Method for monitoring servo driver, digital twin platform and electronic equipment

Abstract

The invention provides a method for monitoring a servo driver, a digital twin platform and electronic equipment. The method comprises the steps that a digital twin platform obtains a three-dimensional model of the servo driver, and the three-dimensional model represents a physical structure of the servo driver; the digital twin platform obtains real-time data and historical data of the servo driver, and constructs a digital model of the servo driver based on the real-time data and the historical data; in a digital twin platform, a three-dimensional model is adopted to display the physical structure of the servo driver, and a digital model is applied to control the behavior action of the servo driver, so that the actual operation state of the servo driver is monitored and predicted, and / or fault diagnosis and prediction are carried out on the servo driver. The servo driver is mapped to a digital twin platform, and precise monitoring and fault diagnosis of the state of the servo driver are realized through a digital twin technology, so that high-precision prediction and analysis are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of servo drives, and in particular to a method for monitoring a servo drive, a digital twin platform, a computer-readable storage medium, and an electronic device. Background Art

[0002] A servo drive is a device used to precisely control an electric motor. It plays a vital role in industrial automation systems. The servo drive controls the speed, position, and torque of the motor by regulating current and voltage to achieve high-precision motion control. It is widely used in robots, CNC machine tools, conveyor systems, and other fields.

[0003] Traditional servo drives face many challenges in practical applications, such as increased system complexity, higher accuracy requirements, and difficulty in fault diagnosis. Currently, there is a lack of a solution for accurately monitoring and predicting the status of a servo drive, and / or accurately diagnosing and predicting faults in a servo drive. Summary of the invention

[0004] The main purpose of the present application is to provide a method for monitoring a servo drive, a digital twin platform, a computer-readable storage medium and an electronic device, so as to at least solve the problem of the current lack of a solution for accurately monitoring and predicting the status of a servo drive, and / or accurately diagnosing and predicting faults of a servo drive.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for monitoring a servo drive is provided, comprising: a digital twin platform obtains a three-dimensional model of a servo drive, wherein the three-dimensional model represents the physical structure of the servo drive; the digital twin platform obtains real-time data and historical data of the servo drive, and constructs a digital model of the servo drive based on the real-time data and the historical data; the three-dimensional model is used in the digital twin platform to display the physical structure of the servo drive, and the digital model is applied to control the behavior of the servo drive, so as to monitor and predict the actual operating status of the servo drive, and / or diagnose and predict faults of the servo drive.

[0006] Optionally, constructing a digital model of the servo drive based on the real-time data and the historical data includes: preprocessing the real-time data and the historical data to obtain preprocessed data, wherein the preprocessing at least includes data cleaning processing, data standardization processing and feature engineering processing; and processing the preprocessed data using a preset algorithm model to obtain the digital model.

[0007] Optionally, the digital model includes a state monitoring and prediction model and a fault diagnosis and prediction model, which monitors and predicts the actual operating state of the servo drive, including: using the state monitoring and prediction model to monitor and predict the actual operating state of the servo drive; and performs fault diagnosis and prediction on the servo drive, including: using the fault diagnosis and prediction model to perform fault diagnosis and prediction on the servo drive.

[0008] Optionally, the digital model also includes a maintenance strategy optimization model, wherein the input of the maintenance strategy optimization model is real-time data and historical data of the servo drive, and the output is a maintenance strategy for the servo drive. The method also includes: feeding back the maintenance strategy output by the maintenance strategy optimization model to the servo system to maintain the fault of the servo drive.

[0009] Optionally, the digital model also includes a life prediction model, wherein the life prediction model input is real-time data and historical data of the servo drive, and the output is the predicted life of the servo drive. The method also includes: using the life prediction model to predict the life of the servo drive.

[0010] Optionally, the digital twin platform obtains real-time data and historical data of the servo drive, including: the digital twin platform establishes a connection with an edge server; the digital twin platform obtains real-time data and historical data of the servo drive stored in the edge server, wherein a connection is established between the edge server and the servo drive.

[0011] Optionally, the method further comprises: storing the behavior action data of the servo driver in a preset database.

[0012] According to another aspect of the present application, a digital twin platform is provided, including: an acquisition unit, used to acquire a three-dimensional model of a servo drive, the three-dimensional model representing the physical structure of the servo drive; an acquisition and construction unit, used to acquire real-time data and historical data of the servo drive, and construct a digital model of the servo drive based on the real-time data and the historical data; a processing unit, used to use the three-dimensional model to display the physical structure of the servo drive in the digital twin platform, and apply the digital model to control the behavior of the servo drive, so as to monitor and predict the actual operating status of the servo drive, and / or perform fault diagnosis and prediction on the servo drive.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for monitoring a servo drive.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the methods for monitoring a servo drive.

