Driving motor state data monitoring and processing method based on multistage feedback scheduling

Abstract

The invention discloses a driving motor state data monitoring and processing method based on multistage feedback scheduling, and relates to the technical field of driving motor monitoring, and the method comprises the steps: obtaining the real-time state data of a driving motor through the state data of a to-be-processed driving motor; acquiring the priority of the real-time state data of the driving motor according to the real-time state data of the driving motor; and according to the real-time state data of the driving motor, performing multi-level feedback scheduling processing by using the priority of the real-time state data of the driving motor to obtain a monitoring processing result of the state data of the driving motor. According to the method, a scheduling algorithm combining priority preemption scheduling, round-robin scheduling and a first arrival first service strategy is adopted, system resources can be fully utilized, and the data processing efficiency is improved. Compared with the condition that the data processing sequence is disordered in the prior art, the real-time requirement of motor state data monitoring processing can be better met.

Description

Technical Field

[0001] The present invention relates to the technical field of drive motor monitoring, and in particular to a drive motor state data monitoring and processing method based on multi-level feedback scheduling. Background Art

[0002] With the rapid development of industrial automation and electric vehicles, the stability and reliability of drive motors are becoming increasingly important. Traditional data monitoring and processing methods are often unable to efficiently process large amounts of complex motor status data, resulting in untimely and inaccurate monitoring of motor operating status, and insufficient fault prediction and diagnosis capabilities. Existing drive motor status data monitoring and processing methods have the following specific defects and deficiencies: (1) Data collection: The accuracy of the sensor is limited, and the acquisition frequency cannot meet the monitoring requirements of the motor's rapidly changing state. The sensor is easily affected by factors such as electromagnetic interference in the motor's operating environment. The collected data has large errors. At the same time, the data formats of sensors from different manufacturers and models are not unified, which makes it difficult to integrate the data, increasing the complexity of data processing. (2) Multi-level feedback scheduling: There is a lack of effective data scheduling mechanism, the data processing order is chaotic, and high-priority key data (such as dangerous signals such as motor overheating and overcurrent) may be delayed in processing, affecting the safe operation of the motor. When the operating status of the motor changes, the existing scheduling mechanism may not be able to adjust the priority and resource allocation in time, resulting in a decrease in monitoring and processing effect; (3) Data processing and analysis: As the motor is used for a longer time, its performance and failure mode may change. Existing data processing and analysis modules are difficult to update in a timely manner, resulting in a decrease in the accuracy of monitoring and processing. In addition, the current data processing and analysis algorithms may have certain limitations and cannot fully adapt to various complex motor operating conditions. For example, machine learning algorithms require a large amount of training data to achieve good results. However, in actual applications, it may be difficult to obtain sufficient high-quality training data.

[0003] Therefore, there is an urgent need for a drive motor state data monitoring and processing method based on multi-level feedback scheduling to solve the problems of poor effectiveness of the data multi-level feedback scheduling mechanism in the prior art and low reliability and safety of drive motor abnormal monitoring and processing. Summary of the invention

[0004] The purpose of the present invention is to propose a drive motor state data monitoring and processing method based on multi-level feedback scheduling, to achieve reasonable management of data through multi-level feedback scheduling, to timely discover motor operation abnormalities and perform fault prediction, to improve the reliability and safety of the motor, and to reduce maintenance costs.

[0005] To achieve the above object, the present invention provides a method for monitoring and processing state data of a drive motor based on multi-level feedback scheduling, comprising the following steps: S1, using the to-be-processed driving motor state data to obtain the real-time state data of the driving motor; S2, obtaining the priority of the real-time state data of the driving motor according to the real-time state data of the driving motor; S3. Perform multi-level feedback scheduling processing according to the real-time state data of the drive motor and the priority of the real-time state data of the drive motor to obtain a monitoring processing result of the state data of the drive motor.

[0006] Optionally, the step of acquiring the real-time state data of the drive motor by using the state data of the drive motor to be processed includes: S1-1, obtaining the to-be-processed driving motor state data of the driving motor based on the sensor acquisition device; S1-2, performing time series comprehensive processing according to the drive motor state data to be processed to obtain the drive motor state data; S1-3, constructing a historical drive motor state database according to the drive motor state data and corresponding historical drive motor state data; S1-4, using the historical drive motor state database to obtain the real-time state data of the drive motor based on the drive motor state data; Wherein, the driving motor state data is the running state data of the driving motor.

[0007] Optionally, performing time series comprehensive processing according to the drive motor state data to be processed to obtain the drive motor state data includes: S1-2-1, using the state data of the drive motor to be processed to perform time-series division processing to obtain initial state data of the drive motor; S1-2-2, performing time series processing on the initial state data of the driving motor to obtain a timestamp of the initial state data of the driving motor; S1-2-3, determine whether the drive motor initial state data all contain the timestamp of the drive motor initial state data, if so, execute S1-2-4, otherwise, retain the current drive motor initial state data, and use the corresponding drive motor initial state data without the timestamp to update the drive motor state data to be processed, and return to S1-2-1; S1-2-4. Use the timestamp of the initial state data of the driving motor and the initial state number of the driving motor to obtain the state data of the driving motor.

