CN-121982099-A - Intelligent assembling and optimizing method for power motor parts

Abstract

The invention relates to an intelligent assembling and optimizing method of a power motor part in the technical field of new generation information, which comprises the steps of adjusting a test protocol according to the potential defect position, wherein the test protocol comprises a sensor monitoring rule, acquiring real-time sensor signal data, marking as an abnormal event if the sensor signal data fluctuation exceeds a preset range, obtaining a performance evaluation index, extracting working condition adaptation characteristics from the performance evaluation index, wherein the working condition adaptation characteristics reflect the response capability of a motor to environmental change, evaluating the stability of the motor under complex working conditions by adopting a classification algorithm, judging a life prediction result, obtaining an optimizing suggestion sequence, updating production parameters aiming at the optimizing suggestion sequence, wherein the production parameters relate to assembling and testing configuration, acquiring feedback cycle data, and locking parameter configuration if the feedback cycle data indicate consistency improvement, thus obtaining a final production model.

Inventors

• DING XINCAN
• HUANG SHENGXIN

Assignees

• 东莞市勇飞五金制品有限公司

Dates

Publication Date

20260505

Application Date

20260108

Claims (9)

1. 1. A power motor part intelligent assembling and optimizing method is characterized by comprising the steps of obtaining initial positioning data of a power motor part through image collecting equipment, processing the initial positioning data by a neural network algorithm to identify edges and center points of the part to obtain accurate part coordinate information, controlling a mechanical arm to execute automatic alignment operation according to the part coordinate information, monitoring displacement deviation data in real time in an alignment process, adjusting mechanical arm speed parameters to obtain a stable assembling position if the displacement deviation data exceeds a preset threshold value, obtaining connection parameters from the assembling position, analyzing the connection parameters by a classification algorithm to judge a fastening state, determining assembling quality data, introducing the assembling quality data into a simulated operation environment to simulate, analyzing characteristics in a virtual operation sequence by the neural network algorithm to determine potential defect positions, adjusting a test protocol according to the potential defect positions, obtaining real-time sensor signal data, marking the sensor signal data to be an abnormal event if the sensor signal data fluctuation exceeds a preset range, obtaining a performance evaluation index, extracting working condition adaptation characteristics from the performance evaluation index, adopting a classification algorithm to evaluate motor stability, judging a service life prediction result, generating an optimized recommended sequence, updating the recommended production sequence, and obtaining the recommended circulation feedback sequence, and finally obtaining the production parameter configuration and the recommended circulation feedback data if the optimized sequence is consistent.
2. 2. The intelligent power motor component assembling and optimizing method is characterized in that initial positioning data of a power motor component are obtained through an image acquisition device, the initial positioning data are processed through a neural network algorithm to identify component edges and center points, accurate component coordinate information is obtained, the power motor component is subjected to data acquisition through the image acquisition device, format processing is conducted on the captured image data to obtain standardized first image data, a pre-established neural network model is adopted to conduct edge detection on the first image data, boundary lines of the component are identified, position information of the component edges is determined, geometric center points are calculated according to the position information of the component edges, spatial coordinate data of the center points are obtained, if the spatial coordinate data of the center points are not identical with a preset reference coordinate range, local area enhancement processing is conducted on the first image data to obtain second image data, relative position relation between the component edges and the center points is judged by conducting edge detection on the second image data again, final position of the component is calculated according to the relative position relation, and accurate coordinate information of the component is obtained, and accurate positioning structure information of the component is completely distributed.
3. 3. The intelligent assembling and optimizing method for the power motor part is characterized by comprising the steps of carrying out automatic alignment operation according to the part coordinate information, monitoring displacement deviation data in real time in the alignment process, adjusting a speed parameter of the mechanical arm to obtain a stable assembling position if the displacement deviation data exceeds a preset threshold value, acquiring initial position data through sensor equipment to acquire the part coordinate information, determining starting point configuration of the mechanical arm, driving the mechanical arm to carry out automatic alignment task according to the initial position data, monitoring the displacement deviation data in real time in the moving process to obtain a current deviation state, comparing the current deviation state with a preset threshold value, triggering dynamic adjustment of the speed parameter if the deviation state exceeds the preset threshold value, acquiring an adjusted control instruction, continuously acquiring the displacement deviation data fed back by a sensor through the adjusted control instruction, judging whether the mechanical arm is close to the stable state or not, optimizing the speed parameter again and updating the control instruction if the continuously acquired displacement deviation data exceeds the preset threshold value, obtaining a more accurate and stable assembling position according to the more accurate and stable moving state of the mechanical arm, and finally determining the stable assembling position.
