key parameters of the chain;

[0016] The data acquisition module is responsible for collecting data from the sensor module and performing preliminary processing;

[0017] The data processing module is used to perform in-depth analysis and processing on the collected data;

[0018] The storage module is used to provide data storage and management;

[0019] The alarm system module is used to monitor the chain status in real time and issue an alarm through the warning light bar in time when an abnormality occurs;

[0020] The dual system redundancy module is used to provide redundancy protection for the PLC module;

[0021] The data archiving module is used to archive infrequently used data;

[0022] The data consistency assurance module is used to ensure the consistency and integrity of data in the system.

[0023] Preferably, the sensor module includes a vibration sensor, a temperature sensor and a displacement sensor, and the key parameters include the vibration of the chain, the temperature of the chain and the gears, and the slack and displacement of the chain.

[0024] Preferably, the data acquisition module includes:

[0025] Data analysis unit, which uses intelligent algorithms to analyze data and identify potential faults;

[0026] Consensus algorithm unit, which is used to achieve distributed consensus and ensure data consistency;

[0027] Asynchronous processing unit, which is used to dynamically balance data consistency and performance.

[0028] Preferably, the storage module includes:

[0029] A main storage unit, which is used to store real-time data and analysis results;

[0030] The backup storage unit is used for redundant data storage to ensure high availability.

[0031] Preferably, the dual system redundancy module comprises:

[0032] The main system unit is responsible for main data processing and real-time monitoring;

[0033] A backup system unit that automatically takes over when the primary system fails.

[0034] Preferably, the data archiving module includes:

[0035] An archiving strategy unit, which is used to classify data into three categories and store them in different storage media;

[0036] The three types of data include:

[0037] Hot data: data that is frequently accessed and processed in real time, usually key data for business operations, stored in high-performance storage media;

[0038] Warm data: data that is no longer frequently accessed but still needs to be accessed regularly. It is usually older business data that may be used in analysis and reporting. It is stored on medium-performance storage media.

[0039] Cold data: Data that is rarely or almost never accessed, usually data for historical records or compliance requirements, stored in the cloud;

[0040] An archival storage unit that automatically identifies and archives infrequently used data based on data usage frequency;

[0041] The automatic identification criteria include:

[0042] Data access log recording: It is necessary to record the access log of each data item, including the access time, access times, and operation type, which includes read, write, and update;

[0043] Data usage frequency analysis: Regularly analyze data access logs to determine the access frequency of each data item.

[0044] Preferably, the data consistency assurance module includes:

[0045] A data synchronization unit is used to periodically synchronize the data status of the primary and standby systems. The data synchronization unit operates using a dual mechanism and includes:

[0046] a. Cross-system consensus mechanism to ensure that the main system and backup system remain consistent when data is changed, using the Raft distributed consensus algorithm;

[0047] The Raft distributed consensus algorithm includes:

[0048] Leader election: Nodes are in three states: leader, follower, and candidate. When a follower node does not receive a heartbeat message from the leader, it enters the candidate state and starts a new election. The candidate increases its term number and requests votes from other nodes. If it receives more than half of the votes, it becomes the leader.

[0049] Log replication: When a leader receives a client request, it converts the request into a log entry and replicates it to all followers;

[0050] Security guarantee: A log entry is considered committed only when it is confirmed by the majority of nodes;

[0051] b. Regular status synchronization mechanism to ensure that the data and configuration of the two systems are consistent, and to promptly detect and correct possible data inconsistencies;

[0052] A cleaning and review unit is used to regularly review and clean up archived data.

[0053] Preferably, the dual system redundancy module further comprises:

[0054] Real-time data synchronization mechanism: while the main system is processing high-concurrency requests, data is asynchronously synchronized to the backup system;

[0055] Intelligent load balancing technology: dynamically dispatch requests to the main system or backup system based on the real-time data request volume;

[0056] The data synchronization mechanism includes the following steps:

[0057] S1. Data change detection: In the main system, any data change is detected through the event trigger mechanism, using database triggers or application layer event listeners to monitor data changes;

[0058] S2. Asynchronous data synchronization: Use message queues to perform asynchronous data synchronization and send changed data messages to the backup system.

