Testing method, system and equipment of GM drive and control all-in-one machine and medium

Abstract

The invention relates to the technical field of all-in-one machine testing, in particular to a testing method, system and device of a GM drive and control all-in-one machine and a medium. The method comprises the following steps: firstly, establishing communication connection between a to-be-tested GM drive and control all-in-one machine and auxiliary test equipment, and ensuring the reliability of data transmission by monitoring a communication state in real time; the test host comprehensively detects the input and output functions of the equipment; then carrying out a servo twin trawling test at a rated rotating speed and under an overload condition, and collecting key operation parameters such as the rotating speed, the torque, the current and the like; meanwhile, an operating system kernel version and a servo software version are verified, so that the compatibility of a software system is ensured; finally, based on all data collected by the test host, a unified qualification judgment standard is established, and only when all detection items meet requirements, the verified factory parameters can be written into the equipment; the problems of missing detection and misjudgment possibly occurring in subentry testing are effectively avoided, and the consistency and reliability of product quality are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of all-in-one machine testing, and in particular to a testing method, system, equipment and medium for a GM drive and control all-in-one machine. Background Art

[0002] As industrial automation continues to increase, GM integrated drive and control units, as new actuators integrating servo drives and motors, are widely used in industrial robots, CNC machine tools, and other fields. Their performance directly impacts the reliability and stability of the entire automation system, making factory testing increasingly important.

[0003] Currently, testing GM integrated drive and control systems relies primarily on manual operation, requiring testers to manually connect equipment, adjust parameters, record data, and analyze it. This testing method requires extensive expertise and experience, and is tedious and time-consuming.

[0004] However, manual testing methods are easily affected by subjective factors such as the operator's skill level and working status, and are unable to conduct comprehensive and accurate testing of products, resulting in large discreteness of test parameters, low production efficiency, and difficulty in meeting the quality control requirements of large-scale production. This situation needs further improvement. Summary of the Invention

[0005] To address the problem that existing testing methods are unable to comprehensively and accurately test products, resulting in large discreteness of test parameters, low production efficiency, and difficulty in meeting the quality control requirements of large-scale production, this application provides a testing method, system, equipment, and medium for a GM drive-control integrated machine, using the following technical solutions: In a first aspect, the present application provides a method for testing a GM drive-control integrated machine, characterized by comprising the following steps: Connect the GM drive control integrated machine to be tested with the auxiliary test equipment to obtain the communication connection status between the devices; The test host detects the input and output functions of the device under test and obtains the function test results; Perform servo drag test under rated speed and overload conditions to obtain servo operation data; The operating system kernel version and servo software version of the device under test are verified by the test host to obtain the version verification result; Based on the communication connection status, the functional test results, the servo operation data and the version verification results, it is determined whether all test items are qualified. When the judgment result is qualified, the factory parameters are written into the device under test.

[0006] By adopting the above technical solution, this application first establishes a communication connection between the GM drive and control integrated machine to be tested and the auxiliary test equipment, and ensures the reliability of data transmission by real-time monitoring of the communication status; the test host conducts a comprehensive test of the input and output functions of the equipment, including the response characteristics of the digital and analog interfaces; then the servo drag test is carried out under rated speed and overload conditions to collect key operating parameters such as speed, torque, and current; at the same time, the operating system kernel version and the servo software version are verified to ensure the compatibility of the software system; finally, based on all the data collected by the test host, a unified qualification judgment standard is established. Only when all the test items meet the requirements can the verified factory parameters be written to the equipment; this application can detect the speed loop of the servo drive and its overload capacity, and improve the comprehensiveness and accuracy of the test through data correlation analysis, effectively avoiding the problems of missed detection and misjudgment that may occur in sub-item tests, and ensuring the consistency and reliability of product quality.

[0007] Optionally, the input and output functions of the device under test are tested by a test host, specifically including the following steps: Obtaining port configuration information of the device under test, wherein the port configuration information includes the number of digital ports and the number of analog channels; generating a test instruction sequence according to the port configuration information; Executing the test instruction sequence and collecting port response data; The port response data is analyzed to determine the working status of each port.