[0015] Applying the technical solution of the present application, a three-dimensional model of the servo drive is obtained through a digital twin platform, and the three-dimensional model represents the physical structure of the servo drive; the digital twin platform obtains the real-time data and historical data of the servo drive, and constructs a digital model of the servo drive based on the real-time data and the historical data; the three-dimensional model is used in the digital twin platform to display the physical structure of the servo drive, and the digital model is used to control the behavior of the servo drive to monitor and predict the actual operating status of the servo drive, and / or to diagnose and predict faults of the servo drive. The servo drive is mapped to the digital twin platform, and accurate monitoring and fault diagnosis of the servo drive status are achieved through digital twin technology, thereby ensuring high-precision prediction and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A schematic flow chart of a method for monitoring a servo drive provided according to an embodiment of the present application is shown;

[0018] Figure 2 A functional architecture diagram of a digital twin system according to an embodiment of the present application is shown;

[0019] Figure 3 A servo drive data acquisition flow chart provided according to an embodiment of the present application is shown;

[0020] Figure 4 A structural block diagram of a digital twin platform provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] As introduced in the background technology, the prior art lacks a solution for accurately monitoring and predicting the status of a servo drive, and / or, accurately diagnosing and predicting faults of a servo drive. In order to solve the problem of lack of a solution for accurately monitoring and predicting the status of a servo drive, and / or, accurately diagnosing and predicting faults of a servo drive, the embodiments of the present application provide a method for monitoring a servo drive, a digital twin platform, a computer-readable storage medium, and an electronic device.

[0025] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0026] In this embodiment, a method for monitoring servo drives of a digital twin platform is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Figure 1 1 is a flow chart of a method for monitoring a servo drive according to an embodiment of the present application. Figure 1As shown, the method comprises the following steps:

[0028] Step S101, the digital twin platform obtains a three-dimensional model of the servo drive, where the three-dimensional model represents the physical structure of the servo drive;

[0029] Among them, digital twin is a virtual model that accurately reflects the entity or system in the real world. The main features of digital twin include:

[0030] 1. Accurate mapping: Digital twins can accurately map the characteristics, status, and behavior of physical entities, including their historical data and real-time data.

[0031] 2. Real-time updates: Through Internet of Things (IoT) devices and sensors, digital twins can receive data from physical entities in real time, thus keeping in sync with the real world.

[0032] 3. Simulation and prediction: Using advanced data analysis and simulation techniques, digital twins can perform scenario simulations and predict future states and behaviors, as well as possible problems.

[0033] 4. Optimization and improvement: By simulating different operating conditions and parameters, digital twins can help engineers and decision makers optimize product designs, production processes, and maintenance strategies.

[0034] 5. Remote monitoring and control: Digital twins can enable monitoring and control of remote physical entities, improving operational flexibility and responsiveness.

[0035] 6. Cost-effectiveness: By testing and validating in a virtual environment, digital twins can reduce the cost and risk of actual testing.

[0036] Among them, a variety of 3D modeling technologies can be used to build a three-dimensional model of the servo drive, and then the constructed three-dimensional model of the servo drive can be imported into the digital twin platform. Of course, 3D modeling software can also be integrated into the digital twin platform to directly build a three-dimensional model of the servo drive on the digital twin platform. However, the second method requires extra computing power of the digital twin platform, which needs to be taken into account in practical applications.

[0037] As mentioned above, a variety of 3D modeling technologies include: SolidWorks, Autodesk Inventor, Siemens NX, PTC Creo, CATIA, Rhinoceros 3D (Rhino), etc. can be selected according to actual needs;

[0038] The constructed 3D model can not only display the contour features of the servo drive, but also accurately display the internal detailed features; it supports virtual simulation and structural analysis.

[0039] Step S102, the digital twin platform obtains real-time data and historical data of the servo drive, and builds a digital model of the servo drive based on the real-time data and the historical data;

[0040] Among them, from the perspective of servo drives, the real-time data and historical data of servo drives can be obtained through the Internet of Things system. Servo drives are often used in industrial automation and robot control, and their performance monitoring and management can be achieved through the Internet of Things system. The Internet of Things system usually includes sensors, actuators and controllers, which can monitor the status and performance of servo drives in real time. The acquired data is then transmitted to the digital twin platform.

[0041] Among them, the real-time data of the servo drive includes: operation data and status data. The operation data includes current, voltage, speed, position, temperature and other data; the status data includes fault status, operation mode, environmental factors, etc.; among them, environmental factors include ambient temperature, ambient humidity, dust and pollution, vibration and impact, power supply fluctuations, electromagnetic interference and other factors.

[0042] The historical data of the servo drive includes: previous operation records, maintenance logs, fault records, etc.

[0043] As mentioned above, the three-dimensional model is a visual model that can be displayed in a display interface and can be understood as a virtual servo drive, but the digital model is a model of underlying calculations and does not have a visual effect.

