Servo driver remote control system and method

Abstract

The invention provides a servo driver remote control system and method, and relates to the technical field of servo control and transmission, and the system comprises a data collection module which is used for collecting the operation data, including the position, speed, acceleration and current parameters, of a servo driver on stage entertainment equipment in real time; the data analysis module is used for analyzing the operation data of the data acquisition module, identifying trends, abnormities and relevance in the data and transmitting the operation data to a remote control center; and the remote monitoring module is used for receiving and displaying the operation data of the data analysis module and remotely monitoring and adjusting the servo driver through a remote operation interface. According to the invention, through real-time data acquisition, analysis, remote monitoring and instruction transmission, efficient and accurate control of stage entertainment equipment is realized, the system has real-time fault diagnosis and protection functions, and the safety, stability and controllability of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of servo control and transmission technology, and in particular to a servo driver remote control system and method. Background Technology

[0002] Traditional servo drive control systems can only perform local operation and control, limiting the operator's range of activities and hindering unified management and monitoring of equipment located in different areas. Parameter adjustments and function controls typically require operators to be physically present on-site, increasing workload and reducing production efficiency. They also lack intelligent fault diagnosis capabilities, or their fault diagnosis algorithms are relatively simple, making it difficult to accurately and quickly identify complex faults. When equipment malfunctions, operators need to spend a significant amount of time troubleshooting and locating the problem, resulting in long repair cycles and impacting production schedules.

[0003] Furthermore, the lack of data sharing capabilities hinders data exchange between different devices, limiting collaborative optimization of the production process. Operators struggle to obtain real-time information on the operational status of other equipment or production lines, making it difficult to make globally optimal decisions. The tight coupling between hardware and software necessitates replacing the entire system or a large amount of hardware during upgrades or maintenance, resulting in high costs. With continuous technological advancements, traditional systems may become incompatible with new control algorithms or communication protocols, leading to system obsolescence. The lack of robust security measures makes the system vulnerable to external attacks or interference, potentially resulting in data leaks or equipment damage.

[0004] Due to limitations in system architecture and technology, traditional systems may exhibit lower reliability when facing complex operating conditions or extreme environments. In remote or distributed production environments, achieving real-time monitoring and control across geographical regions is difficult, limiting the flexibility and efficiency of production management. Furthermore, they cannot meet the needs of application scenarios requiring remote debugging, maintenance, and diagnostics. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a remote control system and method for servo drives, which realizes remote monitoring and fault diagnosis of servo drives.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a servo drive remote control system includes:

[0008] The data acquisition module is used to collect real-time operating data of the servo drives on stage entertainment equipment, including position, speed, acceleration and current parameters;

[0009] The data analysis module is used to analyze the operational data of the data acquisition module, identify trends, anomalies and correlations in the data, and transmit the operational data to the remote control center.

[0010] The remote monitoring module is used to receive and display the operating data of the data analysis module, and to remotely monitor and adjust the servo drive through a remote operation interface;

[0011] The instruction generation and transmission module is used to automatically generate operation instructions for controlling the servo drive based on the operation instructions of the remote monitoring module, and transmit the operation instructions to the servo drive on the stage entertainment equipment.

[0012] The execution control module is used to receive operation instructions from the instruction generation and transmission module, and control the servo driver to move according to the instruction requirements, thereby controlling the stage entertainment equipment;

[0013] The fault diagnosis and protection module is used to perform real-time fault diagnosis of the servo drive based on the operating data of the data acquisition module, and when a fault is detected, it sends alarm information to the remote control center and automatically takes safety measures.

[0014] Furthermore, the operational data of the data acquisition module is analyzed to identify trends, anomalies, and correlations within the data, and the operational data is transmitted to the remote control center, including:

[0015] Based on the operational data of the data acquisition module, an optimization model is constructed, and an objective function is defined in the optimization model;

[0016] The optimization model is solved iteratively, and the current solution is updated in each iteration. This internal iteration is repeated multiple times until the preset number of iterations is reached, and the final solution of the optimization model is obtained, including the identified data trends, outliers and correlation information.

[0017] The final solution of the optimization model is analyzed and processed, including predicting future data change trends based on identified trends, detecting potential faults based on outliers, and discovering the interaction and influence between different parameters based on correlation, so as to obtain the analyzed and processed operating data.

[0018] The analyzed and processed operational data, including identified trends, anomalies, and correlations, are transmitted to the remote control center.

[0019] Furthermore, it receives and displays the operating data from the data analysis module, and remotely monitors and adjusts the servo drive through a remote operation interface, including:

[0020] The data analysis module analyzes the operational data to extract the status, operating parameters, and fault alarm information of the servo drive.

[0021] The remote operation interface automatically refreshes the status, operating parameters and fault alarm information of the servo drive to reflect data changes in real time. If the real-time data contains fault alarm information, the remote operation interface displays the corresponding alarm prompt.

[0022] The adjustment command is sent to the servo drive through the input control on the remote operation interface. The servo drive parses the adjustment command to identify the command type and parameters, and adjusts its own operating parameters according to the command type and parameters, so as to realize remote monitoring and adjustment of the servo drive.

[0023] Furthermore, based on the operation instructions from the remote monitoring module, operation instructions for controlling the servo drive are automatically generated and transmitted to the servo drive on the stage entertainment equipment, including:

[0024] The operation commands of the remote monitoring module are parsed, including checking the command format, check code, serial number, and identifying the type of operation command, including start, stop, acceleration, deceleration, positioning, and parameter setting. Each type corresponds to different control logic and servo driver response.

[0025] Based on the operation instructions, the target servo drive is determined, and the instruction values, including speed, position, and acceleration, are extracted from the operation instructions.

[0026] Based on the instruction type, target servo driver, and instruction value, generate the corresponding operation instructions to control the servo driver;

[0027] The operation instructions for controlling the servo drive are encapsulated, including adding a checksum, serial number, and target address information, to obtain the encapsulated operation instructions for controlling the servo drive. The encapsulated operation instructions for controlling the servo drive are then transmitted to the servo drive on the stage entertainment equipment.

[0028] Furthermore, the system receives operation commands from the command generation and transmission module and controls the servo driver to move according to the command requirements, thereby controlling the stage entertainment equipment, including:

[0029] Based on the operation instructions of the instruction generation and transmission module, initialize the relevant parameters of the artificial fish swarm, including the fish swarm size, search space, number of iterations, visual range, and step size. Based on the motion commands and control objectives of the servo driver, define an evaluation function to evaluate the motion effect of the servo driver.

[0030] Simulate the foraging behavior of fish schools in the search space. Each artificial fish represents a set of control parameters and continuously updates its position through swarming, tail chasing and foraging behaviors.

[0031] In each iteration, the position of each artificial fish is evaluated according to the evaluation function, and the state of the fish group is updated.

[0032] The process involves multiple iterations, with each iteration simulating the foraging behavior of the fish school to update its position and adjusting the state of the fish school according to the evaluation function, until the preset number of iterations is reached to obtain the final combination of control parameters.

[0033] The final combination of control parameters is sent to the servo driver, which controls the movement according to the instructions, including start, stop, acceleration, deceleration, and positioning actions, thereby controlling the stage entertainment equipment.

[0034] Secondly, a remote control method for a servo drive includes:

[0035] The servo driver automatically and in real time collects operating data, including position, speed, acceleration, and current parameters.

[0036] The running data is preprocessed, including filtering and calibration, to obtain processed data. The processed data is then analyzed to identify trends, anomalies, and correlations in the data, and to obtain analysis results.

[0037] Based on the analysis results, receive and parse the control commands from the remote control center, including start, stop, and parameter adjustment operation requirements;

[0038] Based on the control instructions from the remote control center, the corresponding control signals are automatically generated and transmitted to the servo driver to control the movement of the servo motor.

