A permanent magnet motor performance testing method and device

Abstract

The invention discloses a permanent magnet motor performance testing method and device, and specifically relates to the technical field of permanent magnet motor performance testing. By monitoring a large-scale permanent magnet motor model testing process, an intelligent task coordination allocation strategy is formulated, and the task coordination efficiency between processing units is determined. When the task coordination efficiency between processing units decreases, it is judged whether the communication performance between processing units is abnormal, and the intelligent task coordination allocation strategy is adjusted. When the communication performance of a processing unit is abnormal, the overall working state between processing units during the adjustment of the intelligent task coordination allocation strategy is judged, and the adjustment execution effect is evaluated and fed back, and the efficiency abnormality of communication between processing units is monitored in a timely and effective manner, thereby improving the communication performance between processing units, and the execution effect of the formulated intelligent task coordination strategy is fed back, thereby ensuring the stability of the overall working state between processing units.

Description

Technical Field

[0001] The present invention relates to the technical field of permanent magnet motor performance testing, and in particular to a permanent magnet motor performance testing method and device. Background Art

[0002] Permanent magnet motor performance testing is a process of measuring, analyzing and evaluating the key performance parameters of a permanent magnet motor when it is running under various conditions. It aims to ensure that the motor can meet the design requirements in actual applications and provide data for improvement or optimization. In the prior art, static magnetic field simulation analysis is performed on the spatial magnetic field generated by the permanent magnet array by using numerical tools such as the finite element method. The static and dynamic thrust fluctuations of the planar motor are simulated and analyzed by analytical calculation and the finite element method. A planar motor thrust test device is designed based on the thrust fluctuation characteristics and motor size. The device is usually composed of a linear motor and a ball screw linear motion unit to provide a load for the planar motor test. The test device can be used to verify the performance of the motor under actual working loads. For large permanent magnet motor models, detailed meshing is required to ensure the accuracy of the model. As the complexity of the model increases, the number of elements also increases accordingly. Therefore, parallel computing technology may be required to distribute the computing tasks to multiple processing units or computing nodes to improve computing efficiency. However, in parallel computing, processing units need to communicate to coordinate tasks and transfer data. Highly parallel tasks may cause a large amount of communication overhead, resulting in abnormal communication performance, affecting the efficiency of communication between processing units, and further affecting the formulation of task coordination strategies. If the formulated intelligent task coordination strategy does not have a good execution effect, it may affect the overall working status between processing units. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for testing the performance of a permanent magnet motor to overcome the deficiencies in the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a method for testing the performance of a permanent magnet motor, comprising the following steps;

[0005] S1: Monitor the large-scale permanent magnet motor model test process, obtain historical data information when coordinating tasks between processing units in parallel computing, formulate intelligent task coordination allocation strategies, and determine the efficiency of task coordination between processing units;

[0006] S2: When the task coordination efficiency between the processing units decreases, the communication status between the processing units is analyzed, and the communication frequency deviation value between the processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed, and the abnormal current fluctuation index of the permanent magnet motor is evaluated;

[0007] S3: monitor the magnetic field distribution around the permanent magnet motor, and when the change in magnetic field strength around the permanent magnet motor causes interference to the communication line, evaluate the magnetic field strength variation index at this time;

[0008] S4: Comprehensively analyze the communication frequency deviation value, the current abnormal fluctuation index, and the magnetic field strength abnormal variation index to determine whether the communication performance between the processing units is abnormal, and adjust the intelligent task coordination allocation strategy;

[0009] S5: When the communication performance of the processing unit is abnormal, the load impact degree of the processing unit during the adjustment of the task coordination allocation strategy is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is calculated;

[0010] S6: Comprehensively analyze the load balancing ratio of the processing units and the overall power loss value, determine the overall working status between the processing units during the adjustment of the intelligent task coordination allocation strategy, and evaluate and feedback the adjustment execution effect.

[0011] In a preferred embodiment, in S2, the communication status between the processing units is analyzed, and the communication frequency deviation value between the processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed, and the abnormal current fluctuation index of the permanent magnet motor is evaluated;

[0012] The method for obtaining the communication frequency deviation value is as follows: the communication status between the processing units is monitored, n samples of the communication frequency data are obtained, and the data points are marked with x1, x2, ... xn, where n is a positive integer greater than 0, respectively, and the average value of the communication frequency data is calculated, and the average value is subtracted from each collected communication frequency data point to obtain the difference between each data point and the average value, and each difference is squared to obtain the square difference, and the standard deviation of the average square difference is calculated, that is, the communication frequency deviation value is calculated;

[0013] The method for obtaining the abnormal current fluctuation index is as follows: obtaining the real-time current data I of the permanent magnet motor s , using the collected current data, draw the current waveform of the permanent magnet motor and determine the amplitude of the waveform kh e and frequency f l ;

[0014] The current waveform is converted into frequency domain representation by using wavelet transform, and the peak value mp is calculated from the collected current waveform. r ;

[0015] Use fast Fourier transform to convert the current waveform into frequency domain and set the sampling frequency to f h , select the sampling frequency range: f n ~f m, calculate the frequency spectrum within the frequency range, sum the harmonics, and calculate the harmonic content md in the current waveform w ;

[0016] Determine the peak points in the current waveform by using a peak finding algorithm and calculate the number of peak points k;

[0017] Obtain the standard peak value, standard frequency and standard harmonic content of the permanent magnet motor current waveform under the preset standard state, compare them with the peak value, frequency and harmonic content obtained in real time, calculate the peak deviation value, frequency deviation value and harmonic content deviation value under the real-time state, sum them and compare them with the sum of the standard peak value, standard frequency and standard harmonic content under the standard state, and calculate the current abnormal fluctuation index.

