Motor device and operation control method thereof

Abstract

The present invention relates to the field of motor control technology, and in particular to a motor device and an operation control method thereof. The method comprises the following steps: real-time acquisition of the operation data of the motor device in a numerically controlled machine tool under various working conditions to obtain an initial operation data set; using the initial operation data set to train a neural network model to identify the harmonic characteristics under various working conditions and generate a harmonic spectrum recognition model; real-time acquisition of the current and voltage signals of the motor device and inputting them into the harmonic spectrum recognition model for recognition to generate dynamic harmonic characteristic monitoring data; comparing the dynamic harmonic characteristic monitoring data with a preset threshold value to generate an isolation instruction, and dynamically adjusting the frequency response range of the motor device to obtain optimized signal response data. The present invention improves the adaptability, efficiency and stability of the motor device through real-time data acquisition, intelligent model training, dynamic adjustment and feedback mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a motor device and an operation control method thereof. Background Art

[0002] As the core component of CNC machine tools, motor equipment directly determines the machine tool's operational stability, machining accuracy, and energy consumption. Traditional motor equipment control systems typically rely on preset fixed parameters, controlling the motor's operation by setting certain frequency and current parameters. During actual operation, CNC machine tool motor equipment often encounters a variety of complex operating conditions, including varying load conditions, varying ambient temperatures, and other external interference factors. Traditional control methods use fixed parameters and have difficulty responding to these changes in real time, resulting in poor operational stability under varying loads or environmental conditions, and can cause problems such as overheating, vibration, or reduced accuracy. The lack of dynamic adjustment capabilities makes it easy for the equipment to exceed its safe operating range under complex operating conditions, leading to equipment damage or production accidents.

[0003] Harmonics are current or voltage fluctuations caused by the switching operation of nonlinear loads or motor equipment. They can interfere with motor operation, reducing efficiency, increasing energy consumption, and even disrupting other equipment. Existing motor control technologies are relatively weak in harmonic identification and control, failing to effectively filter or isolate harmonic interference, resulting in reduced equipment efficiency and shortened component life.

[0004] Under conditions of sustained high load or high-speed operation, motor equipment can rapidly heat up. Traditional control methods often rely solely on passive cooling measures, such as ventilation or cooling systems, but lack the ability to dynamically adjust cooling based on real-time temperature monitoring. Existing cooling systems often start and stop based on preset thresholds and lack the ability to dynamically adjust based on the motor's real-time thermal state and load. This approach fails to respond promptly to overheating or excessive cooling, which not only affects normal motor operation but can also cause equipment damage. Summary of the Invention

[0005] Based on this, it is necessary for the present invention to provide a motor device and an operation control method thereof to solve at least one of the above technical problems.

[0006] To achieve the above object, a method for controlling the operation of a motor device includes the following steps:

[0007] Step S1: collecting the operating data of the motor equipment in the CNC machine tool under various working conditions in real time to obtain an initial operating data set;

[0008] Step S2: Using the initial operating data set to train the neural network model to identify harmonic characteristics under various operating conditions and generate a harmonic spectrum recognition model;

[0009] Step S3: The current and voltage signals of the motor equipment are collected in real time and input into the harmonic spectrum recognition model for identification to generate dynamic harmonic characteristic monitoring data; the dynamic harmonic characteristic monitoring data is compared with a preset threshold value to generate an isolation instruction, and the frequency response range of the motor equipment is dynamically adjusted to obtain optimized signal response data;

[0010] Step S4: monitoring the temperature of the motor driver and the heat dissipation system in the motor device in real time according to the optimized signal response data to generate comprehensive thermal state monitoring data;

[0011] Step S5: adjusting the current input parameters of the motor device driver based on the comprehensive thermal state monitoring data, optimizing the operating state of the heat dissipation system, and generating hardware adaptive control parameters;

[0012] Step S6: Optimizing the load dynamic adjustment module of the motor equipment based on the hardware adaptive control parameters and the dynamic harmonic characteristic monitoring data, generating load adaptability optimization parameters to improve the operational stability of the motor equipment.

[0013] This invention collects real-time operating data from CNC machine tool motors under various operating conditions and uses a neural network model to identify harmonic characteristics, enabling the system to rapidly respond to diverse operating environments and load conditions. This real-time acquisition and analysis process significantly improves the device's adaptability to complex operating conditions and effectively copes with various environmental fluctuations. Using a harmonic spectrum recognition model to identify harmonic characteristics in current and voltage, the system monitors the device's operating status in real time, helping to promptly detect potential abnormal signals and reduce harmonic interference on device operation, effectively reducing operating losses and extending the device's service life. By comparing and analyzing harmonic characteristic data with preset thresholds, generating isolation instructions and dynamically adjusting the frequency response range effectively suppresses harmonic interference and ensures device stability under various operating conditions. This control method allows the device's frequency response range to adapt to real-time data, improving motor efficiency and response speed while reducing unnecessary power consumption. By monitoring the temperature of the motor driver and cooling system in real time, the system dynamically collects thermal status data and achieves precise temperature control. This real-time thermal management effectively prevents equipment failures caused by overheating and extends the stable operation of the motor. Temperature data monitoring and feedback enable the system to adaptively adjust the cooling system under load or high-speed operation, preventing efficiency degradation caused by overheating and ensuring the safety and efficiency of the equipment. Based on thermal status monitoring data, the system automatically adjusts the current input parameters of the motor driver and optimizes the operation of the cooling system. This adaptive control method enhances the equipment's adaptability under varying loads and environments, improving overall performance. The generation of hardware-adaptive control parameters enables intelligent dynamic adjustment of the driver and cooling system, reducing the frequency of manual commissioning, lowering maintenance costs, and ensuring continuous and efficient operation of the equipment under various conditions. By optimizing the motor equipment's load dynamic adjustment module using hardware-adaptive control parameters and dynamic harmonic characteristic monitoring data, the generated load-adaptive optimization parameters significantly improve the stability and operating efficiency of the motor equipment. This load optimization approach ensures the equipment remains stable under high load conditions and adjusts operating parameters in real time based on varying loads, reducing stress and wear on the equipment and improving its overall reliability.

[0014] The present invention further provides a motor device, comprising a housing, an armature, a drive circuit, and a control system, wherein the armature is disposed within the housing, the drive circuit is electrically connected to the armature, and the control system is installed within the drive circuit. The control system is used to execute an operation control method for the motor device, and the control system comprises:

[0015] The operation data acquisition module is used to collect the operation data of the motor equipment in the CNC machine tool under various working conditions in real time to obtain the initial operation data set;

[0016] The harmonic spectrum modeling module is used to train the neural network model using the initial operating data set to identify the harmonic characteristics under various operating conditions and generate a harmonic spectrum recognition model;

[0017] The dynamic harmonic monitoring and optimization module is used to collect the current and voltage signals of the motor equipment in real time, input them into the harmonic spectrum recognition model for identification, and generate dynamic harmonic characteristic monitoring data; the dynamic harmonic characteristic monitoring data is compared with the preset threshold, an isolation instruction is generated, and the frequency response range of the motor equipment is dynamically adjusted to obtain optimized signal response data;

[0018] Thermal status monitoring and management module, which is used to monitor the temperature of the motor driver and cooling system in the motor equipment in real time based on the optimized signal response data and generate comprehensive thermal status monitoring data;

[0019] Adaptive current and heat dissipation control module, which adjusts the current input parameters of the motor device driver based on comprehensive thermal status monitoring data, optimizes the operating status of the heat dissipation system, and generates hardware adaptive control parameters;

[0020] The load adaptability optimization module is used to optimize the load dynamic adjustment module of the motor equipment based on the hardware adaptive control parameters and dynamic harmonic characteristic monitoring data, and generate load adaptability optimization parameters to improve the operating stability of the motor equipment.

[0021] This invention collects real-time operating data from CNC machine tool motors under various operating conditions to generate an initial operating data set, providing accurate data support for subsequent analysis and model training. This real-time data collection function improves the system's sensitivity to different operating states, enabling the motors to quickly adapt to various complex operating conditions, thereby enhancing the overall system's flexibility and intelligence. The collected initial operating data is used to train a neural network model to generate a harmonic spectrum recognition model, enabling the system to accurately identify and analyze harmonic characteristics under different operating conditions. This refined harmonic modeling enhances the system's monitoring and diagnostic capabilities, effectively suppresses the impact of harmonics on the equipment, reduces the motor's power consumption and failure probability, and improves its operational stability. By monitoring the motor's current and voltage signals in real time and identifying and optimizing harmonic characteristics, the module generates dynamic harmonic characteristic monitoring data. Through comparative analysis, it generates isolation instructions to dynamically adjust the motor's frequency response range. This function enables the motor to maintain stable response performance under varying harmonic interference, optimizes signal response accuracy, effectively improves the device's anti-interference capability, and ensures efficient operation. By monitoring the temperature of the motor driver and cooling system in real time and generating thermal status monitoring data, the module ensures thermal safety under high-load or high-speed operation. This module prevents equipment wear and failure due to overheating, extends equipment life through precise temperature control, and ensures the stability and safety of the motor equipment during long-term operation. Based on comprehensive thermal status monitoring data, the module dynamically adjusts the motor driver's current input parameters and optimizes the cooling system to generate hardware-adaptive control parameters. This adaptive control approach enhances device stability under varying environments and loads, reduces efficiency degradation caused by temperature rise, and reduces the need for manual commissioning, further improving device reliability and maintenance efficiency. The module utilizes hardware-adaptive control parameters and dynamic harmonic characteristic monitoring data to dynamically optimize the load to generate load-adaptive optimization parameters, ensuring smoother operation of the motor equipment under varying load conditions. This module effectively enhances the equipment's operational adaptability under high loads, reduces the stress impact on the equipment, and improves overall system safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0023] Figure 1 Schematic diagram of the steps of the operation control method of the motor equipment of the present invention;

[0024] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0025] Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0026] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0027] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0028] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0029] To achieve this, please refer to Figures 1 to 3 The present invention provides an operation control method for a motor device, the method comprising the following steps:

[0030] Step S1: collecting the operating data of the motor equipment in the CNC machine tool under various working conditions in real time to obtain an initial operating data set;

[0031] Step S2: Using the initial operating data set to train the neural network model to identify harmonic characteristics under various operating conditions and generate a harmonic spectrum recognition model;

[0032] Step S3: The current and voltage signals of the motor equipment are collected in real time and input into the harmonic spectrum recognition model for identification to generate dynamic harmonic characteristic monitoring data; the dynamic harmonic characteristic monitoring data is compared with a preset threshold value to generate an isolation instruction, and the frequency response range of the motor equipment is dynamically adjusted to obtain optimized signal response data;

[0033] Step S4: monitoring the temperature of the motor driver and the heat dissipation system in the motor device in real time according to the optimized signal response data to generate comprehensive thermal state monitoring data;

[0034] Step S5: adjusting the current input parameters of the motor device driver based on the comprehensive thermal state monitoring data, optimizing the operating state of the heat dissipation system, and generating hardware adaptive control parameters;

[0035] Step S6: Optimizing the load dynamic adjustment module of the motor equipment based on the hardware adaptive control parameters and the dynamic harmonic characteristic monitoring data, generating load adaptability optimization parameters to improve the operational stability of the motor equipment.

