Motor remote monitoring and optimizing method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of motor remote monitoring and optimization. The invention relates to a motor remote monitoring and optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the real-time operation information of a motor, inputting the real-time operation information of the motor into a trained fault diagnosis model, obtaining fault early warning information, and carrying out the remote monitoring and optimization of the motor according to the fault early warning information and the historical operation information of the motor. The method comprises the steps of generating a motor operation optimization suggestion in combination with a preset motor performance optimization rule, pushing the motor operation optimization suggestion to terminal equipment to obtain optimization operation information confirmed by a user, generating motor control information used for executing optimization operation according to the optimization operation information, and sending the motor control information to the terminal equipment. The motor control information is used for adjusting motor operation parameters corresponding to the motor operation optimization suggestions. The method has the effect of intelligently adjusting and optimizing the operation parameters of the motor.

Description

Technical Field

[0001] The present invention relates to the technical field of motor remote monitoring and optimization, and in particular to a motor remote monitoring and optimization method, device, computer equipment and storage medium. Background Art

[0002] With the continuous development of industrial automation technology, motors are the core components of industrial equipment, and the health status of motors directly affects the stable operation of the entire production system. Motor fault monitoring and operation optimization have become the key links to improve equipment efficiency and reduce maintenance costs.

[0003] At present, remote monitoring and optimization methods for motors have been widely used in the industrial field. They usually obtain real-time operating information of the motor and combine it with historical operating data for fault diagnosis and performance optimization. However, in the process of generating optimization suggestions and controlling motor operating parameters, existing technologies often rely on fixed performance optimization rules and lack the ability to dynamically adjust according to real-time changes in the motor's operating status. This results in limited optimization effects on motor operating parameters when facing complex operating conditions or abnormal situations, and is unable to fully improve the motor's operating efficiency and reliability.

[0004] Regarding the above-mentioned related technologies, there are defects in that they rely on fixed rules for optimization and are difficult to flexibly adjust the motor operating parameters according to real-time fault warnings and historical operating data. Summary of the invention

[0005] In order to achieve intelligent adjustment and optimization of motor operating parameters, the present application provides a motor remote monitoring and optimization method, device, computer equipment and storage medium.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A motor remote monitoring and optimization method, the motor remote monitoring and optimization method comprising: Get real-time operation information of the motor; Inputting the real-time operation information of the motor into a trained fault diagnosis model to obtain fault warning information; Generate motor operation optimization suggestions based on the fault warning information and the motor historical operation information and in combination with pre-set motor performance optimization rules; Pushing the motor operation optimization suggestion to the terminal device to obtain the optimization operation information confirmed by the user; According to the optimization operation information, motor control information for performing the optimization operation is generated, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion.

[0007] By adopting the above technical solution, by obtaining the real-time operation information of the motor and inputting it into the trained fault diagnosis model, it is possible to timely identify potential faults in the motor operation process and generate corresponding fault warning information, thereby improving the real-time and accuracy of fault diagnosis, and effectively reducing the risk of motor shutdown or damage due to failure to detect potential problems in time; based on the fault warning information and the historical operation information of the motor, combined with the pre-set motor performance optimization rules, motor operation optimization suggestions are generated, and optimization solutions can be proposed before the fault occurs to prevent the problem from worsening, while improving the overall operation efficiency and performance of the motor, thereby improving the operation reliability and energy saving effect of the motor; the motor operation optimization suggestions are pushed to the terminal device to obtain the optimization operation information confirmed by the user, so that the user can verify, confirm or modify the optimization suggestions through the terminal device to ensure that the optimization solution is consistent with the actual operation needs of the user. Thereby, the flexibility of motor control and user participation are improved; based on the optimization operation information confirmed by the user, motor control information for executing the optimization operation is generated, which can ensure the accuracy and timeliness of the optimization control instructions, and the optimization operation is performed based on the parameter adjustment confirmed by the user, which effectively improves the adjustment efficiency of the motor operating parameters and ensures the stability of the motor in actual operation; the motor operating parameters corresponding to the motor operation optimization suggestions are adjusted according to the optimization operation information, which can achieve precise adjustment of the motor operating parameters, including speed, load distribution and temperature, etc., thereby improving the optimization effect during motor operation, helping to extend the life of the motor and reduce energy consumption; the motor control information is used to adjust the motor operating parameters corresponding to the motor operation optimization suggestions, which can automatically adjust the motor based on the optimization suggestions to ensure that the motor operating parameters are continuously in the best state, thereby further improving the stability of the motor operation.

[0008] In a preferred example, the present application may be further configured as follows: the real-time operation information of the motor is input into a trained fault diagnosis model to obtain fault warning information, which includes: Dividing the motor historical operation information into a model training set and a test set; Using the model training set to train the fault diagnosis model to obtain a fault diagnosis model after preliminary training; Using the model test set to verify the fault diagnosis model after preliminary training, and generating a model verification result; The fault diagnosis model after preliminary training is adjusted according to the model verification result to obtain the trained fault diagnosis model.