[0008] Optionally, using the historical drive motor state database to acquire the real-time state data of the drive motor based on the drive motor state data includes: S1-4-1, using the historical drive motor state database to obtain historical drive motor state data; S1-4-2, determine whether the historical drive motor state data completely covers the drive motor state data, if so, use the historical drive motor state data as the historical drive motor state data feature, otherwise, use the corresponding drive motor state data not covered to update the historical drive motor state database, and return to S1-4-1; S1-4-3, obtaining the current moment as the initial moment t of the driving motor state data feature; S1-4-4, respectively obtain the state data characteristics of the drive motor at the initial time t, time t+1 and time t+2; S1-4-5, determine whether the drive motor state data characteristics at the initial time t, time t+1 and time t+2 are completely consistent with the historical drive motor state data characteristics, if so, execute S1-4-6, otherwise, retain the current drive motor state data characteristics, and use the corresponding inconsistent drive motor state data characteristics to update the historical drive motor state database, and return to S1-4-1; S1-4-6. According to the state data characteristics of the drive motor at the initial time t, time t+1 and time t+2, the corresponding continuous drive motor state data is obtained as the real-time state data of the drive motor.

[0009] Optionally, the priority of obtaining the real-time status data of the driving motor according to the real-time status data of the driving motor includes: S2-1, using the real-time state data of the driving motor to perform weight distribution to obtain the real-time state data weight of the driving motor; S2-2. Obtaining the priority of the real-time status data of the driving motor according to the weight of the real-time status data of the driving motor.

[0010] Optionally, obtaining the priority of the real-time status data of the drive motor according to the weight of the real-time status data of the drive motor includes: S2-2-1, obtaining the real-time state data of the driving motor corresponding to the real-time state data weight of the driving motor according to the real-time state data weight of the driving motor as the initial real-time state data of the driving motor; S2-2-2, acquiring historical real-time state data of the drive motor according to the real-time state data weight of the drive motor and the corresponding historical real-time state data weight of the drive motor; S2-2-3, constructing a priority evaluation model according to the historical real-time status data of the drive motor; S2-2-4. Utilize the initial real-time status data of the drive motor to obtain the priority of the real-time status data of the drive motor based on the priority evaluation model.

[0011] Optionally, constructing a priority evaluation model according to the historical real-time state data of the drive motor includes: S2-2-3-1. Acquire a first historical drive motor real-time state data feature according to the historical drive motor real-time state data; S2-2-3-2, taking the first historical driving motor real-time state data feature as input, and the first historical driving motor real-time state data feature corresponding priority as output, and establishing an initial priority evaluation model based on a deep learning algorithm; S2-2-3-3, using the first historical drive motor real-time state data feature as a first training set to obtain a first verification set as the corresponding second historical drive motor real-time state data feature and the corresponding third historical drive motor real-time state data feature; S2-2-3-4, inputting the corresponding second historical drive motor real-time state data feature and the corresponding third historical drive motor real-time state data feature into the priority evaluation initial model respectively to obtain the priority corresponding to the second historical drive motor real-time state data feature and the priority corresponding to the third historical drive motor real-time state data feature; S2-2-3-5 determines whether the priority corresponding to the second historical drive motor real-time status data feature is consistent with the priority corresponding to the first historical drive motor real-time status data feature. If so, execute S2-2-3-6; otherwise, update the historical drive motor real-time status data using the priority corresponding to the second historical drive motor real-time status data feature, and return to S2-2-3-1; S2-2-3-6, determine whether the priority corresponding to the third historical drive motor real-time state data feature is consistent with the priority corresponding to the first historical drive motor real-time state data feature, if so, use the priority evaluation initial model as the priority evaluation model, otherwise, use the priority corresponding to the third historical drive motor real-time state data feature to update the historical drive motor real-time state data, and return to S2-2-3-1; Among them, the priority corresponding to the first historical drive motor real-time status data feature, the priority corresponding to the second historical drive motor real-time status data feature and the priority corresponding to the third historical drive motor real-time status data feature are the order determined according to the first historical drive motor real-time status data feature, the second historical drive motor real-time status data feature and the third historical drive motor real-time status data feature when monitoring the drive motor status data.

