A servo motor driver parameter management method and system

Abstract

The application discloses a kind of servo motor driver parameter management method and system, it is related to motor parameter management technical field, comprising: parameter database initialization, output database initialization result;Based on database initialization result, execute state machine control flow;State machine state information, execute corresponding parameter synchronization strategy, output parameter synchronization state;Based on parameter synchronization state and state machine state information, execute hierarchical error detection and processing, identify abnormality through time window-based fault detection algorithm, and carry out fault handling according to hierarchical recovery strategy, output system health state;According to system health state and parameter synchronization state, execute system diagnosis and performance optimization, read and write optimization are carried out through parameter cache layer, and predictive maintenance is implemented, complete parameter management.The storage and management of unified data parameters are improved, the reliability of servo motor parameter management is improved, with the advantages of high fault identification accuracy and significant improvement of system stability.

Description

Technical Field

[0001] This invention relates to the field of motor technology, and specifically to a method and system for managing parameters of a servo motor driver. Background Technology

[0002] Currently, the development process of host computers for servo motor drivers generally adopts parameter table management to manage driver debugging parameters. Due to the outdated data storage model design, most host computer systems rely on file formats such as INI, XML, or Excel for parameter storage. Such file structures lack transaction processing mechanisms and are prone to data corruption and inconsistency when encountering abnormal power outages or system failures.

[0003] Existing solutions lack effective data integrity protection mechanisms. When parameter files are corrupted, the system cannot recognize the error state and continues to load corrupted data into memory, leading to driver malfunctions and even equipment safety risks. Furthermore, the parameter synchronization process lacks state awareness, failing to distinguish between offline and online operation scenarios during communication interruptions, resulting in parameter modification conflicts and data overwrite risks. Error handling mechanisms are also inadequate, failing to establish a time-window-based anomaly detection and tiered recovery strategy. This makes it easy for transient fluctuations to be misjudged as serious faults, while genuine faults lack targeted repair methods. These deficiencies collectively lead to frequent interruptions in the debugging process and decreased system stability, severely restricting the engineering application performance of servo motor drivers. Therefore, there is an urgent need to build a new parameter management system with robust data management capabilities. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a servo motor driver parameter management method and system. By unifying the storage and management of data parameters, it improves the reliability of servo motor parameter management and has the advantages of strong parameter synchronization accuracy, high fault identification accuracy, and significantly improved system stability.

[0005] This invention provides a servo motor driver parameter management method, the method comprising:

[0006] Step S1: Initialize the parameter database, establish a unified database table structure containing parameter ID, variable name, current value, parameter category, and data checksum, and output the database initialization result;

[0007] Step S2: Based on the database initialization result, execute the state machine control process. When the database initialization is successful, enter the offline state. When a communication connection is established with the lower-level machine and all parameters are successfully read, enter the online state and output the current state machine status information.

[0008] Step S3: Based on the state machine state information, execute the corresponding parameter synchronization strategy. In the offline state, parameter modification is allowed but marked as pending synchronization. In the online state, ensure that the parameters are synchronized with the lower-level machine in real time and output the parameter synchronization status.

[0009] Step S4: Based on the parameter synchronization status and state machine status information, perform hierarchical error detection and processing, identify anomalies through a time window-based fault detection algorithm, perform fault processing according to the hierarchical recovery strategy, and output the system health status.

[0010] Step S5: Based on the system health status and parameter synchronization status, perform system diagnosis and performance optimization, optimize read and write operations through the parameter caching layer, and implement predictive maintenance to complete parameter management.

[0011] Furthermore, step S1 includes:

[0012] Establish a parameter database table structure, including parameter category fields to distinguish between equipment information, motor parameters, speed loop parameters, and encoder parameters;

[0013] Create a version number field and a data checksum field for each parameter, and output parameter version information and integrity verification data;

[0014] Based on the parameter version information, implement parameter version management and data integrity protection;

[0015] When database initialization fails, an error message is output and an error pop-up is triggered. The user is asked to correct the error before the initialization is re-executed.

[0016] Furthermore, step S2 includes:

[0017] Receive the database initialization result, check whether the database has been successfully initialized, and if the initialization is successful, enter offline state;

[0018] In offline mode, monitor the communication connection status with the lower-level machine. Once the connection is established and the parameters are successfully read, output a status transition command.