[0036] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a method for controlling the operation of a motor device according to the present invention. In this example, the method for controlling the operation of the motor device includes the following steps:

[0037] Step S1: collecting the operating data of the motor equipment in the CNC machine tool under various working conditions in real time to obtain an initial operating data set;

[0038] Before the motor equipment is put into operation, an embodiment of the present invention connects an operating data acquisition module to key parts of the motor equipment, ensuring that each sensor corresponds to a corresponding monitoring point. The data acquisition system is initialized and the sensors are calibrated to ensure data accuracy. Once the motor equipment begins operation, the operating data acquisition module automatically and continuously collects operating data from the motor under different operating conditions. For example, under various operating conditions, such as start-up, acceleration, constant speed, deceleration, and stop, the module records the dynamic changes in current, voltage, speed, and temperature. The collected data is labeled and stored according to different operating conditions, forming an initial operating data set.

[0039] Step S2: Using the initial operating data set to train the neural network model to identify harmonic characteristics under various operating conditions and generate a harmonic spectrum recognition model;

[0040] The embodiment of the present invention inputs the initial operating data set into the harmonic spectrum modeling module. First, data cleaning and normalization are performed to remove abnormal data and noise to ensure the integrity and consistency of the input data. The data normalization process can process parameters such as current and voltage at the same scale to facilitate the training effect of the model. Feature extraction is performed on the preprocessed data set to extract harmonic characteristic indicators under different working conditions, including the amplitude, phase angle, frequency component, etc. of each harmonic. These features will be used to identify the harmonic distribution of motor equipment under different load, speed and temperature conditions, and provide effective feature vectors for neural network training. The extracted harmonic characteristic indicators are input into the neural network model to train the model. During the training process, the model continuously adjusts parameters to adapt to the harmonic characteristics of different working conditions, thereby gradually improving the recognition accuracy and response speed. The neural network model will perform multiple rounds of iterations based on a specific learning rate and optimization algorithm, and eventually form a stable harmonic spectrum recognition model.

[0041] Step S3: The current and voltage signals of the motor equipment are collected in real time and input into the harmonic spectrum recognition model for identification to generate dynamic harmonic characteristic monitoring data; the dynamic harmonic characteristic monitoring data is compared with a preset threshold value to generate an isolation instruction, and the frequency response range of the motor equipment is dynamically adjusted to obtain optimized signal response data;

[0042] In this embodiment of the present invention, a real-time monitoring device continuously collects current and voltage signals from motor equipment within a CNC machine tool and transmits the collected data to a dynamic harmonic monitoring and optimization module. This real-time data reflects the operating characteristics of the motor equipment in its current operating state, providing the raw data source for subsequent harmonic characteristic identification. The collected current and voltage signals are input into a harmonic spectrum recognition model. Based on the training results from step S2, this model can identify the harmonic characteristics of the motor equipment under different operating conditions. By analyzing the harmonic characteristics of the current signal, real-time dynamic harmonic characteristic monitoring data is generated to reflect the harmonic distribution of the current operating conditions. This real-time dynamic harmonic characteristic monitoring data is compared and analyzed with a preset safety threshold. If the harmonic characteristic data exceeds the threshold, the system determines that the current operating state is abnormal or unstable and generates a corresponding isolation instruction. This process is used to prevent the risk of performance degradation or damage to the motor equipment caused by excessive harmonic fluctuations. Based on the isolation instruction's determination, the frequency response range of the motor equipment is dynamically adjusted to optimize the equipment's operating state. For example, when the harmonic characteristics exceed the safety range, the response frequency can be adjusted to suppress specific harmonic components, thereby improving the equipment's electrical performance. This adjustment process ensures the harmonic stability of the equipment under varying operating conditions. After the frequency response adjustment is completed, optimized signal response data is generated.

[0043] Step S4: monitoring the temperature of the motor driver and the heat dissipation system in the motor device in real time according to the optimized signal response data to generate comprehensive thermal state monitoring data;

[0044] In this embodiment of the present invention, guided by optimized signal response data, temperature sensors collect the temperatures of key components within a motor device (including the motor driver and cooling system) in real time. This temperature collection process accurately captures the thermal status of each component, providing raw data for subsequent thermal analysis. The collected temperature data is compiled and aggregated to generate comprehensive thermal status monitoring data. This data includes the temperature changes of the motor driver and the heat exchange efficiency of the cooling system, reflecting the overall thermal load of the motor device under current operating conditions. The comprehensive thermal status monitoring data is compared and analyzed with the system's preset safety temperature threshold to determine whether the current thermal status is within a safe range. If the temperature exceeds the safety threshold, the system determines that there is an overheating risk and further triggers the need for optimization and control of the cooling system. Based on the temperature comparison results, it dynamically determines whether the operating status of the cooling system needs to be adjusted. For example, when the motor driver temperature exceeds the preset threshold, the cooling system's fan speed is automatically increased or enhanced cooling mode is enabled to reduce the temperature and prevent thermal damage. If the temperature is within the normal range, thermal status monitoring continues. After the cooling system is adjusted, updated comprehensive thermal status monitoring data is generated again.

[0045] Step S5: adjusting the current input parameters of the motor device driver based on the comprehensive thermal state monitoring data, optimizing the operating state of the heat dissipation system, and generating hardware adaptive control parameters;

[0046] In an embodiment of the present invention, the system analyzes the temperature level and operating status of the motor driver based on comprehensive thermal status monitoring data to determine whether there is a temperature rise caused by excessive current input. If the monitoring data indicates that the driver temperature is close to the upper safety limit, the current input is reduced or dynamically adjusted to reduce the temperature load of the motor driver, thereby preventing overheating. This adjustment process generates optimized current input parameters to ensure that the device operates within a controllable temperature range. If the comprehensive thermal status monitoring data indicates that the current operating mode of the cooling system cannot effectively reduce the driver temperature, the adaptive current and heat dissipation control module automatically adjusts the operating state of the cooling system. For example, the fan speed can be increased, a more efficient cooling mode can be enabled, or the operating frequency of the cooling device can be adjusted to improve the cooling effect and ensure that the motor driver temperature remains within the safety threshold. After completing the optimization of the current input parameters and the cooling system, the system will generate hardware adaptive control parameters, including the optimal operating settings of the current and cooling system under the current environment.

[0047] Step S6: Optimizing the load dynamic adjustment module of the motor equipment based on the hardware adaptive control parameters and the dynamic harmonic characteristic monitoring data, generating load adaptability optimization parameters to improve the operational stability of the motor equipment.

[0048] Embodiments of the present invention use dynamic harmonic characteristic monitoring data to analyze the vibration and harmonic characteristics of the motor equipment under current load conditions to determine whether harmonic characteristics or load fluctuations exceed preset thresholds. This analysis can reveal abnormal changes in the equipment's operating status and provide data support for subsequent optimization. Based on hardware adaptive control parameters, the load adaptability optimization module dynamically calculates optimized parameters adapted to the current load, including current, speed, and load distribution. These adaptive parameters take into account the real-time harmonic characteristics of the motor equipment, enabling the equipment to quickly adapt to load changes and reducing the impact on equipment operational stability. The load adaptive parameters are applied to the load dynamic adjustment module to adjust the motor equipment's current input, speed control, and load distribution in real time, optimizing the motor's frequency response under different loads to eliminate or reduce unnecessary harmonic components and improve the equipment's load adaptability and stability. After applying the load adaptive optimization parameters, the load adaptive optimization module continuously monitors the motor equipment's load status, harmonic characteristics, and equipment stability to ensure the expected adjustment results. If new load fluctuations or harmonic characteristic changes are detected, the system automatically updates the load adaptive parameters and adjusts the load dynamic adjustment module again to maintain stable operation of the motor equipment.

[0049] This invention collects real-time operating data from CNC machine tool motors under various operating conditions and uses a neural network model to identify harmonic characteristics, enabling the system to rapidly respond to diverse operating environments and load conditions. This real-time acquisition and analysis process significantly improves the device's adaptability to complex operating conditions and effectively copes with various environmental fluctuations. Using a harmonic spectrum recognition model to identify harmonic characteristics in current and voltage, the system monitors the device's operating status in real time, helping to promptly detect potential abnormal signals and reduce harmonic interference on device operation, effectively reducing operating losses and extending the device's service life. By comparing and analyzing harmonic characteristic data with preset thresholds, generating isolation instructions and dynamically adjusting the frequency response range effectively suppresses harmonic interference and ensures device stability under various operating conditions. This control method allows the device's frequency response range to adapt to real-time data, improving motor efficiency and response speed while reducing unnecessary power consumption. By monitoring the temperature of the motor driver and cooling system in real time, the system dynamically collects thermal status data and achieves precise temperature control. This real-time thermal management effectively prevents equipment failures caused by overheating and extends the stable operation of the motor. Temperature data monitoring and feedback enable the system to adaptively adjust the cooling system under load or high-speed operation, preventing efficiency degradation caused by overheating and ensuring the safety and efficiency of the equipment. Based on thermal status monitoring data, the system automatically adjusts the current input parameters of the motor driver and optimizes the operation of the cooling system. This adaptive control method enhances the equipment's adaptability under varying loads and environments, improving overall performance. The generation of hardware-adaptive control parameters enables intelligent dynamic adjustment of the driver and cooling system, reducing the frequency of manual commissioning, lowering maintenance costs, and ensuring continuous and efficient operation of the equipment under various conditions. By optimizing the motor equipment's load dynamic adjustment module using hardware-adaptive control parameters and dynamic harmonic characteristic monitoring data, the generated load-adaptive optimization parameters significantly improve the stability and operating efficiency of the motor equipment. This load optimization approach ensures the equipment remains stable under high load conditions and adjusts operating parameters in real time based on varying loads, reducing stress and wear on the equipment and improving its overall reliability.