[0039] The real-time operating status, control results, and processed data of the servo drive are sent to the remote control center to monitor and evaluate the performance of the servo drive in real time.

[0040] Based on real-time operating status and preset fault diagnosis models, the system automatically detects and analyzes faults. If an anomaly is detected, it triggers protection schemes, including emergency shutdown, power reduction, and sends fault alarm information to the remote control center.

[0041] Furthermore, based on the control commands from the remote control center, corresponding control signals are automatically generated and transmitted to the servo driver to control the movement of the servo motor, including:

[0042] The current status of the servo motor, including position, speed, acceleration, and current parameters, is monitored in real time by sensors on the servo driver.

[0043] Calculate the deviation between the current state and the desired state of the servo motor, and dynamically adjust the control parameters, including proportional, integral and derivative parameters, based on the deviation.

[0044] The final control signal is calculated using adaptive control and control parameters;

[0045] Based on the final control signal, the corresponding control command is automatically generated and transmitted to the servo motor through the control interface of the servo driver. The servo motor adjusts its own motion state, including position, speed and acceleration, according to the control command.

[0046] Furthermore, based on real-time operating status and preset fault diagnosis models, fault detection and analysis are automatically performed. If an anomaly is detected, a protection scheme is triggered, including emergency shutdown and power reduction, and fault alarm information is sent to the remote control center, including:

[0047] Real-time operating status data is input into a preset fault diagnosis model for analysis. By comparing the difference between the current state and the normal state, it is determined whether there is an abnormality or fault. If an abnormality or fault is identified, the fault diagnosis model analyzes the type and severity of the fault to obtain the fault analysis results.

[0048] Based on the fault analysis results, the corresponding protection scheme is automatically triggered, and fault alarm information is sent to the remote control center, including the type of fault, the time of occurrence, and the severity.

[0049] The above-described solution of the present invention has at least the following beneficial effects:

[0050] The data acquisition module can collect real-time operating data from the servo drives, including key parameters such as position, speed, acceleration, and current, providing a solid data foundation for precise system control. Through the data analysis module's rapid processing of real-time data, the system can quickly identify trends, anomalies, and correlations within the data, ensuring a comprehensive and accurate understanding of the motion status of the stage entertainment equipment. The remote monitoring module allows operators to view the servo drive's operating status in real-time via a remote interface, enabling necessary adjustments and controls without being physically present on-site. This remote monitoring capability greatly improves operational convenience, reduces labor costs, and also enhances production efficiency and safety.

[0051] The instruction generation and transmission module automatically generates specific control instructions for the servo drives based on the operation instructions from the remote monitoring module, and transmits them quickly and accurately to the servo drives on the stage entertainment equipment. This automated instruction generation and efficient transmission mechanism ensures the timeliness and accuracy of control instructions, making the movement of the stage entertainment equipment smoother and more coordinated. The execution control module can quickly receive and respond to the operation instructions from the instruction generation and transmission module, controlling the servo drives to perform precise movements according to the instructions. This flexible control capability and rapid response mechanism enable the stage entertainment equipment to quickly adapt to different performance needs, enhancing the performance effect and audience experience.

[0052] The fault diagnosis and protection module can perform real-time fault diagnosis on the servo drive. Once an anomaly or fault is detected, it immediately sends an alarm message to the remote control center. Simultaneously, the module can automatically take safety measures, such as emergency shutdown and power reduction, effectively preventing the fault from escalating and protecting the safety of equipment and personnel. This fault early warning and safety assurance mechanism reduces the risks and losses caused by equipment failure. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of a servo drive remote control system provided by an embodiment of the present invention.

[0054] Figure 2 This is a flowchart illustrating a servo drive remote control system method provided by an embodiment of the present invention. Detailed Implementation

[0055] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0056] like Figure 1 As shown, an embodiment of the present invention proposes a servo driver remote control system, comprising:

[0057] Data acquisition module 11 is used to acquire the operating data of the servo drive on the stage entertainment equipment in real time, including position, speed, acceleration and current parameters;

[0058] The data analysis module 12 is used to analyze the operating data of the data acquisition module, identify trends, anomalies and correlations in the data, and transmit the operating data to the remote control center.

[0059] The remote monitoring module 13 is used to receive and display the operating data of the data analysis module, and to remotely monitor and adjust the servo drive through the remote operation interface;

[0060] The instruction generation and transmission module 14 is used to automatically generate operation instructions for controlling the servo drive according to the operation instructions of the remote monitoring module, and transmit the operation instructions to the servo drive on the stage entertainment equipment.

[0061] The execution control module 15 is used to receive operation instructions from the instruction generation and transmission module, and control the servo driver to move according to the instruction requirements, thereby realizing the control of the stage entertainment equipment;

[0062] The fault diagnosis and protection module 16 is used to perform real-time fault diagnosis on the servo drive based on the operating data of the data acquisition module, and to send alarm information to the remote control center and take safety measures automatically when a fault is detected.

[0063] In this embodiment of the invention, key operational data such as the position, speed, acceleration, and current of the servo drive on the stage entertainment equipment are collected in real time. This ensures that the system can reflect the current state of the servo drive in an instant, providing the possibility for remote monitoring and timely adjustment.

[0064] In-depth analysis of the collected operational data can identify trends, anomalies, and correlations, providing valuable decision support for the remote control center. Through data analysis, the system can proactively detect potential faults or performance degradation, allowing for timely intervention and minimizing the impact of equipment malfunctions on the performance. Transmitting operational data to the remote control center enables operators to remotely monitor equipment status, improving management convenience and efficiency.

[0065] Receiving and displaying operational data from the data analysis module enables operators to remotely monitor the status of the servo drives in real time, improving the flexibility and coverage of monitoring. Through the remote operation interface, operators can remotely adjust and control the servo drives without being physically present, reducing operational difficulty and cost. The implementation of remote monitoring enhances the controllability and safety of stage entertainment equipment, providing strong support for the smooth running of performances.

[0066] Based on the operation instructions from the remote monitoring module, control instructions for the servo drive are automatically generated, achieving automated instruction generation and transmission and improving control efficiency. Accurate instruction transmission ensures that the servo drive can move precisely according to the operator's intentions, enhancing the performance effect of the stage entertainment equipment.

[0067] The system receives and responds to operation commands from the command generation and transmission module, controlling the servo drive to move according to the command requirements, thus achieving precise control of the stage entertainment equipment. The rapid response and precise control of the execution control module ensure that the stage entertainment equipment can quickly and accurately complete various actions, enhancing the smoothness and entertainment value of the performance.

[0068] Real-time fault diagnosis of the servo drive is performed based on the operational data from the data acquisition module, enabling timely detection and handling of faults and preventing their escalation and deterioration. Upon detection of a fault, an alarm message is sent to the remote control center, allowing operators to quickly understand the fault situation and take appropriate measures. Automatic safety measures, such as emergency shutdown and power reduction, effectively protect the safety of equipment and personnel, reducing the risks and losses caused by faults.

[0069] In a preferred embodiment of the present invention, real-time acquisition of operating data of the servo drive on the stage entertainment equipment, including position, speed, acceleration, and current parameters, may include:

[0070] In this embodiment of the invention, high-precision, high-response sensors are selected to measure the position, velocity, and acceleration of the servo drive. A data acquisition card supporting high-speed data acquisition is chosen to ensure real-time capture of the dynamic changes of the servo drive. The position, velocity, and acceleration sensors are connected to the data acquisition card through appropriate interfaces (such as analog signal interfaces, digital signal interfaces, etc.). Stable and reliable connections are ensured to avoid data loss or interference. A current sensor or current clamp is used to measure the operating current of the servo drive. The current sensor is connected to the corresponding channel of the data acquisition card.