[0008] By adopting the above technical solution, the present application first reads the port configuration information from the device to be tested, including the number of digital ports and the number of analog channels, which reflects the hardware resource characteristics of the device; then, based on the obtained configuration information, the test host automatically generates a test instruction sequence that matches the current device port structure to ensure the targetedness of the test; then, the test is executed in sequence according to the generated instruction sequence, and the response data of each port is collected in real time during this process; finally, by analyzing the collected response data, it is determined whether the working status of each port is normal; by dynamically adapting the test scheme, the comprehensiveness of the test is guaranteed, and the test efficiency is improved, which effectively solves the problem that traditional fixed test schemes are difficult to cope with device differences.

[0009] Optionally, a servo drag test is performed at rated speed and overload conditions, including the following steps: Read the nominal parameters of the motor of the device under test; Calculate the set values of rated working condition and overload working condition according to the nominal parameters of the motor; Collecting motor operating parameters under rated working conditions and overload working conditions respectively according to the set values; The steady-state performance and dynamic response characteristics of the motor are analyzed based on the operating parameters.

[0010] By adopting the above technical solution, the present application first reads the nominal parameters of the motor from the device to be tested; then, based on these parameters, automatically calculates the rated operating condition and overload operating condition setting values that are adapted to the current motor characteristics, where the overload operating condition is usually set to 150% of the rated value; then, tests are carried out under the calculated rated operating condition and overload operating condition, respectively, to collect key parameters such as speed, torque, current, and temperature rise during the operation of the motor; finally, by analyzing the collected operating parameters, the steady-state performance indicators (such as speed fluctuation rate, torque accuracy, etc.) and dynamic response characteristics (such as acceleration time, overshoot, etc.) of the motor under different loads are evaluated; by automatically matching the test conditions, the effectiveness and safety of the test are ensured, and the targetedness of the test is improved, effectively solving the problem that traditional fixed operating condition test schemes are difficult to adapt to motors of different specifications.

[0011] Optionally, analyzing the steady-state performance and dynamic response characteristics of the motor according to the operating parameters specifically includes the following steps: Monitor the operating status of the drive end and the load end and obtain error code information; Collect feedback current, torque and speed of each test point on the drive end to obtain test data; Calculate the effective value of the three-phase current UVW at the drive end to obtain the current change characteristics; Obtain temperature data of IPM power module and motor, and establish temperature monitoring curve; Based on the error code information, the test data, the current variation characteristics and the temperature monitoring curve, the steady-state performance and dynamic response characteristics of the motor are determined.

[0012] By adopting the above technical solution, this application first monitors the operating status of the drive end and the load end in real time, records various error code information generated by the system, and promptly discovers potential faults; secondly, basic parameters such as feedback current, torque and speed are collected at key test points to establish the basic characteristic curve of the equipment operation; then, by calculating the effective value of the UVW three-phase current at the drive end, the balance and distortion of the current are analyzed, and the electrical performance of the drive system is evaluated; at the same time, the temperature data of the IPM power module and the motor are continuously collected, and the thermal characteristics of the system are mastered through the temperature monitoring curve; finally, based on the obtained error code information, test data, current change characteristics and temperature monitoring curves and other multi-dimensional information, the steady-state performance and dynamic response characteristics of the motor under various working conditions are comprehensively evaluated; a comprehensive evaluation of the system performance is achieved, which effectively solves the problem that a single parameter evaluation method is difficult to reflect the overall performance of the system.

[0013] Optionally, the method further comprises the following steps: Monitor the real-time data collected by the test host to determine whether there are any abnormal parameters; If there are abnormal parameters, the fault type is determined according to the abnormal parameters, and the fault type includes overcurrent fault, overtemperature fault, communication fault and encoder fault; Obtaining a fault level based on the fault type and generating a test alarm log, wherein the fault level includes an emergency stop level, a rapid stop level, and a warning prompt level; Matching emergency response strategies according to the test alarm log, wherein the emergency response strategies include immediate power off, deceleration and shutdown, and parameter adjustment; Execute corresponding protection actions according to the emergency handling strategy and record the fault handling results.

[0014] By adopting the above-mentioned technical solution, the present application first continuously monitors the real-time data collected by the test host, and immediately triggers the fault diagnosis process once it finds that the parameters are out of the normal range; then, according to the type and characteristics of the abnormal parameters, the fault is accurately classified as overcurrent, overtemperature, communication or encoder fault, so as to achieve accurate identification of the fault; then, based on the degree of harm of the fault, it is divided into three levels: emergency shutdown, rapid shutdown or only warning prompt, and a test alarm log containing information such as fault time, type, and level is generated; at the same time, the corresponding emergency processing strategy is automatically matched according to the fault type and level, and the emergency shutdown level fault takes immediate power-off measures, the rapid shutdown level fault executes the deceleration shutdown procedure, and the warning prompt level fault automatically adjusts the parameters; finally, the corresponding protection action is executed according to the selected emergency processing strategy, and the entire fault handling process is recorded and archived; intelligent protection of the test process is realized, and the problems of delayed response and single processing of traditional protection methods are effectively solved.

