A multi-dimensional dynamic motor testing system

Abstract

The present invention discloses a multi-dimensional dynamic motor testing system, which relates to the field of motor testing technology and includes: establishing a digital model of the motor, performing simulated motor testing, and obtaining motor simulation parameters; inputting the motor simulation parameters into a pre-built parameter error analysis model for data analysis to obtain simulated operation error coefficients; performing actual testing on the motor to obtain actual motor operation parameters, which are input into a pre-built parameter error analysis model to obtain actual operation error coefficients; performing error correction on the motor simulation parameters and the actual motor operation parameters based on the simulated operation error coefficients and the actual operation error coefficients to obtain correction calculation results; performing motor performance verification based on the correction calculation results and outputting motor test results. The present invention solves the technical problems that the prior art cannot comprehensively and accurately evaluate motor performance and the test results are greatly interfered with by environmental factors, thereby achieving the technical effect of comprehensively evaluating motor performance.

Description

Technical Field

[0001] The present invention relates to the technical field of motor testing, and in particular to a multi-dimensional dynamic motor testing system. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, motors, as core drive components, have been widely used in industrial equipment, transportation, consumer electronics, and other fields. However, with the continuous improvement of equipment operating requirements and the increasing complexity of process flows, motor performance testing and evaluation are facing increasing challenges. Traditional motor testing methods often rely on single simulation tests or actual tests. These methods suffer from incomplete test data, insufficient accuracy, and sensitivity to environmental factors, making them unable to meet the modern industrial demand for efficient, accurate, and reliable testing. Summary of the Invention

[0003] The present application provides a multi-dimensional dynamic motor testing system, which is used to solve the technical problems that the existing technology cannot comprehensively and accurately evaluate the motor performance and the test results are greatly affected by environmental factors.

[0004] In view of the above problems, the present application provides a multi-dimensional dynamic motor testing system.

[0005] The present application provides a multi-dimensional dynamic motor testing system, the system comprising:

[0006] A digital model construction module, the digital model construction module acquires motor operating parameters and establishes a motor digital model; a simulation test module, the simulation test module performs a simulated motor test based on the motor digital model to obtain motor simulation parameters; a first error coefficient acquisition module, the first error coefficient acquisition module inputs the motor simulation parameters into a pre-built parameter error analysis model for data analysis to obtain simulated operation error coefficients; a second error coefficient acquisition module, the second error coefficient acquisition module performs an actual test on the motor to obtain actual motor operating parameters, and inputs the actual motor operating parameters into a pre-built parameter error analysis model to obtain actual operation error coefficients; an error correction module, the error correction module performs error correction on the motor simulation parameters and the actual motor operating parameters according to the simulated operation error coefficients and the actual operation error coefficients to obtain correction calculation results, the correction calculation results including motor simulation correction parameters and motor actual correction parameters; a motor test result acquisition module, the motor test result acquisition module performs motor performance verification according to the correction calculation results and outputs motor test results.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application obtains motor operating parameters and establishes a motor digital model; based on the motor digital model, simulates the motor test to obtain motor simulation parameters; inputs the motor simulation parameters into a pre-built parameter error analysis model for data analysis to obtain simulated operation error coefficients; performs actual testing on the motor to obtain actual motor operating parameters, inputs the actual motor operating parameters into a pre-built parameter error analysis model to obtain actual operation error coefficients; performs error correction on the motor simulation parameters and the actual motor operating parameters based on the simulated operation error coefficients and the actual operation error coefficients to obtain correction calculation results, the correction calculation results including motor simulation correction parameters and motor actual correction parameters; performs motor performance verification based on the correction calculation results and outputs motor test results. The present invention solves the technical problems that the prior art cannot comprehensively and accurately evaluate motor performance and the test results are greatly affected by environmental factors. By obtaining motor operating parameters to establish a digital model, combining simulated test and actual test data into an error analysis model, generating error correction results, optimizing the simulated and actual parameters through error correction, and then performing performance verification, the present invention ultimately outputs accurate motor test results, achieving the technical effect of comprehensively evaluating motor performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of the structure of a multi-dimensional dynamic motor testing system provided in an embodiment of the present application;

[0011] Figure 2 A flowchart illustrating execution steps of a motor test result acquisition module in a multi-dimensional dynamic motor test system provided in an embodiment of the present application.