[0018] In a preferred embodiment, in S3, when the change in magnetic field strength around the permanent magnet motor causes interference to the communication line, the magnetic field strength variation index at this time is evaluated;

[0019] The method for obtaining the magnetic field intensity variation index is as follows: collecting the variation information of the magnetic field intensity in the real-time magnetic field data, establishing a dynamic model to describe the variation law of the magnetic field, and using it for state estimation using the recursive least squares method;

[0020] Obtain historical data information on changes in magnetic field intensity to initialize model parameters and adjust the algorithm in real time. In the real-time data stream, apply recursive least squares method to each new magnetic field measurement value.

[0021] Acquire real-time magnetic field intensity change data and compare it with preset standard magnetic field intensity change data, calculate the standard deviation between the real-time magnetic field intensity change value and the preset standard magnetic field intensity change value, and calculate the magnetic field intensity variation index based on the optimal state model parameters obtained by the recursive least squares method;

[0022] The magnetic field intensity variation index is compared with the risk threshold. When the magnetic field intensity variation index is greater than or equal to the risk threshold, a magnetic field anomaly signal is issued; when the magnetic field intensity variation index is less than the risk threshold, no magnetic field anomaly signal is issued.

[0023] In a preferred embodiment, in S4, the communication frequency deviation value, the current abnormal fluctuation index and the magnetic field strength abnormal variation index are comprehensively analyzed;

[0024] The communication frequency deviation value, the current abnormal fluctuation index and the magnetic field strength variation index are normalized, and the communication performance stability coefficient between the processing units is calculated through the normalized communication frequency deviation value, the current abnormal fluctuation index and the magnetic field strength variation index.

[0025] In a preferred embodiment, the communication performance stability coefficient between the processing units is compared with a standard threshold. If the communication performance stability coefficient between the processing units is greater than or equal to the standard threshold, a normal communication signal is issued; if the communication performance stability coefficient between the processing units is less than the standard threshold, a communication abnormality signal is issued and the intelligent task coordination allocation strategy is adjusted.

[0026] In a preferred embodiment, in S5, the load influence degree of the processing unit during the task coordination allocation strategy adjustment process is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is calculated;

[0027] The method for obtaining the load balancing ratio is as follows: obtain the number of processing units j in the process of adjusting the task coordination allocation strategy within the time period s, and label them with j = 1, 2, 3...c, where c is a positive integer greater than 0, calculate the number of tasks executed by each processing unit and the average execution time, and calculate the effective execution efficiency of the processing unit;

[0028] The effective execution efficiency of all processing units in the time period s is set up, the mean of the set, i.e., the effective execution efficiency mean, is calculated, the effective execution efficiency of each processing unit is analyzed with the effective execution efficiency mean, the standard deviation of the effective load balancing degree of the processing unit is calculated, the standard deviation of the effective load balancing degree is compared with the preset standard load balancing degree of the processing unit, and the load balancing ratio is calculated;

[0029] The method for obtaining the overall power loss value is as follows: collecting power data at the input and output points of the processing unit, calculating the power input value and output value of each processing unit through the collected power data, calculating the power loss value of each processing unit, and accumulating the power loss values ​​of each processing unit to obtain the overall power loss value;

[0030] The overall power loss value is compared with the expected power loss value. If the overall power loss value is greater than or equal to the expected power loss value, an abnormal power loss signal is output. If the overall power loss value is less than the expected power loss value, no abnormal power loss signal is output.

[0031] In a preferred embodiment, in S6, the load balancing ratio of the processing unit and the overall power loss value are comprehensively analyzed;

[0032] The load balancing ratio and the overall power loss value are normalized, and the execution effect coefficient of the processing unit after the intelligent task coordination allocation strategy is adjusted is calculated through the normalized load balancing ratio and the overall power loss value.

[0033] In a preferred embodiment, the obtained execution effect coefficient is compared with the optimization threshold. If the execution effect coefficient is greater than the optimization threshold, no warning signal is issued; if the execution effect coefficient is less than or equal to the optimization threshold, a warning signal is issued.

[0034] The present invention also provides a permanent magnet motor performance test device, including an information acquisition module, an abnormality identification module, a magnetic field monitoring module, a comprehensive analysis module, a strategy adjustment module and a judgment and early warning module;

[0035] Information collection module: monitors the large-scale permanent magnet motor model test process, obtains historical data information when coordinating tasks between processing units in parallel computing, formulates intelligent task coordination allocation strategies, and determines the efficiency of task coordination between processing units;

[0036] Abnormal identification module: When the task coordination efficiency between processing units decreases, the communication status between processing units is analyzed and the communication frequency deviation value between processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed and the abnormal current fluctuation index of the permanent magnet motor is evaluated;

[0037] Magnetic field monitoring module: monitors the magnetic field distribution around the permanent magnet motor. When the change in magnetic field strength around the permanent magnet motor causes interference to the communication line, the magnetic field strength variation index at this time is evaluated;

[0038] Comprehensive analysis module: conducts comprehensive analysis on the communication frequency deviation value, current abnormal fluctuation index and magnetic field strength variation index to determine whether the communication performance between processing units is abnormal and adjust the intelligent task coordination allocation strategy;

[0039] Strategy adjustment module: When the communication performance of the processing unit is abnormal, the load impact of the processing unit in the task coordination allocation strategy adjustment process is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is calculated;

[0040] Evaluation and feedback module: Comprehensively analyze the load balancing ratio of the processing units and the overall power loss value, determine the overall working status between the processing units during the adjustment of the intelligent task coordination allocation strategy, and evaluate and feedback the adjustment execution effect.