[0071] Configure the data acquisition card using its accompanying software or driver, setting parameters such as sampling rate, data format, and channel selection. Ensure the data acquisition card operates as expected. Write a data acquisition program using a programming language (such as C++, Python, LabVIEW, etc.). The program should be able to cyclically read data from the data acquisition card and store it in memory or write it to a file. Parse the acquired raw data to extract key parameters such as position, velocity, acceleration, and current. Perform necessary preprocessing on the data, such as filtering, noise reduction, and calibration, to improve the accuracy and reliability of the data. Establish a data transmission channel between the data acquisition system and the remote control center using network communication technologies (such as TCP / IP, UDP, etc.) or serial communication. Ensure the stability and bandwidth of the data transmission channel to meet the needs of real-time data transmission. Package the parsed and processed data into an appropriate data format (such as JSON, XML, etc.). Send the data packets to the remote control center through the data transmission channel. Establish a database or file system at the remote control center or locally to store the received data. Classify, index, and back up the data.

[0072] In a preferred embodiment of the present invention, analyzing the operational data of the data acquisition module, identifying trends, anomalies, and correlations in the data, and transmitting the operational data to a remote control center may include:

[0073] Based on the operational data of the data acquisition module, an optimization model is constructed, and an objective function is defined in the optimization model;

[0074] The optimization model is solved iteratively, and the current solution is updated in each iteration. This internal iteration is repeated multiple times until the preset number of iterations is reached, and the final solution of the optimization model is obtained, including the identified data trends, outliers and correlation information.

[0075] The final solution of the optimization model is analyzed and processed, including predicting future data change trends based on identified trends, detecting potential faults based on outliers, and discovering the interaction and influence between different parameters based on correlation, so as to obtain the analyzed and processed operating data.

[0076] The analyzed and processed operational data, including identified trends, anomalies, and correlations, are transmitted to the remote control center.

[0077] In this embodiment of the invention, a suitable optimization model, such as linear regression, is selected based on the characteristics of the data collected by the data acquisition module. The optimization objective is clearly defined within the optimization model. For example, for trend recognition, the objective function might be to minimize the prediction error; for anomaly detection, the objective function is to maximize the distinguishability between outliers and normal data; for correlation analysis, the objective function is to find the strongest correlation between parameters. Initial parameters are set for the optimization model, including the learning rate, number of iterations, and regularization coefficient.

[0078] The iterative process begins using the selected optimization algorithm (interior point method). In each iteration, the current solution is updated based on the objective function. Multiple internal iterations are required within each outer iteration to refine the solution quality. This includes adjusting model parameters and recalculating the objective function value. The iteration stops when the preset number of iterations is reached, yielding the final solution of the optimized model. Based on the identified data trends, the loaded model is used to predict future data trends. Depending on the model's requirements, a certain length of historical data is needed as the basis for prediction. The prediction timeframe is set, such as predicting data for the next hour, day, or week. The prediction is executed, yielding the predicted value of the future data trend: Y(t+h)=c+∑(z). l ×g(tl)); where Y(t+h) is the predicted value h time units after time point t; c is a constant term; zl is the weight corresponding to each past time point; g(tl) is the historical data value l time units before time point t.

[0079] During or after prediction, models or additional anomaly detection algorithms (such as Isolation Forest, LOF, etc.) are used to identify outliers in the data. Outliers are extreme values, sudden changes, or points in the data that do not conform to the normal data distribution. Combining domain knowledge of the servo drive, anomalies are analyzed to determine whether they represent potential faults or abnormal events. The context of anomaly occurrences, such as equipment usage and maintenance history, is considered to more accurately determine their significance. Based on the analysis results of anomalies, corresponding handling suggestions are proposed, such as conducting equipment inspections, adjusting parameter settings, or scheduling maintenance. The correlations between parameters discovered by the model are used to deeply analyze the interactions and influences between different parameters. This involves calculating correlation coefficients between parameters, performing causal analysis, or constructing association rules. Based on the correlation analysis results, clues are provided for fault diagnosis, such as identifying parameter combinations or trends that may lead to faults. A basis is provided for performance optimization, such as adjusting parameter settings to improve equipment performance, reduce energy consumption, or extend service life.

[0080] Package the analyzed and processed operational data (including trend forecasts, outlier information, correlation analysis results, etc.) into a format suitable for transmission. Choose an appropriate packaging method, such as organizing the data into JSON objects, CSV files, or binary data streams. Send the packaged data to the remote control center using the selected transmission method.

[0081] Suppose a servo drive for a stage entertainment device collects data on its position, speed, and current parameters. An optimization model is constructed, and an objective function is defined to minimize the position prediction error. During the iterative solution process, an algorithm is used, and multiple internal iterations are performed to optimize the model parameters. Ultimately, a model capable of accurately predicting position trends is obtained. Simultaneously, the model identifies several outliers related to potential servo drive failures. Furthermore, a strong correlation between speed and current parameters is discovered, providing clues for optimizing the servo drive's performance.

[0082] By constructing and iteratively solving optimization models, we can more accurately predict future data trends of servo drives, providing a scientific basis for the operation planning of stage entertainment equipment. Anomalies identified by the model help us promptly detect potential servo drive failures, allowing us to take preventative measures and reduce downtime and maintenance costs. Correlation analysis provides a deeper understanding of the interactions and impacts between different servo drive parameters, offering strong support for equipment optimization and performance improvement. Transmitting the analyzed and processed operational data to a remote control center enables operators to remotely monitor equipment status, make timely decisions, and improve monitoring efficiency and response speed.

[0083] In a preferred embodiment of the present invention, the formula for calculating the objective function is:

[0084]

[0085] Where F represents the value of the objective function; α, β, and γ represent weight coefficients; N represents the number of samples; i represents the sample index; y i The value of the i-th sample is represented by d; the number of features is represented by j; and the feature index is represented by x. ij θ represents the j-th feature value of the i-th sample; j w represents the j-th element of the optimization model parameter vector; i T represents the weight of the i-th sample; M represents the number of outliers considered in anomaly detection; T represents the weight of the i-th sample. Pj F represents the number of true instances of the j-th outlier; Nj K represents the number of false negatives for the j-th outlier; K represents the number of itemsets considered in association rule mining; k represents; C(A k ∩B k ) indicates that it contains A k and B k The number of transactions; A k B represents the antecedent in the k-th itemset; k T represents the consequent in the k-th itemset; k C(A) represents the total number of transactions in the k-th itemset; k ) indicates that it contains A k The number of transactions.

[0086] In this embodiment of the invention, the values ​​of weighting coefficients α, β, and γ are determined. These values ​​will be used to balance the contributions of prediction error, anomaly detection, and association rule mining to the objective function. The number of samples N, the number of features d, the number of anomalies M, and the number of itemsets K considered in association rule mining are determined. For each sample i (from 1 to N), the difference between the predicted value and the actual value is calculated. Where x ij θ is the j-th feature value of the i-th sample. j This represents the j-th element of the optimized model parameter vector. The difference is squared and multiplied by the sample weight w. i Calculate the average of the weighted squared errors of all samples, i.e. Subtract this average from 1 to get a measure of prediction accuracy, then multiply by α.

[0087] For each outlier j (from 1 to M), determine the number of true cases T. Pj And the number of false negatives F Nj Calculate the proportion of true instances among all outliers, i.e. Multiply this ratio by β.

[0088] For each itemset k (from 1 to K), compute the value containing A. k and B k The number of transactions C(A) k ∩B k ), and containing A k The number of transactions C(A) k ).calculate and Where T k This is the total number of transactions in the k-th itemset. Calculate the average of these ratios, i.e. and Multiply the product of the two averages by y. Multiply the prediction error, anomaly detection accuracy, and association rule mining quality components together to obtain the objective function F.