[0050] Preferably, step S1 includes the following steps:

[0051] Step S11: monitoring the operating parameters of the motor in real time under various operating conditions of the motor equipment of the CNC machine tool to obtain real-time operating data of the motor equipment;

[0052] Step S12: pre-processing the real-time operation data to obtain cleaned operation data;

[0053] Step S13: performing feature extraction on the cleaned operating data to identify key feature parameters under various operating conditions and generate an operating data feature set;

[0054] Step S14: Classify and label the operating data feature set according to various operating conditions to generate an initial operating data set.

[0055] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0056] Step S11: monitoring the operating parameters of the motor in real time under various operating conditions of the motor equipment of the CNC machine tool to obtain real-time operating data of the motor equipment;

[0057] The operating data acquisition module of the embodiment of the present invention collects the operating parameters of the motor equipment in real time according to the actual working environment of the CNC machine tool. The operating parameters include various data such as the motor's current, voltage, speed, temperature and load conditions. These data reflect the real-time operating status of the motor equipment under different working conditions. The motor equipment is monitored under all working conditions in various working modes and different loads of the CNC machine tool. The collection of operating parameters under multiple working conditions ensures the comprehensiveness of the data, covering various working conditions such as low load, high load, overload, standby, etc., and generates comprehensive real-time operating data of the motor equipment.

[0058] Step S12: pre-processing the real-time operation data to obtain cleaned operation data;

[0059] The data preprocessing module of the embodiment of the present invention cleans the real-time operation data to remove noise, outliers and missing values. By setting reasonable threshold standards, parameters such as current, voltage, speed, temperature, etc. are screened to ensure that only reasonable and valid data information is retained. Statistical methods or machine learning algorithms are used to detect outliers in real-time operation data. For example, the standard deviation and mean can be used to calculate the reasonable range of each data item. Data outside the range will be marked as abnormal and processed, and the processing methods include correction, deletion or replacement. The cleaned operation data is uniformly converted into a standard format to ensure the consistency and comparability of each data. This includes timestamp formatting, unit conversion, data type standardization, etc. For missing data caused by sensor failure or data acquisition problems, interpolation or other appropriate methods are applied to fill in the missing data to ensure the integrity of the data set. The cleaned operation data is stored in the database for feature extraction and analysis in subsequent steps. The cleaned operation data is accompanied by reference information of the original data to ensure traceability.

[0060] Step S13: performing feature extraction on the cleaned operating data to identify key feature parameters under various operating conditions and generate an operating data feature set;

[0061] The embodiment of the present invention determines the key feature parameters that need to be extracted based on the operating characteristics of the motor equipment and the working requirements under various working conditions. These parameters may include indicators such as current, voltage, speed, temperature, and power. Feature selection can be performed through expert knowledge or using a feature selection algorithm. A suitable feature extraction method is used to analyze the cleaned operating data. Based on the extracted features, a combined feature is constructed to enhance the expressive power of the model. For example, the ratio of current to voltage can be used as a new feature, or more representative features can be generated by multiplication, difference, etc. The extracted feature parameters are integrated into a unified operating data feature set.

[0062] Step S14: Classify and label the operating data feature set according to various operating conditions to generate an initial operating data set.

[0063] The embodiment of the present invention first defines the working condition definition of the motor equipment under different working conditions. These working conditions include normal operation, no-load operation, overload operation, starting process and braking process, etc. The performance and characteristic parameters of the motor under each working condition will be different. According to the defined working conditions, the characteristic parameters belonging to each working condition are identified by comparing the operating data feature set. A rule-based matching method is used to automatically identify and classify features. On the basis of confirming the feature matching, each set of feature data is labeled and its corresponding working condition type is recorded. The labeling can include the working condition name, category label and related metadata to ensure the traceability and clarity of the data. The labeled data is organized into a structured format, usually in a table format, with each row representing a feature sample and each column representing a feature value and working condition label to form a complete initial operating data set.

[0064] The present invention ensures the timeliness and accuracy of data through real-time monitoring and data preprocessing of motor operating parameters. Real-time monitoring captures the status of the motor under different working conditions, and data cleaning eliminates noise and outliers, improving data quality and laying an accurate foundation for subsequent analysis. Feature extraction further identifies key characteristic parameters, focusing on the core factors affecting motor performance, and enhancing the targeted nature of the analysis. The initial operating data set is formed through classification and labeling, making model training and optimization control more efficient. The entire process builds a comprehensive and reliable operating data support, providing a solid foundation for the intelligent and automated control of motor equipment, thereby improving the production efficiency and safety of the equipment.

[0065] Preferably, step S2 includes the following steps:

[0066] Step S21: grouping, sorting and unifying the initial running data set to obtain normalized training data;

[0067] Step S22: performing feature decomposition on the normalized training data, extracting key feature parameters, and generating a feature data set;

[0068] Step S23: Divide the feature dataset into a training set and a validation set to construct input data for the neural network model, thereby obtaining a model training dataset and a model validation dataset;

[0069] Step S24: using the model training data set to train the neural network model to identify and classify the harmonic characteristics under various working conditions, and obtain a preliminary harmonic characteristic model;

[0070] Step S25: using the model verification data set to perform real-time verification on the preliminary harmonic characteristic model to obtain a verification set test result;

[0071] Step S26: Optimize the preliminary harmonic feature model based on the validation set test results, adjust the network layer structure, learning rate and weight, and obtain a harmonic spectrum recognition model.

[0072] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0073] Step S21: grouping, sorting and unifying the initial running data set to obtain normalized training data;

[0074] The embodiment of the present invention groups the data in the initial operation data set according to different operating conditions. The data in each operating condition group should have similar characteristic parameters to ensure the homogeneity of the data during the training process. This process can be implemented through programming, and the grouping function in the data framework is used for operation. On the basis of grouping, the data in each operating condition group is sorted to ensure consistency in subsequent processing. The sorting basis can be timestamp, characteristic parameter value or other indicators to ensure that the data order of each group meets the analysis requirements. The data in each operating condition group is unified, and missing values ​​are filled or deleted; the characteristic parameters are standardized so that their mean is 0 and the standard deviation is 1; the data is filtered to eliminate noise and outliers. The processed data of each operating condition group is merged into a unified normalized training data.

[0075] Step S22: performing feature decomposition on the normalized training data, extracting key feature parameters, and generating a feature data set;

[0076] When processing normalized training data, the embodiment of the present invention first determines the key parameters that need to be feature decomposed by analyzing the correlation of the data. These key feature parameters include the operating frequency, speed, current, voltage, etc. of the motor equipment. This step can use statistical analysis methods to identify important features that affect the operating performance of the motor. Select a suitable feature decomposition method to process the normalized training data. After completing the feature decomposition, the key feature parameters are extracted based on the selected feature decomposition method. These parameters should be able to effectively represent the operating status of the motor equipment under various working conditions and have high explanatory power and recognition. The extraction process involves algorithm calculation, matrix transformation or model training, etc. The extracted key feature parameters are integrated into a new feature data set.

[0077] Step S23: Divide the feature dataset into a training set and a validation set to construct input data for the neural network model, thereby obtaining a model training dataset and a model validation dataset;

[0078] According to common machine learning practices, the embodiment of the present invention selects an appropriate ratio to divide the feature data set into a training set and a validation set. In this step, it is ensured that the divided data set can cover the diversity under various working conditions and that the model can learn effective features. The division is performed using a random sampling method to avoid potential bias. A random number generator can be used to ensure the randomness of the data during each division. At the same time, the proportion of samples of each category in the training set and the validation set is relatively balanced to avoid the model's biased learning of a certain category of samples. According to the set division ratio and random sampling method, data is extracted from the feature data set to generate two new data sets. The model training data set contains input data for training the neural network model, ensuring that the sample size of the data set is sufficient to improve the generalization ability of the model. The model validation data set contains data for real-time verification of the model performance. This data set will be used to test the performance of the model on unseen data to ensure that the model can accurately identify and classify the harmonic features under various working conditions. The generated model training data set and model validation data set are formatted to ensure that the data structure is consistent and meets the input requirements of the neural network model.

[0079] Step S24: using the model training data set to train the neural network model to identify and classify the harmonic characteristics under various working conditions, and obtain a preliminary harmonic characteristic model;

[0080] The embodiments of the present invention select an appropriate neural network model architecture based on the desired application scenario and data characteristics. Ensure that the selected model can effectively handle the harmonic feature recognition task. Before training begins, the model parameters, including weights and biases, are randomly initialized. He initialization is selected as the initialization method to accelerate convergence and improve model performance. The model training dataset is input into the selected neural network model, ensuring that the data format is consistent with the model's input layer requirements. Data normalization is typically required to improve the efficiency and stability of model training. Based on the nature of the task, an appropriate loss function is selected to evaluate the difference between the model output and the true label. Simultaneously, an optimizer is selected to update the model parameters to minimize the loss function. During training, appropriate hyperparameters are set, including the learning rate, batch size, and number of training rounds. The choice of learning rate directly affects the model's convergence speed and final performance and should be adjusted through methods such as cross-validation or grid search. During training, forward and backward propagation are repeated. The input data is passed through the neural network to calculate the predicted result; the loss between the predicted result and the true label is calculated using the loss function; the gradient is calculated using the chain rule, and the model parameters are updated to reduce the loss. During training, monitor the model's learning progress by recording metrics such as loss and accuracy. Visualization tools help track the training process and promptly identify overfitting or underfitting. Set up early stopping based on the performance of the validation set. If the performance on the validation set does not improve within a few epochs, stop training to prevent overfitting. After training is complete, save the trained preliminary harmonic feature model.

[0081] Step S25: using the model verification data set to perform real-time verification on the preliminary harmonic characteristic model to obtain a verification set test result;

[0082] The embodiment of the present invention ensures that the model validation dataset is prepared and the format matches the input requirements of the preliminary harmonic feature model. The data should undergo the same preprocessing steps as the training data, including standardization and normalization, to ensure consistency. The model validation dataset is input into the preliminary harmonic feature model and the forward propagation process is performed. The model generates a predicted output for the input data by calculating the activation values ​​of each layer. This process involves using the weights and biases in the model to weight and bias the input features, and ultimately obtain the model's prediction results. According to the preset loss function, the difference between the model prediction results and the actual labels in the model validation dataset is calculated. This loss value reflects the performance of the model on the validation data. In addition to the loss value, other evaluation indicators are calculated to fully understand the model performance, such as accuracy, precision, recall rate, and F1 score, to obtain the validation set test results.