[0044] Step S103: Using the three-dimensional model in the digital twin platform to display the physical structure of the servo drive, and using the digital model to control the behavior of the servo drive, so as to monitor and predict the actual operating status of the servo drive, and / or perform fault diagnosis and prediction on the servo drive.

[0045] Due to the integration and linkage of the three-dimensional model and the digital model, under the control of the digital model, the three-dimensional model can be displayed on the display interface in correspondence with the actual servo drive, and due to the calculation function of the digital model, it can not only monitor the actual operating state of the servo drive and diagnose the faults of the servo drive, but also predict the actual operating state of the servo drive and predict the faults of the servo drive. Of course, the prediction results of the actual operating state mentioned above are related to the fault prediction results. For example, the first operating state should be executed, but the prediction is to execute the second predicted state. In this case, there is a high probability that a fault has occurred and a fault warning is required.

[0046] The method for monitoring a servo drive of the present application obtains a three-dimensional model of the servo drive through a digital twin platform, wherein the three-dimensional model represents the physical structure of the servo drive; the digital twin platform obtains real-time data and historical data of the servo drive, and constructs a digital model of the servo drive based on the real-time data and the historical data; the three-dimensional model is used in the digital twin platform to display the physical structure of the servo drive, and the digital model is used to control the behavior of the servo drive, so as to monitor and predict the actual operating status of the servo drive, and / or to perform fault diagnosis and prediction on the servo drive. The servo drive is mapped to the digital twin platform, and accurate monitoring and fault diagnosis of the servo drive status are achieved through digital twin technology, thereby ensuring high-precision prediction and analysis.

[0047] The solution of this application optimizes the control strategy of the servo drive through a digital model to achieve more efficient energy use and better performance. For example, the control parameters of the servo drive are adjusted to improve the response speed and accuracy. The drive is fully managed to provide real-time feedback, performance optimization and fault warning.

[0048] The structural diagram of the digital twin platform of this application can be found in Figure 2 , specifically including: data management, information management and model management, among which the data management part includes market data, R&D data, production data and operation data, the information management part includes data dictionary, identification resolution, metadata description, etc.; the model management part includes geometric model, simulation model, business model, data model, etc. Based on the digital twin platform, the linkage between digital space and physical objects is realized. Through the application of digital twins, the historical data and real-time data of the servo drive are mapped to the digital space, and then fed back to the physical object after the description, diagnosis, prediction and disposal of the digital space. In the whole process, the physical space is predicted through the digital space, and the data of the digital space is verified in the physical space. After the most relevant model is obtained through the mutual verification process, some bottleneck problems of the servo drive are optimized, and the model can also be used to predict faults, life, etc.

[0049] Furthermore, a digital model of the servo drive is constructed based on the real-time data and the historical data, including:

[0050] Preprocessing the real-time data and the historical data to obtain preprocessed data, wherein the preprocessing at least includes data cleaning processing, data standardization processing and feature engineering processing;

[0051] Among them, data cleaning processing specifically includes: removing duplicate data and erroneous values ​​(such as abnormal current or temperature readings); processing missing values, which can be replaced by interpolation, mean value or deletion of corresponding records.

[0052] Data standardization specifically includes: standardizing data from different sources for comparison and analysis. Standardization usually involves normalizing data to make them on the same scale.

[0053] Among them, feature engineering processing specifically includes: extracting meaningful features from the original data, including: operating efficiency (such as the ratio of motor load to rated load); fault precursors (such as temperature rise trends); frequency domain features (extracting signal frequency characteristics through Fourier transform).

[0054] In addition to the above data preprocessing operations, it also includes: using big data analysis technology to process massive amounts of data and identify potential patterns or trends. Available analysis tools include: Apache Spark: for large-scale data processing and analysis. Hadoop: stores and processes large amounts of structured and unstructured data.

[0055] Optionally, data visualization tools (such as Tableau, Power BI) are used to graph the data in the identification of specific faults to help identify patterns and anomalies in the data.

[0056] The preprocessed data is processed using a preset algorithm model to obtain the digital model.

[0057] Among them, the preset algorithm model includes a machine learning model, and the algorithms involved in the machine learning model include: regression analysis: used to predict continuous values ​​(such as motor speed and power); classification algorithm: used to identify fault states (such as normal and faulty); time series prediction: used to predict future performance trends based on historical data.

[0058] Of course, the steps to build a digital model generally include:

[0059] Data partitioning: Divide the dataset into training set and test set.

[0060] Model training: Apply the selected algorithm on the training set and adjust the model parameters to improve prediction accuracy.

[0061] Model validation: Evaluate the performance of the model on the test set, using metrics such as mean squared error (MSE), precision, recall, etc.