[0089] The objective function F comprehensively considers the accuracy of the prediction model, the accuracy of anomaly detection, and the quality of association rule mining, providing a comprehensive evaluation metric for the optimization process. By adjusting the weight coefficients α, β, and γ, the importance of prediction, anomaly detection, and association rule mining can be flexibly adjusted according to actual needs. The value of the objective function F can be directly used to guide the optimization process of model parameters. By maximizing the value of F, the final optimized model parameters are found, thereby improving overall performance. In the data analysis and prediction scenario of servo drives for stage entertainment equipment, accurate prediction, timely anomaly detection, and valuable association rule mining are crucial for improving equipment performance, reducing failure rates, and optimizing operating strategies. The objective function F provides strong support for achieving these goals.

[0090] In a preferred embodiment of the present invention, receiving and displaying the operating data of the data analysis module, and remotely monitoring and adjusting the servo driver through a remote operation interface, may include:

[0091] The data analysis module analyzes the operational data to extract the status, operating parameters, and fault alarm information of the servo drive.

[0092] The remote operation interface automatically refreshes the status, operating parameters and fault alarm information of the servo drive to reflect data changes in real time. If the real-time data contains fault alarm information, the remote operation interface displays the corresponding alarm prompt.

[0093] The adjustment command is sent to the servo drive through the input control on the remote operation interface. The servo drive parses the adjustment command to identify the command type and parameters, and adjusts its own operating parameters according to the command type and parameters, so as to realize remote monitoring and adjustment of the servo drive.

[0094] In this embodiment of the invention, the data analysis module periodically or in real-time sends the servo drive's operating data to the remote monitoring system. The remote monitoring system establishes a data receiving interface to receive data packets from the data analysis module. The remote monitoring system parses the received data packets and, according to a preset data format and protocol, extracts the servo drive's status information (such as power-on, power-off, and running), operating parameters (such as speed, position, and current), and fault alarm information (such as overcurrent, overheating, and communication failure). The parsed data is stored in the remote monitoring system's database for subsequent querying and analysis.

[0095] Design a remote operation interface, including a status display area, a parameter display area, and an alarm display area. The status display area shows the current status of the servo drive; the parameter display area shows the various operating parameters of the servo drive; and the alarm display area displays alarm information when a fault occurs. Bind the servo drive status, operating parameters, and fault alarm information stored in the remote monitoring system to the corresponding areas of the remote operation interface. Set a timer or use an event-driven mechanism to refresh the data on the remote operation interface periodically or in real time. When the data changes, the remote operation interface can update immediately to reflect the latest status, operating parameters, and fault alarm information of the servo drive. When the parsed data contains fault alarm information, the remote operation interface displays the corresponding alarm prompt in the alarm display area, such as a flashing icon or red text prompt, to attract the operator's attention.

[0096] Input controls, such as text boxes, drop-down menus, and buttons, are designed on the remote operation interface for operators to input adjustment commands. Once the operator inputs and confirms the command, the remote monitoring system packages it into a specific format and sends it to the servo drive via the communication interface. Upon receiving the command, the servo drive parses it, identifying the command type (e.g., speed adjustment, position adjustment, parameter setting) and parameters (e.g., target speed, target position, parameter value). Based on the parsed command type and parameters, the servo drive adjusts its own operating parameters, such as adjusting speed, position, or modifying internal parameter settings. After adjusting the parameters, the servo drive can send a feedback signal to the remote monitoring system to confirm that the command has been executed. Upon receiving the feedback signal, the remote monitoring system can display a message indicating successful command execution on the remote operation interface.

[0097] Suppose there is a stage lighting control system where servo drivers control the angle and brightness of the lights. A data analysis module periodically (e.g., every second) sends the servo driver's operating data (including status, operating parameters, and fault alarm information) to a remote monitoring system. The remote monitoring system's data receiving interface receives one data packet per second from the data analysis module. The remote monitoring system parses the data packet and extracts the following information:

[0098] Status information: The servo drive is currently in the "running" state.

[0099] Operating parameters: The current angle of the light is 45 degrees, and the brightness is 80%.

[0100] Fault alarm information: None.

[0101] Remote operation interface display:

[0102] Status display area: Displays "Servo drive: Running".

[0103] Parameter display area: Displays "Light angle: 45 degrees" and "Light brightness: 80%".

[0104] Alarm notification area: No alarm information is displayed as there is no fault.

[0105] Every second, the data on the remote control interface is automatically refreshed to reflect the latest status and operating parameters of the servo drive. Suppose at some point, the data analysis module detects an overheating fault in the servo drive and sends this information in a data packet to the remote monitoring system. After parsing the data packet, the remote monitoring system displays a flashing red icon in the alarm prompt area, accompanied by the text "Servo drive overheating!". The operator observes that the light angle needs adjustment, so they enter the target angle of 60 degrees in the input control on the remote control interface and click the "Confirm" button. The remote monitoring system packages the adjustment command into a specific format, such as {"Command Type":"Angle Adjustment","Parameter":{"Target Angle":60}}, and sends it to the servo drive via the communication interface. Upon receiving the command, the servo drive parses it to determine the command type as "Angle Adjustment" and obtains the target angle as 60 degrees. The servo drive adjusts its own operating parameters to adjust the light angle to 60 degrees. After adjustment, the servo drive sends a feedback signal to the remote monitoring system, such as {"Command Execution Result":"Success"}. After receiving the feedback signal, the remote monitoring system displays the message "Command executed successfully: Light angle adjusted to 60 degrees" on the remote operation interface.

[0106] The remote operation interface displays the servo drive's status, operating parameters, and fault alarm information in real time, allowing operators to promptly understand the servo drive's operating status and respond quickly to abnormal situations. Operators can monitor and adjust the servo drive remotely without being physically present, reducing operational difficulty and cost. By inputting adjustment commands through the remote operation interface, the servo drive can quickly respond and adjust its parameters, improving work efficiency and accuracy. The remote monitoring system can monitor servo drive fault alarm information in real time and promptly alert operators when faults occur, helping to prevent potential safety hazards and accidents.

[0107] In a preferred embodiment of the present invention, automatically generating control commands for the servo drive based on the operation commands of the remote monitoring module, and transmitting the operation commands to the servo drive on the stage entertainment equipment, may include:

[0108] The operation commands of the remote monitoring module are parsed, including checking the command format, check code, serial number, and identifying the type of operation command, including start, stop, acceleration, deceleration, positioning, and parameter setting. Each type corresponds to different control logic and servo driver response.

[0109] Based on the operation instructions, the target servo drive is determined, and the instruction values, including speed, position, and acceleration, are extracted from the operation instructions.

[0110] Based on the instruction type, target servo driver, and instruction value, generate the corresponding operation instructions to control the servo driver;

[0111] The operation instructions for controlling the servo drive are encapsulated, including adding a checksum, serial number, and target address information, to obtain the encapsulated operation instructions for controlling the servo drive. The encapsulated operation instructions for controlling the servo drive are then transmitted to the servo drive on the stage entertainment equipment.

[0112] In this embodiment of the invention, the remote monitoring module receives operation instructions from the operator through a communication interface. It checks whether the format of the operation instructions conforms to preset standards, such as instruction length and field arrangement. It calculates the checksum of the operation instructions and compares it with the checksum attached to the instructions to verify the integrity of the instructions. It checks the sequence number of the operation instructions to ensure the correct order of the instructions and avoid duplicate execution or omissions. Based on specific fields in the operation instructions, it identifies the type of instruction, such as start, stop, acceleration, deceleration, positioning, parameter setting, etc. Based on the target identifier or address information in the operation instructions, it determines the target servo drive to be controlled. It extracts specific instruction values ​​from the operation instructions, such as speed, position, acceleration, etc., which will be used for subsequent control operations. Based on the instruction type, it selects the corresponding control logic, such as start logic, stop logic, acceleration logic, etc. Combining the identifier of the target servo drive and the extracted instruction values, it generates specific operation instructions to control the servo drive. It calculates a new checksum for the generated operation instructions and adds it to the instructions. It assigns a unique sequence number to each operation instruction to ensure the uniqueness and order of the instructions. The target servo drive's address information is added to the command so that the servo drive can recognize and respond. All the above information is combined into a complete, encapsulated command to control the servo drive. This encapsulated command is sent to the target servo drive on the stage entertainment equipment via the communication interface. The system waits for and receives feedback signals from the servo drive to confirm that the command has been successfully transmitted and received.