[0041] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0042] 1. The present invention monitors the large-scale permanent magnet motor model test process, obtains historical data information when the processing units in parallel computing perform coordination tasks, formulates an intelligent task coordination allocation strategy, and determines the task coordination efficiency between the processing units. When the task coordination efficiency between the processing units decreases, it determines whether the communication performance between the processing units is abnormal, and adjusts the intelligent task coordination allocation strategy. Based on the results of comprehensive analysis, the intelligent task coordination allocation strategy can be adjusted in real time to adapt to different working environments and system requirements, thereby improving the adaptability and flexibility of the system. At the same time, by timely discovering and handling abnormal situations, the probability of system failures can be reduced, the system downtime can be reduced, and the stability and availability of the system can be improved.

[0043] 2. When the communication performance of the processing unit is abnormal, the present invention determines the overall working state between the processing units during the adjustment of the intelligent task coordination allocation strategy, and evaluates and feeds back the adjustment execution effect. With the help of the intelligent task coordination allocation strategy, the system can adjust the task allocation according to the actual situation to optimize load balancing, reduce power loss, and improve overall work efficiency. By comprehensively analyzing the load balancing ratio and power loss value, the system can determine the impact of the adjustment of the intelligent task coordination allocation strategy on the overall working state between the processing units, and then evaluate the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0045] Figure 1 The figure is a flow chart of the method of the present invention.

[0046] Figure 2 It is a device module diagram of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] See also Figure 1 and Figure 2As shown, a permanent magnet motor performance testing method described in this embodiment includes the following steps:

[0050] S1: Monitor the large-scale permanent magnet motor model test process, obtain historical data information when coordinating tasks between processing units in parallel computing, formulate intelligent task coordination allocation strategies, and determine the efficiency of task coordination between processing units;

[0051] S2: When the task coordination efficiency between the processing units decreases, the communication status between the processing units is analyzed, and the communication frequency deviation value between the processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed, and the abnormal current fluctuation index of the permanent magnet motor is evaluated;

[0052] S3: monitor the magnetic field distribution around the permanent magnet motor, and when the change in magnetic field strength around the permanent magnet motor causes interference to the communication line, evaluate the magnetic field strength variation index at this time;

[0053] S4: Comprehensively analyze the communication frequency deviation value, the current abnormal fluctuation index, and the magnetic field strength abnormal variation index to determine whether the communication performance between the processing units is abnormal, and adjust the intelligent task coordination allocation strategy;

[0054] S5: When the communication performance of the processing unit is abnormal, the load impact degree of the processing unit during the adjustment of the task coordination allocation strategy is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is calculated;

[0055] S6: Comprehensively analyze the load balancing ratio of the processing units and the overall power loss value, determine the overall working status between the processing units during the adjustment of the intelligent task coordination allocation strategy, and evaluate and feedback the adjustment execution effect.

[0056] Among them, in S1, the large-scale permanent magnet motor model test process is monitored, the historical data information of the coordination tasks between processing units in parallel computing is obtained, the intelligent task coordination allocation strategy is formulated, and the task coordination efficiency between processing units is determined:

[0057] Large permanent magnet motors usually have complex geometries and structures, such as windings of different shapes, the distribution of permanent magnets, etc. In order to accurately describe these complex structures, detailed meshing is required so that each element can adapt to the changes in geometry. As the complexity of the model increases, the required meshing becomes more complex, increasing the demand for computing resources.

[0058] More elements generally mean more accurate approximations, especially for areas with small features. However, increasing the number of elements can also significantly increase the computational burden. When solving complex problems such as nonlinear, multi-physics, and unsteady problems, higher element density is required to maintain model accuracy, but this can also lead to an increase in computing resource requirements.

[0059] To better capture features at different scales in your model during simulation, you may need to use an adaptive meshing method, which refines the mesh in critical areas to improve the accuracy of the solution, but also increases computational complexity.

[0060] For large-scale grid division and number of elements, it may be necessary to use parallel computing technology to distribute the computing tasks to multiple processing units or computing nodes to improve computing efficiency.

[0061] Choose appropriate parallel algorithms and data structures to minimize communication requirements. Some algorithms may be better suited to run in a parallel environment, reducing communication overhead. Try to calculate the required data locally to reduce dependence on other processing units, thereby reducing communication overhead.

[0062] Use dynamic load balancing algorithm to dynamically allocate tasks according to the real-time status of tasks and the performance of processing units to ensure that the task coordination efficiency of processing units is within the standard range. The dynamic load balancing algorithm mainly includes:

[0063] Work Stealing Algorithm: Each processing unit maintains a task queue. When a processing unit completes its own task, it can steal tasks from the queues of other processing units to execute. In this way, the task load is dynamically balanced among the processing units.

[0064] Local task adjustment algorithm: When executing tasks, each processing unit regularly checks the load of the local task queue. If an imbalance is found, some tasks can be actively transferred to other processing units to achieve load balancing.

[0065] By formulating an intelligent task coordination allocation strategy, the efficiency of task coordination between processing units is determined, including: clarifying the overall goals of the system, such as maximizing throughput, minimizing task completion time, and maintaining load balance to the greatest extent. Define specific indicators of task coordination efficiency, such as the variance of task completion time and system load balance index.

[0066] Divide tasks by priority, taking into account factors such as the importance and urgency of the tasks. Set appropriate priorities for each task so that high-priority tasks can be prioritized during intelligent allocation. Design algorithms or rules that can dynamically adjust task allocation based on real-time monitoring of system status and task conditions. For example, dynamically decide on task allocation based on the current load and task priority of the processing unit, thereby controlling the efficiency of task coordination between processing units.