[0083] Step S26: Optimize the preliminary harmonic feature model based on the validation set test results, adjust the network layer structure, learning rate and weight, and obtain a harmonic spectrum recognition model.

[0084] This embodiment of the present invention analyzes the validation set test results to identify differences in model performance under different operating conditions, focusing particularly on categories with low recognition accuracy. This analysis helps understand the model's shortcomings and areas for improvement. Based on the validation results, the learning rate is adjusted appropriately. If the model converges too slowly or too quickly, the learning rate needs to be increased or decreased to find an appropriate training speed. Based on the model's performance on the validation set, it can be decided whether to increase or decrease the number of neural network layers or the number of nodes per layer to improve the model's expressiveness. The model weights are fine-tuned, and different optimization algorithms are used to better adapt to the data characteristics. After adjusting the parameters, the preliminary harmonic signature model is retrained using the model training dataset. This ensures that the model training process is stable under the new parameters and that the harmonic signatures are effectively learned. During training, a cross-validation strategy is implemented, using different combinations of training and validation sets to assess the model's robustness and stability, ensuring consistent performance across different datasets. During retraining, the loss function and accuracy are monitored in real time to assess the model's training effectiveness. Appropriate early stopping conditions are set to avoid overfitting. After retraining, the optimized model performance is evaluated again using the model validation dataset. Calculate new validation set test results and compare them with previous results to observe the effectiveness of the improvements. Use visualization tools (such as loss curves and accuracy curves) to display the model's performance during training, helping you intuitively understand the model's learning process and effectiveness. Save the optimized model as a harmonic spectrum recognition model for subsequent application and deployment.

[0085] The present invention generates standardized data suitable for model training by grouping, sorting and normalizing the initial operating data set, thereby improving model performance. Feature decomposition extracts key parameters, constructs an efficient feature data set, and provides focused input for harmonic feature recognition. The feature data set is divided into a training set and a validation set to ensure the reliability and fairness of the model on different data. The neural network model is trained using the training set to achieve automatic recognition of harmonic features and improve efficiency. Real-time testing of the validation set ensures the accuracy of the model and provides a basis for optimization. By optimizing the model structure, learning rate and weight, a high-precision harmonic spectrum recognition model is obtained. This method effectively supports the intelligent control of motor equipment, enabling the equipment to autonomously monitor and identify harmonic features, thereby achieving more efficient operation management.

[0086] Preferably, step S3 includes the following steps:

[0087] Step S31: monitoring the current and voltage signals of the motor equipment in real time during operation, and preprocessing the data to extract characteristic parameters of the current and voltage signals to generate real-time signal characteristic data;

[0088] Step S32: inputting the real-time signal characteristic data into the harmonic spectrum recognition model to identify the harmonic characteristics of the current signal and obtain dynamic harmonic characteristic monitoring data;

[0089] Step S33: extracting key harmonic parameters from the dynamic harmonic characteristic monitoring data to obtain key parameters of dynamic harmonic characteristics;

[0090] Step S34: recording the vibration frequency of the motor device in real time, and extracting and analyzing key parameters to obtain real-time vibration frequency key parameters;

[0091] Step S35: Based on the real-time vibration frequency key parameter, the harmonic frequency and harmonic frequency amplitude in the dynamic harmonic characteristic key parameter are compared and analyzed with the preset vibration stability threshold to obtain a stability assessment report;

[0092] Step S36: Determine whether the harmonics meet the isolation conditions based on the stability assessment report. If the harmonics exceed the vibration frequency threshold, generate an isolation instruction. If the harmonics do not exceed the vibration frequency threshold, continuously monitor the harmonics of the motor equipment.

[0093] Step S37: confirming the harmonic frequency range and amplitude requirements currently required to be isolated according to the isolation instruction, and generating frequency response range adjustment parameters;

[0094] Step S38: dynamically configuring the frequency response module of the motor device according to the frequency response range adjustment parameter to obtain optimized frequency response configuration data;

[0095] Step S39: performing an isolation operation according to the optimized frequency response configuration data, suppressing harmonic signal interference within a specific frequency range, and generating optimized signal response data.

[0096] As an embodiment of the present invention, in the embodiment of the present invention, step S3 includes the following steps:

[0097] Step S31: monitoring the current and voltage signals of the motor equipment in real time during operation, and preprocessing the data to extract characteristic parameters of the current and voltage signals to generate real-time signal characteristic data;

[0098] In an embodiment of the present invention, high-precision sensors are used to monitor current and voltage signals in real time during the operation of the motor equipment. These sensors are capable of recording signal changes and outputting the instantaneous values ​​of current and voltage in digital form. The acquired current and voltage signals are subjected to data preprocessing, including denoising, normalization, and time synchronization. A feature extraction algorithm is used to extract key characteristic parameters from the preprocessed current and voltage signals, including effective values, peak values, and frequency characteristics. The extracted characteristic parameters are organized into structured data to generate real-time signal characteristic data, including the effective values, peak values, frequency components, and other statistical characteristics of the current and voltage.

[0099] Step S32: inputting the real-time signal characteristic data into the harmonic spectrum recognition model to identify the harmonic characteristics of the current signal and obtain dynamic harmonic characteristic monitoring data;

[0100] The embodiment of the present invention organizes the real-time signal feature data into a suitable input format to ensure that the data can be correctly received and processed by the harmonic spectrum recognition model. The data includes the effective value, peak value, frequency characteristics, etc. of the current and voltage. The trained harmonic spectrum recognition model is loaded from the storage system. The model is generated based on the previous training process and can effectively identify the harmonic characteristics in the current and voltage signals. The real-time signal feature data is input into the harmonic spectrum recognition model. The feature data is received and parsed through the input layer of the model. The harmonic spectrum recognition model analyzes the input signal and uses the parameters learned during its training to identify the harmonic components in the signal. The input signal is Fourier transformed to convert it into the frequency domain for harmonic characteristic analysis. The harmonic frequency and amplitude in the signal are identified, and the existence and intensity of each harmonic component are determined. After the identification is completed, the model will generate dynamic harmonic characteristic monitoring data.

[0101] Step S33: extracting key harmonic parameters from the dynamic harmonic characteristic monitoring data to obtain key parameters of dynamic harmonic characteristics;

[0102] The embodiments of the present invention parse dynamic harmonic characteristic monitoring data and identify the data structure and content, including each harmonic frequency and its corresponding amplitude. Based on actual application requirements, key harmonic parameters to be extracted are determined, including but not limited to harmonic frequencies and harmonic amplitudes. Harmonic frequencies and amplitudes are screened. Threshold criteria are set to filter harmonic frequency and amplitude data below the threshold. Key harmonic frequencies and amplitudes above the threshold are recorded to form a parameter list, generating key parameters for dynamic harmonic characteristics.

[0103] Step S34: recording the vibration frequency of the motor device in real time, and extracting and analyzing key parameters to obtain real-time vibration frequency key parameters;

[0104] The embodiment of the present invention is configured with a vibration sensor and an acquisition device to ensure that the vibration frequency of the motor equipment can be monitored in real time, and the installation position should be able to accurately capture the vibration signal of the motor during operation. During the operation of the motor equipment, its vibration frequency signal is acquired in real time. These signals can be directly acquired by the vibration sensor, and the data acquisition frequency should be high enough to ensure that the rapidly changing vibration characteristics are captured. The acquired vibration signal is preprocessed, including operations such as denoising, smoothing and filtering to improve signal quality and accuracy. This process involves the use of high-pass or low-pass filters to eliminate unnecessary frequency components. The real-time vibration frequency key parameters are extracted from the preprocessed vibration signal, the main vibration frequency components are extracted, the amplitude of the vibration signal is recorded, and the waveform characteristics of the vibration signal are analyzed. The extracted real-time vibration frequency key parameters are organized into a structured data format for subsequent processing and analysis.

[0105] Step S35: Based on the real-time vibration frequency key parameter, the harmonic frequency and harmonic frequency amplitude in the dynamic harmonic characteristic key parameter are compared and analyzed with the preset vibration stability threshold to obtain a stability assessment report;

[0106] The embodiment of the present invention presets a vibration stability threshold value, which is determined based on the operating standards and historical data of the motor equipment. The threshold value is used to determine whether the vibration state of the motor equipment is within the safe operating range. The acquired real-time vibration frequency key parameters are compared one by one with the preset vibration stability threshold value. The harmonic frequencies in the dynamic harmonic characteristic key parameters are compared with the real-time vibration frequency key parameters one by one to check whether there are frequency components that exceed the threshold value. The relationship between the harmonic frequency amplitude and the vibration stability threshold value is evaluated to determine whether the current operating state is normal. Based on the comparative analysis results, a stability assessment report is generated, which includes the following content: identifying the harmonic frequencies and amplitudes that exceed the vibration stability threshold value; providing comparative analysis data and charts to show the relationship between the vibration characteristics and harmonic characteristics of the current motor equipment; and providing safety assessments and recommendations for equipment operation.

[0107] Step S36: Determine whether the harmonics meet the isolation conditions based on the stability assessment report. If the harmonics exceed the vibration frequency threshold, generate an isolation instruction. If the harmonics do not exceed the vibration frequency threshold, continuously monitor the harmonics of the motor equipment.

[0108] The embodiment of the present invention determines the isolation condition of the harmonics, which is usually based on a preset vibration frequency threshold. If any value in the real-time vibration frequency key parameter exceeds the threshold, it is determined that the harmonic needs to be isolated. The data in the stability assessment report is compared with the preset vibration frequency threshold. Check whether the harmonic frequency amplitude in each dynamic harmonic characteristic key parameter exceeds the vibration frequency threshold. For harmonic frequencies that exceed the threshold, their type and amplitude are recorded to prepare for the subsequent generation of isolation instructions. If a harmonic frequency is detected that exceeds the vibration frequency threshold, an isolation instruction is generated to instruct the isolation operation of the harmonic signal in a specific frequency range. If no threshold is exceeded, the system records this status and maintains continuous monitoring of the harmonics of the motor equipment to ensure that the equipment is not disturbed during normal operation.

[0109] Step S37: confirming the harmonic frequency range and amplitude requirements currently required to be isolated according to the isolation instruction, and generating frequency response range adjustment parameters;

[0110] The embodiment of the present invention parses the information in the isolation instruction and extracts the harmonic frequency range and amplitude requirements. Ensure that the specific frequency segment that needs to be isolated and its amplitude limit are understood. Ensure that the extracted frequency range and amplitude meet the safety standards and technical requirements of the motor equipment. According to the operating characteristics of the motor equipment, confirm whether the extracted harmonic frequency range is within the acceptable range of the equipment. If it is found that the extracted frequency range exceeds the safety threshold of the equipment, necessary adjustments and corrections must be made to ensure the safety of the operation. Based on the confirmed harmonic frequency range and its amplitude requirements, the corresponding frequency response range adjustment parameters are generated.