[0113] Suppose an operator sends an operation command via a remote monitoring module, requesting that a stage lighting device (controlled by a servo driver) be moved to a specific position and accelerated at a certain speed. The remote monitoring module receives the operation command and checks that its format, checksum, and serial number are correct. It identifies the command type as "positioning" and "acceleration." It determines that the target servo driver is the one controlling the lighting device. It extracts the command values: target position (X, Y, Z coordinates), acceleration A. Based on the control logic of "positioning" and "acceleration," and combining the target position and acceleration value, it generates a specific operation command. It adds a new checksum, serial number, and target servo driver address information to the operation command. The encapsulated operation command is sent to the target servo driver, and it waits for a feedback signal confirming that the command has been executed.

[0114] By precisely parsing operation commands and extracting specific command values, accurate control of servo drives can be achieved, improving the control precision of stage entertainment equipment. Verification using checksums and serial numbers ensures the integrity and sequence of operation commands, preventing errors or loss and enhancing system reliability. Support for the recognition and response of multiple command types allows operators to flexibly control stage entertainment equipment according to actual needs, improving system flexibility. Automatic generation and encapsulation of operation commands simplifies the operator's workflow and improves work efficiency. Simultaneously, it reduces the professional skills required of operators, making it easier for more people to learn and operate the equipment.

[0115] In a preferred embodiment of the present invention, receiving operation instructions from the instruction generation and transmission module and controlling the servo driver to move according to the instruction requirements to achieve control of the stage entertainment equipment may include:

[0116] Based on the operation instructions of the instruction generation and transmission module, initialize the relevant parameters of the artificial fish swarm, including the fish swarm size, search space, number of iterations, visual range, and step size. Based on the motion commands and control objectives of the servo driver, define an evaluation function to evaluate the motion effect of the servo driver.

[0117] Simulate the foraging behavior of fish schools in the search space. Each artificial fish represents a set of control parameters and continuously updates its position through swarming, tail chasing and foraging behaviors.

[0118] In each iteration, the position of each artificial fish is evaluated according to the evaluation function, and the state of the fish group is updated.

[0119] The process involves multiple iterations, with each iteration simulating the foraging behavior of the fish school to update its position and adjusting the state of the fish school according to the evaluation function, until the preset number of iterations is reached to obtain the final combination of control parameters.

[0120] The final combination of control parameters is sent to the servo driver, which controls the movement according to the instructions, including start, stop, acceleration, deceleration, and positioning actions, thereby controlling the stage entertainment equipment.

[0121] In this embodiment of the invention, an operation instruction containing a control target and motion commands is received from the instruction generation and transmission module. Based on the operation instruction and control requirements, the relevant parameters of the artificial fish swarm algorithm are initialized, including:

[0122] Fish school size: Determines the number of artificial fish participating in the search.

[0123] Search space: Defines the range in which the artificial fish can move, usually corresponding to the control range of the servo drive.

[0124] Iteration count: Sets the maximum number of iterations the algorithm can run, which serves as a stopping condition.

[0125] Visual range: Defines the range within which an artificial fish perceives its surroundings.

[0126] Step size: Determines the distance the artificial fish moves each time.

[0127] Based on the motion commands and control objectives of the servo drive, design an evaluation function to measure the performance of the artificial fish (i.e., the combination of control parameters). The evaluation function should reflect the quality of the servo drive's motion performance, such as position accuracy and speed stability. Randomly generate the initial positions of the artificial fish within the search space, with each position representing a set of control parameters. Perform the following operations for each artificial fish:

[0128] Swarming: Based on the visual range, the density of artificial fish in the surrounding area is calculated, and the fish moves to areas with higher density to simulate the swarming behavior of fish.

[0129] Tail-chasing: Select the best-performing artificial fish in the vicinity (i.e., the one with the highest evaluation function value) and move toward its position to simulate the tail-chasing behavior of a school of fish.

[0130] Foraging: Attempt to search for new locations from the current position. If the evaluation function value of the new location is higher, move to the new location to simulate the foraging behavior of a school of fish.

[0131] The current position (i.e., the combination of control parameters) of each artificial fish is evaluated using an evaluation function to obtain an evaluation value. Based on the evaluation value, the state of the artificial fish is updated, including its position and speed, and the current optimal artificial fish and its position are recorded. This process of simulating foraging behavior and evaluating and updating the state is repeated until a preset number of iterations is reached. After the last iteration, the final combination of control parameters is determined based on the evaluation value of the artificial fish. The final combination of control parameters is sent to the servo driver via a communication interface. The servo driver, based on the received control parameter combination, executes actions such as start, stop, acceleration, deceleration, and positioning to achieve precise control of the stage entertainment equipment.

[0132] Assume the stage entertainment equipment is a rotatable lighting rig, whose rotation angle and speed need to be controlled by a servo driver. The operator sends an operation command via the command generation and transmission module, requesting the lighting rig to rotate to a specific angle and maintain rotation at a certain speed. Initialize the artificial fish swarm parameters: swarm size is set to 30, search space is 0° to 360°, iteration count is set to 100, visual range is set to 10°, and step size is set to 1°. Define an evaluation function: evaluate the quality of the artificial fish based on the deviation between the actual rotation angle and the target angle of the lighting rig, as well as the smoothness of the rotation speed. Initialize 30 artificial fish, randomly distributed within the range of 0° to 360°. Each artificial fish continuously updates its position (i.e., the control parameter combination) based on swarming, tail-chasing, and foraging behaviors. In each iteration, the evaluation function is used to evaluate the position of each artificial fish, and the swarm state is updated based on the evaluation value. After 100 iterations, the final control parameter combination is obtained. The final control parameter combination is sent to the servo driver, and the lighting rig rotates to the specified angle and maintains rotation at a specific speed according to the command.

[0133] The artificial fish swarm algorithm, with its global search capability, can find optimal combinations of control parameters, improving the control precision of stage entertainment equipment. It can automatically adjust control parameters based on different control objectives and motion commands, making it suitable for various control scenarios in stage entertainment equipment. Through iterative search and evaluation, it can more effectively utilize computing resources, avoiding waste. The artificial fish swarm algorithm is simple to understand, easy to program, and can be extended and optimized as needed to meet more complex control requirements.

[0134] In a preferred embodiment of the present invention, the calculation formula for the evaluation function is as follows:

[0135]

[0136] Where E represents the evaluation value; w1, w2, w3, w4, w5, and w6 represent weighting coefficients; P t Indicates the target location; P a Indicates the actual location; t f t0 represents the end time of the evaluation period; v represents the start time of the evaluation period. a (t) represents the speed of the servo drive at time t; v t (t) represents the target speed of the servo drive at time t; a a (t) represents the acceleration of the servo driver at time t; dP a Indicates position P a Differential with respect to time t; dP t Indicates position P t The derivative with respect to time t; u(t) represents the control signal received by the servo driver at time t; u r(t) represents the reference control signal received by the servo driver at time t; P a (t) represents the actual position of the servo drive at time t.