[0012] Explanation of the reference numerals: digital model building module 11 , simulation test module 12 , first error coefficient acquisition module 13 , second error coefficient acquisition module 14 , error correction module 15 , motor test result acquisition module 16 . DETAILED DESCRIPTION

[0013] This application provides a multi-dimensional dynamic motor testing system to solve the technical problems that the existing technology cannot comprehensively and accurately evaluate the motor performance and the test results are greatly interfered by environmental factors. By obtaining the motor operating parameters, a digital model is established, and the error analysis model is input by combining simulation test and actual test data to generate error correction results. After optimizing the simulation and actual parameters through error correction, performance verification is performed, and finally accurate motor test results are output, thereby achieving the technical effect of comprehensive evaluation of motor performance.

[0014] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0016] Examples, such as Figure 1 As shown, an embodiment of the present application provides a multi-dimensional dynamic motor testing system, the system comprising:

[0017] The digital model building module 11 obtains motor operating parameters and builds a digital model of the motor.

[0018] In the embodiments of this application, acquiring motor operating parameters is the foundation for building a digital model. Operating parameters refer to key values reflecting the motor's operating state, including but not limited to speed, torque, current, voltage, and temperature. These parameters are typically collected in real time by a multi-dimensional sensor array mounted on the motor. The sensors convert mechanical and electrical signals into digital signals, providing raw data support for subsequent processing.

[0019] Next, the digital model building module preprocesses the collected operating parameters. Preprocessing involves cleaning, noise reduction, and standardization of the raw data to improve its quality and consistency. For example, filtering algorithms can be used to remove noise signals, and normalization methods can be used to map parameter values to a uniform range. After preprocessing, the data is more suitable for model building.

[0020] Then, the preprocessed data is used for performance fitting, which involves fitting a mathematical model to the motor's actual operating behavior. Performance fitting is an approximate calculation method based on the relationship between parameters. Its purpose is to describe the dynamic characteristics of the motor's operation using mathematical formulas or data-driven algorithms (such as the least squares method). For example, the motor's load characteristic curve can be fitted based on the relationship between speed and torque, while a thermal balance model can be fitted based on current and temperature data.

[0021] After the running fitting is completed, a motor digital model is established based on the fitting results.

[0022] Furthermore, in the system provided in the embodiment of the application, the digital model construction module 11 is further used to:

[0023] Data is collected on the motor to be tested to obtain motor operating parameters; and operation fitting is performed based on the motor operating parameters to obtain the motor digital model.

[0024] In an embodiment of the present application, the operating parameters of the motor to be tested are first obtained through a data acquisition module. Operating parameters refer to the dynamic performance indicators exhibited during the operation of the motor, usually including speed, torque, current, voltage, vibration and temperature. These parameters are collected in real time by sensors installed on the motor (such as torque sensors, temperature sensors, current transformers, etc.). The sensor converts the physical signal into a digital signal and transmits it to the processing system through a data acquisition device. The goal of data acquisition is to ensure the high accuracy and stability of the operating parameters and provide real and reliable data for subsequent analysis.

[0025] The collected operating parameters are then preprocessed. The purpose of data preprocessing is to clean and standardize the collected data to eliminate the impact of external interference on the operating parameters. Specifically, high-frequency noise is removed through low-pass filtering. Low-pass filtering preserves the primary low-frequency components of the signal while eliminating high-frequency fluctuations caused by environmental vibrations or electronic interference, resulting in smoother and more physically meaningful data.

[0026] After preprocessing, the operation fitting phase begins. Operation fitting is the process of mathematically modeling the relationships between the motor's operating parameters. Its goal is to generate a mathematical model that describes the motor's dynamic characteristics. Specifically, polynomial regression is used to fit the parameters and establish relationship curves between key parameters such as speed and torque, and temperature and current.

[0027] Finally, a digital model of the motor is constructed based on the results of the operational fitting. The digital model, a collection of mathematical expressions derived from the fitting, reflects the motor's performance characteristics under different operating conditions. For example, the digital model can be used to predict the motor's operating state under specific load or environmental conditions, providing a basis for subsequent testing and verification.

[0028] The simulation test module 12 performs a simulation motor test based on the motor digital model to obtain motor simulation parameters.

[0029] In this embodiment, the simulation test module 12 simulates the motor's performance under different operating conditions by setting test conditions based on the motor's digital model. After loading the motor's digital model, the simulation module 12 starts the simulation and generates motor simulation parameters corresponding to the operating conditions, including speed, torque, power, temperature, and vibration.