[0062] This application scheme uses the trained model to achieve real-time monitoring of the servo drive, timely identify potential faults and perform predictive maintenance, thereby reducing downtime and maintenance costs.

[0063] In a specific implementation, the above digital model includes a state monitoring and prediction model and a fault diagnosis and prediction model.

[0064] Monitoring and predicting the actual operating state of the servo drive, including: using the state monitoring and prediction model to monitor and predict the actual operating state of the servo drive;

[0065] Performing fault diagnosis and prediction on the servo drive includes: using the fault diagnosis and prediction model to perform fault diagnosis and prediction on the servo drive.

[0066] In addition, the above-mentioned digital model also includes a maintenance strategy optimization model, wherein the input of the above-mentioned maintenance strategy optimization model is the real-time data and historical data of the above-mentioned servo drive, and the output is the maintenance strategy for the above-mentioned servo drive. The above-mentioned method also includes: feeding back the above-mentioned maintenance strategy output by the above-mentioned maintenance strategy optimization model to the servo system to maintain the fault of the above-mentioned servo drive.

[0067] Furthermore, the digital model also includes a life prediction model, wherein the life prediction model inputs the real-time data and historical data of the servo drive, and outputs the predicted life of the servo drive, and the method also includes: using the life prediction model to predict the life of the servo drive. It is particularly pointed out that the life of the servo drive is easily affected by environmental factors, and of course, to a certain extent, it is also affected by factors such as fault status and operating mode.

[0068] The condition monitoring and prediction model, fault diagnosis and prediction model, maintenance strategy optimization model and life prediction model of the present application are all trained using machine learning technology.

[0069] In order to obtain the data of the servo drive, the above-mentioned digital twin platform obtains the real-time data and historical data of the above-mentioned servo drive, including: the above-mentioned digital twin platform establishes a connection with the edge server; the above-mentioned digital twin platform obtains the real-time data and historical data of the above-mentioned servo drive stored in the above-mentioned edge server, wherein a connection is established between the above-mentioned edge server and the above-mentioned servo drive.

[0070] See Figure 3 The servo drive is connected to the edge server through the enterprise private network and transmits the data to the edge server. After the data is transmitted to the edge server, data cleaning and other processes are carried out, and then the useful information is transmitted to the public cloud (i.e., the digital twin platform) through the Internet. In other words, data cleaning and other steps can be performed in the edge server, which can save the computing power of the digital twin platform. Of course, if the computing power of the digital twin platform is sufficient, data preprocessing operations can be performed on the digital twin platform, and the choice is made according to the actual situation.

[0071] In addition, some servo drives cannot upload data to the cloud platform through the edge computing server due to version issues or network problems. The process of uploading data to the edge server through the enterprise intranet and then uploading to the Internet through the public network can be changed to connecting the servo drive to upload data to the Internet through the cellular network.

[0072] The edge server is introduced as follows:

[0073] Edge Server is a type of server in a distributed computing architecture that is located at the edge of the network, close to data sources and users. The main purpose of edge servers is to reduce latency, increase data transfer speeds, and reduce the load on central servers. Here are some key features of edge servers:

[0074] 1. Geographic location: Edge servers are usually deployed close to users, such as in local data centers, at the edge of telecom operators’ networks, or in enterprise intranets.

[0075] 2. Low latency: Since the edge server is close to the data source and users, it can provide faster data processing and response speed, thereby reducing latency.

[0076] 3. Load distribution: Edge servers can help distribute network traffic, reduce the load on central servers, and improve the performance and reliability of the entire network.

[0077] 4. Real-time data processing: Edge servers can process and analyze data close to the data source in real time without transmitting the data to a remote central server.

[0078] 5. Content distribution: Edge servers can cache and distribute content, such as video streaming, web pages, etc., to improve content access speed and user experience.

[0079] 6. Security: Edge servers can provide additional layers of security between users and data centers, such as data encryption and network security.

[0080] 7. Application scenarios: Edge servers are suitable for scenarios that require low latency, high bandwidth, and real-time data processing, such as online games, video surveillance, and Internet of Things (IoT) applications.

[0081] Furthermore, the method further includes: storing the behavior data of the servo drive in a preset database. This method supports unified monitoring of multiple servo drives in a factory. Since the occurrence of servo drive failures is uncertain, the system saves all relevant data so that the cause of the problem can be quickly located and optimized when a failure occurs. Through real-time analysis and prediction of digital models, optimized management of drive status and fault prevention can be achieved.

[0082] Specifically, an IoT system for servo drives is established for real-time data collection and historical data storage. This system needs to realize real-time monitoring of the drive and save the drive's historical data for subsequent analysis and modeling. That is, the IoT system provides a data foundation to support real-time monitoring of the drive status and retrospective analysis of historical data.