[0009] By adopting the above technical scheme, the historical operation information of the motor is divided into a model training set and a test set, which can ensure the integrity and rationality of the model training data, thereby improving the generalization ability and accuracy of the fault diagnosis model; the fault diagnosis model is trained using the model training set to obtain a fault diagnosis model after preliminary training, which can enable the model to have preliminary fault recognition ability through an effective training process, thereby improving the model's prediction ability for faults; the fault diagnosis model after preliminary training is verified using the model test set to generate a model verification result, which can verify the performance of the model through the test set, detect the accuracy and reliability of the model, thereby ensuring the stability and effectiveness of the diagnosis model; the fault diagnosis model after preliminary training is adjusted according to the model verification result to obtain the trained fault diagnosis model, which can further optimize the parameter setting of the model through the verification feedback information, thereby improving the accuracy and adaptability of the fault diagnosis model, so that it can better cope with actual fault conditions in motor operation.

[0010] In a preferred example, the present application can be further configured as follows: the real-time operation information of the motor is input into a trained fault diagnosis model to obtain fault warning information, including: Comparing the real-time operation information of the motor with the normal working state information of the motor in the trained fault diagnosis model to obtain a comparison result; The fault warning information is generated according to the comparison result and in combination with a preset fault judgment threshold.

[0011] By adopting the above technical scheme, the real-time operation information of the motor is compared with the normal working state information of the motor in the trained fault diagnosis model, so that the running state of the motor can be monitored in real time and abnormalities can be discovered in time, thereby improving the timeliness and accuracy of motor fault diagnosis; according to the comparison result, combined with the preset fault judgment threshold, the fault warning information is generated, which can ensure the accuracy of fault judgment, so that the system can issue a warning in time when the threshold is exceeded, thereby effectively avoiding the further expansion of potential faults, ensuring the safe operation of the motor and extending the service life of the motor.

[0012] In a preferred example, the present application may be further configured as follows: generating motor operation optimization suggestions based on the fault warning information and the motor historical operation information in combination with the preset motor performance optimization rules, including: Determine the cause of the current motor failure according to the abnormality type identified in the fault warning information and the motor failure occurrence trend recorded in the motor historical operation information; The motor operation optimization suggestion is obtained by using the preset motor performance optimization rules in combination with the current fault cause of the motor and the real-time operating conditions of the motor.

[0013] By adopting the above technical scheme, according to the abnormality type identified in the fault warning information, combined with the motor fault occurrence trend recorded in the motor historical operation information, the current fault cause of the motor can be accurately determined, thereby providing a reliable basis for subsequent optimization and maintenance; through the pre-set motor performance optimization rules, combined with the current fault cause of the motor and the real-time operating conditions of the motor, more targeted motor operation optimization suggestions can be generated, thereby effectively improving the operating state of the motor, reducing the probability of faults, and improving the overall operating efficiency and performance of the motor.

[0014] In a preferred example, the present application may be further configured as follows: the motor operation optimization suggestion is obtained by combining the preset motor performance optimization rule with the current fault cause of the motor and the real-time operating condition of the motor, including: According to the preset motor performance optimization rules, analyzing the current fault cause of the motor to obtain corresponding fault analysis results; Based on the fault analysis result and in combination with the real-time operating conditions of the motor, optimized operating parameters are generated. The optimized operating parameters include adjusting the motor speed, load distribution and temperature setting, etc., to generate the motor operation optimization suggestions.

[0015] By adopting the above technical solution, according to the preset motor performance optimization rules, the current fault cause of the motor is analyzed, the motor fault can be effectively diagnosed in depth, and the corresponding fault analysis results can be obtained, thereby providing accurate data support for subsequent optimization; based on the fault analysis results, combined with the real-time operating conditions of the motor, optimized operating parameters are generated, and the key parameters of the motor, such as speed, load distribution and temperature setting, can be dynamically adjusted according to different fault types and operating conditions, thereby generating more accurate motor operation optimization suggestions, and ultimately improving the stability and efficiency of the motor operation.

[0016] In a preferred example, the present application may be further configured as follows: the motor operation optimization suggestion is pushed to the terminal device to obtain the optimization operation information confirmed by the user, including: Pushing the motor operation optimization suggestion to the terminal device via a network; According to the selection or adjustment operation of the user on the motor operation optimization suggestion on the terminal device, the optimization operation information confirmed by the user is generated, and the optimization operation information confirmed by the user includes the confirmation or modification of the optimization suggestion by the user.

[0017] By adopting the above-mentioned technical solution, the motor operation optimization suggestion is pushed to the terminal device through the network, which can realize remote transmission of the optimization suggestion, so that the user can obtain the motor operation optimization suggestion in real time, thereby improving the efficiency of information transmission; according to the user's selection or adjustment operation of the motor operation optimization suggestion on the terminal device, the optimization operation information confirmed by the user is generated, which can enable the user to independently select or adjust the optimization suggestion, thereby ensuring that the optimization plan meets the actual needs of the user.

[0018] In a preferred example, the present application may be further configured as follows: generating motor control information for performing the optimization operation according to the optimization operation information, wherein the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion, including: Determine target operating parameters of the motor according to the optimization operation information, wherein the target operating parameters of the motor include but are not limited to speed, load current, temperature, etc.; Based on the motor target operating parameters and the current real-time operating parameters of the motor, the required adjustment amplitude is calculated to obtain the motor amplitude adjustment result; The motor control information is generated according to the motor amplitude adjustment result, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion.