[0030] Furthermore, in the system provided in the embodiment of the application, the simulation test module 12 is further used to:

[0031] Based on the motor digital model, simulation calculation is performed to obtain the motor simulation parameters; wherein the motor simulation parameters include speed, torque, power, temperature, and vibration.

[0032] In an embodiment of the present application, the digital model of the motor is loaded first. The digital model of the motor integrates the relationship between operating parameters such as speed, torque, and current, and can reflect the dynamic behavior of the motor under different working conditions. After loading the model, the simulation environment is initialized to simulate the operating state of the motor. Next, the technical experts set the simulation conditions. This step defines the input variables of the simulation calculation, such as input voltage, ambient temperature, load conditions, etc. The setting of these conditions needs to fit the actual usage scenario to ensure the credibility of the simulation results. For example, by setting the voltage range, the performance of the motor under different power supply conditions can be evaluated.

[0033] Next, the simulation is initiated. Based on the motor's digital model and input conditions, a dynamic simulation tool (such as MATLAB Simulink) is used to simulate the motor's operation. During the simulation, the motor's dynamic response at different time points or load conditions is calculated, generating a series of output parameters. After the simulation is complete, the generated motor simulation parameters are extracted and organized. These parameters include speed, torque, power, temperature, and vibration. Speed reflects the motor's speed performance under load; torque represents the motor's output capacity; power measures the motor's energy output under different electrical conditions; temperature assesses the motor's thermal stability; and vibration reflects the motor's mechanical smoothness.

[0034] Finally, the organized motor simulation parameters are output, saved in digital form, and transmitted to other modules of the system for subsequent error analysis and performance verification.

[0035] The first error coefficient acquisition module 13 inputs the motor simulation parameters into a pre-built parameter error analysis model for data analysis to obtain a simulation operation error coefficient.

[0036] In this embodiment of the present application, a first error coefficient acquisition module receives motor simulation parameters and then uses a parameter error analysis model to calculate the error. This model is trained based on a large amount of historical sample data. The parameter error analysis model is used to analyze the parameter error analysis model to obtain the simulated operation error coefficient.

[0037] Furthermore, in the system provided in the embodiment of the application, the first error coefficient acquisition module 13 is further configured to:

[0038] Obtain sample motor operating parameters, and annotate the sample motor operating parameters with error coefficients to obtain a corresponding sample error coefficient set; perform supervised training based on the sample motor operating parameters and the sample error coefficient set to obtain the parameter error analysis model; analyze the motor simulation parameters according to the parameter error analysis model to obtain the simulated operation error coefficient.

[0039] In this example, we first collected sample motor operating parameters, including speed, torque, power, temperature, and vibration, using a sensor array under different operating environments and test conditions. These operating parameters not only reflect motor performance but also include potential impacts of environmental factors such as temperature fluctuations, humidity, and load conditions.

[0040] Next, the sample motor operating parameters are annotated with error coefficients. The error coefficient is a correction factor used to adjust test data to account for the effects of environmental interference, bringing the adjusted operating parameters closer to actual performance. Error coefficient annotation is performed by technical experts based on historical data and empirical rules. For example, based on previous operating data under similar conditions, experts can assign specific error coefficients to operating parameters under certain conditions to reflect the impact of test conditions on performance. Once annotated, a sample error coefficient set is generated, where each error coefficient corresponds to a corresponding sample operating parameter.

[0041] Next, supervised training is performed based on a set of sample motor operating parameters and sample error coefficients. Supervised training builds a mathematical model capable of automatically predicting error coefficients by learning the relationship between parameters and error coefficients. During the training process, the sample operating parameters are used as input and the sample error coefficients are used as output. Machine learning algorithms, such as support vector machines, are used for training, ultimately generating a parameter error analysis model with predictive capabilities.

[0042] After the model is built, the motor simulation parameters are fed into the parameter error analysis model for data analysis. During the analysis, the parameter error analysis model outputs the simulation run error coefficient corresponding to each simulation parameter based on the simulation parameters and the error relationship learned during training.

[0043] The second error coefficient acquisition module 14 performs actual testing on the motor to obtain actual operating parameters of the motor, and inputs the actual operating parameters of the motor into a pre-built parameter error analysis model to obtain actual operating error coefficients.