[0019] Based on the state transition command, the system transitions from offline to online and initiates a real-time parameter synchronization mechanism.

[0020] When online, it continuously monitors the communication status. When communication is lost, it outputs a status rollback command to return to the offline status.

[0021] Furthermore, step S2 also includes:

[0022] Verify the state transition of the servo motor. Before the state transition, verify the legality of the transition conditions and output the verification results.

[0023] Based on the verification results, a decision is made as to whether to perform a state transition.

[0024] When verification fails, exception handling is performed and an exception handling report is output.

[0025] Based on the exception handling report, record the state transition failure log and provide repair suggestions.

[0026] Furthermore, step S3 includes:

[0027] Receive the state machine state information and select a synchronization strategy mode based on the current state;

[0028] In offline mode, record parameter modification operations and mark them as pending synchronization, and output a list of parameters to be synchronized;

[0029] Upon entering online status, conflict detection is performed based on the list of parameters to be synchronized, and a parameter conflict report is output.

[0030] Based on the parameter conflict report, an intelligent synchronization strategy is adopted to resolve the data inconsistency problem.

[0031] Furthermore, the step of using an intelligent synchronization strategy to resolve data inconsistency issues based on the parameter conflict report includes:

[0032] Receive parameter conflict reports, analyze parameter importance and security level, and output priority assessment results;

[0033] Based on the priority evaluation results, a synchronization strategy mode is selected;

[0034] Perform parameter synchronization operations and output the synchronization results;

[0035] Based on the synchronization execution results, update the parameter version information and synchronization status.

[0036] Furthermore, step S4 includes:

[0037] Receive the state machine status information and parameter synchronization status, and determine the current error detection level;

[0038] The algorithm analyzes the trend of parameter changes based on the time window, filters out false alarms caused by instantaneous fluctuations, and outputs the fault identification results.

[0039] Based on the fault identification results, a graded recovery strategy is executed, and the fault handling status is output.

[0040] Based on the fault handling status, a recovery verification test is performed to confirm system stability.

[0041] Furthermore, step S5 includes:

[0042] Receive the system health status and parameter synchronization status, and evaluate the current system performance;

[0043] Based on the current system performance evaluation results, read and write optimizations are implemented through the parameter caching layer, and the performance optimization effect is output.

[0044] Based on parameter change trends and system operation data, perform predictive maintenance analysis and output maintenance recommendations;

[0045] Based on the maintenance recommendations, the parameter management strategy will be optimized to improve system reliability.

[0046] Furthermore, the management method also includes:

[0047] During parameter database operations, WAL mode is enabled to ensure data consistency and output the transaction processing status;

[0048] Based on the transaction processing status, perform a periodic backup operation and output the backup completion status;

[0049] Based on the system health status, execute the data recovery process when necessary and output the data recovery results;

[0050] Based on the data recovery results, verify the database integrity and update the system status.

[0051] The present invention also provides a servo motor driver parameter management system, the management system being used to implement the aforementioned management method, the management system comprising:

[0052] Parameter storage module: Used to store servo motor driver parameters according to the optimized SQLite database table structure;

[0053] Intelligent state machine control module: used to implement multi-state management with state transition verification and exception recovery mechanism based on Qt framework, including initial state check, offline state, and online state data synchronization verification;

[0054] Hierarchical error handling module: used to employ time-window-based fault detection algorithms and hierarchical self-recovery mechanisms;

[0055] Parameter caching module: used to implement read / write separation and batch operation support;

[0056] System diagnostic module: Used to monitor the system health status in real time and perform predictive maintenance.

[0057] This invention provides a servo motor driver parameter management method and system. By unifying the storage and management of data parameters, it improves the reliability of servo motor parameter management and has the advantages of strong parameter synchronization accuracy, high fault identification accuracy, and significantly improved system stability. Attached Figure Description

[0058] Figure 1 This is a flowchart of the servo motor driver parameter management method in an embodiment of the present invention;

[0059] Figure 2 This is a logic block diagram of the parameter management method in an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of the servo motor driver parameter management system in an embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1:

[0063] Figure 1 A flowchart of the servo motor driver parameter management method in an embodiment of the present invention is shown. Figure 2 A logic block diagram of a parameter management method in an embodiment of the present invention is shown. The management method includes:

[0064] Step S1: Initialize the parameter database, establish a unified database table structure containing parameter ID, variable name, current value, parameter category, and data checksum, and output the database initialization result.