[0137] In this embodiment of the invention, a time range [t0, t] is defined. f [ ] represents the start and end times of the motion. Define the target position P. t Actual location P a Target velocity v t (t), actual speed v a (t), actual acceleration a a (t), actual control input u(t) and reference control input u r Functions such as (t). Determine weighting coefficients w1, w2, w3, w4, w5, and w6 to balance the importance of each indicator. For each time point t, calculate (P). t -P a ) 2 Summing or integrating these values ​​yields the total sum of squares of the position error. For each time point t, calculate (v... a (t)-v t (t)) 2 For these values ​​in [t0, t... f Integrate over [a] to obtain the square integral of the velocity error. For each time point t, calculate (a) a (t)) 2 For these values ​​in [t0, t... f Integrate over [a certain value] to assess the stationarity of acceleration. Calculate the difference in the rate of change of velocity at each time point t. Find the maximum value among these values ​​to assess the smoothness of the velocity change. For each time point t, calculate (u(t) - u r (t)) 2 For these values ​​in [t0, t... f Integrating over [the value] to evaluate the accuracy of the control input. For the actual position P... a (t) in [t0, t f Integrate the above values ​​to assess the overall range of motion. Multiply each of the above terms by its corresponding weighting coefficient and sum them to obtain the final evaluation function value E.

[0138] The sum of squares of the position error directly excites the control algorithm to reduce position deviation, thereby improving positioning accuracy. The integral of the square of the velocity error prompts the control algorithm to track the target velocity more closely, reducing velocity fluctuations. The integral of the square of the acceleration and the maximum deviation term of the rate of change of velocity together ensure smooth motion and reduce mechanical shock. The integral of the square of the control input error encourages the control algorithm to match the reference input more accurately, improving control efficiency. The position integral term provides an evaluation basis for the overall motion range, prompting the control algorithm to consider the overall motion effect while pursuing local accuracy. By adjusting the weighting coefficients, the evaluation function can be customized according to specific application requirements to optimize specific performance indicators. The evaluation function comprehensively considers multiple aspects such as position, velocity, acceleration, and control input, providing a comprehensive evaluation index for the performance of the servo drive.

[0139] In a preferred embodiment of the present invention, real-time fault diagnosis of the servo drive is performed based on the operating data of the data acquisition module, and when a fault is detected, an alarm message is sent to the remote control center, and safety measures are automatically taken, which may include:

[0140] In this embodiment of the invention, based on the characteristics of the servo drive and common fault types, key parameters to be monitored are determined, such as motor temperature, current, voltage, speed, position, acceleration, and control input. Appropriate data acquisition hardware (such as sensors, data acquisition cards, etc.) and software (such as data acquisition software, drivers, etc.) are selected. The data acquisition module is configured to collect data on the above parameters at a set sampling rate, ensuring the accuracy and reliability of the data. The collected data is transmitted in real time to a data processing unit (such as a PC, embedded system, etc.). A data storage mechanism is designed. A fault model is established based on the normal operation data and historical fault data of the servo drive. This can include threshold models, statistical models, machine learning models, etc. Specific fault detection rules are defined for each fault type. The collected real-time data is preprocessed, such as denoising and normalization. The fault detection model is applied to analyze the preprocessed data to identify potential faults. Based on the analysis results of the fault detection model, it is determined whether a fault exists. If a fault is detected, the fault type, severity, and possible causes are further determined. The format of alarm information is defined, including fault type, severity, occurrence time, and possible causes. Ensure alarm messages are concise and clear, facilitating quick understanding and response from the remote control center. Select an appropriate communication method (such as wired network, wireless network, SMS, email, etc.) to send alarm messages to the remote control center. Ensure the reliability and real-time nature of the communication method so that the remote control center can receive alarm messages promptly. When the fault detection module determines that a fault exists, it immediately sends alarm messages to the remote control center according to the set format and communication method.

[0141] Define corresponding safety measures based on the type and severity of the fault. These may include shutdown, deceleration, switching to a backup system, and initiating emergency braking. Configure actuators (such as relays, solenoid valves, and brakes) capable of performing these safety measures. Ensure the reliability and response speed of the actuators so that measures can be taken quickly in the event of a fault. When the fault detection module determines that safety measures need to be implemented, it immediately triggers the actuators to execute the corresponding safety measures via control signals. Monitor the implementation of safety measures to ensure they are properly implemented and effectively curb the development of the fault. Integrate data acquisition, fault detection, alarm transmission, and safety measure execution into a complete system.

[0142] like Figure 2 As shown, embodiments of the present invention also provide a remote control method for a servo driver, comprising:

[0143] Step 21: Automatically and in real time collect operating data, including position, speed, acceleration, and current parameters, through the sensors on the servo driver;

[0144] Step 22: Preprocess the running data, including filtering and calibration, to obtain processed data. Analyze the processed data to identify trends, anomalies, and correlations in the data, and obtain analysis results.

[0145] Step 23: Based on the analysis results, receive and parse the control commands from the remote control center, including start, stop, and parameter adjustment operation requirements;

[0146] Step 24: Based on the control instructions from the remote control center, automatically generate the corresponding control signal and transmit the control signal to the servo driver to control the movement of the servo motor;

[0147] Step 25: Send the real-time operating status, control results, and processed data of the servo drive to the remote control center to monitor and evaluate the performance of the servo drive in real time.

[0148] Step 26: Based on the real-time operating status and the preset fault diagnosis model, automatically perform fault detection and analysis. If an abnormality is detected, trigger the protection scheme, including emergency shutdown, power reduction, and send fault alarm information to the remote control center.

[0149] In this embodiment of the invention, high-precision position sensors, velocity sensors, acceleration sensors, and current sensors are installed on the servo driver to ensure accurate measurement of relevant parameters. A reasonable sampling frequency is set to ensure the real-time performance and accuracy of the data. Polling or interrupt methods are used to read sensor data to reduce data loss. The collected data is transmitted to the data processing unit in real time via a high-speed, stable communication interface (such as Ethernet, CAN bus, etc.).

[0150] Step 22: Apply digital filtering algorithms (such as mean filtering) to remove noise and interference from the data, improving data quality. Calibrate the collected data based on sensor characteristics and environmental factors to ensure accuracy. Use time series analysis methods (moving average) to identify trends in the data and predict future conditions. Identify outliers in the data by setting thresholds and applying machine learning algorithms (such as anomaly detection algorithms). Analyze the correlation between parameters using correlation analysis methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) to discover potential problems.

[0151] Step 23: Determine the communication protocol with the remote control center to ensure correct transmission and parsing of commands. Receive control commands from the remote control center in real time via the communication interface. Parse the received commands to extract operational requirements such as start, stop, and parameter adjustment. Verify the parsed commands to ensure their legality and validity.

[0152] Step 24: Based on the parsed control commands and the current state of the servo driver, generate a control signal using a suitable control algorithm (adaptive control). Transmit the generated control signal to the servo driver through a suitable interface (such as a PWM signal) to control the movement of the servo motor.

[0153] Step 25: Monitor the servo drive's operating status in real time, including motor position, speed, acceleration, and current. Package the real-time status, control results, and processed data into a standard format data packet. Send the data packet to the remote control center via the communication interface for monitoring and evaluation.

[0154] Step 26: Establish a fault diagnosis model based on historical fault data and expert experience for automatic fault detection and analysis. Real-time comparison of operational status data with the fault diagnosis model identifies anomalies and faults. Further analysis of detected faults determines the fault type, cause, and severity. Based on the fault type and severity, automatically trigger corresponding protection schemes (such as emergency shutdown, power reduction, etc.). Send fault alarm information, including fault type, occurrence time, and severity, to the remote control center.

[0155] By acquiring and preprocessing operational data in real time and applying advanced control algorithms, the control accuracy and stability of servo drives can be improved. Sending control commands to a remote control center and receiving real-time feedback enables remote monitoring and control of the servo drives, improving production efficiency and management. Automatic fault detection and analysis, triggering corresponding protection schemes, allows for the timely identification and handling of potential faults, preventing production accidents and equipment damage. Sending real-time status and data to the remote control center enables data visualization and in-depth analysis, providing strong support for production optimization and decision-making.