[0044] In this embodiment, the second error coefficient acquisition module first performs actual motor testing, acquiring actual operating parameters using sensors in a real-world operating environment. These operating parameters, including speed, torque, power, temperature, and vibration, are important indicators of the motor's dynamic performance under actual operating conditions. The sensors collect data in real time, and the data transmission module converts the signals into digitized operating parameters.

[0045] The collected actual operating parameters are then fed into the parameter error analysis model. The model is then invoked to perform error calculations. Based on the actual operating parameters and the model's characteristic mapping, corresponding error coefficients are calculated. These error coefficients quantify the degree to which environmental factors affect the actual test data. Finally, the actual operating error coefficients are output and associated with each actual operating parameter for subsequent error correction and performance verification steps.

[0046] The error correction module 15 performs error correction on the motor simulation parameters and the motor actual operation parameters according to the simulation operation error coefficient and the actual operation error coefficient to obtain a correction calculation result, wherein the correction calculation result includes the motor simulation correction parameter and the motor actual correction parameter.

[0047] In this embodiment of the present application, the error correction module first receives a simulated operation error coefficient and an actual operation error coefficient. Next, the motor simulation parameters are corrected. This correction method multiplies the simulated operation error coefficients by the motor simulation parameters one by one to calculate the motor simulation correction parameters. The actual motor operation parameters are then corrected. By multiplying the actual operation error coefficients by the actual operation parameters, the effects of environmental and test conditions on the actual operation data are corrected, resulting in the actual motor correction parameters.

[0048] Finally, the motor simulation correction parameters and the motor actual correction parameters are integrated to generate the correction calculation results and output them.

[0049] Furthermore, in the system provided in the embodiment of the application, the error correction module 15 is further configured to:

[0050] The simulated operation error coefficients are mapped one-to-one with the motor simulation parameters, and the actual operation error coefficients are mapped one-to-one with the motor actual operation parameters; the simulated operation error coefficients are multiplied by the corresponding motor simulation parameters to generate the motor simulation correction parameters; the actual operation error coefficients are multiplied by the corresponding motor actual operation parameters to generate the motor actual correction parameters; the motor simulation correction parameters and the motor actual correction parameters are integrated and output as the correction calculation result.

[0051] In the embodiment of the present application, a one-to-one mapping operation is first performed. The simulated operation error coefficients are matched item by item with the motor simulation parameters, and the actual operation error coefficients are matched item by item with the motor actual operation parameters, ensuring that each error coefficient corresponds to a specific operating parameter. This mapping process is intended to clarify the applicable scope of error correction and make subsequent calculations more targeted. For example, the simulated operation error coefficients correspond to simulated parameters such as speed and torque, while the actual operation error coefficients correspond to actual parameters such as power and temperature.

[0052] Next, the motor simulation parameters are calibrated. Each simulated operation error coefficient is multiplied by the corresponding simulation parameter to generate the corrected motor simulation calibration parameters. For example, the speed in the simulation parameter is multiplied by the corresponding simulated operation error coefficient to obtain the correction parameter for the speed in the motor simulation parameter. These corrected parameters are combined to obtain the motor simulation calibration parameters. Similarly, the actual motor operating parameters are calibrated by multiplying each actual operation error coefficient by the corresponding actual operating parameter to generate the corrected motor actual calibration parameters.

[0053] Finally, the motor simulation correction parameters and the motor actual correction parameters are integrated to generate the final correction calculation results.

[0054] The motor test result acquisition module 16 performs motor performance verification according to the correction calculation result and outputs the motor test result.

[0055] In an embodiment of the present application, first, the motor test result acquisition module receives the correction calculation results, which include the motor simulation correction parameters and actual correction parameters that have been corrected for errors. Then, a performance verification analysis is performed. Performance verification is to compare the corrected parameters with the preset performance standards to evaluate whether the performance of the motor under different working conditions meets the design requirements. Then, the performance deviation evaluation is started. By analyzing the difference between the correction parameters and the performance standards, a performance deviation result is generated. The performance deviation result quantifies the degree of deviation between the actual operating state of the motor and the ideal design target, and can intuitively reflect the operating performance of the motor in different dimensions.

[0056] Finally, the results of performance verification and deviation assessment are integrated to generate and output the motor test results.

[0057] Further, such as Figure 2 As shown, in the system provided by the embodiment of the application, the motor test result acquisition module 16 is further used to:

[0058] According to the correction calculation results, the corresponding parameters in the motor simulation correction parameters and the motor actual correction parameters are averaged to obtain the motor performance mean index; the motor performance mean index is compared with the preset performance standard to generate a performance deviation result; based on the performance deviation result, the motor operating performance is evaluated to generate a motor test result.