[0019] By adopting the above technical solution, the motor target operating parameters are determined according to the optimization operation information, which can ensure that the motor operating parameters meet the target settings in the optimization suggestions, covering parameters such as speed, load current and temperature, so that the motor operating state is more accurately controllable; based on the motor target operating parameters and the current real-time operating parameters of the motor, the required adjustment amplitude is calculated to obtain the motor amplitude adjustment result, which can accurately evaluate the deviation between the current and target states of the motor and ensure the rationality and effectiveness of the adjustment amplitude; based on the motor amplitude adjustment result, the motor control information is generated, and the motor control information is used to adjust the motor operating parameters corresponding to the motor operation optimization suggestion, which can adjust the motor operating parameters in real time to make it close to or reach the optimized state, thereby effectively improving the motor's operating efficiency and performance stability.

[0020] The second object of the invention is achieved by the following technical solutions: A motor remote monitoring and optimization device, the motor remote monitoring and optimization device comprising: Real-time information acquisition module, used to obtain real-time operation information of the motor; A fault diagnosis module, used for inputting the real-time operation information of the motor into a trained fault diagnosis model to obtain fault warning information; An optimization suggestion generating module, used for generating motor operation optimization suggestions according to the fault warning information and the motor historical operation information, combined with the preset motor performance optimization rules; A user interaction module, used to push the motor operation optimization suggestion to the terminal device and obtain the optimization operation information confirmed by the user; A control information generating module is used to generate motor control information for performing an optimization operation according to the optimization operation information, wherein the motor control information is used to adjust motor operation parameters corresponding to the motor operation optimization suggestion.

[0021] By adopting the above technical solution, by obtaining the real-time operation information of the motor and inputting it into the trained fault diagnosis model, it is possible to timely identify potential faults in the motor operation process and generate corresponding fault warning information, thereby improving the real-time and accuracy of fault diagnosis, and effectively reducing the risk of motor shutdown or damage due to failure to detect potential problems in time; based on the fault warning information and the historical operation information of the motor, combined with the pre-set motor performance optimization rules, motor operation optimization suggestions are generated, and optimization solutions can be proposed before the fault occurs to prevent the problem from worsening, while improving the overall operation efficiency and performance of the motor, thereby improving the operation reliability and energy saving effect of the motor; the motor operation optimization suggestions are pushed to the terminal device to obtain the optimization operation information confirmed by the user, so that the user can verify, confirm or modify the optimization suggestions through the terminal device to ensure that the optimization solution is consistent with the actual operation needs of the user. Thereby, the flexibility of motor control and user participation are improved; based on the optimization operation information confirmed by the user, motor control information for executing the optimization operation is generated, which can ensure the accuracy and timeliness of the optimization control instructions, and the optimization operation is performed based on the parameter adjustment confirmed by the user, which effectively improves the adjustment efficiency of the motor operating parameters and ensures the stability of the motor in actual operation; the motor operating parameters corresponding to the motor operation optimization suggestions are adjusted according to the optimization operation information, which can achieve precise adjustment of the motor operating parameters, including speed, load distribution and temperature, etc., thereby improving the optimization effect during motor operation, helping to extend the life of the motor and reduce energy consumption; the motor control information is used to adjust the motor operating parameters corresponding to the motor operation optimization suggestions, which can automatically adjust the motor based on the optimization suggestions to ensure that the motor operating parameters are continuously in the best state, thereby further improving the stability of the motor operation.

[0022] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned motor remote monitoring and optimization method when executing the computer program.

[0023] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned motor remote monitoring and optimization method are implemented.