[0015] Optionally, the fault type further includes a motor demagnetization fault, a mechanical jam fault, and a bearing fault. Monitoring the real-time data collected by the test host and determining the fault type further includes the following steps: Obtaining a preset multi-operating-condition dynamic feature library, wherein the multi-operating-condition dynamic feature library includes fault feature matrix templates under different speeds and different loads; Acquire current spectrum data and torque ripple data during load gradient; Calculating the amplitude ratio of the fundamental wave to the characteristic harmonics of the current spectrum data and the fluctuation rate of the torque pulsation data to form a characteristic matrix sequence; Extracting characteristic harmonic amplitude ratio change gradient, torque fluctuation rate mutation characteristics and spectrum energy reconstruction error from the characteristic matrix sequence to obtain a fault feature vector; The fault feature vector is pattern matched with the multi-operating-condition dynamic feature library to determine the fault type.

[0016] By adopting the above technical solution, the GM drive-control integrated machine may have various electromechanical coupling faults such as motor demagnetization, mechanical jamming and bearings during actual operation; since this type of fault has dynamic evolution characteristics, its characterization parameters will change with the working conditions, and traditional fault diagnosis methods are difficult to accurately identify the fault type; this application first establishes a dynamic feature library containing fault feature matrix templates under different speeds and different load conditions. The dynamic feature library covers the typical characteristics of various types of faults under different working conditions; then, during the load gradient test, the current spectrum data and torque pulsation data are synchronously collected to obtain the dynamic response characteristics of the fault; then the current is calculated The amplitude ratio of the fundamental wave to the characteristic harmonics in the spectrum, as well as the fluctuation rate of the torque pulsation, are organized into a characteristic matrix sequence that reflects the dynamic evolution process of the fault. The changing gradient of the characteristic harmonic amplitude ratio and the mutation characteristics of the torque fluctuation rate are extracted from the characteristic matrix sequence, and the reconstruction error of the spectrum energy is calculated to construct a multi-dimensional fault feature vector. Finally, the extracted feature vector is pattern matched with the preset multi-operating condition dynamic feature library, and the specific fault type is determined through matching degree analysis. By establishing a dynamic feature library and a multi-dimensional feature extraction mechanism, accurate identification of the fault type is achieved, effectively solving the problem that traditional static diagnosis methods are difficult to adapt to changes in operating conditions.

[0017] Optionally, the pattern matching process includes the following steps: Presetting a fault feature weight matrix, wherein the fault feature weight matrix includes a characteristic harmonic amplitude ratio change gradient weight, a torque fluctuation rate mutation feature weight, and a spectrum energy reconstruction error weight; Constructing a fault feature vector, wherein the fault feature vector includes an amplitude ratio change gradient, a torque fluctuation rate, and a spectrum energy reconstruction error; Calculating a weighted matching degree between the fault feature vector and the fault feature weight matrix; The fault type is determined by comparing the weighted matching degree with a preset fault threshold.

[0018] By adopting the above technical solution, since different fault features have different contributions to the fault type, the use of simple feature matching is likely to cause deviations in the diagnostic results. For example, under certain working conditions, harmonic features may be more able to reflect demagnetization faults than torque features, and torque fluctuations may be more sensitive to mechanical faults. The present application first presets a fault feature weight matrix that reflects the importance of each feature, including the characteristic harmonic amplitude ratio change gradient weight, the torque fluctuation rate mutation feature weight, and the spectrum energy reconstruction error weight. Then, a fault feature vector containing the amplitude ratio change gradient, the torque fluctuation rate, and the spectrum energy reconstruction error is constructed. The fault feature vector is then multiplied by the weight matrix to obtain a weighted matching degree that comprehensively considers the importance of each feature. Finally, the calculated weighted matching degree is compared with a pre-set fault threshold. When the matching degree of a certain type of fault exceeds the corresponding threshold, the specific fault type can be determined. Differentiated processing of fault features is achieved, which effectively solves the problem of insufficient accuracy of the traditional equal-weight matching method and provides strong support for the intelligent fault diagnosis.