[0059] S3, backup system receiving: The backup system periodically pulls messages from the message queue and updates the received data to its database;

[0060] S4, Data consistency verification: Apply verification methods in the backup system to verify data integrity and ensure that the received data is consistent with the data in the primary system;

[0061] The intelligent load balancing technology adopts a load balancing algorithm to implement an intelligent load balancing strategy, and its formula is:

[0062]

[0063] Among them, Server Score j Represents the comprehensive score of server j. The lower the score, the more suitable it is for receiving new requests. Connections j is the number of active connections established with server j, reflecting the current load of the server, Weight j is the processing capacity weight of server j, which determines the server's ability to process requests. j is the most recent response time of server j, indicating the speed of processing requests, N is the total number of servers, and argmin indicates selecting the server with the lowest score. By selecting the server with the lowest comprehensive score, it can dynamically allocate requests to the server with the best performance to ensure load balancing under high concurrency.

[0064] Preferably, the archiving storage unit uses a support vector machine algorithm for auxiliary identification, and the objective function of the algorithm formula is:

[0065]

[0066] The constraints are:

[0067]

[0068] Among them, w is the weight vector, which affects the decision boundary and represents the importance of each feature to fault identification; ||w|| is the norm of the weight vector, the smaller it is, the better the classification performance; y iis the label of the sample (+1 for normal, -1 for failure), which is used to train the model; x i is the input feature vector, which contains data from vibration sensors, temperature sensors and displacement sensors; b is the bias term, which adjusts the offset of the classification hyperplane.

[0069] Working principle: When a chain fails, it will be detected by the detection box, causing the warning light bar to start flashing and alarming. At this time, the centralized controller will control the detection box through the PLC module to perform self-diagnosis on the chain. Data and control instructions are transmitted through cables protected by protective tubes. The completion of self-diagnosis is detected by the centralized controller. If necessary, the diagnostic status of the detection box can be manually controlled through the control knob.

[0070] The present invention provides a novel self-diagnosis device for a chain transmission system, which has the following beneficial effects:

[0071] 1. The present invention greatly improves the data availability and fault tolerance of the system by combining dual system redundancy design with distributed database. While the main system processes high concurrent requests, the backup system ensures instant data update through a real-time data synchronization mechanism. Even if the main system fails, the backup system can take over seamlessly to ensure the continuous operation of the chain drive system. This redundancy mechanism effectively reduces the risk of downtime caused by system failures, ensures business continuity and stability, and provides users with a reliable operating environment.

[0072] 2. The present invention automatically identifies and classifies data into hot data, warm data and cold data, and stores them on different storage media. This hierarchical storage strategy not only improves the efficiency of data access and ensures rapid response of hot data in high-performance storage, but also reduces storage costs. By transferring infrequently used data to cloud storage or low-cost media, the optimal allocation of resources is achieved. In addition, the intelligent archiving and cleaning functions ensure the automatic processing of infrequently used data, so that the system can still maintain an efficient management level after long-term operation.

[0073] 3. The present invention implements a cross-system consensus mechanism and a periodic status synchronization mechanism, which effectively ensures the data consistency between the main system and the backup system. The application of the Raft distributed consensus algorithm ensures that all nodes can remain synchronized when the data changes, thereby eliminating the hidden dangers of data inconsistency. In addition, the periodic verification mechanism can promptly detect and correct possible data inconsistencies, thereby improving the reliability and security of the system. These measures enhance the transparency of data management, ensure the data accuracy of the chain drive system at critical moments, and provide users with a safe and reliable operating environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A perspective view of the present invention;

[0075] Figure 2 It is a schematic diagram of a PLC module of the present invention;

[0076] Figure 3 is a schematic diagram of a sensor module of the present invention;

[0077] Figure 4 It is a schematic diagram of a data acquisition module of the present invention;

[0078] Figure 5 It is a schematic diagram of a storage module of the present invention;

[0079] Figure 6 It is a schematic diagram of a dual system redundant module of the present invention;

[0080] Figure 7 It is a schematic diagram of a data archiving module of the present invention;

[0081] Figure 8 It is a schematic diagram of the data consistency assurance module of the present invention.

[0082] Among them, 1. detection box; 2. control knob; 3. centralized controller; 4. protection tube; 5. chain; 6. warning light bar. DETAILED DESCRIPTION

[0083] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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.