[0012] Optionally, performing multi-level feedback scheduling processing according to the real-time state data of the drive motor and utilizing the priority of the real-time state data of the drive motor to obtain the monitoring processing result of the state data of the drive motor includes: S3-1, performing multi-level feedback scheduling on the real-time state data of the drive motor according to the priority of the real-time state data of the drive motor to obtain a multi-level feedback scheduling result of the real-time state data of the drive motor; S3-2, obtaining historical multi-level feedback scheduling results as a second training set according to the multi-level feedback scheduling results of the real-time state data of the drive motor and the corresponding historical real-time state data of the drive motor; S3-3, obtaining the historical multi-level feedback scheduling result corresponding to the second training set as a second verification set; S3-4, taking the second training set as input, and the monitoring processing result corresponding to the second training set as output, and constructing a basic model for monitoring and processing of drive motor state data based on a support vector machine; S3-5, inputting the second verification set into the driving motor state data monitoring and processing basic model to obtain a monitoring and processing result corresponding to the second verification set; S3-6, judging whether the monitoring processing result corresponding to the second verification set is completely consistent with the output result of the drive motor state data monitoring processing basic model, if so, using the drive motor state data monitoring processing basic model as the drive motor state data monitoring processing model, and executing S3-7, otherwise, retaining the current monitoring processing result corresponding to the second verification set, and using the inconsistent monitoring processing result corresponding to the second verification set to update the second training set, and returning to S3-4; S3-7. Based on the multi-level feedback scheduling result of the real-time state data of the drive motor, a monitoring and processing result of the real-time state data of the drive motor is obtained by using a drive motor state data monitoring and processing model as the drive motor state data monitoring and processing result.

[0013] Optionally, performing multi-level feedback scheduling on the real-time state data of the drive motor according to the priority of the real-time state data of the drive motor to obtain the multi-level feedback scheduling result of the real-time state data of the drive motor includes: S3-1-1, using the priority of the real-time state data of the drive motor and the priority of the corresponding historical real-time state data of the drive motor to obtain a multi-level feedback scheduling threshold value of the corresponding historical real-time state data of the drive motor; S3-1-2, dividing the real-time state data of the driving motor according to the priority of the real-time state data of the driving motor to obtain classification data of the real-time state data of the driving motor as the real-time state priority data of the driving motor; S3-1-3, the scheduling strategies for obtaining the real-time status priority data of the driving motor according to the real-time status priority data of the driving motor are respectively a preemptive priority scheduling strategy, a time slice round-robin scheduling strategy and a first-come-first-served strategy; S3-1-4, obtaining a multi-level feedback scheduling result of the real-time state data of the drive motor using the real-time state priority data of the drive motor according to the priority of the real-time state data of the drive motor, the multi-level feedback scheduling threshold of the corresponding historical real-time state data of the drive motor and the scheduling strategy of the real-time state priority data of the drive motor; The multi-level feedback scheduling threshold is a threshold of a multi-level feedback scheduling result at different priorities after scheduling the real-time status data of the drive motor.

[0014] Optionally, obtaining a multi-level feedback scheduling result of the real-time state data of the drive motor by using the real-time state priority data of the drive motor according to the priority of the real-time state data of the drive motor, the multi-level feedback scheduling threshold of the corresponding historical real-time state data of the drive motor and the scheduling strategy of the real-time state priority data of the drive motor includes: S3-1-4-1, obtaining the multi-level feedback scheduling thresholds corresponding to the historical drive motor real-time status data as the first-level feedback scheduling threshold, the second-level feedback scheduling threshold and the third-level feedback scheduling threshold respectively; S3-1-4-2, obtaining a first feedback scheduling result of the real-time state data of the drive motor by using the preemptive priority scheduling strategy according to the priority of the real-time state data of the drive motor and the real-time state priority data of the drive motor; S3-1-4-3, determine whether the first feedback scheduling result of the real-time status data of the drive motor is consistent with the first-level scheduling threshold of the historical real-time status data of the drive motor. If so, obtain the first scheduling real-time status data of the drive motor according to the first feedback scheduling result of the real-time status data of the drive motor, and execute S3-1-4-4; otherwise, use the real-time status data of the drive motor corresponding to the first feedback scheduling result of the inconsistent real-time status data of the drive motor as the real-time status priority data of the drive motor, and return to S3-1-4-2; S3-1-4-4, obtaining a second feedback scheduling result of the real-time state data of the drive motor by using the time slice round-robin scheduling strategy according to the priority of the real-time state data of the drive motor and the first scheduling real-time state data of the drive motor; S3-1-4-5, determine whether the second feedback scheduling result of the real-time status data of the drive motor is consistent with the secondary scheduling threshold of the historical real-time status data of the drive motor. If so, obtain the second scheduled real-time status data of the drive motor according to the second feedback scheduling result of the real-time status data of the drive motor, and execute S3-1-4-6; otherwise, use the real-time status data of the drive motor corresponding to the second feedback scheduling result of the inconsistent real-time status data of the drive motor as the first scheduled real-time status data of the drive motor, and return to S3-1-4-4; S3-1-4-6, obtaining a third feedback scheduling result of the real-time status data of the drive motor by using the first-come-first-served strategy according to the priority of the real-time status data of the drive motor and the second scheduling real-time status data of the drive motor; S3-1-4-7, determine whether the third feedback scheduling result of the real-time state data of the drive motor is consistent with the third-level scheduling threshold of the historical real-time state data of the drive motor. If so, obtain the third-scheduled real-time state data of the drive motor according to the third feedback scheduling result of the real-time state data of the drive motor, and execute S3-1-4-8; otherwise, use the real-time state data of the drive motor corresponding to the third feedback scheduling result of the inconsistent real-time state data of the drive motor as the second-scheduled real-time state data of the drive motor, and return to S3-1-4-6; S3-1-3-8. Obtain the first feedback scheduling result of the real-time status data of the drive motor, the second feedback scheduling result of the real-time status data of the drive motor, and the third feedback scheduling result of the real-time status data of the drive motor as the multi-level feedback scheduling result of the real-time status data of the drive motor.