[0019] In a second aspect, the present application provides a GM drive-control integrated machine testing system, comprising: Test host, auxiliary test equipment and GM drive control integrated machine to be tested; among them, The test host is connected to the auxiliary test equipment and is used to perform test tasks and process test data; The auxiliary test equipment is connected to the GM drive and control integrated machine to be tested, and is used to provide a test load; The test host includes: A communication detection module is used to detect the communication connection status between the GM drive and control integrated machine to be tested and the auxiliary test equipment; Functional testing module, used to detect the input and output functions of the GM drive-control integrated machine to be tested; Servo test module, used to perform servo drag test under rated speed and overload conditions; A version verification module is used to verify the operating system kernel version and servo software version of the GM drive-control integrated machine to be tested; A determination module, configured to determine whether a test item is qualified based on the communication connection status, function test results, servo operation data, and version verification results; The parameter writing module is used to write the factory parameters into the GM drive-control integrated machine to be tested when the judgment result is qualified.

[0020] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned GM drive and control integrated machine testing method when executing the computer program.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned GM drive and control integrated machine testing method.

[0022] In summary, this application includes at least one of the following beneficial technical effects: 1. This application first establishes a communication connection between the GM drive-control integrated machine to be tested and the auxiliary test equipment, and ensures the reliability of data transmission by real-time monitoring of the communication status; the test host conducts a comprehensive test of the input and output functions of the equipment, including the response characteristics of the digital and analog interfaces; then, a servo drag test is carried out under rated speed and overload conditions to collect key operating parameters such as speed, torque, and current; at the same time, the operating system kernel version and the servo software version are verified to ensure the compatibility of the software system; finally, based on all the data collected by the test host, a unified qualification judgment standard is established. Only when all the test items meet the requirements can the verified factory parameters be written to the equipment; this application can detect the speed loop of the servo drive and its overload capacity, and improve the comprehensiveness and accuracy of the test through data correlation analysis, effectively avoiding the problems of missed detection and misjudgment that may occur in sub-item tests, and ensuring the consistency and reliability of product quality; 2. This application first reads the nominal parameters of the motor from the device under test; then, based on these parameters, automatically calculates the rated operating condition and overload condition setting values that are adapted to the current motor characteristics, where the overload condition is usually set to 150% of the rated value; then, tests are carried out under the calculated rated operating condition and overload condition, respectively, collecting key parameters such as speed, torque, current, and temperature rise during motor operation; finally, by analyzing the collected operating parameters, the steady-state performance indicators (such as speed fluctuation rate, torque accuracy, etc.) and dynamic response characteristics (such as acceleration time, overshoot, etc.) of the motor under different loads are evaluated; by automatically matching the test conditions, the effectiveness and safety of the test are ensured, and the targetedness of the test is improved, effectively solving the problem that traditional fixed-condition test solutions are difficult to adapt to motors of different specifications; 3. This application first establishes a dynamic feature library containing fault feature matrix templates under different speeds and different load conditions. The dynamic feature library covers the typical characteristics of various types of faults under different working conditions; then, during the load gradient test, the current spectrum data and torque pulsation data are synchronously collected to obtain the dynamic response characteristics of the fault; then, the amplitude ratio of the fundamental wave and the characteristic harmonics in the current spectrum, as well as the fluctuation rate of the torque pulsation are calculated, and organized into a feature matrix sequence that reflects the dynamic evolution process of the fault; the change gradient of the characteristic harmonic amplitude ratio and the mutation characteristics of the torque fluctuation rate are extracted from the feature matrix sequence, and the reconstruction error of the spectrum energy is calculated to construct a multi-dimensional fault feature vector; finally, the extracted feature vector is pattern matched with the preset multi-working condition dynamic feature library, and the specific fault type is determined by matching degree analysis; by establishing a dynamic feature library and a multi-dimensional feature extraction mechanism, accurate identification of the fault type is achieved, effectively solving the problem that traditional static diagnostic methods are difficult to adapt to changes in working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method for testing a GM drive-control integrated machine according to an embodiment of the present application; Figure 2 This is a flow chart of step S130 in a method for testing a GM drive-control integrated machine according to an embodiment of the present application; Figure 3 This is a flow chart of step S134 in a method for testing a GM drive-control integrated machine according to an embodiment of the present application; Figure 4 This is a flowchart of a fault handling method in a GM drive-control integrated machine testing method according to an embodiment of the present application; Figure 5 This is a flow chart of obtaining a fault feature vector in a testing method of a GM drive-control integrated machine according to an embodiment of the present application; Figure 6 This is a schematic diagram of a pattern matching process in a testing method of a GM drive-control integrated machine according to an embodiment of the present application; Figure 7 This is a module diagram of a test system for a GM drive-control integrated machine according to an embodiment of the present application; Figure 8 This is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0025] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0026] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0027] In the first aspect, the present application provides a method for testing a GM drive-control integrated machine, referring to Figure 1 , including the following steps: S110: Connect the GM drive-control integrated machine to be tested to the auxiliary test equipment to obtain the communication connection status between the devices.