[0084] Please see attached Figure 1 -Attached Figure 2 The embodiment of the present invention provides a novel self-diagnosis device for a chain transmission system, comprising:

[0085] The detection box 1 has a plurality of control knobs 2 installed on the front side thereof, and the control knobs 2 are used to manually control the detection state of the detection box 1;

[0086] A centralized controller 3, which is installed on the top of the detection box 1 and is used to display the diagnosis progress and results;

[0087] The protection tube 4 is installed on both sides of the centralized controller 3 to protect the cables of the centralized controller 3 controlling the detection box 1;

[0088] A chain 5 is provided throughout the interior of the detection box 1 for transmission operation;

[0089] A warning light bar 6, which is installed on the upper and lower sides of the control knob 2 and connected to the detection box 1, is used to flash and warn when the chain 5 fails and is in a self-diagnosis state;

[0090] A PLC module, which is installed inside the centralized controller 3 and is used to control the self-diagnosis operation of the detection box 1;

[0091] The PLC module includes a sensor module, a data acquisition module, a data processing module, a storage module, an alarm system module, a dual system redundancy module, a data archiving module and a data consistency assurance module;

[0092] The sensor module is responsible for real-time monitoring of key parameters of the chain 5;

[0093] The data acquisition module is responsible for collecting data from the sensor module and performing preliminary processing;

[0094] The data processing module is used to conduct in-depth analysis and processing of the collected data;

[0095] The storage module is used to provide data storage and management;

[0096] The alarm system module is used to monitor the status of the chain 5 in real time and issue an alarm through the warning light bar 6 in time when an abnormality occurs;

[0097] Dual system redundancy modules are used to provide redundancy protection for PLC modules;

[0098] The data archiving module is used to archive infrequently used data;

[0099] The data consistency assurance module is used to ensure the consistency and integrity of data in the system.

[0100] Specifically, in the detection box 1, the chain 5 runs through it for transmission operation. The operator manually sets the detection state through the control knob 2 to start the self-diagnosis process. The centralized controller 3 is located on the top of the detection box 1 and is responsible for displaying the current diagnosis progress and results. Once the self-diagnosis is started, the sensor module starts working immediately to monitor the key parameters of the chain 5 in real time, including vibration, temperature and displacement. Each sensor transmits the collected data to the data acquisition module, which is responsible for preliminary processing of the data to ensure the integrity and accuracy of the data. After preliminary processing, the data will be sent to the data processing module, which uses intelligent algorithms to conduct in-depth analysis of the data to identify potential faults or anomalies. When an abnormality is detected, the alarm system module will respond quickly and send a flashing alarm through the warning light bar 6 to prompt the operator that the chain 5 has a fault; At the same time, the storage module will store the processed data in real time to facilitate subsequent analysis and backtracking. For infrequently used data, the data archiving module will automatically archive it according to the frequency of use and store it in low-cost storage media to ensure the efficient operation of the main storage module; in order to ensure the high availability and data consistency of the system, the dual-system redundancy module will monitor the status of the main system and the backup system in real time. When the main system processes high-concurrency requests, the backup system will update the data through an asynchronous data synchronization mechanism to ensure that when the main system fails, the backup system can take over seamlessly and maintain data availability; finally, the data consistency assurance module is responsible for regularly synchronizing the data status of the main and backup systems to ensure the consistency of all data between the two systems, and promptly discover and correct possible data inconsistencies to further improve the reliability and security of the entire system.

[0101] Please see attached Figure 3 -Attached Figure 5 ,The sensor module includes vibration sensor, temperature sensor and displacement sensor.,Key parameters include chain vibration, chain and gear temperature, and chain slack and displacement;

[0102] The data acquisition module includes:

[0103] Data analysis unit, which uses intelligent algorithms to analyze data and identify potential faults;

[0104] Consensus algorithm unit, which is used to achieve distributed consensus and ensure data consistency;

[0105] Asynchronous processing unit, which is used to dynamically balance data consistency and performance;

[0106] The storage module includes:

[0107] A main storage unit, which is used to store real-time data and analysis results;

[0108] The backup storage unit is used for redundant data storage to ensure high availability.

[0109] Specifically, the data analysis unit of the data acquisition module receives the sensor data and uses an intelligent algorithm to perform preliminary analysis to identify potential failure modes. The results of the data analysis are then transmitted to the consensus algorithm unit, which ensures data consistency and synchronizes data between multiple nodes through a distributed consensus algorithm, thereby maintaining data consistency between the main system and the backup system. At the same time, the asynchronous processing unit plays an important role in this process. It is responsible for optimizing the balance between data processing performance and consistency. In the case of high concurrent requests, the asynchronous processing unit enables data to be quickly collected and analyzed without delays caused by waiting for consistency verification. The data after analysis and consensus is stored in the main storage unit in the storage module to ensure the security and accessibility of real-time data and analysis results. At the same time, the backup storage unit of the storage module receives the data from the main storage unit for redundant storage, thereby improving the high availability of the system and ensuring that when the main storage unit fails, the backup data can be immediately called to maintain the continuous operation of the system. Through this collaborative operation, the sensor module, the data acquisition module and the storage module achieve efficient data monitoring, analysis and storage, providing strong support for the self-diagnosis of the chain drive system and ensuring that the system can quickly respond to and handle potential failures in real-time monitoring.