[0083] In order to better understand the technical solution of the present application, the management and optimization of the servo drive based on data twin implementation of the present application includes:

[0084] Part I: Optimizing Management Technology Solutions

[0085] 1. Real-time monitoring and data collection

[0086] Sensor integration: Various sensors (such as temperature, vibration, current, and voltage sensors) are installed on the servo drive to collect equipment operation data in real time.

[0087] Data transmission: Through IoT technology, data is transmitted to the cloud or local server for centralized processing and analysis.

[0088] 2. Data Analysis and Visualization

[0089] Real-time data analysis: Use big data processing platforms (such as Apache Spark) to stream real-time data and quickly identify anomalies.

[0090] Visual dashboards: Use data visualization tools (such as Tableau, Power BI) to create real-time monitoring dashboards that intuitively display the performance indicators, status, and health of the drives.

[0091] 3. Optimize control strategy

[0092] Intelligent control algorithm: Based on real-time data analysis, intelligent control algorithms (such as fuzzy control, adaptive control) are applied to dynamically adjust the control parameters of the drive to achieve higher energy efficiency and better response speed.

[0093] Load optimization: Optimize the working state of the servo drive according to the operating load and demand changes to avoid overload and inefficient operation.

[0094] 4. Performance Benchmarks and Evaluation

[0095] Build a baseline model: Create a baseline performance model of the servo drive under normal operating conditions and regularly compare it with real-time data to evaluate the operating efficiency of the equipment.

[0096] KPI monitoring: Set key performance indicators (KPIs), such as efficiency, response time, and failure rate, for continuous monitoring and evaluation.

[0097] Part II: Fault Prevention Technical Solutions

[0098] 1. Fault prediction model

[0099] Machine learning algorithms: Machine learning models (e.g., random forest, support vector machine) are trained on historical operating data to identify potential failure modes and trends.

[0100] Time series analysis: Use time series forecasting technology to analyze trends in equipment operation data and identify possible failures in advance.

[0101] 2. Anomaly Detection

[0102] Real-time anomaly detection: Monitor device status in real time by setting thresholds or using anomaly detection algorithms (such as isolation forest) to promptly detect signs of deviation from normal working conditions.

[0103] Early warning mechanism: When an abnormality is detected, the early warning mechanism is automatically triggered to notify relevant maintenance personnel to conduct inspection and processing.

[0104] 3. Predictive maintenance

[0105] Maintenance strategy optimization: Based on the prediction model, a predictive maintenance strategy is formulated to perform maintenance based on equipment health rather than fixed time intervals to reduce maintenance costs and downtime.

[0106] Maintenance plan adjustment: Update maintenance plans in real time to ensure that key equipment operates in optimal conditions and avoid production interruptions due to unexpected failures.

[0107] 4. Fault history analysis

[0108] Failure Mode Analysis: Conduct in-depth analysis of historical failure data to identify common failure modes and causes to improve design and operational processes.

[0109] Feedback mechanism: Feedback fault handling experience and data into the system to help continuously optimize prediction models and maintenance strategies.

[0110] The solution of this application achieves the following technical effects:

[0111] 1. Real-time monitoring: Through digital twin technology, real-time status monitoring of servo drives can be achieved, and problems can be discovered and solved in a timely manner, that is, rapid response to external instructions and environmental changes;

[0112] 2. Performance optimization: The digital twin model can help analyze the performance bottleneck of the servo drive, optimize the control parameters, and improve the overall efficiency of the system. It can also maintain the high precision of the servo drive under various working conditions, especially in the case of load fluctuations and environmental changes.

[0113] 3. Predictive maintenance: Through the analysis of the digital twin model, potential failures can be predicted and predictive maintenance can be implemented, no longer relying on experience, and reducing downtime risks and maintenance costs.

[0114] In addition, the application of data twin technology overcomes the defects of various compatibility and performance matching that need to be resolved in the integration and coordination of servo drives in complex automation systems.

[0115] The embodiment of the present application also provides a digital twin platform. It should be noted that the digital twin platform of the embodiment of the present application can be used to execute the method for monitoring a servo drive provided in the embodiment of the present application. The digital twin platform is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the digital twin platform described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0116] The following is an introduction to the digital twin platform provided in the embodiments of the present application.

[0117] Figure 4 Schematic diagram of a digital twin platform according to an embodiment of the present application. Figure 4 As shown in the figure, the digital twin platform includes:

[0118] An acquisition unit 41 is used to acquire a three-dimensional model of the servo drive, wherein the three-dimensional model represents the physical structure of the servo drive;

[0119] An acquisition and construction unit 42, used for acquiring the real-time data and historical data of the servo drive, and constructing a digital model of the servo drive based on the real-time data and the historical data;

[0120] The processing unit 43 is used to use the above-mentioned three-dimensional model to display the physical structure of the above-mentioned servo drive in the above-mentioned digital twin platform, and apply the above-mentioned digital model to control the behavior of the above-mentioned servo drive, so as to monitor and predict the actual operating status of the above-mentioned servo drive, and / or perform fault diagnosis and prediction on the above-mentioned servo drive.