[0015] Compared with the closest prior art, the present invention has the following beneficial effects: The present invention obtains the priority of the real-time status data of the drive motor based on the historical real-time status data of the drive motor and the priority evaluation model, can more comprehensively and accurately understand the priority of the drive motor status data, improve the accuracy of the priority evaluation, and can greatly improve the accuracy of the analysis results, and can provide a more effective decision-making basis for the maintenance and management of the motor; the scheduling algorithm combining priority preemptive scheduling, time slice round-robin scheduling and first-come-first-served strategy is adopted, which can make full use of system resources and improve the efficiency of data processing. In addition, compared with the situation in which the data processing order is chaotic in the prior art, the present invention can better meet the real-time requirements of motor status data monitoring and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 The present invention is a flowchart of a method for monitoring and processing driving motor state data based on multi-level feedback scheduling according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in combination with the specific embodiment of the present invention and the corresponding drawings. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] The terms used in the embodiments of the present invention are only used to explain the specific embodiments of the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, an embodiment of the present invention provides a method for monitoring and processing state data of a driving motor based on multi-level feedback scheduling, comprising the following steps: S1, using the to-be-processed driving motor state data to obtain the real-time state data of the driving motor; S2, obtaining the priority of the real-time state data of the driving motor according to the real-time state data of the driving motor; S3. Perform multi-level feedback scheduling processing according to the real-time state data of the drive motor and the priority of the real-time state data of the drive motor to obtain a monitoring processing result of the state data of the drive motor.

[0021] S1 specifically includes: S1-1, obtaining the to-be-processed driving motor state data of the driving motor based on the sensor acquisition device; S1-2, performing time series comprehensive processing according to the drive motor state data to be processed to obtain the drive motor state data; S1-3, constructing a historical drive motor state database according to the drive motor state data and corresponding historical drive motor state data; S1-4, using the historical drive motor state database to obtain the real-time state data of the drive motor based on the drive motor state data; Wherein, the driving motor state data is the running state data of the driving motor.

[0022] S1-2 specifically includes: S1-2-1, using the state data of the drive motor to be processed to perform time-series division processing to obtain initial state data of the drive motor; S1-2-2, performing time series processing on the initial state data of the driving motor to obtain a timestamp of the initial state data of the driving motor; S1-2-3, determine whether the drive motor initial state data all contain the timestamp of the drive motor initial state data, if so, execute S1-2-4, otherwise, retain the current drive motor initial state data, and use the corresponding drive motor initial state data without the timestamp to update the drive motor state data to be processed, and return to S1-2-1; S1-2-4. Use the timestamp of the initial state data of the driving motor and the initial state number of the driving motor to obtain the state data of the driving motor.

[0023] S1-4 specifically includes: S1-4-1, using the historical drive motor state database to obtain historical drive motor state data; S1-4-2, determine whether the historical drive motor state data completely covers the drive motor state data, if so, use the historical drive motor state data as the historical drive motor state data feature, otherwise, use the corresponding drive motor state data not covered to update the historical drive motor state database, and return to S1-4-1; S1-4-3, obtaining the current moment as the initial moment t of the driving motor state data feature; S1-4-4, respectively obtain the state data characteristics of the drive motor at the initial time t, time t+1 and time t+2; S1-4-5, determine whether the drive motor state data characteristics at the initial time t, time t+1 and time t+2 are completely consistent with the historical drive motor state data characteristics, if so, execute S1-4-6, otherwise, retain the current drive motor state data characteristics, and use the corresponding inconsistent drive motor state data characteristics to update the historical drive motor state database, and return to S1-4-1; S1-4-6. According to the state data characteristics of the drive motor at the initial time t, time t+1 and time t+2, the corresponding continuous drive motor state data is obtained as the real-time state data of the drive motor.

[0024] S2 specifically includes: S2-1, using the real-time state data of the driving motor to perform weight distribution to obtain the real-time state data weight of the driving motor; S2-2, obtaining the priority of the real-time state data of the driving motor according to the weight of the real-time state data of the driving motor; In this embodiment, the weights of the current, temperature and rotation speed in the real-time status data of the drive motor are set to 0.4, 0.3 and 0.3 respectively.

[0025] S2-2 specifically includes: S2-2-1, obtaining the real-time state data of the driving motor corresponding to the real-time state data weight of the driving motor according to the real-time state data weight of the driving motor as the initial real-time state data of the driving motor; S2-2-2, acquiring historical real-time state data of the drive motor according to the real-time state data weight of the drive motor and the corresponding historical real-time state data weight of the drive motor; S2-2-3, constructing a priority evaluation model according to the historical real-time status data of the drive motor; S2-2-4. Utilize the initial real-time status data of the drive motor to obtain the priority of the real-time status data of the drive motor based on the priority evaluation model.