[0024] In summary, the present application includes at least one of the following beneficial technical effects: 1. By acquiring the real-time operation information of the motor and inputting it into the trained fault diagnosis model, it is possible to timely identify potential faults in the motor operation process and generate corresponding fault warning information, thereby improving the real-time and accuracy of fault diagnosis, and effectively reducing the risk of motor shutdown or damage due to failure to detect potential problems in time; based on the fault warning information and the historical operation information of the motor, combined with the pre-set motor performance optimization rules, motor operation optimization suggestions are generated, which can propose optimization plans before the fault occurs to prevent the problem from worsening, and at the same time improve the overall operation efficiency and performance of the motor, thereby improving the operation reliability and energy saving effect of the motor; the motor operation optimization suggestions are pushed to the terminal device to obtain the optimization operation information confirmed by the user, so that the user can verify, confirm or modify the optimization suggestions through the terminal device to ensure that the optimization plan is consistent with the actual operation needs of the user, thereby improving The flexibility of motor control and user participation are improved; based on the optimization operation information confirmed by the user, motor control information for executing the optimization operation is generated, which can ensure the accuracy and timeliness of the optimization control instructions, and the optimization operation is performed based on the parameter adjustment confirmed by the user, which effectively improves the adjustment efficiency of the motor operation parameters and ensures the stability of the motor in actual operation; the motor operation parameters corresponding to the motor operation optimization suggestion are adjusted according to the optimization operation information, which can achieve accurate adjustment of the motor operation parameters, including speed, load distribution and temperature, etc., thereby improving the optimization effect during the motor operation process, helping to extend the motor life and reduce energy consumption; the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion, which can automatically adjust the motor based on the optimization suggestion to ensure that the motor operation parameters are continuously in the best state, thereby further improving the stability of the motor operation; 2. According to the preset motor performance optimization rules, the current fault cause of the motor is analyzed, and the motor fault can be effectively diagnosed in depth to obtain the corresponding fault analysis results, thereby providing accurate data support for subsequent optimization; based on the fault analysis results, combined with the real-time operating conditions of the motor, the optimized operating parameters are generated, and the key parameters of the motor, such as speed, load distribution and temperature setting, can be dynamically adjusted according to different fault types and operating conditions, thereby generating more accurate motor operation optimization suggestions, and ultimately improving the stability and efficiency of the motor operation; 3. Determine the target operating parameters of the motor based on the optimization operation information, ensure that the operating parameters of the motor meet the target settings in the optimization suggestions, and cover various parameters such as speed, load current and temperature, so that the motor operating state is more accurate and controllable; calculate the required adjustment amplitude based on the motor target operating parameters and the current real-time operating parameters of the motor, and obtain the motor amplitude adjustment result, so as to accurately evaluate the deviation between the current motor and the target state, and ensure the rationality and effectiveness of the adjustment amplitude; generate the motor control information based on the motor amplitude adjustment result, and the motor control information is used to adjust the motor operating parameters corresponding to the motor operation optimization suggestion, and can adjust the motor operating parameters in real time to make it close to or reach the optimized state, thereby effectively improving the motor's operating efficiency and performance stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of a motor remote monitoring and optimization method in one embodiment of the present application; Figure 2 This is a flowchart of the implementation before step S20 in the motor remote monitoring and optimization method in one embodiment of the present application; Figure 3 This is a flowchart for implementing step S20 in the motor remote monitoring and optimization method in one embodiment of the present application; Figure 4 This is a flowchart for implementing step S30 in the motor remote monitoring and optimization method in one embodiment of the present application; Figure 5 It is a flowchart for implementing step S302 in the motor remote monitoring and optimization method in one embodiment of the present application; Figure 6 It is a flowchart for implementing step S40 in the motor remote monitoring and optimization method in one embodiment of the present application; Figure 7 It is a flowchart for implementing step S50 in the motor remote monitoring and optimization method in one embodiment of the present application; Figure 8 This is a principle block diagram of a motor remote monitoring and optimization device in one embodiment of the present application; Fig. 9 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The present application is further described in detail below in conjunction with the accompanying drawings.

[0027] In one embodiment, if Figure 1 As shown, the present application discloses a motor remote monitoring and optimization method, which specifically includes the following steps: S10: Obtain the real-time operation information of the motor.

[0028] Specifically, the process of obtaining the real-time operation information of the motor can be achieved by installing multiple sensors on the motor, including temperature sensors, vibration sensors, current sensors, etc. These sensors are responsible for collecting key operating parameters such as the motor's temperature, vibration amplitude, and current intensity, and transmitting these data to the system in real time through the field bus or wireless communication. The system will classify and filter the received data, eliminate invalid information, and perform preliminary data preprocessing, such as removing noise and calibrating sensor errors, to ensure the accuracy and real-time nature of the motor's operation information.

[0029] Furthermore, the system is intended to monitor and analyze the operating status of the motor, generate optimization suggestions based on early warning information and historical data, and finally achieve fault prevention and performance optimization by controlling the operating parameters of the motor.

[0030] S20: Input the real-time operation information of the motor into the trained fault diagnosis model to obtain fault warning information.

[0031] Specifically, the real-time operating data obtained from the motor is standardized to conform to the input format of the fault diagnosis model. Then, the central control system calls the trained fault diagnosis model, which is built on a deep learning algorithm and has been trained using a large amount of historical motor fault data. The model compares the real-time data with the historical data, and analyzes the difference between the current operating state of the motor and the normal operating conditions to detect whether there are abnormalities or potential faults. Through the output of the fault diagnosis model, the system can identify specific fault types or abnormal trends, and generate corresponding fault warning information to help users prevent motor failures in advance.

[0032] S30: Generate motor operation optimization suggestions based on the fault warning information and the motor historical operation information in combination with pre-set motor performance optimization rules.

[0033] Specifically, the system will classify the fault type and severity in the fault warning information, and retrieve the historical operation information of the motor from the database, including previous fault records, maintenance records, and performance data of the motor under different operating loads. Then, the system will use the pre-set motor performance optimization rules, which are optimization plans based on motor characteristics, load conditions, energy efficiency indicators and other conditions. By comparing the real-time fault warning of the motor with historical data, the system can identify the deviation between the current state of the motor and the optimal operating state, and generate optimization suggestions, such as adjusting the motor speed, reducing the operating load, or improving the cooling efficiency.

[0034] S40: Push the motor operation optimization suggestion to the terminal device, and obtain the optimization operation information confirmed by the user.

[0035] Specifically, the process of pushing the motor operation optimization suggestions to the terminal device communicates through the Internet or local network. The system will package the generated motor operation optimization suggestions into messages and push these optimization suggestions to the user's terminal device through a secure communication protocol (such as MQTT or HTTP protocol). The terminal device can be a mobile phone, tablet or industrial computer. The user can view the details of the optimization suggestions on the terminal device, including the recommended optimization operations, the expected performance improvement effects, etc. The user can choose to confirm, adjust or reject these optimization suggestions based on his own judgment, and send the confirmed optimization operation information back to the system through the terminal device. After receiving the user's feedback, the system will record the user's choice and generate motor control information based on the optimization operation information confirmed by the user.