[0110] Please see attached Figure 6 -Attached Figure 8 , dual system redundancy modules include:

[0111] The main system unit is responsible for main data processing and real-time monitoring;

[0112] A backup system unit that automatically takes over when the primary system fails;

[0113] The data archiving module includes:

[0114] An archiving strategy unit, which is used to classify data into three categories and store them in different storage media;

[0115] The three types of data include:

[0116] Hot data: data that is frequently accessed and processed in real time, usually key data for business operations, stored in high-performance storage media;

[0117] Warm data: data that is no longer frequently accessed but still needs to be accessed regularly. It is usually older business data that may be used in analysis and reporting. It is stored on medium-performance storage media.

[0118] Cold data: Data that is rarely or almost never accessed, usually data for historical records or compliance requirements, stored in the cloud;

[0119] An archival storage unit that automatically identifies and archives infrequently used data based on data usage frequency;

[0120] The criteria for automatic identification include:

[0121] Data access log recording: It is necessary to record the access log of each data item, including access time, access times, and operation type. The operation type includes read, write, and update.

[0122] Data usage frequency analysis: Regularly analyze data access logs to determine the access frequency of each data item;

[0123] The data consistency assurance module includes:

[0124] The data synchronization unit is used to periodically synchronize the data status of the primary and standby systems. The data synchronization unit operates using a dual mechanism, which includes:

[0125] a. Cross-system consensus mechanism to ensure that the main system and backup system remain consistent when data is changed, using the Raft distributed consensus algorithm;

[0126] The Raft distributed consensus algorithm includes:

[0127] Leader election: Nodes are in three states: leader, follower, and candidate. When a follower node does not receive a heartbeat message from the leader, it enters the candidate state and starts a new election. The candidate increases its term number and requests votes from other nodes. If it receives more than half of the votes, it becomes the leader.

[0128] Log replication: When a leader receives a client request, it converts the request into a log entry and replicates it to all followers;

[0129] Security guarantee: A log entry is considered committed only when it is confirmed by the majority of nodes;

[0130] b. Regular status synchronization mechanism to ensure that the data and configuration of the two systems are consistent, and to promptly detect and correct possible data inconsistencies;

[0131] A cleaning and review unit, which is used to regularly review and clean up archived data;

[0132] The dual system redundancy module further includes:

[0133] Real-time data synchronization mechanism: while the main system is processing high-concurrency requests, data is asynchronously synchronized to the backup system;

[0134] Intelligent load balancing technology: dynamically dispatch requests to the main system or backup system based on the real-time data request volume;

[0135] The data synchronization mechanism includes the following steps:

[0136] S1. Data change detection: In the main system, any data change is detected through the event trigger mechanism, using database triggers or application layer event listeners to monitor data changes;

[0137] S2. Asynchronous data synchronization: Use message queues to perform asynchronous data synchronization and send changed data messages to the backup system.

[0138] S3, backup system receiving: The backup system periodically pulls messages from the message queue and updates the received data to its database;

[0139] S4, Data consistency verification: Apply verification methods in the backup system to verify data integrity and ensure that the received data is consistent with the data in the primary system;

[0140] Intelligent load balancing technology uses a load balancing algorithm to implement an intelligent load balancing strategy. The formula is:

[0141]

[0142] Among them, Server Score j Represents the comprehensive score of server j. The lower the score, the more suitable it is for receiving new requests. Connections j is the number of active connections established with server j, reflecting the current load of the server, Weight j is the processing capacity weight of server j, which determines the server's ability to process requests. j is the most recent response time of server j, indicating the speed of processing requests, N is the total number of servers, and argmin indicates selecting the server with the lowest score. By selecting the server with the lowest comprehensive score, it can dynamically allocate requests to the server with the best performance to ensure load balancing under high concurrency conditions;

[0143] The archiving storage unit uses the support vector machine algorithm for auxiliary identification, and its algorithm formula objective function is:

[0144]

[0145] The constraints are:

[0146]

[0147] Among them, w is the weight vector, which affects the decision boundary and represents the importance of each feature to fault identification; ||w|| is the norm of the weight vector, the smaller it is, the better the classification performance; y iis the label of the sample (+1 for normal, -1 for failure), which is used to train the model; x i is the input feature vector, which contains data from vibration sensors, temperature sensors and displacement sensors; b is the bias term, which adjusts the offset of the classification hyperplane.