[0111] Step S38: dynamically configuring the frequency response module of the motor device according to the frequency response range adjustment parameter to obtain optimized frequency response configuration data;

[0112] The embodiment of the present invention parses the frequency response range adjustment parameters and extracts the specific values ​​of each frequency segment and the corresponding configuration requirements. Confirm the integrity and accuracy of the parameters to ensure that they meet the technical specifications and operating requirements of the equipment. Ensure that the frequency response module of the motor equipment is in a configurable state, check whether all related systems and components are working properly, and avoid failures during the dynamic configuration process. According to the parsed frequency response range adjustment parameters, the frequency response module of the motor equipment is dynamically configured. Set the corresponding frequency response characteristics, such as suppressing or amplifying specific harmonic frequencies, to ensure that the motor equipment can effectively cope with different harmonic interferences. After the configuration is completed, the performance of the frequency response module is monitored in real time to ensure that the new configuration can effectively optimize the frequency response. Based on the monitoring results, make necessary adjustments to the settings of the frequency response module to achieve the expected optimization effect. The completed optimized frequency response configuration data is recorded in the system database for subsequent maintenance and auditing.

[0113] Step S39: performing an isolation operation according to the optimized frequency response configuration data, suppressing harmonic signal interference within a specific frequency range, and generating optimized signal response data.

[0114] In an embodiment of the present invention, optimized frequency response configuration data is input into the frequency response module of the motor device. The configuration data includes the harmonic frequency range to be isolated and the corresponding frequency amplitude limit requirements. Based on the optimized frequency response configuration data, the harmonic isolation mode is activated. This mode monitors harmonics within a preset frequency range in real time while the motor device is operating to determine whether isolation conditions are met. Harmonic signals within the target frequency range are identified by real-time acquired current and voltage signal characteristics, and their characteristics are compared with the optimized frequency response configuration data to ensure that the detected harmonics are within the preset range of the configuration data. Based on the comparison results, if the detected harmonic signal exceeds the amplitude limit in the configuration data, a suppression strategy is triggered. The suppression strategy includes adjusting the frequency response of the motor device to automatically lower the frequency response sensitivity within the preset harmonic frequency range, or reducing the harmonic amplification factor in this frequency band through software and hardware methods to achieve a harmonic suppression effect. As the suppression strategy is implemented, the change data of each frequency response of the motor device after the isolation operation is recorded to generate the final optimized signal response data.

[0115] The present invention monitors the current and voltage signals of motor equipment in real time during operation and performs data preprocessing to rapidly extract signal characteristic parameters and generate real-time signal characteristic data. This process enhances the system's response speed and effectively identifies potential issues. The extracted real-time signal characteristic data is then input into a harmonic spectrum recognition model for harmonic characteristic identification, generating dynamic harmonic monitoring data. This makes real-time harmonic characteristic identification more accurate and provides a reliable foundation for subsequent analysis. By extracting key dynamic harmonic parameters, the system can clearly identify important harmonic information affecting the equipment, thereby enhancing the depth of data analysis and the ability to understand equipment status. Furthermore, the system records vibration frequency in real time and extracts key parameters to promptly detect abnormal vibrations, ensuring safe equipment operation. Based on the real-time vibration frequency key parameters, the harmonic frequency and amplitude are compared and analyzed with preset vibration stability thresholds to generate a stability assessment report. This assessment report helps identify potential risks and provides data support for harmonic isolation. The stability assessment report determines whether to issue an isolation instruction, allowing dynamic adjustment of the strategy to prevent equipment failure. Subsequently, based on the isolation instruction, the harmonic frequency range and amplitude requirements to be isolated are determined, and frequency response adjustment parameters are generated to ensure the accuracy of the isolation operation and reduce operational errors. Ultimately, isolation operations are performed using optimized frequency response configuration data, effectively suppressing harmonic signal interference and generating optimized signal response data, significantly improving equipment stability and extending service life.

[0116] Preferably, step S4 includes the following steps:

[0117] Step S41: collecting and recording real-time temperature data of a motor driver in a CNC machine tool motor device according to the optimized signal response data to generate driver thermal state monitoring data;

[0118] In one embodiment of the present invention, a temperature sensor is configured in the motor driver to ensure real-time monitoring of the motor driver's temperature changes. The temperature sensor is connected to a data acquisition system to facilitate real-time data transmission and recording. A temperature monitoring program is initiated to periodically acquire real-time temperature data from the temperature sensor. The acquisition frequency is set, for example, once per second, to ensure the timeliness and accuracy of the monitored data. The acquired real-time temperature data is stored in a database to form driver thermal status monitoring data.

[0119] Step S42: Compare the driver thermal status monitoring data with a preset safety temperature threshold to generate a thermal status threshold comparison result;

[0120] The embodiment of the present invention sets an appropriate safety temperature threshold based on the technical specifications of the motor driver and the manufacturer's recommendations. This threshold should take into account the optimal operating temperature under normal working conditions and provide a guarantee for the long life of the motor. The threshold can be a fixed value or can be dynamically adjusted according to factors such as different workloads and environmental conditions. Real-time temperature data for a recent period of time is extracted from the thermal status monitoring data of the driver to form a set of temperature data to be compared. Ensure that the data format is consistent to facilitate comparative analysis. Develop a comparison algorithm to compare the extracted real-time temperature data with the preset safety temperature threshold one by one. The result of the comparison should include whether each data point is below, equal to, or exceeds the threshold. The results of the comparison are organized into a thermal status threshold comparison result, which includes the following information: the number of temperature records exceeding the threshold, the highest and lowest temperature values, and an overall judgment on whether the current temperature is within a safe range.

[0121] Step S43: Based on the comparison result of the thermal state threshold, determine whether the cooling system of the motor device needs to be activated. If the temperature exceeds the threshold, adjust the fan speed of the cooling system to generate the motor device cooling control parameters; if the temperature does not exceed the threshold, continue to monitor the motor device status;

[0122] The embodiment of the present invention executes judgment logic based on the comparison result of the thermal state threshold. If any item of the current temperature data exceeds the safe temperature threshold, it indicates that the operating temperature of the motor driver is too high. If all the temperature data do not exceed the safe temperature threshold, it is considered that the motor device is in a safe state. If the judgment result shows that the temperature exceeds the safe temperature threshold, an instruction to start the cooling system is generated, and the fan speed of the cooling system is adjusted according to specific needs to improve the cooling effect. The adjustment of the fan speed can be determined according to the degree to which the current temperature exceeds the threshold. Generate the heat dissipation control parameters of the motor device, which include information such as the fan speed setting and the start time. If the judgment result shows that the temperature does not exceed the safe temperature threshold, continue to monitor the status of the motor device, maintain real-time monitoring of temperature changes, and ensure that potential overheating risks can be responded to in a timely manner at any time.

[0123] Step S44: Comprehensively analyze the heat dissipation control parameters of the motor equipment and the optimized signal response data to generate comprehensive thermal state monitoring data.

[0124] The embodiment of the present invention integrates the heat dissipation control parameters of the motor equipment with the optimized signal response data to form a comprehensive data set. The data integration process includes the unification of data formats, the alignment of timestamps, and necessary identifiers for subsequent analysis. The main purpose of analyzing the integrated data is to evaluate the operating safety and performance of the motor equipment under the current heat dissipation state. The analysis includes three aspects: temperature and response relationship, fan efficiency evaluation, and safety evaluation. Among them, the temperature and response relationship is to analyze the impact of temperature changes on the response performance of the motor equipment and determine whether there is a performance degradation caused by excessive temperature; the fan efficiency evaluation is to evaluate whether the current fan speed is sufficient to meet the heat dissipation requirements of the motor driver and whether the fan speed needs to be further adjusted to improve the heat dissipation efficiency; the safety assessment is based on comprehensive data to determine whether the motor equipment is still in a safe operating state and whether additional measures need to be taken to optimize heat dissipation and performance. Based on the results of the comprehensive analysis, comprehensive thermal status monitoring data is generated.

[0125] The present invention generates thermal status monitoring data by collecting motor driver temperature data in real time, ensuring accurate monitoring of equipment temperature and timely detection of anomalies to ensure safety. After comparing the monitoring data with the safety temperature threshold, a threshold comparison result is generated to achieve over-temperature warning and enhance the system's risk identification ability. Based on the comparison results, the system automatically adjusts the heat dissipation system and dynamically controls the fan speed to prevent overheating and improve the operating stability of the equipment. If the temperature is normal, the system continues to monitor to avoid unnecessary heat dissipation startup, thereby improving energy efficiency. Through comprehensive analysis with the optimized signal response data, comprehensive thermal status monitoring data is generated to provide reliable support for subsequent decision-making and equipment optimization. This process enables the equipment to achieve intelligent regulation under different working conditions, extend its service life and optimize overall performance.

[0126] Preferably, step S44 includes the following steps:

[0127] Step S441: collecting the temperature of the motor driver in real time through a temperature sensor, establishing a temperature data curve, and analyzing the temperature change trend using optimized signal response data to obtain abnormal thermal state information data;

[0128] In this embodiment of the present invention, a temperature sensor installed on the motor driver begins real-time monitoring of its operating temperature. The sensor periodically records temperature data and sends it to the control system. The collected data includes timestamps, current temperature values, and other relevant information for comprehensive analysis. The collected temperature data is processed to generate a temperature data curve. This curve shows the trend of the motor driver's temperature over time, facilitating the observation of temperature fluctuations. The temperature data can be visualized using charting software or data analysis tools to more intuitively present temperature changes. The temperature data curve is analyzed using optimized signal response data. This includes identifying the rate of temperature increase or decrease, as well as periodic changes. Statistical analysis methods are used to smooth the temperature curve to reduce noise and extract valid temperature trends. Based on the temperature trend analysis, criteria for temperature anomalies are determined. For example, an abnormal temperature condition can be identified when the temperature exceeds a preset threshold or when the temperature changes too rapidly within a short period of time. Abnormal temperature information is recorded, including the time the anomaly occurred, its duration, and the operating status at the time. The analysis results are compiled into abnormal thermal condition information data.