[0065] Specifically, the parameter database is initialized by establishing a unified database table structure that includes parameter IDs, variable names, current values, parameter categories, and data checksums. In practical applications, a unified database table structure refers to the process of defining a structured data storage format to centrally manage parameter information. This can be implemented using a relational database management system, such as SQLite or MySQL, to create data tables. Parameter categories are identified by predefined classification codes, and data checksums are generated using a cyclic redundancy check algorithm. Specifically, the data checksum can be calculated using CRC32 or MD5 hash functions to verify the integrity of data after an abnormal power outage. This is mainly to ensure the reliability of data storage and prevent the misuse of damaged data.

[0066] Specifically, step S1 includes:

[0067] Establish a parameter database table structure, including parameter category fields to distinguish between equipment information, motor parameters, speed loop parameters, and encoder parameters; by using enumeration types or predefined string sets to classify and manage parameters in a modular way, the resulting parameter category fields can effectively structure data organization and quickly locate the range of damaged parameters in the event of initialization anomalies.

[0068] Create a version number field and a data checksum field for each parameter, and output parameter version information and integrity verification data; by recording the historical change status of the parameter using an integer incrementing sequence or timestamp format, the resulting version number field can track the parameter modification trajectory and provide a basis for data recovery.

[0069] Based on the parameter version information, parameter version management and data integrity protection are implemented; by using cyclic redundancy check or hash algorithm to generate fixed-length check codes, the resulting data checksum field can detect data tampering or storage errors in real time, ensuring the integrity of stored data.

[0070] When database initialization fails, an error message is output and an error pop-up is triggered. Initialization is then re-executed after the user corrects the error. By providing interactive error feedback through a modal dialog box in a graphical user interface, the resulting error pop-up can interrupt the fault process and guide the user to intervene.

[0071] A structured data organization framework is established using parameter category fields, and a version number and checksum field are generated synchronously during initialization to form a dual verification mechanism. When the system performs database initialization, it first constructs a categorized storage structure based on the parameter category fields, and then instantiates a version number and checksum field for each parameter, enabling the system to trace parameter changes and verify data integrity. During initialization, checksum values ​​are compared in real time. If data anomalies are detected, a version rollback process is triggered, using historical version information to restore valid data. If initialization completely fails, the system immediately interrupts subsequent operations, outputting structured error information through an error pop-up. After the user corrects the configuration error, the initialization process is restarted. At this point, the system performs consistency checks based on version information to ensure that only complete and valid parameter data is loaded. This design creates a closed-loop processing logic of "structure definition - state tracking - anomaly detection - user intervention" during the initialization process, effectively avoiding the risk of continuing to use corrupted data.

[0072] Step S2: Based on the database initialization result, execute the state machine control flow. When the database initialization is successful, enter the offline state. When a communication connection is established with the lower-level machine and all parameters are successfully read, enter the online state and output the current state machine status information.

[0073] The system receives the database initialization result and checks whether the database has been successfully initialized. If the initialization is successful, it enters an offline state. This can be implemented using a finite state machine model, for example, by defining state transition rules through a state transition table. When the database initialization is successful, the system enters an offline state. The decision to enter an offline state is made by first checking the database initialization result, ensuring that the system only starts subsequent operations when the database is valid. In the offline state, the system continuously monitors the communication connection status and only outputs a state transition instruction when the connection is established and all parameters are read, thereby ensuring the reliability of state transitions.

[0074] In offline mode, the communication connection status with the lower-level machine is monitored. When the connection is established and the parameters are successfully read, a state transition command is output. After the communication connection with the lower-level machine is established and the parameters are successfully read, the system enters online mode. The state transition can be driven by event triggers or timers, without relying on the state transition verification conditions. This is mainly to ensure the security of the state transition and provide real-time status information.

[0075] Based on the state transition instructions, the system transitions from an offline state to an online state and initiates a real-time parameter synchronization mechanism. By generating control signals that trigger state machine state changes based on an event-driven mechanism, the resulting state transition instructions can safely achieve the transition to the data synchronization stage.