[0028] In this embodiment, the GM integrated drive and control machine factory test system consists of four main components: a central control computer, the device under test (DUT), auxiliary equipment, and a safety module. The central control computer runs the test software, enabling human-machine interaction and test process control. The DUT includes the GM integrated drive and control machine and the drive motor. The auxiliary equipment includes the MC12TS test board, GR servo drive, and load motor. The safety module includes a safety door switch, a wire-hanging switch, an emergency stop switch, and a system operation status indicator. The test system can simultaneously support seven test stations to meet the testing needs of different product models.

[0029] Specifically, the test process first requires establishing a reliable device communication connection. The system has a pre-set communication address mapping table, using DIP switches to determine the IP address of each device. The default IP address for the first group of auxiliary devices is 192.168.177.187, and subsequent stations decrease in sequence. The device under test uses the three DI lines (X20 / X21 / X22) as address lines, reading the status of the auxiliary device's DIP switches to determine its own IP address. The first group defaults to 192.168.177.177 to avoid address conflicts. After communication is established, the system exchanges data via CAN bus, EtherCAT, and RS485. The CAN bus connects to the load servo drive with a baud rate of 1Mbps; EtherCAT is used for high-speed periodic data exchange; and RS485 is used for parameter reading and writing.

[0030] S120: Detect the input and output functions of the device under test through the test host to obtain a function test result.

[0031] In this embodiment, when the test is executed, the system first performs input and output function tests. Digital IO testing uses a point-by-point activation method, setting only one output point at a time and verifying IO function by comparing the written and readback values. Analog IO testing uses a step-by-step method. The system presets the input-output conversion formula y=kx+b, calculates the expected value based on the slope k and intercept b of each channel, and determines whether the measured value is within the allowable error range.

[0032] S130. Perform a servo drag test at rated speed and overload conditions to obtain servo operation data.

[0033] In this embodiment, the system presets a test condition database for motors of different power levels. During the test, the parameters are automatically matched according to the motor type, and the test is performed using a master-slave drive towing method. The master drive operates in speed mode and the slave drive operates in torque mode. The test is divided into two test points: rated operating condition (speed 100% rated value, torque 80% rated value) and overload condition (speed 100% rated value, torque 150% rated value), and each test point lasts 30 seconds. The system evaluates the servo performance by monitoring indicators such as speed fluctuation rate, torque accuracy, three-phase current balance, and temperature rise characteristics.

[0034] S140: Verify the operating system kernel version and servo software version of the device under test through the test host to obtain a version verification result.

[0035] In this embodiment, to ensure the consistency and reliability of software versions in shipped products, the system establishes a version verification database. This database contains key information such as the standard kernel version number, servo software version number, and file verification code for each product model. The verification process uses a hierarchical comparison mechanism, first verifying version number matching, then performing file integrity verification using the MD5 algorithm, and finally verifying functional characteristics.

[0036] Specifically, the test host first reads the kernel version information of the device under test via the RS485 interface at a baud rate of 115200bps, obtaining a hexadecimal version number, such as "0x0123." It then reads the servo software version number, also in hexadecimal format, such as "0x4567." The system compares the read version information with a pre-set version comparison table to verify compliance with shipping requirements. For critical system files, a 32-bit checksum is calculated using the MD5 algorithm and compared with a standard checksum to ensure the software has not been tampered with. If the version numbers match and the file verification passes, the version verification is considered qualified.

[0037] S150. Determine whether all test items are qualified based on the communication connection status, function test results, servo operation data and version verification results. When the result is qualified, write the factory parameters into the device under test.