[0036] S50: generating motor control information for performing the optimization operation according to the optimization operation information, wherein the motor control information is used to adjust motor operation parameters corresponding to the motor operation optimization suggestion.

[0037] Specifically, the system will determine the motor operating parameters that need to be adjusted, such as speed, load current, temperature, etc., based on the content of the optimization operation information. Then, the system will calculate the adjustment range of each parameter to ensure that the adjusted operating state meets the optimization recommendations. For example, based on the gap between the current motor operating state and the target operating state, the system will calculate the specific value by which the speed needs to be increased or decreased, as well as the adjustment range of other related parameters. After the calculation is completed, the system will generate the corresponding motor control information and send this control information to the motor controller through the field control bus or wireless module. The motor controller will adjust the motor's operating parameters based on this control information to achieve optimized operation.

[0038] In one embodiment, if Figure 2 As shown, before step S20, the real-time operation information of the motor is input into the trained fault diagnosis model to obtain fault warning information, which includes: S11: Divide the motor historical operation information into a model training set and a test set.

[0039] Specifically, the system will obtain the operating data of the motor under different working conditions, including the motor's speed, load, temperature, current and other parameters, and classify these data by chronological order or working condition category. Subsequently, the system divides these historical operating information into model training sets and test sets, ensuring that the training set contains enough instance data of motor failures and normal operation, while the test set is used to verify the generalization ability of the model. The training set and test set are divided in a ratio of 80% for training and 20% for testing, to ensure the model training effect while retaining enough data for verification.

[0040] S12: Train the fault diagnosis model using the model training set to obtain a fault diagnosis model after preliminary training.

[0041] Specifically, the system inputs the training set into the fault diagnosis model for training. The algorithm used by the model can be a deep learning algorithm. During the training process, the model gradually forms features that can identify the normal operation and fault status of the motor by learning the different state data of the motor. The system adjusts the weights of the model through back propagation to minimize the prediction error. The training process will iterate multiple times until the classification accuracy of the model on the training set reaches the set threshold. Finally, the generated preliminary trained model can make a preliminary judgment on the state of the motor.

[0042] S13: Use the model test set to verify the fault diagnosis model after preliminary training and generate a model verification result.

[0043] Specifically, the system inputs the previously divided test set into the model after preliminary training to carry out the model verification process. Through the test set, the system compares the model prediction results with the actual motor operation status, and calculates the model's accuracy, precision, recall rate and other indicators on the test data. The model verification results can reflect the model's generalization ability and performance on unknown data.

[0044] S14: adjusting the fault diagnosis model after preliminary training according to the model verification result to obtain a trained fault diagnosis model.

[0045] Specifically, the system will further adjust and optimize the model after preliminary training based on the results of the test set verification. If the model performs poorly on the test set, the system will adjust the model's hyperparameters or introduce regularization and other techniques to prevent the model from overfitting or underfitting. The adjustment process may include increasing the complexity of the model, expanding the feature space, or performing data enhancement to improve the model's diagnostic capabilities under different operating conditions. After adjustment, the system will verify the model's performance again to ensure that the model achieves the expected accuracy and stability in the fault diagnosis task, and finally obtain a trained fault diagnosis model.

[0046] In one embodiment, if Figure 3 As shown, in step S20, the real-time operation information of the motor is input into the trained fault diagnosis model to obtain fault warning information, including: S201: Compare the real-time operation information of the motor with the normal working state information of the motor in the trained fault diagnosis model to obtain a comparison result.

[0047] Specifically, the system first collects the motor's operating information in real time, including key parameters such as voltage, current, speed, and temperature, and inputs this information into the trained fault diagnosis model. The model contains the benchmark data of the motor's normal working state, which is the characteristic values ​​extracted by analyzing the historical operating information of a large number of motors under normal working conditions. The system compares the motor parameters collected in real time with the normal working condition data in the model one by one, calculates the deviation values ​​of each parameter, and generates the comparison results by comprehensively analyzing these deviations.

[0048] S202: Generate fault warning information based on the comparison result and a preset fault judgment threshold.

[0049] Specifically, the system will judge the operating status of the motor based on the deviation value identified in the comparison result and the preset fault judgment threshold. Each parameter deviation will have a corresponding threshold. For example, if the motor temperature exceeds a certain value or the abnormal current fluctuation exceeds the normal range, the system will determine whether to trigger a fault warning based on these thresholds. If the deviation in the comparison result exceeds the threshold, the system will immediately generate the corresponding fault warning information. The warning information will include specific abnormal parameters, degree of deviation, and possible causes of the fault, so that maintenance personnel can be notified in time to conduct investigation and repair, thereby avoiding potential motor failure or damage.

[0050] In one embodiment, if Figure 4 As shown, in step S30, based on the fault warning information and the historical operation information of the motor, combined with the preset motor performance optimization rules, a motor operation optimization suggestion is generated, including: S301: Determine the cause of the current motor fault according to the abnormality type identified in the fault warning information and the motor fault occurrence trend recorded in the motor historical operation information.