[0059] In this embodiment, the motor's simulated calibration parameters and the corresponding performance parameters from the actual calibration parameters are averaged to generate a motor performance average index. This process combines simulated and actual calibration data to comprehensively reflect the motor's operating performance, avoiding the potential bias caused by single data.

[0060] The average motor performance index is then compared to pre-set performance standards. These standards consist of maximum and minimum values for each performance parameter, defining the normal operating range. For example, the speed standard sets maximum and minimum speed values to ensure the motor neither overspeeds nor underruns the starting requirements. The torque standard specifies maximum and minimum output torques to ensure proper load-carrying capacity and starting performance.

[0061] The performance deviation results are then generated based on the comparison results. During the comparison process, if the mean performance indicator falls between the maximum and minimum values of the corresponding parameter, the performance is considered normal; if it exceeds the maximum value or falls below the minimum value, the performance is considered unsatisfactory. For example, if the mean speed exceeds the maximum allowable range, it may indicate a motor overspeed risk, while falling below the minimum value may indicate starting or load issues.

[0062] Finally, the motor's operating performance is comprehensively evaluated based on the performance deviation results, generating a motor test result. The test result clearly indicates whether the motor's performance meets the required standards, along with specific deviation information. If all performance indicators meet the pre-set performance standards, the test result displays "Pass." If any parameter exceeds the standard range, the result is marked as "Failed," with a list of performance items requiring improvement.

[0063] Furthermore, in the system provided in the application embodiment, the preset performance standards include speed range standards, torque range standards, power range standards, temperature range standards, and vibration amplitude standards.

[0064] In an embodiment of the present application, the preset performance standards are used to define the normal range of key performance parameters during motor operation, including speed range standards, torque range standards, power range standards, temperature range standards and vibration amplitude standards. Each standard consists of a maximum value and a minimum value, which are used to guide performance verification and test result judgment.

[0065] The preset speed range standard includes maximum speed and minimum speed values. For example, the speed under rated load must not be lower than the designed minimum value, and the speed under high load must not exceed the set maximum value.

[0066] The torque range standard includes a maximum torque value and a minimum torque value. The minimum value indicates the motor's starting capability under light load conditions, while the maximum value indicates the motor's carrying capacity under heavy load conditions.

[0067] The power range standard reflects the motor's energy conversion efficiency. The preset maximum value is used to limit excessive power consumption to ensure energy-saving performance; the minimum value ensures that the motor can maintain normal operation under rated load.

[0068] Temperature range standards include maximum and minimum temperature values for safe operation. For example, the maximum temperature value is used to limit material damage caused by overheating, while the minimum temperature value is used to prevent lubrication performance degradation or component failure caused by low temperatures.

[0069] Vibration amplitude is a key indicator for evaluating a motor's mechanical stability. A preset maximum value is used to limit mechanical wear or failure caused by excessive vibration, while a minimum value is set to ensure smooth motor operation. For example, a vibration amplitude exceeding the maximum value during high-load operation may indicate loose mechanical components, while a vibration amplitude below the minimum value during low-load operation may indicate abnormal operating conditions.

[0070] Furthermore, in the system provided in the embodiment of the application, after obtaining the motor test results, the system is used to:

[0071] The motor test results are visualized on the terminal; and a terminal interface is displayed based on the corresponding relationship between the motor test results and the motor digital model.

[0072] In the examples of this application, the test result data is first processed and converted into an intuitive graphical form using visualization technology. These test results include evaluation data and deviation analysis results for key performance indicators such as speed, torque, power, temperature, and vibration. Visualization formats typically include trend graphs, bar charts, and pie charts, which are used to display parameter change trends, comparison results with preset performance standards, and the distribution of the proportion of qualified and unqualified performance items.

[0073] The motor test results are then dynamically displayed on the terminal interface based on the correspondence between them and the motor digital model. By mapping the test result data structure to the characteristics of the motor digital model, the interface can intuitively overlay the test results onto the virtual background of the digital model. For example, the speed test results can be dynamically annotated in the speed module area of the digital model, and the torque data can be displayed in the corresponding module to indicate whether it meets the preset standards, with color codes used to indicate whether the performance items meet the requirements.

[0074] Ultimately, a dynamic interactive display is achieved on the terminal interface. Users can filter different performance items and view the corresponding test results and the characteristics associated with the digital model, thereby quickly understanding the motor's operating performance and existing problems.