[0076] In online mode, the system continuously monitors communication status. When communication is lost, a status rollback command is output to return to offline mode. By designing the interrupt signal as a priority signal, the resulting status rollback command can quickly trigger the system to restore to a safe operating state in the event of communication failure.

[0077] Furthermore, upon entering the online state based on this instruction, a real-time parameter synchronization mechanism is immediately activated to maintain data consistency. Simultaneously, the communication status is monitored in real time while online, and a state rollback instruction is output upon detecting a disconnection, allowing the system to safely return to the offline state, forming a complete state management closed loop. This mechanism, through clearly defined entry conditions, transition trigger conditions, and rollback path design, ensures that the state machine remains controllable even in abnormal situations, avoiding parameter synchronization errors caused by state management failures.

[0078] Specifically, step S2 further includes:

[0079] Verify the state transition of the servo motor. Before the state transition, verify the legality of the transition conditions and output the verification result. Based on the verification result, decide whether to perform the state transition. When the verification fails, perform abnormal state handling and output an abnormal handling report. According to the abnormal handling report, record the state transition failure log and provide repair suggestions.

[0080] State transition verification refers to checking the legality of transition conditions before a state switch, which can be achieved by checking conditions such as communication connection status and parameter integrity. The verification result can be understood as the output of a judgment on the legality of the transition conditions, which can be a Boolean value or a status code. Specifically, abnormal state handling refers to the measures taken when verification fails, which can include rollback operations or entering a safe mode. In addition, logging state transition failures and providing repair suggestions means generating detailed error logs and actionable suggestions, which can be in a structured log format.

[0081] Specifically, the solution in this application embeds a verification mechanism before state switching to proactively check key conditions such as communication connection stability and parameter integrity, avoiding blind switching when conditions are not met; based on the verification output as the decision-making basis, it ensures that state transition is only allowed when verification passes; when verification fails, abnormal state handling is immediately executed to prevent the fault from spreading to subsequent processes; and by recording logs and providing repair suggestions, it supports rapid location of the root cause of the problem and system recovery.

[0082] Step S3: Based on the state machine state information, execute the corresponding parameter synchronization strategy. In the offline state, parameter modification is allowed but marked as pending synchronization. In the online state, ensure that the parameters are synchronized with the lower-level machine in real time and output the parameter synchronization status.

[0083] Specifically, the parameter synchronization strategy refers to the method of dynamically adjusting parameter synchronization behavior based on the system status. It can be implemented using a local storage mechanism. For example, in the offline state, parameter modifications are recorded in a log file and marked as pending synchronization. In the online state, data is transmitted through a real-time communication protocol such as Modbus. The synchronization process can be based on a transaction commit mechanism to ensure data consistency. Its main purpose is to isolate offline modifications from interaction with the lower-level machine and avoid the propagation of erroneous data.

[0084] Specifically, step S3 includes:

[0085] The system receives the state machine state information and selects a synchronization strategy mode based on the current state. By using state variables or state codes to indicate the system's operating phase, the obtained state machine state information can provide a real-time basis for selecting the synchronization strategy.

[0086] In offline mode, parameter modification operations are recorded and marked as pending synchronization, and a list of parameters to be synchronized is output. By using flag bits or status fields to identify the temporary storage stage after parameter modification, the resulting pending synchronization status can prevent offline modifications from directly overwriting lower-level machine data and avoid parameter corruption caused by abnormal power outages. By constructing a structured data container for centralized storage of pending synchronization parameter information using linked lists or arrays, the resulting list of pending synchronization parameters can provide a complete input source for conflict detection.

[0087] Upon entering online status, conflict detection is performed based on the list of parameters to be synchronized, and a parameter conflict report is output. By using value comparison or hash verification algorithms to identify the differences between the parameters to be synchronized and the current parameters of the lower-level machine, the conflict detection can detect data inconsistencies in advance.

[0088] By recording conflict details using log files or structured message formats, the resulting parameter conflict report provides an objective basis for synchronization decisions. Based on this report, an intelligent synchronization strategy is employed to resolve data inconsistency issues. By dynamically adjusting synchronization behavior using rule-based decision trees or priority queues, the resulting intelligent synchronization strategy selects the optimal resolution path based on conflict characteristics.