[0121] The digital twin platform of the present application obtains the three-dimensional model of the servo drive through an acquisition unit, and the three-dimensional model represents the physical structure of the servo drive; the acquisition and construction unit obtains the real-time data and historical data of the servo drive, and constructs the digital model of the servo drive based on the real-time data and the historical data; the processing unit uses the three-dimensional model in the digital twin platform to display the physical structure of the servo drive, and uses the digital model to control the behavior of the servo drive to monitor and predict the actual operating status of the servo drive, and / or to diagnose and predict faults of the servo drive. The servo drive is mapped to the digital twin platform, and the digital twin technology is used to accurately monitor and diagnose faults of the servo drive, thereby ensuring high-precision prediction and analysis.

[0122] An optional solution, the acquisition and construction unit includes a construction module, the construction module is used to construct the digital model of the servo drive based on the above-mentioned real-time data and the above-mentioned historical data, wherein the construction module includes a preprocessing submodule and a processing submodule, the preprocessing submodule is used to preprocess the above-mentioned real-time data and the above-mentioned historical data to obtain preprocessed data, wherein the above-mentioned preprocessing at least includes data cleaning processing, data standardization processing and feature engineering processing; the processing submodule is used to process the above-mentioned preprocessed data using a preset algorithm model to obtain the above-mentioned digital model.

[0123] An optional solution, the above-mentioned digital model includes a state monitoring and prediction model and a fault diagnosis and prediction model, the processing unit includes a first monitoring and prediction module and a second monitoring and prediction module, the first monitoring and prediction module is used to monitor and predict the actual operating state of the above-mentioned servo drive, and has the function of using the above-mentioned state monitoring and prediction model to monitor and predict the actual operating state of the above-mentioned servo drive; the second monitoring and prediction module is used to perform fault diagnosis and prediction on the above-mentioned servo drive, and has the function of using the above-mentioned fault diagnosis and prediction model to perform fault diagnosis and prediction on the above-mentioned servo drive.

[0124] An optional solution, the digital model also includes a maintenance strategy optimization model, wherein the input of the maintenance strategy optimization model is the real-time data and historical data of the servo drive, and the output is the maintenance strategy for the servo drive, and the digital twin platform also includes a maintenance unit, which is used to feed back the maintenance strategy output by the maintenance strategy optimization model to the servo system to maintain the fault of the servo drive.

[0125] An optional solution is that the above-mentioned digital model also includes a life prediction model, wherein the above-mentioned life prediction model inputs the real-time data and historical data of the above-mentioned servo drive, and outputs the predicted life of the above-mentioned servo drive, and the above-mentioned digital twin platform also includes a prediction unit, which is used to use the above-mentioned life prediction model to predict the life of the above-mentioned servo drive.

[0126] An optional solution, the acquisition and construction unit includes an acquisition module, the acquisition module includes a connection submodule and an acquisition submodule, the connection submodule is used to establish a connection with the edge server; the acquisition submodule is used to acquire the real-time data and historical data of the servo drive stored in the edge server, wherein a connection is established between the edge server and the servo drive.

[0127] As an optional solution, the digital twin platform further includes a storage unit, which is used to store the behavior action data of the servo drive in a preset database.

[0128] The digital twin platform includes a processor and a memory. The acquisition unit, acquisition and construction unit, and processing unit are stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0129] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to solve the problem of the current lack of a solution for accurately monitoring and predicting the state of the servo drive, and / or accurately diagnosing and predicting faults of the servo drive.

[0130] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0131] An embodiment of the present invention provides an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the above-mentioned methods for monitoring a servo drive.

[0132] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for monitoring a servo drive.

[0133] Specifically, the method for monitoring the servo drive includes:

[0134] Step S101, the digital twin platform obtains a three-dimensional model of the servo drive, where the three-dimensional model represents the physical structure of the servo drive;

[0135] Step S102, the digital twin platform obtains real-time data and historical data of the servo drive, and builds a digital model of the servo drive based on the real-time data and the historical data;

[0136] Step S103: Using the three-dimensional model in the digital twin platform to display the physical structure of the servo drive, and using the digital model to control the behavior of the servo drive, so as to monitor and predict the actual operating status of the servo drive, and / or perform fault diagnosis and prediction on the servo drive.

[0137] Optionally, constructing a digital model of the servo drive based on the real-time data and the historical data includes: preprocessing the real-time data and the historical data to obtain preprocessed data, wherein the preprocessing includes at least data cleaning processing, data standardization processing and feature engineering processing; and processing the preprocessed data using a preset algorithm model to obtain the digital model.