[0148] Specifically, in the self-diagnosis device of the new chain transmission system, the dual system redundant module achieves high availability and high performance through real-time data synchronization mechanism and intelligent load balancing technology. Specifically, the main system unit is responsible for processing all data change operations, and monitors data changes through event triggering mechanism, and detects changes in key data in the chain transmission process by using database triggers or event listeners at the application layer. Once the data change is detected, the main system will immediately synchronize the relevant data through the message queue asynchronously to send the changed data message to the backup system unit. The backup system regularly pulls these messages from the message queue and updates its database to ensure data consistency and real-time performance. In this process, data consistency verification is crucial. After receiving the data, the backup system will verify the integrity of the data through a verification method to ensure that the received data is consistent with the data in the main system, and promptly discover and correct any potential inconsistencies. At the same time, the intelligent load balancing technology dynamically adjusts the system load according to the real-time data request volume and system load. For the distribution of the entire request, the load balancing algorithm uses a comprehensive scoring formula to evaluate the performance of each server, which includes the number of active connections established with the server, the server's processing power weight and the most recent response time, so as to select the server with the lowest score to handle new requests, so as to optimize resource utilization and ensure fast response in high concurrency situations; in the archiving storage module, the system will automatically identify infrequently used data according to the frequency of data access, and classify it into hot data, warm data and cold data. By monitoring the data access log, the system analyzes the access pattern of each data item, and uses the support vector machine algorithm for auxiliary identification to ensure that low-frequency access data is archived in time. Specifically, the support vector machine evaluates the failure mode of the data by calculating the weight vector, sample label and input feature vector, and formulates an archiving strategy based on this to achieve efficient data management and storage. In this collaborative operation process, each module unit works closely together to form an efficient and reliable self-diagnosis system to ensure the stability and safety of the chain drive.

[0149] 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. A novel self-diagnosis device for chain transmission system, characterized in that: include: A detection box (1) having a plurality of control knobs (2) installed on the front side thereof, wherein the control knobs (2) are used to manually control the detection state of the detection box (1); A centralized controller (3), which is mounted on the top of the detection box (1) and is used to display the diagnosis progress and results; A protective tube (4) is installed on both sides of the centralized controller (3) and is used to protect the cables of the centralized controller (3) controlling the detection box (1); A chain (5) is arranged to penetrate the interior of the detection box (1) and is used for transmission operation; A warning light bar (6) is installed on the upper and lower sides of the control knob (2) and connected to the detection box (1), and is used to flash a warning light when the chain (5) fails and is in a self-diagnosis state; A PLC module is installed inside the centralized controller (3) and is used to control the self-diagnosis operation of the detection box (1).

2. A novel self-diagnosis device for a chain transmission system according to claim 1, characterized in that: The PLC module includes a sensor module, a data acquisition module, a data processing module, a storage module, an alarm system module, a dual system redundancy module, a data archiving module and a data consistency assurance module; The sensor module is responsible for real-time monitoring of key parameters of the chain (5); The data acquisition module is responsible for collecting data from the sensor module and performing preliminary processing; The data processing module is used to perform in-depth analysis and processing on the collected data; The storage module is used to provide data storage and management; The alarm system module is used to monitor the status of the chain (5) in real time and to promptly issue an alarm through the warning light bar (6) when an abnormality occurs; The dual system redundancy module is used to provide redundancy protection for the PLC module; The data archiving module is used to archive infrequently used data; The data consistency assurance module is used to ensure the consistency and integrity of data in the system.

3. A novel self-diagnosis device for a chain transmission system according to claim 2, characterized in that: The sensor module includes a vibration sensor, a temperature sensor and a displacement sensor, and the key parameters include the vibration of the chain, the temperature of the chain and the gear, and the relaxation and displacement of the chain.

4. A novel self-diagnosis device for chain transmission system according to claim 2, characterized in that: The data acquisition module comprises: Data analysis unit, which uses intelligent algorithms to analyze data and identify potential faults; Consensus algorithm unit, which is used to achieve distributed consensus and ensure data consistency; An asynchronous processing unit that is used to dynamically balance data consistency and performance.