[0089] Furthermore, by pre-setting parameter processing rule sets (such as offline and online strategies) customized for different communication states, the resulting synchronization strategy mode can adapt to state changes and effectively isolate invalid operations. The selection of the synchronization strategy mode is triggered by state machine state information. In the offline state, parameter modification operations are recorded and marked as pending synchronization states, forming a list of parameters to be synchronized. When entering the online state, conflict detection is performed based on this list to generate a parameter conflict report. Subsequently, an intelligent synchronization strategy is adopted based on the report to resolve data inconsistency issues. This design ensures that parameter modifications during state transitions are managed systematically. The conflict detection stage uses the list of parameters to be synchronized as an input source to ensure that the comparison process covers all offline modifications. The intelligent synchronization strategy makes dynamic decisions based on key information in the conflict report, avoiding the risk of data loss due to direct overwriting, thus maintaining the integrity of parameter synchronization and system stability during state transitions.

[0090] Specifically, the step of using an intelligent synchronization strategy to resolve data inconsistency issues based on the parameter conflict report includes:

[0091] Receive parameter conflict reports, analyze parameter importance and security level, and output priority assessment results; summarize information on data inconsistencies detected during parameter synchronization, which can be implemented using structured data formats such as JSON or XML for easy system parsing and processing; the analysis of parameter importance and security level refers to the process of critically assessing parameters, which can be implemented using a rule engine or a preset weight matrix, providing an objective basis for strategy selection by quantifying the criticality and security impact of parameters.

[0092] Based on the priority evaluation results, a synchronization strategy mode is selected. The priority evaluation results refer to the quantified parameter priority indicators, which can be represented as numerical levels or classification labels to guide subsequent strategy selection. The selection of the synchronization strategy mode refers to dynamically adjusting the synchronization behavior according to the priority. This can be achieved using decision trees or state machines. For example, a forced coverage strategy can be used for high-security-level parameters to ensure real-time performance, while a delayed synchronization strategy can be used for low-importance parameters to reduce system disturbances.

[0093] Perform parameter synchronization operations and output the synchronization execution results. In the actual process of writing or reading data, a database transaction processing mechanism can be used to ensure the atomicity and consistency of the operation. The synchronization execution result refers to the feedback information of the synchronization operation, which may include success status or error codes to verify the effectiveness of synchronization.

[0094] Based on the synchronization execution results, update the parameter version information and synchronization status to ensure that the database status is consistent with the actual synchronization situation, thereby forming a closed-loop reliability guarantee mechanism in the entire parameter management process.

[0095] Step S4: Based on parameter synchronization status and state machine status information, perform hierarchical error detection and processing, identify anomalies through a time window-based fault detection algorithm, process faults according to a hierarchical recovery strategy, and output the system health status.

[0096] Specifically, time-window-based fault detection algorithms can be understood as methods that identify anomalies by analyzing the historical trend of parameter changes. This can be achieved using sliding time window technology, such as setting a fixed time window length, calculating the standard deviation or rate of change of parameters within the window, and determining an anomaly when the value exceeds a preset threshold. Fault handling can be performed according to a predefined recovery level, such as restarting the communication link or rolling back parameter values. The main purpose is to filter out instantaneous fluctuations and block the path of erroneous data being written to memory.

[0097] Step S4 includes:

[0098] Specifically, the system receives the state machine state information and parameter synchronization status to determine the current error detection level. The current error detection level can be determined by dynamically adjusting the sensitivity threshold of fault detection based on the system's operating status, using a rule engine based on the state machine state and parameter synchronization status. For example, when the system is offline, the detection level can be lowered to avoid triggering invalid detection under unstable communication conditions.

[0099] Furthermore, the time window algorithm is used to analyze the parameter change trend, filter out false alarms caused by instantaneous fluctuations, and output fault identification results. The time window algorithm for analyzing parameter change trend can be understood as continuously monitoring parameter data by setting a fixed or variable time window. It can be implemented using sliding window averaging or exponential weighted moving average algorithms to identify persistent anomalies and eliminate instantaneous interference.

[0100] Based on the fault identification results, a graded recovery strategy is executed, and the fault handling status is output. Recovery measures are automatically selected according to the severity of the fault. A predefined fault level mapping table can be used to implement graded recovery operations, so that the system can perform timely error correction according to the actual situation to ensure the normal operation of the system.