[0129] Step S442: Compare the heat dissipation control parameters of the motor device with the real-time temperature, and evaluate the control effect of the current heat dissipation system using the relationship between the fan speed and the temperature change rate to obtain heat dissipation control effect data;

[0130] The embodiment of the present invention continuously obtains real-time temperature data of the motor driver from the temperature sensor. These data will include the current temperature value and its rate of change, ensuring that the thermal state of the motor can be reflected in real time. The heat dissipation control parameters are extracted from the control system of the motor equipment. These parameters include the current fan speed, the working status of the heat dissipation system, and any active or passive heat dissipation measures. The real-time temperature data is compared with the heat dissipation control parameters to calculate the control effect of the current heat dissipation system under actual operating conditions. By comparing the temperature changes in different time periods, the effectiveness of the heat dissipation system in regulating the temperature is analyzed. The relationship between the fan speed and the temperature change rate is used to evaluate the performance of the current heat dissipation system. For example, the effect of the fan speed on the temperature change can be quantified by establishing a mathematical model or using a regression analysis method. Based on the evaluation results, it is determined whether the heat dissipation system has effectively reduced the temperature of the motor driver, and then the heat dissipation control effect data is obtained.

[0131] Step S443: performing cluster analysis on the abnormal temperature based on the abnormal thermal state information data to identify the root cause of the motor equipment overheating and obtain a driver abnormal state report;

[0132] Embodiments of the present invention preprocess abnormal thermal status information data, including data cleaning and normalization, to ensure data accuracy and usability. This involves removing noise, filling missing values, and unifying data from different units. An appropriate clustering algorithm, such as K-means clustering, hierarchical clustering, or DBSCAN, is selected as the basis for identifying abnormal temperature patterns. This selection is based on the characteristics of the data and its distribution to ensure the effectiveness of the clustering effect. The processed abnormal thermal status information data is input into the selected clustering algorithm for analysis. The clustering algorithm identifies different temperature patterns in the data and clusters similar temperature anomalies together to form multiple clusters. The clustering results are analyzed to identify potential root causes of motor equipment overheating. This can be done by examining the center point of each cluster and its degree of dispersion to determine which temperature patterns are abnormal, indicating equipment problems or the influence of environmental factors. Based on the results of the cluster analysis, a drive abnormal status report is generated, which details each identified abnormal temperature pattern and its root cause.

[0133] Step S444: Comprehensively analyze the heat dissipation control effect data and the driver abnormal status report to generate comprehensive thermal status monitoring data.

[0134] An embodiment of the present invention integrates the heat dissipation control effect data with the drive abnormal status report to form a comprehensive data set. During the integration process, it should be ensured that all relevant parameters are matched, especially the timestamps and device status related to abnormal temperatures. An appropriate analysis method is selected to extract valuable information and patterns from the comprehensive data set. The method selection should be based on the complexity of the required results and the characteristics of the data. The comprehensive data set is analyzed using the selected analysis method to identify the relationship between the heat dissipation control effect and the abnormal status. Based on the analysis results, comprehensive thermal status monitoring data is generated, which should include an evaluation of the heat dissipation control effect, the frequency of abnormal status and its corresponding root cause analysis.

[0135] The present invention uses a temperature sensor to collect the temperature of the motor driver in real time and establish a temperature data curve to achieve accurate monitoring of temperature anomalies. At the same time, by optimizing the signal response data to analyze the temperature trend, reliable abnormal thermal state information is provided to ensure the safety of the equipment. The heat dissipation control parameters are compared with the real-time temperature to evaluate the effect of the heat dissipation system, and the fan speed and temperature change rate are analyzed to ensure that the heat dissipation system remains stable under various working conditions. Cluster analysis is performed using abnormal thermal state data to identify the root cause of overheating, provide a basis for troubleshooting, and extend the life of the equipment. The comprehensive thermal state monitoring data generated by the comprehensive heat dissipation effect data and the abnormal state report improves the accuracy of equipment status monitoring and provides data support for subsequent maintenance optimization. The overall process realizes the intelligent management of motor equipment, and can quickly respond, warn and dynamically adjust in the event of an anomaly, thereby reducing downtime and improving production efficiency and operational safety.

[0136] Preferably, step S5 includes the following steps:

[0137] Step S51: performing real-time analysis on the current input parameters of the motor device driver based on the comprehensive thermal state monitoring data to identify the current demand of the driver under various temperature conditions and generate the current input parameters;

[0138] An embodiment of the present invention collects real-time temperature data of a motor device driver from a temperature sensor. Relevant information is extracted from the comprehensive thermal state monitoring data, including the driver's historical current input parameters and current operating state. Based on the historical data, a current demand model is established, and the model should consider the relationship between current and temperature under different temperature conditions. The model can use regression analysis, machine learning algorithms, or other suitable methods to ensure its accuracy and reliability. The real-time temperature data is input into the current demand model for analysis, and the current demand of the driver under the current temperature conditions is calculated in real time. The analysis should consider multiple operating states and load conditions to ensure the comprehensiveness of the results. Based on the real-time analysis results, the corresponding current input parameters are generated.

[0139] Step S52: Optimizing and adjusting the current input parameters of the motor device driver to identify the optimal operating temperature range of the motor device driver and obtain the current optimized input adjustment parameters;

[0140] An embodiment of the present invention collects operational records of a motor device driver under different temperature conditions, including information such as its operating status, power consumption, and efficiency. Based on the collected data, an optimization model is established that analyzes the relationship between current input parameters and temperature. Data analysis methods can be used to determine the correlation between current demand and temperature. The model should consider changes in current demand under different workloads to ensure its applicability. Using the established optimization model, the driver's current input parameters are analyzed under different temperature conditions and the driver's optimal operating state is identified under each temperature condition. The optimal operating temperature range of the motor device driver is determined, typically the temperature range within which the device can operate efficiently without overheating or overloading. Based on the optimal operating temperature range, current optimization input adjustment parameters are generated. These parameters should include recommended current input values ​​within the optimal temperature range and the required adjustment ranges under different temperature conditions. The generated current optimization input adjustment parameters ensure that the driver operates at optimal efficiency and avoids potential failures caused by excessively low or high current.

[0141] Step S53: Based on the current optimization input adjustment parameters, the operating status of the cooling system is monitored in real time, and the fan speed and coolant flow of the cooling system are adjusted in real time to generate cooling system control parameters;

[0142] In this embodiment of the present invention, temperature and current sensors are installed in the motor device driver to collect the motor device's temperature and current input parameters in real time. A data acquisition system compares the real-time collected current input parameters with the motor device's comprehensive thermal status monitoring data to monitor the current operating status of the cooling system. Key cooling system parameters, such as fan speed and coolant flow rate, are set and compared with the real-time monitoring data to analyze the effectiveness of the current cooling system. The motor device's workload and ambient temperature are monitored to evaluate the cooling system's performance under different operating conditions. Parameters are adjusted based on the current optimization input to analyze their impact on the motor device driver's cooling requirements. If the current input parameters indicate the driver is overheating under specific temperature conditions, the system promptly issues adjustment instructions. Based on the real-time monitoring results and the current optimization input adjustment parameters, the cooling system's fan speed and coolant flow rate are dynamically adjusted. For example, when the motor driver temperature rises, the system increases the fan speed to improve air circulation and cooling efficiency; if the temperature drops, the fan speed is appropriately reduced to save energy. Coolant flow rate is controlled to optimize cooling efficiency, ensuring an appropriate flow rate between the motor driver and the heat sink to maintain ideal cooling conditions. After completing the above adjustments, the cooling system control parameters are generated and the new fan speed and coolant flow values ​​are recorded.

[0143] Step S54: integrating the current optimization input adjustment parameters and the heat dissipation system control parameters to generate hardware adaptive control parameters.

[0144] An embodiment of the present invention creates a data integration model that combines current optimization input adjustment parameters with cooling system control parameters. The model can correlate these two types of parameters to evaluate their synergistic effect in the overall operation of the motor equipment. By comparing and analyzing the relationship between current demand and cooling capacity through an algorithm, it is ensured that under different working conditions, the current input of the motor equipment and the working state of the cooling system can maintain the best match. The integrated data is analyzed to evaluate the adaptability between the current input required by the current motor device driver in actual operation and the responsiveness of the cooling system. Potential optimization space is identified, for example, under certain temperature conditions, the fan speed or coolant flow is adjusted to meet the current input demand and maintain a safe operating temperature. Based on the results of the integrated analysis, new hardware adaptive control parameters are generated.

[0145] The present invention accurately identifies the current demand of the motor driver at different temperatures through real-time analysis of comprehensive thermal status monitoring data, ensuring that the equipment operates within a safe range and reducing the risk of failure. The current input parameters are optimized and adjusted to identify the optimal operating temperature range, improve energy efficiency, and reduce damage caused by excessive temperature. The cooling system status is monitored based on the current optimization parameters, and the fan speed and coolant flow are dynamically adjusted to ensure that the cooling system remains in the optimal state under different conditions, effectively extending the life of the equipment. The current optimization and cooling control parameters are integrated to generate hardware adaptive control parameters to achieve intelligent adjustment of the equipment. The overall process enhances the equipment's responsiveness to temperature and current changes, improves operating efficiency, reduces failure downtime, and improves energy saving effects and system stability.

[0146] Preferably, step S53 includes the following steps:

[0147] Step S531: collecting parameters of the heat dissipation system in real time through sensors, and monitoring temperature changes in real time to obtain real-time monitoring parameters of the heat dissipation system;

[0148] In an embodiment of the present invention, a plurality of temperature sensors are arranged at key positions of the heat dissipation system to ensure comprehensive monitoring of the temperature changes of the entire heat dissipation system. These sensors can collect temperature data in real time and transmit the data to a central control system via wireless or wired means. Periodic data collection is implemented and an appropriate sampling frequency is set to ensure that the acquired temperature data is real-time and accurate. Temperature changes are recorded, including key parameters such as the temperature of the fan inlet and outlet, the temperature of the coolant, and the operating temperature of the motor driver. The collected real-time temperature data is input into a data processing module for data cleaning and preprocessing to remove abnormal values ​​or noise. Real-time monitoring parameters are generated from the temperature data, including information such as the current temperature, the temperature change rate, and the temperature historical trend, for subsequent analysis and use. The processed real-time monitoring parameters are fed back to the control system of the motor equipment to provide detailed information about the current operating status of the heat dissipation system. Changes in the real-time monitoring parameters are recorded to obtain the real-time monitoring parameters of the heat dissipation system.