[0075] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0076] The present application obtains motor operating parameters and establishes a motor digital model; based on the motor digital model, simulates the motor test to obtain motor simulation parameters; inputs the motor simulation parameters into a pre-built parameter error analysis model for data analysis to obtain simulated operation error coefficients; performs actual testing on the motor to obtain actual motor operating parameters, inputs the actual motor operating parameters into a pre-built parameter error analysis model to obtain actual operation error coefficients; performs error correction on the motor simulation parameters and the actual motor operating parameters based on the simulated operation error coefficients and the actual operation error coefficients to obtain correction calculation results, the correction calculation results including motor simulation correction parameters and motor actual correction parameters; performs motor performance verification based on the correction calculation results and outputs motor test results. The present invention solves the technical problems that the prior art cannot comprehensively and accurately evaluate motor performance and the test results are greatly affected by environmental factors. By obtaining motor operating parameters to establish a digital model, combining simulated test and actual test data into an error analysis model, generating error correction results, optimizing the simulated and actual parameters through error correction, and then performing performance verification, the present invention ultimately outputs accurate motor test results, achieving the technical effect of comprehensively evaluating motor performance.

[0077] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0079] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A multi-dimensional dynamic motor testing system, characterized in that: The system comprises: a digital model building module, wherein the digital model building module obtains motor operating parameters and builds a motor digital model; A simulation test module, wherein the simulation test module performs a simulated motor test based on the motor digital model to obtain motor simulation parameters; a first error coefficient acquisition module, which inputs the motor simulation parameters into a pre-built parameter error analysis model for data analysis to obtain a simulation operation error coefficient; a second error coefficient acquisition module, which performs actual testing on the motor to obtain actual operating parameters of the motor, and inputs the actual operating parameters of the motor into a pre-built parameter error analysis model to obtain actual operating error coefficients; an error correction module, wherein the error correction module performs error correction on the motor simulation parameters and the motor actual operation parameters according to the simulated operation error coefficient and the actual operation error coefficient to obtain a correction calculation result, wherein the correction calculation result includes the motor simulation correction parameter and the motor actual correction parameter; a motor test result acquisition module, which performs motor performance verification based on the correction calculation result and outputs a motor test result; The first error coefficient acquisition module: Obtaining sample motor operating parameters, and marking the sample motor operating parameters with error coefficients to obtain a corresponding sample error coefficient set; Performing supervised training based on the sample motor operating parameters and the sample error coefficient set to obtain the parameter error analysis model; Analyzing the motor simulation parameters according to the parameter error analysis model to obtain the simulation operation error coefficient; The error correction module: Performing a one-to-one mapping between the simulated operation error coefficient and the motor simulation parameter, and performing a one-to-one mapping between the actual operation error coefficient and the motor actual operation parameter; multiplying the simulation operation error coefficients by corresponding motor simulation parameters to generate the motor simulation correction parameters; Multiplying the actual operation error coefficients by the corresponding actual operation parameters of the motors to generate the actual correction parameters of the motors; Integrating the motor simulation correction parameters and the motor actual correction parameters to output as the correction calculation result; The motor test result acquisition module: performing mean calculation on corresponding parameters of the motor simulation correction parameters and the motor actual correction parameters according to the correction calculation result to obtain a motor performance mean index; Comparing the motor performance mean index with a preset performance standard to generate a performance deviation result; Based on the performance deviation result, the motor operating performance is evaluated and a motor test result is generated.

2. A multi-dimensional dynamic motor testing system according to claim 1, characterized in that: Digital model building blocks: Collect data from the motor to be tested and obtain the motor operating parameters; Operation fitting is performed based on the motor operation parameters to obtain the motor digital model.

3. The multi-dimensional dynamic motor testing system according to claim 1, wherein: Simulation test module: Based on the motor digital model, a simulation calculation is performed to obtain the motor simulation parameters; The motor simulation parameters include speed, torque, power, temperature, and vibration.

4. The multi-dimensional dynamic motor testing system according to claim 1, wherein: The preset performance standards include speed range standards, torque range standards, power range standards, temperature range standards, and vibration amplitude standards.

5. The multi-dimensional dynamic motor testing system according to claim 1, characterized in that: After obtaining the motor test results: Performing terminal visualization on the motor test results; Based on the correspondence between the motor test results and the motor digital model, a terminal interface display is performed.

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