[0101] Specifically, based on the aforementioned fault handling status, a recovery verification test is performed to confirm system stability. This verification test can be implemented using simulated operation or actual load testing to confirm parameter synchronization and the restoration of normal state machine operation.

[0102] The error detection level is dynamically determined by receiving state machine state information and parameter synchronization status, making the detection process adaptable to the current system operating state and avoiding overly sensitive detection when parameters are not synchronized or communication is abnormal. Then, the parameter change trend is analyzed based on the time window algorithm. By continuously monitoring parameter fluctuations and filtering instantaneous noise, it is ensured that the fault identification results only reflect the real faults. Next, a graded recovery strategy is executed according to the fault identification results, matching the corresponding recovery measures according to the severity of the fault. This prevents excessive intervention caused by minor fluctuations and ensures that critical faults are handled in a timely manner. Finally, recovery verification tests are performed based on the fault handling status. The system stability is verified through actual operation, forming a closed-loop optimization mechanism, thereby ensuring reliable system recovery after fault handling.

[0103] Step S5: Based on the system health status and parameter synchronization status, perform system diagnosis and performance optimization, optimize read and write operations through the parameter caching layer, and implement predictive maintenance to complete parameter management.

[0104] Specifically, the parameter caching layer refers to the intermediate storage mechanism used to optimize parameter read and write operations. It can be implemented using memory caching technology, such as using local memory to cache frequently accessed parameters, prioritizing data retrieval from the cache for read operations, and employing an asynchronous write strategy for write operations. Predictive maintenance can predict potential problems by analyzing historical parameter data trends. Its main purpose is to reduce the frequency of direct database access and improve system robustness.

[0105] Step S5 includes:

[0106] The system receives the system health status and parameter synchronization status, and evaluates the current system performance. It performs dynamic quantitative analysis on the operating efficiency of the servo motor driver parameter management system, and uses a combination of real-time data acquisition and weighted evaluation algorithm to evaluate the system. This can establish a performance benchmark that is closely dependent on the real-time operating status of the system and avoid evaluation bias caused by using only historical data.

[0107] Based on the current system performance evaluation results, read and write optimization is implemented through the parameter caching layer to output the performance optimization effect; based on the parameter evolution law and the running context, fault prediction can be performed. Predictive operations can be implemented by time series analysis or sliding window algorithm, which can shift the maintenance strategy from passive response to proactive prevention and improve the initiative of system response.

[0108] Based on parameter change trends and system operation data, predictive maintenance analysis is performed to output maintenance recommendations. Based on the maintenance recommendations, parameter synchronization rules and error handling mechanisms are dynamically adjusted to form a closed-loop linkage between strategy adjustment and maintenance feedback.

[0109] Based on the maintenance recommendations, the parameter management strategy is optimized to improve system reliability. These recommendations are further fed back into the parameter management strategy optimization phase, forming a complete closed loop from status monitoring to strategy adjustment, thereby ensuring that the system maintains stable and reliable operation under complex conditions.

[0110] Specifically, the management method also includes:

[0111] During parameter database operations, WAL mode is enabled to ensure data consistency and output transaction processing status. WAL (Write-Ahead Logging) mode is a log mode that can be implemented using the built-in log replay mechanism of the database management system. It can force all data modifications to be recorded in the log before being applied to the main database, thereby ensuring data atomicity and consistency through log recovery in the event of an abnormal power outage.

[0112] Based on the transaction processing status, perform periodic backup operations and output backup completion status; perform backup operations based on real-time feedback information from database operations to ensure the integrity and reliability of system data management. Periodic backup operations can be configured to perform backups only when the data is in a consistent state, avoiding the generation of invalid backup sets.

[0113] Based on the system health status, execute the data recovery process when necessary and output the data recovery results; determine the system health status based on the health assessment results output in step S4, and respond to the system status in a timely manner to ensure the accuracy of data flow within the system.

[0114] Based on the data recovery results, the database integrity is verified and the system status is updated. The data recovery process refers to the process of rebuilding the database from the backup data source. It can be implemented using an automatic triggering mechanism based on health status thresholds. The data recovery results can be understood as the verification output of the recovery operation, which can be implemented using a recovery success or failure status report, providing direct evidence for integrity verification, thereby maintaining the continuity and reliability of parameter management.