4. 4. The intelligent power motor component assembling and optimizing method according to claim 1, wherein the steps of obtaining connection parameters from the assembling position, analyzing the connection parameters by using a classification algorithm to judge the fastening state, and determining assembling quality data comprise the steps of obtaining connection parameter data from the assembling position, including fastening strength and position offset information, and performing preliminary arrangement on the data by using an automatic acquisition tool to obtain a structured connection parameter set; the method comprises the steps of analyzing fastening force and position deviation according to a structured connection parameter set, judging whether the fastening state reaches a preset qualified standard or not by using a classification algorithm, obtaining a state classification result, detecting whether the fastening force is uniform or not according to the state classification result, judging that the fastening force is uniform if the fastening force is within a preset threshold range, obtaining force uniformity data, analyzing the influence of the position deviation on the fastening state according to the force uniformity data and the state classification result, marking as a disqualified state if the position deviation exceeds a preset allowable range, determining deviation influence data, comprehensively evaluating quality performance in the assembly process by using the deviation influence data and the force uniformity data, judging whether potential assembly defects exist or not, obtaining quality evaluation data, generating detailed analysis records of assembly quality according to the quality evaluation data, and determining a final assembly quality grade through data comparison and history record analysis.
5. 5. The intelligent power motor component assembling and optimizing method is characterized in that the assembling quality data are imported into a simulated operation environment to simulate, characteristics in a virtual operation sequence are analyzed through a neural network algorithm, the positions of potential defects are determined, key operation parameters are extracted from the assembling quality data, preliminary arrangement is conducted on the extracted parameters to generate a structured operation data set, the operation data set is imported into the pre-built simulated operation environment, scene simulation is conducted on various load conditions, virtual sequence data under different working conditions are collected to determine the integrity of the virtual sequence, the characteristics of vibration signals are extracted through the neural network algorithm according to the virtual sequence data, abnormal fluctuation of the vibration signals is analyzed to judge whether potential defect risks exist, according to analysis results of the vibration signals, temperature change data in the virtual sequence are combined, whether the temperature change exceeds a preset threshold range is detected, if the temperature change exceeds the preset threshold range, the temperature change is marked to be abnormal state, abnormal state identification is obtained, specific areas where defects possibly exist are located through the abnormal state identification, relevance of the defect areas and the operation data is conducted through a data comparison tool, the defect location information is obtained, and the defect location information is well-ordered according to the defect location information, and the defect location information is well-ordered is recorded according to the analysis results of the vibration signals.
6. 6. The intelligent power motor component assembling and optimizing method according to claim 1, wherein the method comprises the steps of adjusting a test protocol according to the potential defect position, obtaining real-time sensor signal data, marking the sensor signal data as an abnormal event if the fluctuation of the sensor signal data exceeds a preset range, obtaining a performance evaluation index, comprising the steps of obtaining real-time signal data collected by a sensor monitoring system, carrying out directional data collection aiming at the potential defect position, carrying out preliminary screening on the signal data to obtain a filtered signal data set, carrying out data fluctuation analysis according to the filtered signal data set by adopting a preset monitoring rule, marking the sensor signal data as a suspected abnormal event if the fluctuation of the data exceeds the preset range, determining a suspected abnormal event set aiming at the suspected abnormal event set, obtaining historical signal data related to the potential defect position, judging whether the sensor signal data is a real abnormal event by comparing the fluctuation characteristics of the historical signal data and the current suspected abnormal event, obtaining a real abnormal event list, carrying out analysis on the relevance of the abnormal event and the defect position according to the real abnormal event list and combining an evaluation standard in the test protocol, carrying out preliminary evaluation index, carrying out preliminary evaluation on the performance-related information of the abnormal event and the defect position by adopting a mapping relation, carrying out preliminary evaluation index, carrying out preliminary evaluation on the performance-related information according to the map-related performance-related information, and carrying out a characteristic correction index, if the performance-related information is not carrying out preliminary evaluation on the map-related to the suspected abnormal event set, and carrying out a preliminary evaluation index, and obtaining a final performance evaluation index data set.