[0067] When the task coordination efficiency between processing units decreases, the communication status between processing units is analyzed and the communication frequency deviation value between processing units is calculated, which is specifically:

[0068] The communication frequency deviation value refers to the degree of difference in the communication frequency between processing units, which is usually measured by calculating the standard deviation. In a parallel computing system, processing units may need to communicate with each other to coordinate tasks and share information. The communication frequency deviation value reflects the changes in these communication frequencies.

[0069] The size of the communication frequency deviation value can be used to evaluate the communication stability of the system. A smaller deviation value usually indicates that the communication frequency between processing units is relatively stable, while a larger deviation value may indicate that the communication frequency fluctuates or is unstable.

[0070] An increase in the communication frequency deviation value may indicate a decrease in the efficiency of task coordination. Inconsistency in communication frequencies between processing units may lead to an uneven distribution of communication tasks, thus affecting the efficiency of the overall system.

[0071] Large communication frequency deviation values ​​may be an indicator of load imbalance. Some processing units may be taking on more communication tasks while other processing units are taking on less, resulting in uneven load distribution.

[0072] The method for obtaining the communication frequency deviation value is as follows: the communication status between the processing units is monitored, n samples of the communication frequency data are obtained, and the data points are marked with x1, x2, ... xn, where n is a positive integer greater than 0, and the average value of the communication frequency data is calculated, that is, the sum of all sample values ​​divided by the number of samples;

[0073] Calculate the difference between each data point and the average value and subtract the average value from the collected communication frequency data point. Square each difference to obtain the square difference. Average all the square differences and take the square root. Calculate the square root of the average square difference to obtain the standard deviation, that is, calculate the communication frequency deviation value.

[0074] A large communication frequency deviation value may indicate that the communication system has large fluctuations or instability in the task coordination process between processing units. This may lead to uncertainty in the communication signal, making the communication performance unreliable.

[0075] An increase in the communication frequency deviation value may indicate problems with task allocation and communication coordination, resulting in a decrease in communication efficiency between processing units. A larger frequency deviation may mean that the communication frequencies between some processing units vary greatly, affecting the overall communication effect.

[0076] Large communication frequency deviations may be an indication of uneven load distribution among processing units. Some processing units may be overloaded with communication tasks, while other processing units are relatively idle, resulting in an unbalanced load.

[0077] Large fluctuations in communication frequency may indicate that there is a problem with the task coordination strategy and that adjustments need to be made. This may include the way tasks are allocated, the communication protocol, or other coordination mechanisms.

[0078] The abnormal change degree of the permanent magnet motor current waveform is analyzed to evaluate the abnormal current fluctuation index of the permanent magnet motor, specifically:

[0079] Abnormal fluctuations may cause electromagnetic interference, affecting nearby communication equipment and signal transmission. Abnormal fluctuations may cause the shape of the current waveform to change, which in turn causes signal distortion. Distorted signals may be difficult to correctly decode during communication transmission, resulting in communication errors or loss of information.

[0080] High-frequency current fluctuations may cause spectrum expansion, resulting in a wider spectrum of components in the motor system. This may overlap with the communication band, causing spectrum contention and resulting in degraded communication performance. Abnormal fluctuations may adversely affect nearby communication equipment and even cause equipment failure. This may require interference suppression measures or isolation measures to maintain the normal operation of the communication system.

[0081] The method for obtaining the abnormal current fluctuation index is as follows: obtaining the real-time current data I of the permanent magnet motor s The real-time current data can be collected and monitored by using a current sensor or current detection equipment. The collected current data can be used to draw the current waveform of the permanent magnet motor and determine the amplitude of the waveform kh e and frequency f l ;

[0082] The current waveform is converted into frequency domain representation by using wavelet transform, and the peak value mp is calculated from the collected current waveform. r , which is the difference between the maximum and minimum values ​​of the waveform. The peak value represents the amplitude range of the current waveform;

[0083] Use fast Fourier transform to convert the current waveform into frequency domain and set the sampling frequency to f h , select the sampling frequency range: f n ~f m, calculate the frequency spectrum within the frequency range, then sum the harmonics and calculate the harmonic content md in the current waveform w ;

[0084] The peak point in the current waveform is determined by the peak finding algorithm. The peak point represents the non-periodic part of the current waveform. The number of peak points k is calculated. The more the number of peak points, the more irregular the waveform is, that is, the higher the non-periodicity is.

[0085] Obtain the standard peak value, standard frequency and standard harmonic content of the permanent magnet motor current waveform under the preset standard state, compare them with the peak value, frequency and harmonic content obtained in real time, calculate the peak deviation value, frequency deviation value and harmonic content deviation value under the real-time state, sum them and compare them with the sum of the standard peak value, standard frequency and standard harmonic content under the standard state, and calculate the current abnormal fluctuation index;

[0086] As the fluctuation index increases, the irregularity of the current waveform may lead to an increased probability of signal distortion. Signal distortion may make it more difficult to decode the communication signal, resulting in communication errors.

[0087] Abnormal fluctuations may cause the frequency and amplitude of the current waveform to change, increasing the possibility of electromagnetic interference. Highly volatile current waveforms may cause instability in the communication link, resulting in interruption or fluctuation of the communication connection.

[0088] The spectrum expansion caused by abnormal fluctuations may overlap with the communication frequency band, causing spectrum contention, which in turn leads to conflicts and interference of communication signals. Abnormal fluctuations may cause the working point of communication equipment to deviate from the normal range, affecting the performance and stability of the equipment.