[0156] In another preferred embodiment of the present invention, step 24 above, which involves automatically generating a corresponding control signal based on the control command from the remote control center and transmitting the control signal to the servo driver to control the movement of the servo motor, may include:

[0157] Step 224: Monitor the current status of the servo motor in real time using sensors on the servo driver, including position, speed, acceleration, and current parameters;

[0158] Step 225: Calculate the deviation between the current state and the desired state of the servo motor, and dynamically adjust the control parameters, including proportional, integral and derivative parameters, based on the deviation.

[0159] Step 226: Calculate the final control signal using adaptive control and control parameters. Where u(t) represents the control signal at time t; L p Indicates the proportional control parameter; L J The integral control parameter is represented by: e(t) represents the deviation between the desired state and the actual state at time t; e(t-1) represents the deviation between the desired state and the actual state at time t-1; Δt represents the time interval between two adjacent sampling points; u dp (t) represents the increment of the control signal generated at time t; J represents the index variable;

[0160] Step 227: Based on the final control signal, the corresponding control command is automatically generated and transmitted to the servo motor through the control interface of the servo driver. The servo motor adjusts its motion state, including position, speed and acceleration, according to the control command.

[0161] In this embodiment of the invention, it is ensured that the position sensor, speed sensor, acceleration sensor, and current sensor on the servo driver are all functioning normally, and the corresponding reading programs are configured. The position, speed, acceleration, and current parameters of the servo motor are acquired in real time at a set sampling frequency (e.g., 100Hz), and this data is stored in a buffer or memory. It is ensured that the acquired parameter data are synchronized in time for subsequent deviation calculations and control parameter adjustments.

[0162] Step 225: Based on the collected current state data of the servo motor and the expected state data sent by the remote control center, calculate the deviation values ​​of each parameter (position, speed, acceleration). Based on the deviation values, apply an adaptive control algorithm to dynamically adjust the proportional, integral, and derivative control parameters. The adjustment strategy can be based on empirical rules, fuzzy logic, or machine learning algorithms, etc. The adjusted control parameters are then applied to the control algorithm to prepare for the next step of control signal calculation.

[0163] Step 226: Calculate the control signal u(t) at time t based on the adaptive control algorithm and the adjusted control parameters. Calculate the control signal increment u based on the dynamic characteristics of the servo motor and the control requirements. dp (t) is used to achieve fine adjustment of the control signal.

[0164] Step 227: Generate corresponding control commands based on the calculated control signal u(t). Control commands may include position commands, speed commands, or acceleration commands, depending on the servo motor's control mode. The generated control commands are transmitted to the servo motor through the servo driver's control interface (such as a PWM signal interface, analog signal interface, or digital signal interface). Upon receiving the control commands, the servo motor adjusts its motion state, including position, speed, and acceleration, to achieve precise motion control.

[0165] Taking the servo control of a certain type of industrial robot arm as an example, sensors installed on the robot arm monitor its position, velocity, acceleration, and current parameters in real time. Based on the deviation between the arm's current position and the desired position, the proportional, integral, and derivative control parameters are dynamically adjusted. For example, when the arm's position deviates significantly from the desired position, the proportional control parameter can be increased to quickly reduce the deviation; when the arm approaches the desired position, the proportional control parameter is decreased and the integral control parameter is increased to improve control accuracy. An adaptive control algorithm and the adjusted control parameters are applied to calculate a control signal. This control signal determines how the arm should adjust its motion state to approach the desired position. Corresponding control commands are generated based on the control signal and transmitted to the arm's servo motors through the servo driver's control interface. Upon receiving the commands, the servo motors adjust their motion state to achieve precise position control.

[0166] By monitoring the current state of the servo motor in real time and dynamically adjusting control parameters, control deviation can be significantly reduced, and the control accuracy of the servo motor can be improved. Adaptive control algorithms can adjust control parameters in real time based on the dynamic characteristics of the servo motor, thereby enhancing system stability and robustness. By calculating the control signal increment and adjusting control commands in real time, rapid response and control of the servo motor can be achieved, improving the system's dynamic performance. Combining control commands from a remote control center with the adaptive control algorithm of the servo driver, precise remote control of the servo motor can be achieved, meeting the needs of complex industrial applications.

[0167] In another preferred embodiment of the present invention, step 26 above, which automatically performs fault detection and analysis based on the real-time operating status and a preset fault diagnosis model, and triggers a protection scheme if an anomaly is detected, including emergency shutdown, power reduction, and sending fault alarm information to the remote control center, may include:

[0168] Step 261: Input the real-time operating status data into the preset fault diagnosis model for analysis. By comparing the difference between the current state and the normal state, determine whether there is an abnormality or fault. If an abnormality or fault is identified, the fault diagnosis model analyzes the type and severity of the fault to obtain the fault analysis results.

[0169] Step 262: Based on the fault analysis results, automatically trigger the corresponding protection scheme and send fault alarm information to the remote control center, including the fault type, occurrence time, and severity.

[0170] In this embodiment of the invention, real-time operating status data (such as position, speed, acceleration, current, temperature, etc.) is preprocessed, including data cleaning, normalization, or standardization, to ensure data quality and consistency. The preprocessed data is then input into a preset fault diagnosis model. This model may be built based on machine learning algorithms (such as support vector machines, neural networks, decision trees, etc.) or expert systems, capable of identifying differences between normal and abnormal states. Through model analysis, it is determined whether the current state deviates from the normal range. If an anomaly or fault is identified, the model further analyzes the type of fault (such as motor overheating, overload, short circuit, sensor failure, etc.) and its severity (such as minor, moderate, severe). Based on the model's analysis, fault analysis results are generated, including fault type, fault severity, and possible causes, providing a basis for subsequent protection scheme triggering and fault handling.

[0171] Step 262: Based on the fault analysis results, automatically select the corresponding protection scheme. For example, for severe faults (such as motor overheating potentially causing a fire), emergency shutdown may be selected; for moderate faults (such as overload), reduced power operation may be selected to mitigate the fault risk. The selected protection scheme is executed through the servo drive's control interface or related protection circuits. For example, an emergency shutdown signal is sent to the servo motor, or the power output is adjusted to reduce the load. Simultaneously, fault alarm information is generated, including the fault type, occurrence time (accurate to seconds or milliseconds), severity, and other key information. The fault alarm information is sent to the remote control center in real time via a communication interface (such as Ethernet, CAN bus, etc.). This ensures that the control center can quickly learn about the fault and take appropriate measures.

[0172] Taking the servo drive of a certain type of CNC machine tool as an example, during the operation of the CNC machine tool, the servo drive collects data such as the motor's position, speed, acceleration, current, and temperature in real time. This data is first preprocessed, such as by removing noise and normalizing, to ensure accuracy. The preprocessed data is then input into a neural network-based fault diagnosis model. This model has been trained with a large amount of normal and fault data and can accurately identify various fault types. Model analysis revealed that the motor temperature data was abnormally high, exceeding the normal range. Further analysis determined it to be a motor overheating fault, with a severity level of "moderate." Based on the fault analysis results, a power reduction protection scheme is automatically triggered to reduce the motor load and lower the temperature. Simultaneously, fault alarm information is generated, including the fault type "motor overheating," the occurrence time (e.g., "2023-04-15 10:30:12"), and the severity level ("moderate"), and is sent to the remote control center via Ethernet.

[0173] By employing a pre-defined fault diagnosis model, abnormalities and faults during servo drive operation can be accurately identified, improving the accuracy and reliability of fault detection. Automatically triggered protection schemes, such as emergency shutdown or power reduction, effectively prevent fault escalation and protect equipment and personnel safety. Timely transmission of fault alarm information allows the remote control center to quickly implement maintenance measures, reducing downtime and maintenance costs. Automated fault detection and protection reduce manual intervention, improve production efficiency, and ensure the stable operation of equipment such as CNC machine tools.