[0115] This invention provides a servo motor driver parameter management method. By constructing a unified database table structure, state machine control flow, parameter synchronization strategy, time window-based fault detection algorithm, and parameter caching layer, it systematically solves the problems of outdated data storage models and lack of error correction mechanisms in traditional parameter management. This enables the parameter management process to have data integrity protection and error blocking capabilities, thereby meeting the reliability requirements of servo motor driver host computer development.

[0116] Example 2:

[0117] Figure 3 This diagram illustrates a servo motor driver parameter management system according to an embodiment of the present invention, used to implement a parameter management method, including:

[0118] Parameter storage module 10: Used to store servo motor driver parameters according to the optimized SQLite database table structure; By combining the optimized SQLite database table structure of parameter storage module 10 with the state transition verification mechanism of intelligent state machine control module 20, and introducing the time window-based fault detection algorithm and hierarchical self-recovery mechanism of hierarchical error handling module 30, in case of abnormal power failure, the incomplete operation is automatically rolled back using the built-in transaction mechanism of SQLite. At the same time, the erroneous data propagation path is blocked by strict consistency verification before state transition. This effectively solves the data corruption problem caused by the backwardness of traditional data storage model and the problem of erroneous data being written to memory due to the lack of error correction mechanism, which significantly improves the reliability of parameter management and system robustness.

[0119] The intelligent state machine control module 20 is used to implement multi-state management with state transition verification and anomaly recovery mechanisms based on the Qt framework, including initial state checks, offline state, and online state data synchronization verification. Before the servo motor transitions from the "offline" state to the "online" state, the intelligent state machine control module 20 can actively communicate with the servo driver to verify whether its firmware version and basic parameter list are consistent with the expectations in the database, and confirm the stability of the physical connection. Simultaneously, by querying the parameter cache module 40, it ensures that all parameters to be issued have passed consistency verification, thereby effectively blocking the propagation path of incorrect configurations or abnormal data.

[0120] The graded error handling module 30 is used to employ a time window-based fault detection algorithm and a graded self-recovery mechanism. The time window-based evaluation method can effectively distinguish between occasional jitter and real faults, avoiding unnecessary drastic responses from the system due to temporary interference. At the same time, it can quickly and orderly upgrade the response strategy when a real fault occurs, greatly improving the system's fault tolerance and overall availability.

[0121] Parameter caching module 40: used to implement read-write separation and support batch operations; the parameter caching module 40 ensures, through locking mechanisms and version management strategies, that the cached data of the parameter caching module 40 can provide complete and consistent data of the previous version during batch submission, thereby avoiding read-write conflicts, ensuring strong data consistency, and providing the system with an efficient and secure data access experience.

[0122] System diagnostic module 50: Used to monitor the system health status in real time and perform predictive maintenance. Based on the history of parameter modifications and state transitions, the system diagnostic module 50 can intelligently predict the remaining lifespan of storage modules or potential memory fragmentation problems, enabling maintenance personnel to take maintenance measures before problems occur, changing from passive response to proactive prevention, and greatly improving the system's mean time between failures (MTBF) and maintainability.

[0123] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0124] Furthermore, the embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for managing parameters of a servo motor driver, characterized in that, The management method includes: Step S1: Initialize the parameter database, establish a unified database table structure containing parameter ID, variable name, current value, parameter category, and data checksum, and output the database initialization result; Step S2: Based on the database initialization result, execute the state machine control process. When the database initialization is successful, enter the offline state. When a communication connection is established with the lower-level machine and all parameters are successfully read, enter the online state and output the current state machine status information. Step S3: Based on the state machine state information, execute the corresponding parameter synchronization strategy. In the offline state, parameter modification is allowed but marked as pending synchronization. In the online state, ensure that the parameters are synchronized with the lower-level machine in real time and output the parameter synchronization status. Step S4: Based on the parameter synchronization status and state machine status information, perform hierarchical error detection and processing, identify anomalies through a time window-based fault detection algorithm, perform fault processing according to the hierarchical recovery strategy, and output the system health status. Step S5: Based on the system health status and parameter synchronization status, perform system diagnosis and performance optimization, optimize read and write operations through the parameter caching layer, and implement predictive maintenance to complete parameter management.