[0089] Monitor the magnetic field distribution around the permanent magnet motor. When the change in magnetic field strength around the permanent magnet motor causes interference to the communication line, evaluate the magnetic field strength variation index at this time, specifically:

[0090] When the magnetic field strength around the permanent magnet motor changes, it may cause potential interference to the communication line. This change can cause magnetic field interference to affect the transmission quality of the communication signal, causing signal distortion, spectrum expansion and instability of the communication link, thereby affecting the performance of the overall communication system. By monitoring the magnetic field strength variation index, the degree of this potential interference can be evaluated in time, triggering an alarm mechanism, so that preventive measures can be taken to maintain the normal operation of the communication system.

[0091] The method for obtaining the magnetic field intensity variation index is as follows: the magnetic field intensity variation information in the real-time magnetic field data is collected by a magnetic field sensor or other magnetic field detection equipment, and based on the system's recognition of the magnetic field intensity variation, a dynamic model is established to describe the variation law of the magnetic field, which is used for state estimation of the recursive least squares method;

[0092] The historical data information of magnetic field intensity change is obtained to initialize the model parameters, and the algorithm is adjusted in real time. In the real-time data stream, the recursive least squares method is applied to each new magnetic field measurement value, the real-time magnetic field intensity change data is obtained and compared with the preset standard magnetic field intensity change data, and the standard deviation of the real-time magnetic field intensity change value and the preset standard magnetic field intensity change value is calculated. Based on the optimal state model parameters obtained by the card recursive least squares method, the magnetic field intensity variation index is calculated.

[0093] The magnetic field intensity variation index is compared with the risk threshold. When the magnetic field intensity variation index is greater than or equal to the risk threshold, it means that the change in magnetic field intensity around the permanent magnet motor has a greater interference effect on the communication line. At this time, a magnetic field abnormality signal is issued to remind the staff so that they can respond quickly and take necessary measures; when the magnetic field intensity variation index is less than the risk threshold, it means that the change in magnetic field intensity around the permanent magnet motor has a less interference effect on the communication line. At this time, no magnetic field abnormality signal is issued.

[0094] The increase of the magnetic field intensity variation index usually indicates that the magnetic field around the permanent magnet motor has changed significantly. The main role of the magnetic field intensity variation index in judging whether the communication performance between processing units is abnormal is reflected in the following aspects:

[0095] The change of magnetic field strength may cause interference to communication lines, especially for sensitive communication equipment. The increase of magnetic field strength change index can be used as an interference indicator, indicating that the communication system may be abnormally affected by the external magnetic field.

[0096] As the magnetic field intensity variation index increases, the system can use it as an early warning signal for abnormal events. This helps to detect potential communication problems in advance, allowing the system to take preventive measures to prevent the abnormal communication performance from further deteriorating.

[0097] Real-time monitoring of the magnetic field intensity variation index provides real-time feedback on system performance. System administrators or operation and maintenance personnel can monitor this index to understand the current status of the system and take appropriate measures in time to ensure normal operation of communication performance.

[0098] The communication frequency deviation value, current abnormal fluctuation index and magnetic field strength variation index are comprehensively analyzed to determine whether the communication performance between the processing units is abnormal, and the intelligent task coordination allocation strategy is adjusted, specifically:

[0099] The communication frequency deviation value, the current abnormal fluctuation index and the magnetic field strength variation index are normalized, and the communication performance stability coefficient between the processing units is calculated through the normalized communication frequency deviation value, the current abnormal fluctuation index and the magnetic field strength variation index.

[0100] For example, the present invention may use the following formula to calculate the communication performance stability coefficient, and the calculation expression is: In the formula, eg k is the communication performance stability coefficient, bm d is the communication frequency deviation value, ek s is the current abnormal fluctuation index, nx y is the magnetic field intensity variation index, a1, a2, a3 are the proportional coefficients of the communication frequency deviation value, the current abnormal fluctuation index and the magnetic field intensity variation index, and a2>a3>a1>0;

[0101] The communication performance stability coefficient is used to comprehensively evaluate the stability of communication performance between processing units. When the stability coefficient is high, it means that the combined impact of various indicators has played a positive role in the stability of communication performance. A high stability coefficient may mean that the system is operating normally and various indicators are in a stable state, which helps to improve the reliability and consistency of communication.

[0102] The communication performance stability coefficient between the processing units is compared with the standard threshold. If the communication performance stability coefficient between the processing units is greater than or equal to the standard threshold, it means that the communication performance stability between the processing units is relatively high, and a normal communication signal is issued at this time; if the communication performance stability coefficient between the processing units is less than the standard threshold, it means that the communication performance stability between the processing units is relatively low, and a communication abnormality signal is issued at this time, and the intelligent task coordination allocation strategy is adjusted.

[0103] A processing unit is a basic unit in a system that performs parallel computing tasks. A processing unit can be a core or processor in a computer system, or a node in a distributed system. In the large-scale permanent magnet motor model test process, the task of the processing unit is to perform computing tasks and process data. In parallel computing, coordination tasks need to be performed between processing units to improve overall efficiency.

[0104] In this embodiment, by monitoring the large-scale permanent magnet motor model test process, historical data information when the processing units in parallel computing coordinate tasks is obtained, an intelligent task coordination allocation strategy is formulated, and the task coordination efficiency between the processing units is determined; when the task coordination efficiency between the processing units decreases, the communication status between the processing units is analyzed, and the communication frequency deviation value between the processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed, and the abnormal current fluctuation index of the permanent magnet motor is evaluated; the magnetic field distribution around the permanent magnet motor is monitored, and when the change of the magnetic field strength around the permanent magnet motor interferes with the communication line, the magnetic field strength variation index at this time is evaluated; the communication frequency deviation value, the abnormal current fluctuation index and the magnetic field strength variation index are comprehensively analyzed to determine whether the communication performance between the processing units is abnormal, and the intelligent task coordination allocation strategy is adjusted. Based on the results of the comprehensive analysis, the intelligent task coordination allocation strategy can be adjusted in real time to adapt to different working environments and system requirements, improve the adaptability and flexibility of the system, and at the same time, by timely discovering and handling abnormal situations, the probability of system failure can be reduced, the system downtime can be reduced, and the stability and availability of the system can be improved.