[0026] S2-2-3 specifically includes: S2-2-3-1. Acquire a first historical drive motor real-time state data feature according to the historical drive motor real-time state data; S2-2-3-2, taking the first historical driving motor real-time state data feature as input, and the first historical driving motor real-time state data feature corresponding priority as output, and establishing an initial priority evaluation model based on a deep learning algorithm; S2-2-3-3, using the first historical drive motor real-time state data feature as a first training set to obtain a first verification set as the corresponding second historical drive motor real-time state data feature and the corresponding third historical drive motor real-time state data feature; S2-2-3-4, inputting the corresponding second historical drive motor real-time state data feature and the corresponding third historical drive motor real-time state data feature into the priority evaluation initial model respectively to obtain the priority corresponding to the second historical drive motor real-time state data feature and the priority corresponding to the third historical drive motor real-time state data feature; S2-2-3-5 determines whether the priority corresponding to the second historical drive motor real-time status data feature is consistent with the priority corresponding to the first historical drive motor real-time status data feature. If so, execute S2-2-3-6; otherwise, update the historical drive motor real-time status data using the priority corresponding to the second historical drive motor real-time status data feature, and return to S2-2-3-1; S2-2-3-6, determine whether the priority corresponding to the third historical drive motor real-time state data feature is consistent with the priority corresponding to the first historical drive motor real-time state data feature, if so, use the priority evaluation initial model as the priority evaluation model, otherwise, use the priority corresponding to the third historical drive motor real-time state data feature to update the historical drive motor real-time state data, and return to S2-2-3-1; Among them, the priority corresponding to the first historical drive motor real-time status data feature, the priority corresponding to the second historical drive motor real-time status data feature and the priority corresponding to the third historical drive motor real-time status data feature are the order determined according to the first historical drive motor real-time status data feature, the second historical drive motor real-time status data feature and the third historical drive motor real-time status data feature when monitoring the drive motor status data.

[0027] S3 specifically includes: S3-1, performing multi-level feedback scheduling on the real-time state data of the drive motor according to the priority of the real-time state data of the drive motor to obtain a multi-level feedback scheduling result of the real-time state data of the drive motor; S3-2, obtaining historical multi-level feedback scheduling results as a second training set according to the multi-level feedback scheduling results of the real-time state data of the drive motor and the corresponding historical real-time state data of the drive motor; S3-3, obtaining the historical multi-level feedback scheduling result corresponding to the second training set as a second verification set; S3-4, taking the second training set as input, and the monitoring processing result corresponding to the second training set as output, and constructing a basic model for monitoring and processing of drive motor state data based on a support vector machine; S3-5, inputting the second verification set into the driving motor state data monitoring and processing basic model to obtain a monitoring and processing result corresponding to the second verification set; S3-6, judging whether the monitoring processing result corresponding to the second verification set is completely consistent with the output result of the drive motor state data monitoring processing basic model, if so, using the drive motor state data monitoring processing basic model as the drive motor state data monitoring processing model, and executing S3-7, otherwise, retaining the current monitoring processing result corresponding to the second verification set, and using the inconsistent monitoring processing result corresponding to the second verification set to update the second training set, and returning to S3-4; S3-7. Based on the multi-level feedback scheduling result of the real-time state data of the drive motor, a monitoring and processing result of the real-time state data of the drive motor is obtained by using a drive motor state data monitoring and processing model as the drive motor state data monitoring and processing result.

[0028] S3-1 specifically includes: S3-1-1, using the priority of the real-time state data of the drive motor and the priority of the corresponding historical real-time state data of the drive motor to obtain a multi-level feedback scheduling threshold value of the corresponding historical real-time state data of the drive motor; S3-1-2, dividing the real-time state data of the driving motor according to the priority of the real-time state data of the driving motor to obtain classification data of the real-time state data of the driving motor as the real-time state priority data of the driving motor; S3-1-3, the scheduling strategies for obtaining the real-time status priority data of the driving motor according to the real-time status priority data of the driving motor are respectively a preemptive priority scheduling strategy, a time slice round-robin scheduling strategy and a first-come-first-served strategy; S3-1-4, obtaining a multi-level feedback scheduling result of the real-time state data of the drive motor using the real-time state priority data of the drive motor according to the priority of the real-time state data of the drive motor, the multi-level feedback scheduling threshold of the corresponding historical real-time state data of the drive motor and the scheduling strategy of the real-time state priority data of the drive motor; The multi-level feedback scheduling threshold is a threshold of a multi-level feedback scheduling result at different priorities after scheduling the real-time status data of the drive motor.