[0051] Specifically, after receiving the fault warning information, the system will first extract the description of the abnormality type in the warning information, such as overheating, abnormal vibration or abnormal current. Then, the system will analyze the historical operation information, including the performance data and fault records of the motor under different operating conditions, and identify the fault mode and frequency related to the current abnormality type through time series analysis technology. Analyze the fault occurrence trend in the historical data to accurately locate the cause of the current motor fault, such as whether it is caused by bearing wear, winding short circuit or overload.

[0052] S302: Obtain motor operation optimization suggestions by using pre-set motor performance optimization rules and combining the current fault causes of the motor and the real-time operating conditions of the motor.

[0053] In this embodiment, the preset motor performance optimization rules refer to a series of guiding principles and operating specifications formulated in advance based on the operating characteristics and historical fault data of the motor.

[0054] Specifically, the system will first retrieve the corresponding motor performance optimization rules based on the identified fault cause. These rules usually include detailed instructions on how to adjust the motor operating parameters, such as reducing the speed, adjusting the load distribution, or modifying the cooling strategy. Subsequently, the system will obtain the real-time operating data of the motor, including current, voltage, temperature, etc., and compare it with the preset optimization rules to evaluate the optimization potential under the current operating conditions. Finally, the system will comprehensively analyze the fault cause and real-time data to generate specific operation optimization suggestions, such as suggesting that users reduce the speed to a certain safe range and enhance the cooling system when they find that the motor is overheating, so as to ensure that the motor operates in an optimized operating state.

[0055] In one embodiment, if Figure 5 As shown, in step S302, the motor operation optimization suggestions are obtained by combining the current fault causes of the motor and the real-time operating conditions of the motor through the pre-set motor performance optimization rules, including: S3021: According to the preset motor performance optimization rules, the current fault cause of the motor is analyzed to obtain the corresponding fault analysis result.

[0056] Specifically, after determining the current cause of the motor fault, the system will call the built-in motor performance optimization rule base, which includes optimization solutions for different fault causes. The system determines the corresponding optimization steps by matching the fault cause with the fault handling strategy in the rule base. For example, when the fault cause is caused by overheating of the winding, the system will apply the corresponding temperature control management rules, analyze the possible source of overheating and derive the optimal cooling solution. At the same time, the system will combine historical fault data to generate the fault analysis results, such as the fault impact range, potential fault duration and possible equipment loss.

[0057] S3022: Based on the fault analysis results and in combination with the real-time operating conditions of the motor, generate optimized operating parameters, which include adjusting the motor speed, load distribution, and temperature settings, etc., to generate motor operation optimization suggestions.

[0058] Specifically, after obtaining the fault analysis results, the system will further calculate the optimal operating parameters of the motor based on the real-time operating conditions of the motor, such as the current speed, load status, and temperature. These parameters may include reducing the motor speed to a certain range to avoid further overheating damage, or adjusting the load distribution to balance the operating pressure of the motor. In addition, if the system detects that the temperature of the motor exceeds the safety threshold, the optimization recommendations may include lowering the temperature setting, or starting the backup cooling system to ensure that the motor runs at an appropriate temperature.

[0059] In one embodiment, if Figure 6 As shown, in step S40, the motor operation optimization suggestion is pushed to the terminal device, and the optimization operation information confirmed by the user is obtained, including: S401: Push the motor operation optimization suggestion to the terminal device via the network.

[0060] Specifically, after generating the motor operation optimization suggestions, the system will send the optimization suggestions to the connected terminal device through the network or communication equipment. The terminal device can be the user's mobile phone, computer or dedicated control panel.

[0061] S402: Generate optimization operation information confirmed by the user based on the selection or adjustment operation of the motor operation optimization suggestion on the terminal device, where the optimization operation information confirmed by the user includes the user's confirmation or modification of the optimization suggestion.

[0062] Specifically, the system will provide multiple interactive options on the terminal device, through which the user can confirm or adjust the optimization suggestions. For example, the user can choose to accept the speed adjustment plan in the suggestion, or modify the load distribution parameters according to their own needs. Each operation of the user will be recorded, and the system will generate corresponding confirmation information based on the user's operation. The confirmation information will include whether the user fully accepts the optimization suggestion, or what modifications have been made to some of the suggested parameters. The confirmation information will then be fed back to the system through the terminal device as the final basis for the optimization operation, ensuring that subsequent motor control operations can be performed according to the user's specific choices.

[0063] In one embodiment, if Figure 7 As shown, in step S50, motor control information for performing the optimization operation is generated according to the optimization operation information, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion, including: S501: Determine target operating parameters of the motor according to the optimization operation information. The target operating parameters of the motor include but are not limited to rotation speed, load current, temperature, etc.

[0064] Specifically, after receiving the optimization operation information confirmed by the user, the system will analyze the parameter adjustment requirements involved and set the corresponding target parameters according to the optimization rules. These target operating parameters may involve the core operating indicators of the motor, such as the need to adjust the motor speed to improve efficiency, adjust the load current to reduce power consumption, or adjust the upper or lower temperature limit of the motor according to the operating environment. These target parameters are the optimal operating state calculated by the system based on the current fault state of the motor, user needs and optimization suggestions, with the aim of improving the performance of the motor or extending the service life of the motor.

[0065] S502: Calculate the required adjustment amplitude based on the motor target operating parameters and the current real-time operating parameters of the motor to obtain the motor amplitude adjustment result.