[0138] Optionally, the above-mentioned digital model includes a state monitoring and prediction model and a fault diagnosis and prediction model, which monitors and predicts the actual operating state of the above-mentioned servo drive, including: using the above-mentioned state monitoring and prediction model to monitor and predict the actual operating state of the above-mentioned servo drive; and performs fault diagnosis and prediction on the above-mentioned servo drive, including: using the above-mentioned fault diagnosis and prediction model to perform fault diagnosis and prediction on the above-mentioned servo drive.

[0139] Optionally, the digital model also includes a maintenance strategy optimization model, wherein the input of the maintenance strategy optimization model is the real-time data and historical data of the servo drive, and the output is a maintenance strategy for the servo drive. The method also includes: feeding back the maintenance strategy output by the maintenance strategy optimization model to the servo system to maintain the fault of the servo drive.

[0140] Optionally, the digital model also includes a life prediction model, wherein the life prediction model input is the real-time data and historical data of the servo drive, and the output is the predicted life of the servo drive. The method also includes: using the life prediction model to predict the life of the servo drive.

[0141] Optionally, the digital twin platform obtains real-time data and historical data of the servo drive, including: the digital twin platform establishes a connection with an edge server; the digital twin platform obtains real-time data and historical data of the servo drive stored in the edge server, wherein a connection is established between the edge server and the servo drive.

[0142] Optionally, the method further comprises: storing the behavior action data of the servo driver in a preset database.

[0143] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the method for monitoring a servo drive is executed when the program is run.

[0144] Specifically, the method for monitoring the servo drive includes:

[0145] Step S101, the digital twin platform obtains a three-dimensional model of the servo drive, where the three-dimensional model represents the physical structure of the servo drive;

[0146] Step S102, the digital twin platform obtains real-time data and historical data of the servo drive, and builds a digital model of the servo drive based on the real-time data and the historical data;

[0147] Step S103: Using the three-dimensional model in the digital twin platform to display the physical structure of the servo drive, and using the digital model to control the behavior of the servo drive, so as to monitor and predict the actual operating status of the servo drive, and / or perform fault diagnosis and prediction on the servo drive.

[0148] Optionally, constructing a digital model of the servo drive based on the real-time data and the historical data includes: preprocessing the real-time data and the historical data to obtain preprocessed data, wherein the preprocessing includes at least data cleaning processing, data standardization processing and feature engineering processing; and processing the preprocessed data using a preset algorithm model to obtain the digital model.

[0149] Optionally, the above-mentioned digital model includes a state monitoring and prediction model and a fault diagnosis and prediction model, which monitors and predicts the actual operating state of the above-mentioned servo drive, including: using the above-mentioned state monitoring and prediction model to monitor and predict the actual operating state of the above-mentioned servo drive; and performs fault diagnosis and prediction on the above-mentioned servo drive, including: using the above-mentioned fault diagnosis and prediction model to perform fault diagnosis and prediction on the above-mentioned servo drive.

[0150] Optionally, the digital model also includes a maintenance strategy optimization model, wherein the input of the maintenance strategy optimization model is the real-time data and historical data of the servo drive, and the output is a maintenance strategy for the servo drive. The method also includes: feeding back the maintenance strategy output by the maintenance strategy optimization model to the servo system to maintain the fault of the servo drive.

[0151] Optionally, the digital model also includes a life prediction model, wherein the life prediction model input is the real-time data and historical data of the servo drive, and the output is the predicted life of the servo drive. The method also includes: using the life prediction model to predict the life of the servo drive.

[0152] Optionally, the digital twin platform obtains real-time data and historical data of the servo drive, including: the digital twin platform establishes a connection with an edge server; the digital twin platform obtains real-time data and historical data of the servo drive stored in the edge server, wherein a connection is established between the edge server and the servo drive.

[0153] Optionally, the method further comprises: storing the behavior action data of the servo driver in a preset database.

[0154] An embodiment of the present invention provides an electronic device, the device comprising a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0155] Step S101, the digital twin platform obtains a three-dimensional model of the servo drive, where the three-dimensional model represents the physical structure of the servo drive;

[0156] Step S102, the digital twin platform obtains real-time data and historical data of the servo drive, and builds a digital model of the servo drive based on the real-time data and the historical data;

[0157] Step S103: Using the three-dimensional model in the digital twin platform to display the physical structure of the servo drive, and using the digital model to control the behavior of the servo drive, so as to monitor and predict the actual operating status of the servo drive, and / or perform fault diagnosis and prediction on the servo drive.

[0158] Optionally, constructing a digital model of the servo drive based on the real-time data and the historical data includes: preprocessing the real-time data and the historical data to obtain preprocessed data, wherein the preprocessing includes at least data cleaning processing, data standardization processing and feature engineering processing; and processing the preprocessed data using a preset algorithm model to obtain the digital model.