[0149] Step S532: Comparing and analyzing the real-time monitoring parameters of the heat dissipation system with the current optimization input adjustment parameters, evaluating the heat dissipation effect of the fan speed and coolant flow rate, and obtaining heat dissipation demand comparison result data;

[0150] The embodiment of the present invention inputs the real-time monitoring parameters and the current optimization input adjustment parameters into the analysis module, and sets the corresponding algorithm for comparative analysis. The working efficiency of the real-time heat dissipation system is calculated, including the current temperature and the difference between the actual current demand and the theoretical current demand, to evaluate whether the heat dissipation system can meet the heat dissipation requirements of the motor equipment under different working conditions. Based on the comparative analysis results, the heat dissipation effect of the fan speed and coolant flow is evaluated, including whether there is overheating or insufficient cooling. The heat dissipation requirement comparison result data is recorded, including the conclusion of the evaluated heat dissipation effect and whether the current heat dissipation capacity meets the working requirements of the equipment.

[0151] Step S533: dynamically adjusting the fan speed of the cooling system based on the cooling demand comparison result data to obtain fan speed optimization data;

[0152] The embodiment of the present invention records the current demand of the motor device driver at different operating temperatures and the actual operating parameters of the current cooling system. Based on the cooling demand comparison result data, a dynamic adjustment strategy is set, including the preset fan speed change range, the relationship curve between the fan speed and temperature, and the adjustment ratio of the coolant flow rate. The threshold for fan speed adjustment is set to ensure that the fan speed is automatically increased or decreased when a certain temperature is reached. The real-time monitoring parameters and cooling demand assessment results of the current cooling system are obtained through the control module to determine whether the fan speed needs to be adjusted. If the current cooling effect is insufficient, the fan speed is increased, and a linear or nonlinear adjustment strategy is adopted to gradually increase the fan speed to the required level. If the cooling effect is good, it can be considered to moderately reduce the fan speed to save energy and avoid unnecessary noise and energy consumption. The adjusted fan speed is recorded, and fan speed optimization data is generated, including the newly set fan speed value and the corresponding timestamp.

[0153] Step S534: Adapting and adjusting the coolant flow rate according to the fan speed optimization data to obtain the coolant flow rate optimization data;

[0154] The embodiment of the present invention obtains real-time monitoring parameters of the current heat dissipation system, including information such as coolant flow rate, heat dissipation effect and system temperature. The adaptation strategy of the coolant flow rate is set according to the fan speed optimization data, and the relationship between the fan speed and the coolant flow rate is clarified. The adjustment range of the coolant flow rate is determined to ensure that an appropriate cooling effect can be provided at different fan speeds. The fan speed optimization data is analyzed by the control module to determine the requirements of the current fan speed for the coolant flow rate. If the fan speed increases, the coolant flow rate is increased to meet the overall cooling requirements of the heat dissipation system and prevent the equipment from overheating. If the fan speed decreases, the coolant flow rate is reduced accordingly to optimize the energy consumption and efficiency of the system and avoid excessive cooling. The adjusted coolant flow rate is recorded to generate coolant flow optimization data, including the newly set flow value and the relevant timestamp.

[0155] Step S535: Integrate the fan speed optimization data and the coolant flow optimization data to generate cooling system control parameters.

[0156] The embodiment of the present invention determines the relationship between the fan speed optimization data and the coolant flow optimization data, sets appropriate integration rules, and ensures that the results after data integration can reflect the overall operating status of the cooling system. The structure of the cooling system control parameters is clarified, including the fan speed, coolant flow and their corresponding adjustment strategies. The fan speed optimization data and the coolant flow optimization data are matched item by item to form a comprehensive data set. Timestamps, device status and operating instructions are added to each set of optimization data to generate a complete record of the cooling system control parameters. Through the data integration process, the final cooling system control parameters are generated, including the optimized fan speed settings and the corresponding coolant flow settings.

[0157] The present invention uses sensors to collect and monitor the parameters of the cooling system in real time, quickly identifying cooling problems, ensuring that the motor equipment operates within a safe temperature range, and reducing the risk of equipment damage. By comparing and analyzing the real-time parameters of the cooling system and the current optimization input parameters, the cooling effect of the fan speed and coolant flow rate is accurately evaluated, providing data support for adjustment. Based on demand comparison, the fan speed and coolant flow rate are dynamically adjusted to achieve optimal cooling, avoid equipment overheating, and reduce energy consumption. The fan and coolant data are integrated to generate cooling control parameters, making the cooling system more intelligent and adaptive. The overall process effectively improves the stability, safety, and operating efficiency of the equipment, meeting the industrial needs of energy conservation and environmental protection.

[0158] Preferably, step S6 includes the following steps:

[0159] Step S61: performing real-time analysis on the load dynamic adjustment module of the motor device according to the hardware adaptive control parameters to identify the performance requirements of the motor device under various load conditions and generate load performance requirement analysis data;

[0160] The embodiment of the present invention collects real-time operating data of the motor equipment through sensors and monitoring equipment, including current, speed, torque and load information. Hardware adaptive control parameters are obtained, which may include current optimization input adjustment parameters generated in the previous steps, heat dissipation system control parameters, etc. Based on the real-time data collected, a performance requirement model of the motor equipment under different load conditions is established. This model should take into account the operating characteristics of the motor under various working conditions, such as starting, acceleration, full load and no-load states. The relationship between real-time data and hardware adaptive control parameters is analyzed by algorithm to identify the optimal performance indicators required for the motor equipment under various load conditions, including the required current, speed and torque. The analysis results are organized into load performance requirement analysis data, including the specific values ​​of the current, speed and torque required under each load state and the corresponding working conditions.

[0161] Step S62: Optimizing the configuration of the load dynamic adjustment module based on the load performance demand analysis data and the dynamic harmonic characteristic monitoring data to generate a load optimization strategy;

[0162] The embodiment of the present invention conducts a comprehensive analysis of the load performance demand analysis data and the dynamic harmonic characteristic monitoring data to identify performance deviations under the current load conditions and harmonic characteristics. Through mathematical modeling or simulation calculations, it is evaluated whether the current configuration of the load dynamic adjustment module can meet the identified performance requirements and find potential optimization space. Based on the above evaluation results, a load optimization strategy is formulated, including adjustment of the motor control algorithm, optimization of load distribution, and implementation of harmonic suppression measures. The optimization strategy should clearly define specific adjustment goals, such as reducing the impact of harmonics, improving efficiency, optimizing power factor, etc., and the feasibility and economy of implementation must also be considered.

[0163] Step S63: applying the load optimization strategy to the control module of the motor device, and adjusting the torque and speed of the motor device in real time to generate load adaptability optimization parameters to improve the operation stability of the motor device.

[0164] The embodiment of the present invention inputs the load optimization strategy into the control module of the motor equipment. This strategy includes the optimal torque and speed settings under different load conditions, which aims to optimize the operating performance of the motor equipment. The real-time operating status of the motor equipment is monitored by sensors, and relevant data is collected, including current torque, speed, load conditions, temperature and other information. These real-time data will be used to evaluate whether the performance of the motor equipment meets the requirements of the load optimization strategy. Based on the data monitored in real time, a comparative analysis is performed with the load optimization strategy to identify the parameters that need to be adjusted. If it is found that the current torque and speed of the motor equipment do not meet the optimization target, these parameters are dynamically adjusted through the control algorithm. The control module should have a fast response capability and be able to make adjustments quickly after collecting data in real time to cope with instantaneous load changes. After the torque and speed adjustment is completed, the new torque and speed parameters are recorded to generate load adaptability optimization parameters.

[0165] The present invention analyzes hardware adaptive control parameters in real time, identifies the performance requirements of the motor under different load conditions, generates load performance requirement data, and ensures the performance adaptability and flexibility of the equipment under various loads. Combined with load requirements and dynamic harmonic data, the load adjustment module is optimized and configured to generate a load optimization strategy to improve the operating efficiency of the motor and reduce energy waste. Through the real-time adjustment of torque and speed by the load optimization strategy, the equipment can quickly adapt to load changes, significantly improve operational stability and extend service life, and reduce maintenance costs. This method effectively reduces energy consumption and improves energy efficiency through optimized configuration and real-time parameter adjustment, meets energy conservation and environmental protection needs, and contributes to intelligent manufacturing and sustainable development.

[0166] Preferably, the present invention further provides a motor device for executing the above-mentioned operation control method of the motor device, the motor device comprising:

[0167] The operation data acquisition module is used to collect the operation data of the motor equipment in the CNC machine tool under various working conditions in real time to obtain the initial operation data set;

[0168] The harmonic spectrum modeling module is used to train the neural network model using the initial operating data set to identify the harmonic characteristics under various operating conditions and generate a harmonic spectrum recognition model;

[0169] The dynamic harmonic monitoring and optimization module is used to collect the current and voltage signals of the motor equipment in real time, input them into the harmonic spectrum recognition model for identification, and generate dynamic harmonic characteristic monitoring data; the dynamic harmonic characteristic monitoring data is compared with the preset threshold, an isolation instruction is generated, and the frequency response range of the motor equipment is dynamically adjusted to obtain optimized signal response data;

[0170] Thermal status monitoring and management module, which is used to monitor the temperature of the motor driver and cooling system in the motor equipment in real time based on the optimized signal response data and generate comprehensive thermal status monitoring data;

[0171] Adaptive current and heat dissipation control module, which adjusts the current input parameters of the motor device driver based on comprehensive thermal status monitoring data, optimizes the operating status of the heat dissipation system, and generates hardware adaptive control parameters;

[0172] The load adaptability optimization module is used to optimize the load dynamic adjustment module of the motor equipment based on the hardware adaptive control parameters and dynamic harmonic characteristic monitoring data, and generate load adaptability optimization parameters to improve the operating stability of the motor equipment.