[0038] In this embodiment, the final qualification determination uses a weighted scoring mechanism. The system pre-establishes a scoring table for each test item, assigning different weight coefficients based on the importance of each test item, and then calculates the overall score through weighted calculation. When the score reaches the preset threshold, the preset factory parameters are automatically written.

[0039] Specifically, the system comprehensively evaluates four categories of test results: Communication connection status accounts for 25% of the weight, requiring all three communication methods, CAN / EtherCAT / RS485, to be functional with a packet loss rate of less than 0.1%; functional test results account for 30% of the weight, requiring all digital and analog I / O tests to pass with signal errors within ±1%; servo operation data accounts for 35% of the weight, requiring speed fluctuation less than 1%, torque accuracy less than 2%, and current waveform distortion less than 5%; and version verification results account for 10% of the weight, requiring a perfect match between the version number and the checksum. If the weighted score exceeds 90 points, the system writes factory parameters to the device under test via the RS485 interface. These parameters include control parameters (e.g., PID parameters, acceleration and deceleration times), protection parameters (e.g., overcurrent protection values, overvoltage protection values), and communication parameters (e.g., station ID, baud rate). After writing, the system performs a parameter readback to verify that the settings are correct. If any test fails or the parameter writing fails, the device is deemed defective and requires re-inspection or repair.

[0040] In one embodiment, referring to Figure 2In step S130, a servo drag test is performed under rated speed and overload conditions, specifically including the following steps: S131. Read the nominal parameters of the motor of the device under test.

[0041] In this embodiment, the system pre-establishes a motor parameter configuration table containing nominal parameters for motors of different power levels, such as basic parameters such as rated power, rated speed, rated torque, and rated current, as well as characteristic parameters such as winding resistance, stator inductance, and moment of inertia. The system reads the motor type identification code stored in the device under test via the RS485 interface and, based on this identification code, retrieves the corresponding motor nominal parameters from the configuration table.

[0042] S132. Calculate the set values for the rated operating condition and the overload operating condition according to the nominal parameters of the motor.

[0043] In this embodiment, the system uses a standardized operating condition setting method. Based on the nominal motor parameters, the system pre-sets an operating condition parameter calculation model. This model takes into account the motor's overload capacity, temperature rise characteristics, and mechanical strength limitations, and automatically calculates the speed and torque setting values under rated and overload conditions.

[0044] S133. Collect motor operating parameters under rated operating conditions and overload conditions according to set values.

[0045] In this embodiment, the system uses a high-speed data acquisition module with a sampling frequency of 1kHz to simultaneously collect key parameters such as speed, torque, and current. The system also has a pre-set data buffer area to store complete operating data within a 30-second test cycle for subsequent analysis and processing.

[0046] S134. Analyze the steady-state performance and dynamic response characteristics of the motor based on the operating parameters.

[0047] In this embodiment, the system adopts a hierarchical performance evaluation method. For steady-state performance, an evaluation model based on statistical analysis is established to calculate indicators such as speed fluctuation rate and torque accuracy. For dynamic performance, a spectrum analysis method is used to evaluate system bandwidth and harmonic characteristics.

[0048] In one embodiment, referring to Figure 3 In step S134, the steady-state performance and dynamic response characteristics of the motor are analyzed according to the operating parameters, which specifically includes the following steps: S1341. Monitor the operating status of the drive end and the load end to obtain error code information.

[0049] In this embodiment, the system has a pre-configured standard error code comparison table that contains all possible error types that may occur on the driver and load sides, such as overcurrent, overvoltage, and overtemperature, along with corresponding fault levels and handling suggestions. The system monitors the operating status of the devices on both ends in real time and records error information in the operation log.

[0050] Specifically, the test system reads the GR servo status and error codes from the load side via the CAN bus every 100ms, while simultaneously reading driver status information via the EtherCAT bus. On the driver side, key status bits monitored include PWM enable (0x0001), overcurrent alarm (0x0002), and overvoltage fault (0x0004). On the load side, key monitoring bits include slave communication status (0x05 indicates operational status) and torque control status (0x0008). If any error code is detected, the system records the time of occurrence and operating parameters for subsequent analysis.

[0051] S1342. Collect feedback current, torque, and speed at each test point on the drive end to obtain test data.

[0052] In this embodiment, the system pre-configures a data acquisition scheme with a segmented sampling strategy. A test data buffer pool is established to categorize and store data from different test points. Each test point contains both rated and overload conditions. The system automatically adjusts the sampling frequency and data length to ensure sufficient valid data is collected.