5. A novel self-diagnosis device for chain transmission system according to claim 2, characterized in that: The storage module comprises: A main storage unit, which is used to store real-time data and analysis results; The backup storage unit is used for redundant data storage to ensure high availability.

6. A novel self-diagnosis device for chain transmission system according to claim 2, characterized in that: The dual system redundancy module comprises: The main system unit is responsible for main data processing and real-time monitoring; A backup system unit that automatically takes over when the primary system fails.

7. A novel self-diagnosis device for a chain transmission system according to claim 2, characterized in that: The data archiving module comprises: An archiving strategy unit, which is used to classify data into three types of data and store them in different storage media; The three types of data include: Hot data: data that is frequently accessed and processed in real time, usually key data for business operations, stored in high-performance storage media; Warm data: data that is no longer frequently accessed but still needs to be accessed regularly. It is usually older business data that may be used in analysis and reporting. It is stored on medium-performance storage media. Cold data: data that is rarely or almost never accessed, usually data for historical records or compliance requirements, stored in the cloud; An archival storage unit that automatically identifies and archives infrequently used data based on data usage frequency; The automatic identification criteria include: Data access log recording: It is necessary to record the access log of each data item, including the access time, access times, and operation type, which includes read, write, and update; Data usage frequency analysis: Regularly analyze data access logs to determine the access frequency of each data item.

8. A novel self-diagnosis device for a chain transmission system according to claim 2, characterized in that: The data consistency guarantee module includes: A data synchronization unit is used to periodically synchronize the data status of the primary and standby systems. The data synchronization unit operates using a dual mechanism and includes: a. Cross-system consensus mechanism to ensure that the main system and backup system remain consistent when data is changed, using the Raft distributed consensus algorithm; The Raft distributed consensus algorithm includes: Leader election: Nodes are in three states: leader, follower, and candidate. When a follower node does not receive a heartbeat message from the leader, it enters the candidate state and starts a new election. The candidate increases its term number and requests votes from other nodes. If it receives more than half of the votes, it becomes the leader. Log replication: When a leader receives a client request, it converts the request into a log entry and replicates it to all followers; Security guarantee: A log entry is considered committed only when it is confirmed by the majority of nodes; b. Regular status synchronization mechanism to ensure that the data and configuration of the two systems are consistent, and to promptly detect and correct possible data inconsistencies; A cleaning and review unit is used to regularly review and clean up archived data.

9. A novel self-diagnosis device for a chain transmission system according to claim 6, characterized in that: The dual system redundancy module further comprises: Real-time data synchronization mechanism: while the main system is processing high-concurrency requests, data is asynchronously synchronized to the backup system; Intelligent load balancing technology: dynamically dispatch requests to the main system or backup system based on the real-time data request volume; The data synchronization mechanism includes the following steps: S1. Data change detection: In the main system, any data change is detected through the event trigger mechanism, using database triggers or application layer event listeners to monitor data changes; S2. Asynchronous data synchronization: Use message queues to perform asynchronous data synchronization and send changed data messages to the backup system. S3, backup system receiving: The backup system periodically pulls messages from the message queue and updates the received data to its database; S4, Data consistency verification: Apply verification methods in the backup system to verify data integrity and ensure that the received data is consistent with the data in the primary system; The intelligent load balancing technology adopts a load balancing algorithm to implement an intelligent load balancing strategy, and its formula is: Among them, Server Score j Represents the comprehensive score of server j. The lower the score, the more suitable it is for receiving new requests. Connections j is the number of active connections established with server j, reflecting the current load of the server, Weight j is the processing capacity weight of server j, which determines the server's ability to process requests. j is the most recent response time of server j, indicating the speed of processing requests, N is the total number of servers, and argmin indicates selecting the server with the lowest score. By selecting the server with the lowest comprehensive score, it can dynamically allocate requests to the server with the best performance to ensure load balancing under high concurrency.

10. A novel self-diagnosis device for a chain transmission system according to claim 7, characterized in that: The archiving storage unit uses a support vector machine algorithm for auxiliary identification, and the algorithm formula objective function is: The constraints are: Among them, w is the weight vector, which affects the decision boundary and represents the importance of each feature to fault identification; ||w|| is the norm of the weight vector, the smaller it is, the better the classification performance; y i is the label of the sample (+1 for normal, -1 for failure), which is used to train the model; x i is the input feature vector, which contains data from vibration sensors, temperature sensors and displacement sensors; b is the bias term, which adjusts the offset of the classification hyperplane.

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