7. 7. The intelligent power motor component assembling and optimizing method according to claim 1 is characterized in that working condition adaptation features are extracted from performance evaluation indexes, motor stability is evaluated by means of a classification algorithm, life prediction results are judged, an optimized suggestion sequence is generated, the method comprises the steps of extracting working condition adaptation features from the performance evaluation indexes, conducting feature extraction processing on various environmental change data in the motor operation process, obtaining a working condition adaptation feature set through analysis of historical operation records and data collected in real time, analyzing response capability of a motor under the environmental change according to the working condition adaptation feature set, classifying the feature set by means of a support vector machine algorithm, determining the response capability level of the motor under different environmental changes, judging motor stability according to the response capability level, weighting complex working condition data if the response capability level is lower than a preset threshold value, obtaining a stability evaluation result, predicting the residual working condition life of the motor according to the stability evaluation result, obtaining a life prediction value through comparison of the current stability evaluation result and the historical data, determining a life prediction value according to the service life prediction value, optimizing the key factor according to the working condition prediction value, determining the optimal suggestion sequence according to the response capability level, and the optimal suggestion sequence is generated when the response capability level is lower than a preset threshold value, and the optimal service life is lower than a critical factor.
8. 8. The intelligent assembling and optimizing method for the power motor component according to claim 1 is characterized in that production parameters are updated according to the optimizing suggestion sequence, feedback circulation data are obtained, if the feedback circulation data indicate consistency improvement, parameter configuration is locked, a final production model is obtained, the method comprises the steps of obtaining an adjusting direction of the production parameters through analysis of the optimizing suggestion sequence, carrying out initial updating on the assembling configuration and the testing configuration to obtain an initial adjusting scheme, carrying out configuration updating of the production parameters according to the initial adjusting scheme, recording specific change data of the assembling configuration and the testing configuration, determining updated parameter sets, starting a feedback circulation mechanism according to the updated parameter sets, obtaining real-time data flow from a production process, judging whether the data flow meets preset consistency standards, if the data flow meets the preset parameter sets, locking the current parameter sets, if the data flow does not meet the consistency standards, adjusting fine values of the production parameters based on data difference of the feedback circulation, obtaining corrected parameter sets, carrying out production process again, obtaining new feedback circulation data according to the initial adjusting scheme, judging whether the consistency preset threshold value is met, and if the corrected parameter sets meet the preset threshold value, carrying out stable construction of the production model according to the fixed parameter set, and carrying out stable construction on the production model according to the fixed parameter set.
9. 9. The intelligent power motor component assembling and optimizing method according to claim 1, wherein the steps of importing the assembling quality data into a simulated operation environment for simulation, analyzing features in a virtual operation sequence by using a neural network algorithm, and determining potential defect positions include extracting key operation parameters from the assembling quality data, and performing data arrangement on the extracted parameters to generate a structured operation data set to obtain operation data for subsequent analysis; the method comprises the steps of importing operation data into a pre-constructed simulation environment, conducting scene simulation on various load conditions, collecting virtual sequence data under different working conditions, determining the integrity of a virtual sequence, conducting feature extraction on vibration signals by means of a neural network algorithm on the virtual sequence data, analyzing abnormal fluctuation of the vibration signals, judging whether potential defect risks exist or not, detecting whether temperature changes exceed a preset threshold range according to analysis results of the vibration signals and temperature change data in the virtual sequence, marking the abnormal state if the temperature changes exceed the preset threshold range, obtaining abnormal state identification, locating specific areas where defects possibly exist through the abnormal state identification, analyzing relevance between the defect areas and the operation data by means of a data comparison tool to obtain defect location information, generating detailed records of defect distribution according to the defect location information, conducting classification processing on abnormal points in records, determining priority ranking of defects, and analyzing the influence range of the defects on overall operation by combining the load conditions in the operation data through the defect priority ranking, and obtaining final defect influence evaluation data.