[0105] Example 2

[0106] When the communication performance of the processing unit is abnormal, the load impact of the processing unit during the adjustment of the task coordination allocation strategy is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is evaluated, specifically:

[0107] By calculating the load balance ratio of the processing units, the system can evaluate the overall performance. The load balance ratio reflects the balance of task distribution between processing units, thus providing a comprehensive understanding of system performance.

[0108] Changes in the load balancing ratio can be used to detect abnormalities in the system. When the load balancing ratio exceeds a set threshold or differs significantly from a baseline during normal operation, it may indicate an unbalanced distribution of tasks between processing units, which may be an indication of abnormal communication performance.

[0109] The method for obtaining the load balancing ratio is as follows: obtain the number of processing units j in the process of adjusting the task coordination allocation strategy within the time period s, and label them with j = 1, 2, 3...c, where c is a positive integer greater than 0, calculate the number of tasks executed by each processing unit and the average execution time, and calculate the effective execution efficiency of the processing unit;

[0110] Establish a set of effective execution efficiencies of all processing units within the time period s, calculate the mean of the set, i.e., the mean effective execution efficiency, analyze the effective execution efficiency of each processing unit with the mean effective execution efficiency, calculate the standard deviation of the effective load balancing degree of the processing unit, compare the standard deviation of the effective load balancing degree with the preset standard load balancing degree of the processing unit, and calculate the load balancing ratio.

[0111] The larger the load balancing ratio, the more evenly the load is distributed among the processing units. During the adjustment of the intelligent task coordination allocation strategy, it indicates that the overall working state is more balanced and stable, specifically:

[0112] A larger load balancing ratio indicates that each processing unit is bearing a relatively even load, and the system is more effectively utilizing all available computing resources, which helps improve overall performance and efficiency.

[0113] Balanced load distribution helps avoid situations where some processing units are overloaded while others are idle. A large load balancing ratio means that tasks or requests are more evenly distributed, reducing the possibility of bottlenecks or performance degradation in the system. This helps keep response times low and improves user experience.

[0114] Overall, the large load balancing ratio is the result of excellent system design and intelligent task coordination, indicating that the system has achieved a good balance in resource utilization, performance, and reliability.

[0115] Determine the power loss between the processing units and evaluate the overall power loss value at this time, specifically:

[0116] By monitoring the power loss, potential problems in the system can be discovered in time, which helps to adjust the intelligent task coordination allocation strategy to maximize the performance between processing units. By adjusting the strategy, power loss can be reduced and the overall efficiency of the system can be improved.

[0117] The method for obtaining the overall power loss value is: using power measuring instruments or sensors to collect power data at the input and output points of each processing unit in the system; calculating the power input value and output value of each processing unit through the collected data;

[0118] For each processing unit, calculate its power loss value, which is the difference between the input power and the output power of the processing unit. Add up the power loss values ​​of each processing unit to get the overall power loss value, and compare the overall power loss value with the expected power loss value. If the overall power loss value is greater than or equal to the expected power loss value, an abnormal power loss signal is output. After receiving the abnormal power loss signal, the staff should immediately investigate the cause of the abnormal signal and check the power loss value of each processing unit to determine whether there is a problem with a specific component or system. If the overall power loss value is less than the expected power loss value, no abnormal power loss signal is output.

[0119] It should be noted that the expected power loss value can be the theoretical power loss of the system under normal operating conditions, or the average value in the past working cycle. The comparison results are helpful to determine whether there is an abnormal situation at present.

[0120] Comprehensively analyze the load balancing ratio of the processing units and the overall power loss value, determine the overall working status between the processing units during the adjustment of the intelligent task coordination allocation strategy, and evaluate and feedback the adjustment execution effect, including:

[0121] The load balancing ratio and the overall power loss value are normalized, and the execution effect coefficient of the processing unit after the intelligent task coordination allocation strategy is adjusted is calculated through the normalized load balancing ratio and the overall power loss value.

[0122] For example, the present invention may use the following formula to calculate the execution effect coefficient, and the calculation expression is: In the formula, dg r is the execution effect coefficient, mc J is the load balancing ratio, lf s is the overall power loss value, β1 and β2 are the load balancing ratio and the proportional coefficient of the overall power loss value, and β2>β1>0;

[0123] Obtaining the execution effect coefficient helps system managers comprehensively evaluate the effect of adjusting the intelligent task coordination allocation strategy. If the coefficient is high, it means that the system performs well in terms of load balancing and power utilization; conversely, a low coefficient may indicate that there are problems and improvement measures need to be taken.

[0124] The obtained execution effect coefficient is compared with the optimization threshold. If the execution effect coefficient is greater than the optimization threshold, it means that the overall working status stability between the processing units during the adjustment of the intelligent task coordination allocation strategy is higher, and no warning signal is issued at this time; if the execution effect coefficient is less than or equal to the optimization threshold, it means that the overall working status stability between the processing units during the adjustment of the intelligent task coordination allocation strategy is lower, and a warning signal is issued at this time. After receiving the warning signal, the staff can promptly understand the overall working status of the current processing unit.