2. The servo motor driver parameter management method according to claim 1, characterized in that, Step S1 includes: Establish a parameter database table structure, including parameter category fields to distinguish between equipment information, motor parameters, speed loop parameters, and encoder parameters; Create a version number field and a data checksum field for each parameter, and output parameter version information and integrity verification data; Based on the parameter version information, implement parameter version management and data integrity protection; When database initialization fails, an error message is output and an error pop-up is triggered. The user is asked to correct the error before the initialization is re-executed.

3. The servo motor driver parameter management method according to claim 1, characterized in that, Step S2 includes: Receive the database initialization result, check whether the database has been successfully initialized, and if the initialization is successful, enter offline state; In offline mode, monitor the communication connection status with the lower-level machine. Once the connection is established and the parameters are successfully read, output a status transition command. Based on the state transition command, the system transitions from offline to online and initiates a real-time parameter synchronization mechanism. When online, it continuously monitors the communication status. When communication is lost, it outputs a status rollback command to return to the offline status.

4. The servo motor driver parameter management method according to claim 3, characterized in that, Step S2 further includes: Verify the state transition of the servo motor. Before the state transition, verify the legality of the transition conditions and output the verification results. Based on the verification results, a decision is made as to whether to perform a state transition. When verification fails, exception handling is performed and an exception handling report is output. Based on the exception handling report, record the state transition failure log and provide repair suggestions.

5. The servo motor driver parameter management method according to claim 1, characterized in that, Step S3 includes: Receive the state machine state information and select a synchronization strategy mode based on the current state; In offline mode, record parameter modification operations and mark them as pending synchronization, and output a list of parameters to be synchronized; Upon entering online status, conflict detection is performed based on the list of parameters to be synchronized, and a parameter conflict report is output. Based on the parameter conflict report, an intelligent synchronization strategy is adopted to resolve the data inconsistency problem.

6. The servo motor driver parameter management method according to claim 5, characterized in that, The step of using an intelligent synchronization strategy to resolve data inconsistency issues based on the parameter conflict report includes: Receive parameter conflict reports, analyze parameter importance and security level, and output priority assessment results; Based on the priority evaluation results, a synchronization strategy mode is selected; Perform parameter synchronization operations and output the synchronization results; Based on the synchronization execution results, update the parameter version information and synchronization status.

7. The servo motor driver parameter management method according to claim 1, characterized in that, Step S4 includes: Receive the state machine status information and parameter synchronization status, and determine the current error detection level; The algorithm analyzes the trend of parameter changes based on the time window, filters out false alarms caused by instantaneous fluctuations, and outputs the fault identification results. Based on the fault identification results, a graded recovery strategy is executed, and the fault handling status is output. Based on the fault handling status, a recovery verification test is performed to confirm system stability.

8. The servo motor driver parameter management method according to claim 1, characterized in that, Step S5 includes: Receive the system health status and parameter synchronization status, and evaluate the current system performance; Based on the current system performance evaluation results, read and write optimizations are implemented through the parameter caching layer, and the performance optimization effect is output. Based on parameter change trends and system operation data, perform predictive maintenance analysis and output maintenance recommendations; Based on the maintenance recommendations, the parameter management strategy will be optimized to improve system reliability.

9. The servo motor driver parameter management method according to claim 1, characterized in that, The management method also includes: During parameter database operations, WAL mode is enabled to ensure data consistency and output the transaction processing status; Based on the transaction processing status, perform a periodic backup operation and output the backup completion status; Based on the system health status, execute the data recovery process when necessary and output the data recovery results; Based on the data recovery results, verify the database integrity and update the system status.

10. A servo motor driver parameter management system, characterized in that, The management system is used to implement the management method as described in any one of claims 1 to 9, and the management system includes: Parameter storage module: Used to store servo motor driver parameters according to the optimized SQLite database table structure; Intelligent state machine control module: used to implement multi-state management with state transition verification and exception recovery mechanism based on Qt framework, including initial state check, offline state, and online state data synchronization verification; Hierarchical error handling module: used to employ time-window-based fault detection algorithms and hierarchical self-recovery mechanisms; Parameter caching module: used to implement read / write separation and batch operation support; System diagnostic module: Used to monitor the system health status in real time and perform predictive maintenance.

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