[0174] It should be noted that this method is the same as the method described above for the system. All implementation methods in the above system embodiments are applicable to this embodiment and can achieve the same technical effect.

[0175] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A servo drive remote control system, characterized in that, include: The data acquisition module is used to collect real-time operating data of the servo drives on stage entertainment equipment, including position, speed, acceleration and current parameters; The data analysis module is used to analyze the operational data of the data acquisition module, identify trends, anomalies and correlations in the data, and transmit the operational data to the remote control center. The remote monitoring module is used to receive and display the operating data of the data analysis module, and to remotely monitor and adjust the servo drive through a remote operation interface; The instruction generation and transmission module is used to automatically generate operation instructions for controlling the servo drive based on the operation instructions of the remote monitoring module, and transmit the operation instructions to the servo drive on the stage entertainment equipment. The execution control module is used to receive operation instructions from the instruction generation and transmission module, and control the servo driver to move according to the instruction requirements, thereby controlling the stage entertainment equipment; The fault diagnosis and protection module is used to perform real-time fault diagnosis of the servo drive based on the operating data of the data acquisition module, and when a fault is detected, it sends alarm information to the remote control center and automatically takes safety measures.

2. The servo drive remote control system according to claim 1, characterized in that, Analyze the operational data of the data acquisition module, identify trends, anomalies, and correlations in the data, and transmit the operational data to the remote control center, including: Based on the operational data of the data acquisition module, an optimization model is constructed, and an objective function is defined in the optimization model; The optimization model is solved iteratively, and the current solution is updated in each iteration. This internal iteration is repeated multiple times until the preset number of iterations is reached, and the final solution of the optimization model is obtained, including the identified data trends, outliers and correlation information. The final solution of the optimization model is analyzed and processed, including predicting future data change trends based on identified trends, detecting potential faults based on outliers, and discovering the interaction and influence between different parameters based on correlation, so as to obtain the analyzed and processed operating data. The analyzed and processed operational data, including identified trends, anomalies, and correlations, are transmitted to the remote control center.

3. The servo drive remote control system according to claim 2, characterized in that, It receives and displays the operating data from the data analysis module, and remotely monitors and adjusts the servo drive through a remote operation interface, including: The data analysis module analyzes the operational data to extract the status, operating parameters, and fault alarm information of the servo drive. The remote operation interface automatically refreshes the status, operating parameters and fault alarm information of the servo drive to reflect data changes in real time. If the real-time data contains fault alarm information, the remote operation interface displays the corresponding alarm prompt. The adjustment command is sent to the servo drive through the input control on the remote operation interface. The servo drive parses the adjustment command to identify the command type and parameters, and adjusts its own operating parameters according to the command type and parameters, so as to realize remote monitoring and adjustment of the servo drive.

4. The servo drive remote control system according to claim 3, characterized in that, Based on the operation instructions from the remote monitoring module, the system automatically generates operation instructions to control the servo drive and transmits these instructions to the servo drive on the stage entertainment equipment, including: The operation instructions of the remote monitoring module are parsed, including checking the instruction format, check code, serial number, and identifying the type of operation instruction, including start, stop, acceleration, deceleration, positioning, and parameter setting. Each type corresponds to different control logic and servo driver response. Based on the operation instructions, the target servo drive is determined, and the instruction values, including speed, position, and acceleration, are extracted from the operation instructions.

5. The servo drive remote control system according to claim 4, characterized in that, Based on the operation instructions of the remote monitoring module, the system automatically generates operation instructions to control the servo drive and transmits these instructions to the servo drive on the stage entertainment equipment. This includes generating corresponding operation instructions to control the servo drive based on the instruction type, target servo drive, and instruction value. The operation instructions for controlling the servo drive are encapsulated, including adding a checksum, serial number, and target address information, to obtain the encapsulated operation instructions for controlling the servo drive. The encapsulated operation instructions for controlling the servo drive are then transmitted to the servo drive on the stage entertainment equipment.

6. The servo driver remote control system according to claim 5, characterized in that, The system receives operation commands from the command generation and transmission module and controls the servo driver to move according to the command requirements, thereby controlling the stage entertainment equipment, including: Based on the operation instructions of the instruction generation and transmission module, initialize the relevant parameters of the artificial fish swarm, including the fish swarm size, search space, number of iterations, visual range, and step size. Based on the motion commands and control objectives of the servo driver, define an evaluation function to evaluate the motion effect of the servo driver. Simulating the foraging behavior of fish schools in a search space, each artificial fish represents a set of control parameters and continuously updates its position through swarming, tail chasing, and foraging behaviors.

7. The servo drive remote control system according to claim 6, characterized in that, The system receives operation commands from the command generation and transmission module and controls the servo driver to move according to the command requirements, thereby controlling the stage entertainment equipment, including: In each iteration, the position of each artificial fish is evaluated according to the evaluation function, and the state of the fish group is updated. The process involves multiple iterations, with each iteration simulating the foraging behavior of the fish school to update its position and adjusting the state of the fish school according to the evaluation function, until the preset number of iterations is reached to obtain the final combination of control parameters. The final combination of control parameters is sent to the servo driver, which controls the movement according to the instructions, including start, stop, acceleration, deceleration, and positioning actions, thereby controlling the stage entertainment equipment.

8. A remote control method for a servo driver, wherein the method implements the system as described in any one of claims 1 to 7, characterized in that, include: The servo driver automatically and in real time collects operating data, including position, speed, acceleration, and current parameters. The running data is preprocessed, including filtering and calibration, to obtain processed data. The processed data is then analyzed to identify trends, anomalies, and correlations in the data, and to obtain analysis results. Based on the analysis results, receive and parse the control commands from the remote control center, including start, stop, and parameter adjustment operation requirements; Based on the control instructions from the remote control center, the corresponding control signals are automatically generated and transmitted to the servo driver to control the movement of the servo motor. The real-time operating status, control results, and processed data of the servo drive are sent to the remote control center to monitor and evaluate the performance of the servo drive in real time. Based on real-time operating status and preset fault diagnosis models, the system automatically detects and analyzes faults. If an anomaly is detected, it triggers protection schemes, including emergency shutdown, power reduction, and sends fault alarm information to the remote control center.

9. The servo driver remote control method according to claim 8, characterized in that, Based on the control commands from the remote control center, the system automatically generates corresponding control signals and transmits these signals to the servo driver to control the movement of the servo motor, including: The current status of the servo motor, including position, speed, acceleration, and current parameters, is monitored in real time by sensors on the servo driver. Calculate the deviation between the current state and the desired state of the servo motor, and dynamically adjust the control parameters, including proportional, integral and derivative parameters, based on the deviation. The final control signal is calculated using adaptive control and control parameters; Based on the final control signal, the corresponding control command is automatically generated and transmitted to the servo motor through the control interface of the servo driver. The servo motor adjusts its own motion state, including position, speed and acceleration, according to the control command.

10. The servo driver remote control method according to claim 9, characterized in that, Based on real-time operating status and preset fault diagnosis models, the system automatically detects and analyzes faults. If an anomaly is detected, a protection scheme is triggered, including emergency shutdown and power reduction, and fault alarm information is sent to the remote control center, including: Real-time operating status data is input into a preset fault diagnosis model for analysis. By comparing the difference between the current state and the normal state, it is determined whether there is an abnormality or fault. If an abnormality or fault is identified, the fault diagnosis model analyzes the type and severity of the fault to obtain the fault analysis results. Based on the fault analysis results, the corresponding protection scheme is automatically triggered, and fault alarm information is sent to the remote control center, including the type of fault, the time of occurrence, and the severity.

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