[0066] Specifically, the system will first obtain the real-time operating parameters of the motor from sensors or monitoring devices, including operating data such as current speed, load current, and temperature. Then, the system will compare these real-time parameters with the target operating parameters set previously, and calculate the adjustment range of each parameter. For example, if the target speed is higher than the current speed, the speed range that needs to be increased is calculated; if the load current is too large, the current value that needs to be reduced is calculated. The system will generate a corresponding adjustment range result for each parameter that needs to be adjusted to ensure the accuracy of the adjustment and avoid damage to the motor due to excessive or insufficient adjustments.

[0067] S503: Generate motor control information according to the motor amplitude adjustment result, where the motor control information is used to adjust motor operation parameters corresponding to the motor operation optimization suggestion.

[0068] Specifically, the system generates corresponding control information based on the adjustment range of each parameter. This control information is transmitted to the motor control module in the form of digital instructions, and the motor control module makes real-time adjustments based on these instructions to ensure that the motor's operating status meets the optimization recommendations. For example, if the calculation results show that the speed needs to be increased by 5%, the system will generate corresponding speed control information and send it to the motor controller to perform the speed increase operation. This control information can not only accurately control key parameters such as the motor's speed, load current, and temperature, but also monitor the adjustment effect in real time during the adjustment process to ensure the safety and accuracy of the adjustment operation.

[0069] Furthermore, the motor control module refers to a functional unit or system component used to perform real-time control of the motor's operating state. It is responsible for receiving external input control signals, such as optimized operating parameters, and adjusting the motor's operating state by executing these signals, including motor speed, load current, temperature and other parameters.

[0070] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] In one embodiment, a motor remote monitoring and optimization device is provided, and the motor remote monitoring and optimization device corresponds to the motor remote monitoring and optimization method in the above embodiment. Figure 8 As shown, the motor remote monitoring and optimization device includes a real-time information acquisition module, a fault diagnosis module, an optimization suggestion generation module, a user interaction module and a control information generation module. The detailed description of each functional module is as follows: Real-time information acquisition module, used to obtain real-time operation information of the motor; A fault diagnosis module is used to input the real-time operation information of the motor into the trained fault diagnosis model to obtain fault warning information; An optimization suggestion generation module is used to generate motor operation optimization suggestions based on fault warning information and motor historical operation information combined with pre-set motor performance optimization rules; The user interaction module is used to push the motor operation optimization suggestions to the terminal device and obtain the optimization operation information confirmed by the user; The control information generation module is used to generate motor control information for performing optimization operations according to the optimization operation information, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestions.

[0072] Optionally, the fault diagnosis module previously includes: A historical information acquisition module is used to divide the motor historical operation information into a model training set and a test set; A model preliminary training module is used to train the fault diagnosis model using the model training set to obtain a fault diagnosis model after preliminary training; A model testing module is used to verify the fault diagnosis model after preliminary training using a model test set and generate a model verification result; The model generation module is used to adjust the fault diagnosis model after preliminary training according to the model verification result to obtain a trained fault diagnosis model.

[0073] Optionally, the fault diagnosis module includes: The comparison result submodule is used to compare the real-time operation information of the motor with the normal working state information of the motor in the trained fault diagnosis model to obtain the comparison result; The fault warning submodule is used to generate fault warning information based on the comparison result and the preset fault judgment threshold.

[0074] Optionally, the optimization suggestion generation module includes: The fault cause submodule is used to determine the current fault cause of the motor based on the abnormal type identified in the fault warning information and the motor fault occurrence trend recorded in the motor historical operation information; The optimization suggestion submodule is used to obtain motor operation optimization suggestions through pre-set motor performance optimization rules, combined with the current fault causes of the motor and the real-time operating conditions of the motor.

[0075] Optionally, the optimization suggestion submodule includes: The fault analysis result submodule is used to analyze the current fault cause of the motor according to the preset motor performance optimization rules and obtain the corresponding fault analysis results; The optimization suggestion submodule is used to generate optimized operating parameters based on the fault analysis results and the real-time operating conditions of the motor. The optimized operating parameters include adjusting the motor speed, load distribution and temperature setting, etc., to generate motor operation optimization suggestions.

[0076] Optionally, the user interaction module includes: The operation suggestion sending submodule is used to push the motor operation optimization suggestions to the terminal device through the network; The optimization suggestion confirmation submodule is used to generate user-confirmed optimization operation information based on the user's selection or adjustment operation of the motor operation optimization suggestion on the terminal device. The user-confirmed optimization operation information includes the user's confirmation or modification of the optimization suggestion.

[0077] Optionally, the control information generation module includes: A target operating parameter submodule is used to determine the motor target operating parameters according to the optimization operation information. The motor target operating parameters include but are not limited to speed, load current and temperature; The motor amplitude adjustment result submodule is used to calculate the required adjustment amplitude based on the motor target operating parameters and the current real-time operating parameters of the motor to obtain the motor amplitude adjustment result; The motor control submodule is used to generate motor control information according to the motor amplitude adjustment result, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion.

[0078] For the specific definition of the motor remote monitoring and optimization device, please refer to the definition of the motor remote monitoring and optimization method above, which will not be repeated here. Each module in the above motor remote monitoring and optimization device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0079] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a motor remote monitoring and optimization method is implemented.