Description

Intelligent assembling and optimizing method for power motor parts Technical Field The invention relates to the technical field of new generation information, in particular to an intelligent assembling and optimizing method for power motor parts. Background The power motor is used as a core component of high-precision equipment such as an unmanned aerial vehicle holder, the performance and the quality of the power motor are directly related to the stability and the safety of the operation of the equipment, and the power motor has irreplaceable important value. With the wide popularization of unmanned aerial vehicle application, higher requirements are put forward on the production efficiency and performance consistency of the power motor. However, the current industry still faces challenges in the production and testing links, and technological breakthroughs are needed to drive the development of intelligence and automation. The existing method depends on manual operation in the production of the power motor, so that the efficiency is low, the consistency of products is difficult to ensure, and the quality problem is easily caused by human errors. Meanwhile, performance test is often limited to an off-line mode, real-time monitoring of the running state of the motor is lacked, and the performance of the motor under complex working conditions is difficult to comprehensively evaluate. The method can not find potential problems in time, can not provide effective basis for subsequent optimization, and limits the improvement space of the production process. The technical difficulty of the deeper level is the comprehensiveness of the control of the assembly accuracy and the performance test. The assembly precision directly influences the operation stability and the service life of the motor, but the prior art lacks high-precision automation means in the parts positioning and connecting fastening links, and the situation that the parts are misplaced or connected infirly often occurs. The accuracy of the performance test is further affected by such a problem, because the performance of the motor under various load conditions cannot be truly reflected during the test, for example, overheat or abnormal vibration may be caused by assembly defects during high-load operation, thereby shortening the service life of the motor. Therefore, how to realize high-precision automatic assembly in the production process, and simultaneously capture the performance of the motor under different working conditions through real-time and comprehensive testing means becomes a key problem for improving the quality and the production efficiency of the power motor. The solution to this problem is not only related to the reliability of the individual products, but also to the ability and prospect of the industry to switch to intelligent manufacturing. Disclosure of Invention The invention provides an intelligent assembling and optimizing method for power motor parts, which mainly comprises the following steps: The method comprises the steps of obtaining initial positioning data of a power motor part through image acquisition equipment, processing the initial positioning data by a neural network algorithm to identify the edge and the center point of the part to obtain accurate part coordinate information, controlling a mechanical arm to execute automatic alignment operation according to the part coordinate information, monitoring displacement deviation data in real time in the alignment process, adjusting mechanical arm speed parameters to obtain a stable assembly position if the displacement deviation data exceeds a preset threshold value, obtaining connection parameters from the assembly position, analyzing the connection parameters by a classification algorithm to judge a fastening state, determining assembly quality data, introducing the assembly quality data into a simulated operation environment to simulate, analyzing characteristics in a virtual operation sequence by the neural network algorithm to determine potential defect positions, adjusting a test protocol according to the potential defect positions, obtaining real-time sensor signal data, marking as an abnormal event if the sensor signal data fluctuation exceeds a preset range, obtaining a performance evaluation index, extracting working condition adaptation characteristics from the performance evaluation index, evaluating motor stability by the classification algorithm, judging life prediction results, generating an optimization suggestion sequence, updating production parameters according to the optimization suggestion sequence, obtaining feedback circulation data, and obtaining the feedback circulation data if the circulation data are consistent, and obtaining the final circulation data if the circulation data is consistent. Further, the initial positioning data of the power motor part is obtained through the image acquisition equipment, the initial positioning data are proces