[0125] In this embodiment, when the communication performance of the processing unit is abnormal, the degree of load impact on the processing unit during the adjustment of the task coordination and allocation strategy is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is judged, and the overall power loss value at this time is calculated, and the load balancing ratio of the processing units and the overall power loss value are comprehensively analyzed to judge the overall working status between the processing units during the adjustment of the intelligent task coordination and allocation strategy, and the adjustment execution effect is evaluated and fed back. With the help of the intelligent task coordination and allocation strategy, the system can adjust the task allocation according to the actual situation to optimize load balancing, reduce power loss, and improve overall work efficiency. By comprehensively analyzing the load balancing ratio and power loss value, the system can judge the impact of the adjustment of the intelligent task coordination and allocation strategy on the overall working status between the processing units, and then evaluate the stability and reliability of the system.

[0126] Example 3

[0127] The permanent magnet motor performance test device described in this embodiment includes an information acquisition module, an abnormality identification module, a magnetic field monitoring module, a comprehensive analysis module, a strategy adjustment module and a judgment and warning module;

[0128] Information collection module: monitors the large-scale permanent magnet motor model test process, obtains historical data information when coordinating tasks between processing units in parallel computing, formulates intelligent task coordination allocation strategies, and determines the efficiency of task coordination between processing units;

[0129] Abnormal identification module: When the task coordination efficiency between processing units decreases, the communication status between processing units is analyzed and the communication frequency deviation value between processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed and the abnormal current fluctuation index of the permanent magnet motor is evaluated;

[0130] Magnetic field monitoring module: monitors the magnetic field distribution around the permanent magnet motor. When the change in magnetic field strength around the permanent magnet motor causes interference to the communication line, the magnetic field strength variation index at this time is evaluated;

[0131] Comprehensive analysis module: conducts comprehensive analysis on the communication frequency deviation value, current abnormal fluctuation index and magnetic field strength variation index to determine whether the communication performance between processing units is abnormal and adjust the intelligent task coordination allocation strategy;

[0132] Strategy adjustment module: When the communication performance of the processing unit is abnormal, the load impact of the processing unit in the task coordination allocation strategy adjustment process is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is calculated;

[0133] Evaluation and feedback module: Comprehensively analyze the load balancing ratio of the processing units and the overall power loss value, determine the overall working status between the processing units during the adjustment of the intelligent task coordination allocation strategy, and evaluate and feedback the adjustment execution effect.

[0134] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0135] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0136] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0137] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes 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.

[0138] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0139] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for testing the performance of a permanent magnet motor, characterized in that: The steps include: S1: Monitor the large-scale permanent magnet motor model test process, obtain historical data information when coordinating tasks between processing units in parallel computing, formulate intelligent task coordination allocation strategies, and determine the efficiency of task coordination between processing units; S2: When the task coordination efficiency between the processing units decreases, the communication status between the processing units is analyzed, and the communication frequency deviation value between the processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed, and the abnormal current fluctuation index of the permanent magnet motor is evaluated; S3: monitor the magnetic field distribution around the permanent magnet motor, and when the change of magnetic field strength around the permanent magnet motor causes interference to the communication line, evaluate the magnetic field strength variation index at this time; S4: Comprehensively analyze the communication frequency deviation value, the current abnormal fluctuation index, and the magnetic field strength abnormal variation index to determine whether the communication performance between the processing units is abnormal, and adjust the intelligent task coordination allocation strategy; S5: When the communication performance of the processing unit is abnormal, the load impact degree of the processing unit during the adjustment of the task coordination allocation strategy is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is calculated; S6: Comprehensively analyze the load balancing ratio of the processing units and the overall power loss value, determine the overall working status between the processing units during the adjustment of the intelligent task coordination allocation strategy, and evaluate and feedback the adjustment execution effect.

2. A permanent magnet motor performance testing method according to claim 1, characterized in that: In S2, the communication status between the processing units is analyzed, and the communication frequency deviation value between the processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed, and the abnormal current fluctuation index of the permanent magnet motor is evaluated; The method for obtaining the communication frequency deviation value is as follows: the communication status between the processing units is monitored, n samples of the communication frequency data are obtained, and the data points are marked with x1, x2, ... xn, where n is a positive integer greater than 0, respectively, and the average value of the communication frequency data is calculated, and the average value is subtracted from each collected communication frequency data point to obtain the difference between each data point and the average value, and each difference is squared to obtain the square difference, and the standard deviation of the average square difference is calculated, that is, the communication frequency deviation value is calculated; The method for obtaining the abnormal current fluctuation index is as follows: obtaining the real-time current data I of the permanent magnet motor s , Using the collected current data, draw the current waveform of the permanent magnet motor and determine the amplitude of the waveform kh e and frequency f l ; The current waveform is converted into frequency domain representation by using wavelet transform, and the peak value mp is calculated from the collected current waveform. r ; Use fast Fourier transform to convert the current waveform into frequency domain and set the sampling frequency to f h , select the sampling frequency range: f n ~f m , calculate the frequency spectrum within the frequency range, sum the harmonics, and calculate the harmonic content md in the current waveform w ; Determine the peak points in the current waveform by using a peak finding algorithm and calculate the number of peak points k; Obtain the standard peak value, standard frequency and standard harmonic content of the permanent magnet motor current waveform under the preset standard state, compare them with the peak value, frequency and harmonic content obtained in real time, calculate the peak deviation value, frequency deviation value and harmonic content deviation value under the real-time state, sum them and compare them with the sum of the standard peak value, standard frequency and standard harmonic content under the standard state, and calculate the current abnormal fluctuation index.