[0159] Optionally, the above-mentioned digital model includes a state monitoring and prediction model and a fault diagnosis and prediction model, which monitors and predicts the actual operating state of the above-mentioned servo drive, including: using the above-mentioned state monitoring and prediction model to monitor and predict the actual operating state of the above-mentioned servo drive; and performs fault diagnosis and prediction on the above-mentioned servo drive, including: using the above-mentioned fault diagnosis and prediction model to perform fault diagnosis and prediction on the above-mentioned servo drive.

[0160] Optionally, the digital model also includes a maintenance strategy optimization model, wherein the input of the maintenance strategy optimization model is the real-time data and historical data of the servo drive, and the output is a maintenance strategy for the servo drive. The method also includes: feeding back the maintenance strategy output by the maintenance strategy optimization model to the servo system to maintain the fault of the servo drive.

[0161] Optionally, the digital model also includes a life prediction model, wherein the life prediction model input is the real-time data and historical data of the servo drive, and the output is the predicted life of the servo drive. The method also includes: using the life prediction model to predict the life of the servo drive.

[0162] Optionally, the digital twin platform obtains real-time data and historical data of the servo drive, including: the digital twin platform establishes a connection with an edge server; the digital twin platform obtains real-time data and historical data of the servo drive stored in the edge server, wherein a connection is established between the edge server and the servo drive.

[0163] Optionally, the method further comprises: storing the behavior action data of the servo driver in a preset database.

[0164] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0165] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the program of the method steps of initializing monitoring a servo drive.

[0166] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0167] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0168] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0169] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0170] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0171] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0172] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0173] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0174] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0175] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for monitoring a servo drive, characterized in that: include: The digital twin platform obtains a three-dimensional model of the servo drive, wherein the three-dimensional model represents the physical structure of the servo drive; The digital twin platform acquires real-time data and historical data of the servo drive, and builds a digital model of the servo drive based on the real-time data and the historical data; The three-dimensional model is used in the digital twin platform to display the physical structure of the servo drive, and the digital model is applied to control the behavior of the servo drive to monitor and predict the actual operating status of the servo drive, and / or to diagnose and predict faults of the servo drive.

2. The method for monitoring a servo drive according to claim 1, characterized in that: Building a digital model of the servo drive based on the real-time data and the historical data includes: Preprocessing the real-time data and the historical data to obtain preprocessed data, wherein the preprocessing at least includes data cleaning processing, data standardization processing and feature engineering processing; The preprocessed data is processed using a preset algorithm model to obtain the digital model.

3. The method for monitoring a servo drive according to claim 1, characterized in that: The digital model includes a state monitoring and prediction model and a fault diagnosis and prediction model. Monitoring and predicting the actual operating state of the servo drive, including: using the state monitoring and prediction model to monitor and predict the actual operating state of the servo drive; Performing fault diagnosis and prediction on the servo driver includes: performing fault diagnosis and prediction on the servo driver using the fault diagnosis and prediction model.

4. The method for monitoring a servo drive according to claim 1, characterized in that: The digital model also includes a maintenance strategy optimization model, wherein the input of the maintenance strategy optimization model is the real-time data and historical data of the servo drive, and the output is a maintenance strategy for the servo drive. The method also includes: The maintenance strategy output by the maintenance strategy optimization model is fed back to the servo system to perform maintenance on the fault of the servo drive.

5. The method for monitoring a servo drive according to claim 1, characterized in that: The digital model further includes a life prediction model, wherein the life prediction model inputs real-time data and historical data of the servo drive, and outputs the predicted life of the servo drive. The method further includes: The life prediction model is used to predict the life of the servo drive.

6. The method for monitoring a servo drive according to claim 1, characterized in that: The digital twin platform obtains real-time data and historical data of the servo drive, including: The digital twin platform establishes a connection with an edge server; The digital twin platform obtains real-time data and historical data of the servo drive stored in the edge server, wherein a connection is established between the edge server and the servo drive.

7. The method for monitoring a servo drive according to claim 1, characterized in that: The method further comprises: The behavior data of the servo driver is stored in a preset database.

8. A digital twin platform, characterized in that: include: An acquisition unit, used for acquiring a three-dimensional model of the servo drive, wherein the three-dimensional model represents a physical structure of the servo drive; An acquisition and construction unit, used for acquiring real-time data and historical data of the servo drive, and constructing a digital model of the servo drive based on the real-time data and the historical data; A processing unit is used to display the physical structure of the servo drive using the three-dimensional model in the digital twin platform, and to control the behavior of the servo drive using the digital model, so as to monitor and predict the actual operating status of the servo drive, and / or to diagnose and predict faults of the servo drive.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for monitoring a servo drive according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the monitoring servo drive described in any one of claims 1 to 7.

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