[0029] S3-1-4 specifically includes: S3-1-4-1, obtaining the multi-level feedback scheduling thresholds corresponding to the historical drive motor real-time status data as the first-level feedback scheduling threshold, the second-level feedback scheduling threshold and the third-level feedback scheduling threshold respectively; S3-1-4-2, obtaining a first feedback scheduling result of the real-time state data of the drive motor by using the preemptive priority scheduling strategy according to the priority of the real-time state data of the drive motor and the real-time state priority data of the drive motor; S3-1-4-3, determine whether the first feedback scheduling result of the real-time status data of the drive motor is consistent with the first-level scheduling threshold of the historical real-time status data of the drive motor. If so, obtain the first scheduling real-time status data of the drive motor according to the first feedback scheduling result of the real-time status data of the drive motor, and execute S3-1-4-4; otherwise, use the real-time status data of the drive motor corresponding to the first feedback scheduling result of the inconsistent real-time status data of the drive motor as the real-time status priority data of the drive motor, and return to S3-1-4-2; S3-1-4-4, obtaining a second feedback scheduling result of the real-time state data of the drive motor by using the time slice round-robin scheduling strategy according to the priority of the real-time state data of the drive motor and the first scheduling real-time state data of the drive motor; S3-1-4-5, determine whether the second feedback scheduling result of the real-time status data of the drive motor is consistent with the secondary scheduling threshold of the historical real-time status data of the drive motor. If so, obtain the second scheduled real-time status data of the drive motor according to the second feedback scheduling result of the real-time status data of the drive motor, and execute S3-1-4-6; otherwise, use the real-time status data of the drive motor corresponding to the second feedback scheduling result of the inconsistent real-time status data of the drive motor as the first scheduled real-time status data of the drive motor, and return to S3-1-4-4; S3-1-4-6, obtaining a third feedback scheduling result of the real-time status data of the drive motor by using the first-come-first-served strategy according to the priority of the real-time status data of the drive motor and the second scheduling real-time status data of the drive motor; S3-1-4-7, determine whether the third feedback scheduling result of the real-time state data of the drive motor is consistent with the third-level scheduling threshold of the historical real-time state data of the drive motor. If so, obtain the third-scheduled real-time state data of the drive motor according to the third feedback scheduling result of the real-time state data of the drive motor, and execute S3-1-4-8; otherwise, use the real-time state data of the drive motor corresponding to the third feedback scheduling result of the inconsistent real-time state data of the drive motor as the second-scheduled real-time state data of the drive motor, and return to S3-1-4-6; S3-1-3-8. Obtain the first feedback scheduling result of the real-time status data of the drive motor, the second feedback scheduling result of the real-time status data of the drive motor, and the third feedback scheduling result of the real-time status data of the drive motor as the multi-level feedback scheduling result of the real-time status data of the drive motor.

[0030] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0031] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0032] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0033] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring and processing driving motor state data based on multi-level feedback scheduling, characterized in that: The specific steps include: S1, using the to-be-processed driving motor state data to obtain the real-time state data of the driving motor; S2, obtaining the priority of the real-time state data of the driving motor according to the real-time state data of the driving motor; S3. Perform multi-level feedback scheduling processing according to the real-time state data of the drive motor and the priority of the real-time state data of the drive motor to obtain a monitoring processing result of the state data of the drive motor.

2. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 1 is characterized in that: The method of obtaining the real-time state data of the drive motor by using the state data of the drive motor to be processed comprises: S1-1, obtaining the to-be-processed driving motor state data of the driving motor based on the sensor acquisition device; S1-2, performing time series comprehensive processing according to the drive motor state data to be processed to obtain the drive motor state data; S1-3, constructing a historical drive motor state database according to the drive motor state data and corresponding historical drive motor state data; S1-4, using the historical drive motor state database to obtain the real-time state data of the drive motor based on the drive motor state data; Wherein, the driving motor state data is the running state data of the driving motor.

3. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 2 is characterized in that: Performing time series comprehensive processing according to the drive motor state data to be processed to obtain the drive motor state data includes: S1-2-1, using the state data of the drive motor to be processed to perform time-series division processing to obtain initial state data of the drive motor; S1-2-2, performing time series processing on the initial state data of the driving motor to obtain a timestamp of the initial state data of the driving motor; S1-2-3, determine whether the initial state data of the drive motor all contain the timestamp of the initial state data of the drive motor, if so, execute S1-2-4, otherwise, retain the current initial state data of the drive motor, and use the corresponding initial state data of the drive motor without the timestamp to update the to-be-processed drive motor state data, and return to S1-2-1; S1-2-4. Use the timestamp of the initial state data of the driving motor and the initial state number of the driving motor to obtain the state data of the driving motor.

4. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 2 is characterized in that: Acquiring the real-time state data of the drive motor based on the drive motor state data using the historical drive motor state database includes: S1-4-1, using the historical drive motor state database to obtain historical drive motor state data; S1-4-2, determine whether the historical drive motor state data completely covers the drive motor state data, if so, use the historical drive motor state data as the historical drive motor state data feature, otherwise, use the corresponding drive motor state data not covered to update the historical drive motor state database, and return to S1-4-1; S1-4-3, obtaining the current moment as the initial moment t of the driving motor state data feature; S1-4-4, respectively obtain the state data characteristics of the drive motor at the initial time t, time t+1 and time t+2; S1-4-5, determine whether the drive motor state data characteristics at the initial time t, time t+1 and time t+2 are completely consistent with the historical drive motor state data characteristics, if so, execute S1-4-6, otherwise, retain the current drive motor state data characteristics, and use the corresponding inconsistent drive motor state data characteristics to update the historical drive motor state database, and return to S1-4-1; S1-4-6. According to the state data characteristics of the drive motor at the initial time t, time t+1 and time t+2, the corresponding continuous drive motor state data is obtained as the real-time state data of the drive motor.

5. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 1 is characterized in that: The priority of obtaining the real-time status data of the drive motor according to the real-time status data of the drive motor includes: S2-1, using the real-time state data of the driving motor to perform weight distribution to obtain the real-time state data weight of the driving motor; S2-2. Obtaining the priority of the real-time status data of the driving motor according to the weight of the real-time status data of the driving motor.

6. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 5 is characterized in that: Acquiring the priority of the real-time state data of the drive motor according to the real-time state data weight of the drive motor includes: S2-2-1, obtaining the real-time state data of the driving motor corresponding to the real-time state data weight of the driving motor according to the real-time state data weight of the driving motor as the initial real-time state data of the driving motor; S2-2-2, acquiring historical real-time state data of the drive motor according to the real-time state data weight of the drive motor and the corresponding historical real-time state data weight of the drive motor; S2-2-3, constructing a priority evaluation model according to the historical real-time status data of the drive motor; S2-2-4. Utilize the initial real-time status data of the drive motor to obtain the priority of the real-time status data of the drive motor based on the priority evaluation model.

7. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 6 is characterized in that: Constructing a priority evaluation model based on the historical real-time status data of the drive motor includes: S2-2-3-1. Acquire a first historical drive motor real-time state data feature according to the historical drive motor real-time state data; S2-2-3-2, taking the first historical driving motor real-time state data feature as input, and the first historical driving motor real-time state data feature corresponding priority as output, and establishing an initial priority evaluation model based on a deep learning algorithm; S2-2-3-3, using the first historical drive motor real-time state data feature as a first training set to obtain a first verification set as the corresponding second historical drive motor real-time state data feature and the corresponding third historical drive motor real-time state data feature; S2-2-3-4, inputting the corresponding second historical drive motor real-time state data feature and the corresponding third historical drive motor real-time state data feature into the priority evaluation initial model respectively to obtain the priority corresponding to the second historical drive motor real-time state data feature and the priority corresponding to the third historical drive motor real-time state data feature; S2-2-3-5 determines whether the priority corresponding to the second historical drive motor real-time status data feature is consistent with the priority corresponding to the first historical drive motor real-time status data feature. If so, execute S2-2-3-6; otherwise, update the historical drive motor real-time status data using the priority corresponding to the second historical drive motor real-time status data feature, and return to S2-2-3-1; S2-2-3-6, determine whether the priority corresponding to the third historical drive motor real-time state data feature is consistent with the priority corresponding to the first historical drive motor real-time state data feature, if so, use the priority evaluation initial model as the priority evaluation model, otherwise, use the priority corresponding to the third historical drive motor real-time state data feature to update the historical drive motor real-time state data, and return to S2-2-3-1; Among them, the priority corresponding to the first historical drive motor real-time status data feature, the priority corresponding to the second historical drive motor real-time status data feature and the priority corresponding to the third historical drive motor real-time status data feature are the order determined according to the first historical drive motor real-time status data feature, the second historical drive motor real-time status data feature and the third historical drive motor real-time status data feature when monitoring the drive motor status data.

8. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 1 is characterized in that: Performing multi-level feedback scheduling processing according to the real-time state data of the drive motor and utilizing the priority of the real-time state data of the drive motor to obtain the monitoring processing result of the state data of the drive motor includes: S3-1, performing multi-level feedback scheduling on the real-time state data of the drive motor according to the priority of the real-time state data of the drive motor to obtain a multi-level feedback scheduling result of the real-time state data of the drive motor; S3-2, obtaining historical multi-level feedback scheduling results as a second training set according to the multi-level feedback scheduling results of the real-time state data of the drive motor and corresponding historical real-time state data of the drive motor; S3-3, obtaining the historical multi-level feedback scheduling result corresponding to the second training set as a second verification set; S3-4, taking the second training set as input, and the monitoring processing result corresponding to the second training set as output, and constructing a basic model for monitoring and processing of drive motor state data based on a support vector machine; S3-5, inputting the second verification set into the driving motor state data monitoring and processing basic model to obtain a monitoring and processing result corresponding to the second verification set; S3-6, judging whether the monitoring processing result corresponding to the second verification set is completely consistent with the output result of the drive motor state data monitoring processing basic model, if so, using the drive motor state data monitoring processing basic model as the drive motor state data monitoring processing model, and executing S3-7, otherwise, retaining the current monitoring processing result corresponding to the second verification set, and using the inconsistent monitoring processing result corresponding to the second verification set to update the second training set, and returning to S3-4; S3-7. Based on the multi-level feedback scheduling result of the real-time state data of the drive motor, a monitoring and processing result of the real-time state data of the drive motor is obtained by using a drive motor state data monitoring and processing model as the drive motor state data monitoring and processing result.

9. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 8 is characterized in that: The multi-level feedback scheduling of the real-time state data of the driving motor is performed by using the priority of the real-time state data of the driving motor to obtain the multi-level feedback scheduling result of the real-time state data of the driving motor, including: S3-1-1, using the priority of the real-time state data of the drive motor and the priority of the corresponding historical real-time state data of the drive motor to obtain a multi-level feedback scheduling threshold value corresponding to the historical real-time state data of the drive motor; S3-1-2, dividing the real-time state data of the driving motor according to the priority of the real-time state data of the driving motor to obtain classification data of the real-time state data of the driving motor as the real-time state priority data of the driving motor; S3-1-3, the scheduling strategies for obtaining the real-time status priority data of the driving motor according to the real-time status priority data of the driving motor are respectively a preemptive priority scheduling strategy, a time slice round-robin scheduling strategy and a first-come-first-served strategy; S3-1-4, obtaining a multi-level feedback scheduling result of the real-time state data of the drive motor using the real-time state priority data of the drive motor according to the priority of the real-time state data of the drive motor, the multi-level feedback scheduling threshold of the corresponding historical real-time state data of the drive motor and the scheduling strategy of the real-time state priority data of the drive motor; The multi-level feedback scheduling threshold is a threshold of a multi-level feedback scheduling result at different priorities after scheduling the real-time status data of the drive motor.

10. The driving motor state data monitoring and processing method based on multi-level feedback scheduling according to claim 9 is characterized in that: According to the priority of the real-time state data of the driving motor, the multi-level feedback scheduling threshold of the corresponding historical real-time state data of the driving motor and the scheduling strategy of the real-time state priority data of the driving motor, the multi-level feedback scheduling result of the real-time state data of the driving motor is obtained by using the real-time state priority data of the driving motor, including: S3-1-4-1, obtaining the multi-level feedback scheduling thresholds corresponding to the historical drive motor real-time status data as the first-level feedback scheduling threshold, the second-level feedback scheduling threshold and the third-level feedback scheduling threshold respectively; S3-1-4-2, obtaining a first feedback scheduling result of the real-time state data of the drive motor by using the preemptive priority scheduling strategy according to the priority of the real-time state data of the drive motor and the real-time state priority data of the drive motor; S3-1-4-3, determine whether the first feedback scheduling result of the real-time status data of the drive motor is consistent with the first-level scheduling threshold of the historical real-time status data of the drive motor. If so, obtain the first scheduling real-time status data of the drive motor according to the first feedback scheduling result of the real-time status data of the drive motor, and execute S3-1-4-4; otherwise, use the real-time status data of the drive motor corresponding to the first feedback scheduling result of the inconsistent real-time status data of the drive motor as the real-time status priority data of the drive motor, and return to S3-1-4-2; S3-1-4-4, obtaining a second feedback scheduling result of the real-time state data of the drive motor by using the time slice round-robin scheduling strategy according to the priority of the real-time state data of the drive motor and the first scheduling real-time state data of the drive motor; S3-1-4-5, determine whether the second feedback scheduling result of the real-time status data of the drive motor is consistent with the secondary scheduling threshold of the historical real-time status data of the drive motor. If so, obtain the second scheduled real-time status data of the drive motor according to the second feedback scheduling result of the real-time status data of the drive motor, and execute S3-1-4-6; otherwise, use the real-time status data of the drive motor corresponding to the second feedback scheduling result of the inconsistent real-time status data of the drive motor as the first scheduled real-time status data of the drive motor, and return to S3-1-4-4; S3-1-4-6, obtaining a third feedback scheduling result of the real-time status data of the drive motor by using the first-come-first-served strategy according to the priority of the real-time status data of the drive motor and the second scheduling real-time status data of the drive motor; S3-1-4-7, determine whether the third feedback scheduling result of the real-time state data of the drive motor is consistent with the third-level scheduling threshold of the historical real-time state data of the drive motor. If so, obtain the third-scheduled real-time state data of the drive motor according to the third feedback scheduling result of the real-time state data of the drive motor, and execute S3-1-4-8; otherwise, use the real-time state data of the drive motor corresponding to the third feedback scheduling result of the inconsistent real-time state data of the drive motor as the second-scheduled real-time state data of the drive motor, and return to S3-1-4-6; S3-1-3-8. Obtain the first feedback scheduling result of the real-time status data of the drive motor, the second feedback scheduling result of the real-time status data of the drive motor, and the third feedback scheduling result of the real-time status data of the drive motor as the multi-level feedback scheduling result of the real-time status data of the drive motor.

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