3. A permanent magnet motor performance testing method according to claim 2, characterized in that: In S3, when the change in magnetic field strength around the permanent magnet motor causes interference to the communication line, the magnetic field strength variation index at this time is evaluated; The method for obtaining the magnetic field intensity variation index is as follows: collecting the variation information of the magnetic field intensity in the real-time magnetic field data, establishing a dynamic model to describe the variation law of the magnetic field, and using it for state estimation using the recursive least squares method; Obtain historical data information on changes in magnetic field intensity to initialize model parameters and adjust the algorithm in real time. In the real-time data stream, apply recursive least squares method to each new magnetic field measurement value. Acquire real-time magnetic field intensity change data and compare it with preset standard magnetic field intensity change data, calculate the standard deviation between the real-time magnetic field intensity change value and the preset standard magnetic field intensity change value, and calculate the magnetic field intensity variation index based on the optimal state model parameters obtained by the recursive least squares method; The magnetic field intensity variation index is compared with the risk threshold. When the magnetic field intensity variation index is greater than or equal to the risk threshold, a magnetic field anomaly signal is issued; when the magnetic field intensity variation index is less than the risk threshold, no magnetic field anomaly signal is issued.

4. A permanent magnet motor performance testing method according to claim 3, characterized in that: In S4, the communication frequency deviation value, the current abnormal fluctuation index and the magnetic field intensity abnormal variation index are comprehensively analyzed; The communication frequency deviation value, the current abnormal fluctuation index and the magnetic field strength variation index are normalized, and the communication performance stability coefficient between the processing units is calculated through the normalized communication frequency deviation value, the current abnormal fluctuation index and the magnetic field strength variation index.

5. A permanent magnet motor performance testing method according to claim 4, characterized in that: The communication performance stability coefficient between the processing units is compared with the standard threshold. If the communication performance stability coefficient between the processing units is greater than or equal to the standard threshold, a normal communication signal is issued; if the communication performance stability coefficient between the processing units is less than the standard threshold, a communication abnormality signal is issued and the intelligent task coordination allocation strategy is adjusted.

6. A permanent magnet motor performance testing method according to claim 5, characterized in that: In S5, the load impact degree of the processing unit during the task coordination allocation strategy adjustment process is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is calculated; The method for obtaining the load balancing ratio is as follows: obtain the number of processing units j in the process of adjusting the task coordination allocation strategy within the time period s, and label them with j = 1, 2, 3...c, where c is a positive integer greater than 0, calculate the number of tasks executed by each processing unit and the average execution time, and calculate the effective execution efficiency of the processing unit; The effective execution efficiency of all processing units in the time period s is set up, the mean of the set, i.e., the effective execution efficiency mean, is calculated, the effective execution efficiency of each processing unit is analyzed with the effective execution efficiency mean, the standard deviation of the effective load balancing degree of the processing unit is calculated, the standard deviation of the effective load balancing degree is compared with the preset standard load balancing degree of the processing unit, and the load balancing ratio is calculated; The method for obtaining the overall power loss value is as follows: collecting power data at the input and output points of the processing unit, calculating the power input value and output value of each processing unit through the collected power data, calculating the power loss value of each processing unit, and accumulating the power loss values ​​of each processing unit to obtain the overall power loss value; The overall power loss value is compared with the expected power loss value. If the overall power loss value is greater than or equal to the expected power loss value, an abnormal power loss signal is output. If the overall power loss value is less than the expected power loss value, no abnormal power loss signal is output.

7. A permanent magnet motor performance testing method according to claim 6, characterized in that: In S6, the load balancing ratio of the processing unit and the overall power loss value are comprehensively analyzed; The load balancing ratio and the overall power loss value are normalized, and the execution effect coefficient of the processing unit after the intelligent task coordination allocation strategy is adjusted is calculated through the normalized load balancing ratio and the overall power loss value.

8. A permanent magnet motor performance testing method according to claim 7, characterized in that: The obtained execution effect coefficient is compared with the optimization threshold. If the execution effect coefficient is greater than the optimization threshold, no warning signal is issued; if the execution effect coefficient is less than or equal to the optimization threshold, a warning signal is issued.

9. A permanent magnet motor performance testing device, used to implement a permanent magnet motor performance testing method according to any one of claims 1 to 8, characterized in that: It includes information collection module, anomaly identification module, magnetic field monitoring module, comprehensive analysis module, strategy adjustment module and evaluation feedback module; Information collection module: monitors the large-scale permanent magnet motor model test process, obtains historical data information when coordinating tasks between processing units in parallel computing, formulates intelligent task coordination allocation strategies, and determines the efficiency of task coordination between processing units; Abnormal identification module: When the task coordination efficiency between processing units decreases, the communication status between processing units is analyzed and the communication frequency deviation value between processing units is calculated; the abnormal change degree of the permanent magnet motor current waveform is analyzed and the abnormal current fluctuation index of the permanent magnet motor is evaluated; Magnetic field monitoring module: monitors the magnetic field distribution around the permanent magnet motor. When the change in magnetic field strength around the permanent magnet motor causes interference to the communication line, the magnetic field strength variation index at this time is evaluated; Comprehensive analysis module: conducts comprehensive analysis on the communication frequency deviation value, current abnormal fluctuation index and magnetic field strength variation index to determine whether the communication performance between processing units is abnormal and adjust the intelligent task coordination allocation strategy; Strategy adjustment module: When the communication performance of the processing unit is abnormal, the load impact of the processing unit in the task coordination allocation strategy adjustment process is analyzed, and the load balancing ratio of the processing unit is calculated; the power loss between the processing units is determined, and the overall power loss value at this time is calculated; Evaluation and feedback module: Comprehensively analyze the load balancing ratio of the processing units and the overall power loss value, determine the overall working status between the processing units during the adjustment of the intelligent task coordination allocation strategy, and evaluate and feedback the adjustment execution effect.

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