[0173] This invention collects real-time operating data from CNC machine tool motors under various operating conditions to generate an initial operating data set, providing accurate data support for subsequent analysis and model training. This real-time data collection function improves the system's sensitivity to different operating states, enabling the motors to quickly adapt to various complex operating conditions, thereby enhancing the overall system's flexibility and intelligence. The collected initial operating data is used to train a neural network model to generate a harmonic spectrum recognition model, enabling the system to accurately identify and analyze harmonic characteristics under different operating conditions. This refined harmonic modeling enhances the system's monitoring and diagnostic capabilities, effectively suppresses the impact of harmonics on the equipment, reduces the motor's power consumption and failure probability, and improves its operational stability. By monitoring the motor's current and voltage signals in real time and identifying and optimizing harmonic characteristics, the module generates dynamic harmonic characteristic monitoring data. Through comparative analysis, it generates isolation instructions to dynamically adjust the motor's frequency response range. This function enables the motor to maintain stable response performance under varying harmonic interference, optimizes signal response accuracy, effectively improves the device's anti-interference capability, and ensures efficient operation. By monitoring the temperature of the motor driver and cooling system in real time and generating thermal status monitoring data, the module ensures thermal safety under high-load or high-speed operation. This module prevents equipment wear and failure due to overheating, extends equipment life through precise temperature control, and ensures the stability and safety of the motor equipment during long-term operation. Based on comprehensive thermal status monitoring data, the module dynamically adjusts the motor driver's current input parameters and optimizes the cooling system to generate hardware-adaptive control parameters. This adaptive control approach enhances device stability under varying environments and loads, reduces efficiency degradation caused by temperature rise, and reduces the need for manual commissioning, further improving device reliability and maintenance efficiency. The module utilizes hardware-adaptive control parameters and dynamic harmonic characteristic monitoring data to dynamically optimize the load to generate load-adaptive optimization parameters, ensuring smoother operation of the motor equipment under varying load conditions. This module effectively enhances the equipment's operational adaptability under high loads, reduces the stress impact on the equipment, and improves overall system safety and efficiency.

[0174] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited by the above description. Therefore, it is intended that all changes that fall within the meaning and scope of the equivalent elements of the application documents are included in the present invention.

[0175] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the operation of a motor device, characterized in that: The following steps are involved: Step S1: collecting the operating data of the motor equipment in the CNC machine tool under various working conditions in real time to obtain an initial operating data set; Step S2: Using the initial operating data set, the neural network model is trained to identify harmonic features under various operating conditions and generate a harmonic spectrum recognition model. Step S2 includes the following steps: Step S21: grouping, sorting and unifying the initial running data set to obtain normalized training data; Step S22: performing feature decomposition on the normalized training data, extracting key feature parameters, and generating a feature data set; Step S23: Divide the feature dataset into a training set and a validation set to construct input data for the neural network model, thereby obtaining a model training dataset and a model validation dataset; Step S24: using the model training data set to train the neural network model to identify and classify the harmonic characteristics under various working conditions, and obtain a preliminary harmonic characteristic model; Step S25: using the model verification data set to perform real-time verification on the preliminary harmonic characteristic model to obtain a verification set test result; Step S26: Optimize the preliminary harmonic feature model based on the validation set test results, adjust the network layer structure, learning rate and weight, and obtain a harmonic spectrum recognition model; Step S3: The current and voltage signals of the motor equipment are collected in real time and input into the harmonic spectrum recognition model for identification to generate dynamic harmonic characteristic monitoring data; the dynamic harmonic characteristic monitoring data is compared with a preset threshold value to generate an isolation instruction, and the frequency response range of the motor equipment is dynamically adjusted to obtain optimized signal response data. Step S3 includes the following steps: Step S31: monitoring the current and voltage signals of the motor equipment in real time during operation, and preprocessing the data to extract characteristic parameters of the current and voltage signals to generate real-time signal characteristic data; Step S32: inputting the real-time signal characteristic data into the harmonic spectrum recognition model to identify the harmonic characteristics of the current signal and obtain dynamic harmonic characteristic monitoring data; Step S33: extracting key harmonic parameters from the dynamic harmonic characteristic monitoring data to obtain key parameters of dynamic harmonic characteristics; Step S34: recording the vibration frequency of the motor device in real time, and extracting and analyzing key parameters to obtain real-time vibration frequency key parameters; Step S35: Based on the real-time vibration frequency key parameter, the harmonic frequency and harmonic frequency amplitude in the dynamic harmonic characteristic key parameter are compared and analyzed with the preset vibration stability threshold to obtain a stability assessment report; Step S36: Determine whether the harmonics meet the isolation conditions based on the stability assessment report. If the harmonics exceed the vibration frequency threshold, generate an isolation instruction. If the harmonics do not exceed the vibration frequency threshold, continuously monitor the harmonics of the motor equipment. Step S37: confirming the harmonic frequency range and amplitude requirements currently required to be isolated according to the isolation instruction, and generating frequency response range adjustment parameters; Step S38: dynamically configuring the frequency response module of the motor device according to the frequency response range adjustment parameter to obtain optimized frequency response configuration data; Step S39: performing an isolation operation according to the optimized frequency response configuration data to suppress harmonic signal interference within a specific frequency range and generate optimized signal response data; Step S4: Monitor the temperature of the motor driver and the heat dissipation system in the motor device in real time according to the optimized signal response data to generate comprehensive thermal state monitoring data. Step S4 includes the following steps: Step S41: collecting and recording real-time temperature data of the motor driver in the CNC machine tool motor device according to the optimized signal response data to generate driver thermal state monitoring data; Step S42: Compare the driver thermal status monitoring data with a preset safety temperature threshold to generate a thermal status threshold comparison result; Step S43: Based on the comparison result of the thermal state threshold, determine whether the cooling system of the motor device needs to be activated. If the temperature exceeds the threshold, adjust the fan speed of the cooling system to generate the motor device cooling control parameters; if the temperature does not exceed the threshold, continue to monitor the motor device status; Step S44: Comprehensively analyzing the heat dissipation control parameters of the motor equipment and the optimized signal response data to generate comprehensive thermal state monitoring data. Step S44 includes the following steps: Step S441: collecting the temperature of the motor driver in real time through a temperature sensor, establishing a temperature data curve, and analyzing the temperature change trend using optimized signal response data to obtain abnormal thermal state information data; Step S442: Compare the heat dissipation control parameters of the motor device with the real-time temperature, and evaluate the control effect of the current heat dissipation system using the relationship between the fan speed and the temperature change rate to obtain heat dissipation control effect data; Step S443: performing cluster analysis on the abnormal temperature based on the abnormal thermal status information data to identify the root cause of the motor equipment overheating and obtain a driver abnormal status report; Step S444: performing a comprehensive analysis on the heat dissipation control effect data and the driver abnormality status report to generate comprehensive thermal status monitoring data; Step S5: adjusting the current input parameters of the motor device driver based on the comprehensive thermal state monitoring data, optimizing the operating state of the heat dissipation system, and generating hardware adaptive control parameters; Step S6: Optimizing the load dynamic adjustment module of the motor equipment based on the hardware adaptive control parameters and the dynamic harmonic characteristic monitoring data, generating load adaptability optimization parameters to improve the operational stability of the motor equipment.

2. The operation control method of the motor equipment according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: monitoring the operating parameters of the motor in real time under various operating conditions of the motor equipment of the CNC machine tool to obtain real-time operating data of the motor equipment; Step S12: pre-processing the real-time operation data to obtain cleaned operation data; Step S13: performing feature extraction on the cleaned operating data to identify key feature parameters under various operating conditions and generate an operating data feature set; Step S14: Classify and label the operating data feature set according to various operating conditions to generate an initial operating data set.

3. The operation control method of the motor equipment according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing real-time analysis on the current input parameters of the motor device driver based on the comprehensive thermal state monitoring data to identify the current demand of the driver under various temperature conditions and generate the current input parameters; Step S52: Optimizing and adjusting the current input parameters of the motor device driver to identify the optimal operating temperature range of the motor device driver and obtain the current optimized input adjustment parameters; Step S53: Based on the current optimization input adjustment parameters, the operating status of the cooling system is monitored in real time, and the fan speed and coolant flow of the cooling system are adjusted in real time to generate cooling system control parameters; Step S54: integrating the current optimization input adjustment parameters and the heat dissipation system control parameters to generate hardware adaptive control parameters.

4. The operation control method of the motor equipment according to claim 3, characterized in that: Step S53 includes the following steps: Step S531: collecting parameters of the heat dissipation system in real time through sensors, and monitoring temperature changes in real time to obtain real-time monitoring parameters of the heat dissipation system; Step S532: Comparing and analyzing the real-time monitoring parameters of the heat dissipation system with the current optimization input adjustment parameters, evaluating the heat dissipation effect of the fan speed and coolant flow rate, and obtaining heat dissipation demand comparison result data; Step S533: dynamically adjusting the fan speed of the cooling system based on the cooling demand comparison result data to obtain fan speed optimization data; Step S534: Adapting and adjusting the coolant flow rate according to the fan speed optimization data to obtain the coolant flow rate optimization data; Step S535: Integrate the fan speed optimization data and the coolant flow optimization data to generate cooling system control parameters.

5. The operation control method of the motor equipment according to claim 1, characterized in that: Step S6 includes the following steps: Step S61: performing real-time analysis on the load dynamic adjustment module of the motor device according to the hardware adaptive control parameters to identify the performance requirements of the motor device under various load conditions and generate load performance requirement analysis data; Step S62: Optimizing the configuration of the load dynamic adjustment module based on the load performance demand analysis data and the dynamic harmonic characteristic monitoring data to generate a load optimization strategy; Step S63: applying the load optimization strategy to the control module of the motor device, and adjusting the torque and speed of the motor device in real time to generate load adaptability optimization parameters to improve the operation stability of the motor device.

6. A motor device, characterized in that: The motor comprises a housing, an armature, a drive circuit, and a control system. The armature is disposed in the housing, the drive circuit is electrically connected to the armature, and the control system is installed in the drive circuit. The control system is used to execute the operation control method of the motor device according to claim 1, and the control system includes: The operation data acquisition module is used to collect the operation data of the motor equipment in the CNC machine tool under various working conditions in real time to obtain the initial operation data set; The harmonic spectrum modeling module is used to train the neural network model using the initial operating data set to identify the harmonic characteristics under various operating conditions and generate a harmonic spectrum recognition model; The dynamic harmonic monitoring and optimization module is used to collect the current and voltage signals of the motor equipment in real time, input them into the harmonic spectrum recognition model for identification, and generate dynamic harmonic characteristic monitoring data; the dynamic harmonic characteristic monitoring data is compared with the preset threshold, an isolation instruction is generated, and the frequency response range of the motor equipment is dynamically adjusted to obtain optimized signal response data; Thermal status monitoring and management module, which is used to monitor the temperature of the motor driver and cooling system in the motor equipment in real time based on the optimized signal response data and generate comprehensive thermal status monitoring data; Adaptive current and heat dissipation control module, which adjusts the current input parameters of the motor device driver based on comprehensive thermal status monitoring data, optimizes the operating status of the heat dissipation system, and generates hardware adaptive control parameters; The load adaptability optimization module is used to optimize the load dynamic adjustment module of the motor equipment based on the hardware adaptive control parameters and dynamic harmonic characteristic monitoring data, and generate load adaptability optimization parameters to improve the operating stability of the motor equipment.

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