[0080] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Get real-time operation information of the motor; Input the real-time operation information of the motor into the trained fault diagnosis model to obtain fault warning information; Generate motor operation optimization suggestions based on fault warning information and motor historical operation information combined with pre-set motor performance optimization rules; Push motor operation optimization suggestions to the terminal device and obtain optimization operation information confirmed by the user; According to the optimization operation information, motor control information for performing the optimization operation is generated, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion.

[0081] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Get real-time operation information of the motor; Input the real-time operation information of the motor into the trained fault diagnosis model to obtain fault warning information; Generate motor operation optimization suggestions based on fault warning information and motor historical operation information combined with pre-set motor performance optimization rules; Push motor operation optimization suggestions to the terminal device and obtain optimization operation information confirmed by the user; According to the optimization operation information, motor control information for performing the optimization operation is generated, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion.

[0082] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0083] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0084] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A motor remote monitoring and optimization method, characterized in that: The motor remote monitoring and optimization method comprises: Get real-time operation information of the motor; Inputting the real-time operation information of the motor into a trained fault diagnosis model to obtain fault warning information; Generate motor operation optimization suggestions based on the fault warning information and the motor historical operation information and in combination with pre-set motor performance optimization rules; Pushing the motor operation optimization suggestion to the terminal device to obtain the optimization operation information confirmed by the user; According to the optimization operation information, motor control information for performing the optimization operation is generated, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion.

2. The motor remote monitoring and optimization method according to claim 1, characterized in that: The real-time operation information of the motor is input into a trained fault diagnosis model to obtain fault warning information, which includes: Dividing the motor historical operation information into a model training set and a test set; Using the model training set to train the fault diagnosis model to obtain a fault diagnosis model after preliminary training; Using the model test set to verify the fault diagnosis model after preliminary training, and generating a model verification result; The fault diagnosis model after preliminary training is adjusted according to the model verification result to obtain the trained fault diagnosis model.

3. The motor remote monitoring and optimization method according to claim 1, characterized in that: The step of inputting the real-time operation information of the motor into a trained fault diagnosis model to obtain fault warning information includes: Comparing the real-time operation information of the motor with the normal working state information of the motor in the trained fault diagnosis model to obtain a comparison result; The fault warning information is generated according to the comparison result and in combination with a preset fault judgment threshold.

4. The motor remote monitoring and optimization method according to claim 1, characterized in that: The generating of motor operation optimization suggestions based on the fault warning information and the motor historical operation information in combination with the preset motor performance optimization rules includes: Determine the cause of the current motor failure according to the abnormality type identified in the fault warning information and the motor failure occurrence trend recorded in the motor historical operation information; The motor operation optimization suggestion is obtained by combining the preset motor performance optimization rules with the current fault cause of the motor and the real-time operating conditions of the motor.

5. The motor remote monitoring and optimization method according to claim 4, characterized in that: The motor operation optimization suggestion is obtained by combining the preset motor performance optimization rule with the current fault cause of the motor and the real-time operating condition of the motor, including: According to the preset motor performance optimization rules, analyzing the current fault cause of the motor to obtain corresponding fault analysis results; Based on the fault analysis result and in combination with the real-time operating conditions of the motor, optimized operating parameters are generated. The optimized operating parameters include adjusting the motor speed, load distribution and temperature setting, etc., to generate the motor operation optimization suggestions.

6. The motor remote monitoring and optimization method according to claim 1, characterized in that: The step of pushing the motor operation optimization suggestion to the terminal device and obtaining the optimization operation information confirmed by the user includes: Pushing the motor operation optimization suggestion to the terminal device via a network; According to the selection or adjustment operation of the user on the motor operation optimization suggestion on the terminal device, the optimization operation information confirmed by the user is generated, and the optimization operation information confirmed by the user includes the confirmation or modification of the optimization suggestion by the user.

7. The motor remote monitoring and optimization method according to claim 1, characterized in that: The step of generating motor control information for performing the optimization operation according to the optimization operation information, wherein the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion, includes: Determine target operating parameters of the motor according to the optimization operation information, wherein the target operating parameters of the motor include but are not limited to speed, load current, temperature, etc.; Based on the motor target operating parameters and the current real-time operating parameters of the motor, the required adjustment amplitude is calculated to obtain the motor amplitude adjustment result; The motor control information is generated according to the motor amplitude adjustment result, and the motor control information is used to adjust the motor operation parameters corresponding to the motor operation optimization suggestion.

8. A motor remote monitoring and optimization device, characterized in that: The motor remote monitoring and optimization device comprises: Real-time information acquisition module, used to obtain real-time operation information of the motor; A fault diagnosis module, used for inputting the real-time operation information of the motor into a trained fault diagnosis model to obtain fault warning information; An optimization suggestion generating module, used for generating motor operation optimization suggestions according to the fault warning information and the motor historical operation information, combined with the preset motor performance optimization rules; A user interaction module, used to push the motor operation optimization suggestion to the terminal device and obtain the optimization operation information confirmed by the user; A control information generating module is used to generate motor control information for performing an optimization operation according to the optimization operation information, wherein the motor control information is used to adjust motor operation parameters corresponding to the motor operation optimization suggestion.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the motor remote monitoring and optimization method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the motor remote monitoring and optimization method according to any one of claims 1 to 7 are implemented.

Images