[0053] Specifically, at each test point, the system collects feedback data at a frequency of 1kHz, including actual speed n (rpm), actual torque T (N·m), and phase current I (A). Data is collected for 30 seconds under rated operating conditions and 15 seconds under overload conditions. For speed feedback, the system records the maximum, minimum, and average values; for torque feedback, the RMS value and fluctuation range are calculated; and for current feedback, the RMS value and peak value are recorded.

[0054] S1343. Calculate the effective values of the three-phase currents UV and W at the drive end to obtain the current variation characteristics.

[0055] In this embodiment, the system uses a standardized current analysis method. A pre-configured current characteristics evaluation template includes a phase balance calculation formula, waveform distortion criteria, and dynamic response indicators. The system calculates the three-phase RMS current in real time, establishes a current characteristics curve, and evaluates current quality.

[0056] S1344. Obtain temperature data of the IPM power module and the motor, and establish a temperature monitoring curve.

[0057] In this embodiment, the system has a preset temperature alarm threshold table containing temperature limits for motors of different power levels. The system collects real-time IPM module and motor temperatures to create a temperature rise curve and evaluate heat dissipation performance.

[0058] S1345. Determine the steady-state performance and dynamic response characteristics of the motor based on the error code information, test data, current variation characteristics, and temperature monitoring curve.

[0059] In this embodiment, the system establishes a performance index scoring table, setting scoring standards for steady-state performance and dynamic characteristics. The system analyzes various monitoring data, calculates a comprehensive score, and determines whether the motor performance meets the requirements.

[0060] In one embodiment, referring to Figure 4 , the method further comprises the steps of: S410: Monitor the real-time data collected by the test host to determine whether there are any abnormal parameters.

[0061] In this embodiment, the system pre-configures a parameter monitoring configuration table, which contains information such as key monitoring parameters, sampling periods, and alarm thresholds. Monitoring parameters are categorized into basic parameters (such as current, voltage, and temperature), performance parameters (such as speed and torque), and communication parameters (such as response time and packet loss rate). The system sets different monitoring strategies based on the importance of different parameters.

[0062] S420: If there are abnormal parameters, determine the fault type according to the abnormal parameters. The fault types include overcurrent fault, overtemperature fault, communication fault, and encoder fault.

[0063] In this embodiment, the system presets a fault diagnosis decision table, which maps abnormal parameters to specific fault types and includes a correspondence between parameter characteristics, judgment conditions, and fault types.

[0064] S430: Obtain a fault level based on the fault type and generate a test alarm log. The fault levels include an emergency stop level, a rapid stop level, and a warning prompt level.

[0065] In this embodiment, the system presets a fault level determination table to determine the fault level based on the fault type and abnormality level. The system automatically generates an alarm log in a standard format to record detailed information when the fault occurs.

[0066] Specifically, overcurrent and encoder faults are directly classified as emergency stop, requiring immediate power disconnection; overtemperature faults are classified as rapid stop, allowing the motor to decelerate and stop; communication faults are classified based on their duration: rapid stop for faults exceeding 1 second, and warning for faults below. Alarm logs contain information such as timestamp, fault code, fault description, and on-site parameters, stored in JSON format for easy analysis.

[0067] S440. Match an emergency response strategy based on the test alarm log. The emergency response strategy includes immediate power off, deceleration and shutdown, and parameter adjustment.

[0068] Specifically, for emergency shutdown-level faults, an immediate power-off strategy is executed, and the main power supply and control power supply are cut off at the same time; for rapid shutdown-level faults, a deceleration shutdown strategy is executed, and the speed is first reduced to zero before powering off; for warning-level faults, a parameter adjustment strategy is executed, such as reducing the target speed or load torque.

[0069] S450: Execute corresponding protection actions according to the emergency handling strategy and record the fault handling results.

[0070] In this embodiment, the system pre-sets a processing result evaluation table, which includes processing action verification items and result evaluation criteria. The system records the entire processing process to form a closed-loop fault processing file.

[0071] Specifically, the system records the execution process of the protection action, including the action time, execution sequence and response results.

[0072] In one embodiment, referring to Figure 5 ,fault types also include motor demagnetization fault, mechanical blocking fault and bearing fault.,Monitoring the real-time data collected by the test host and determining the fault type,also includes the following steps: S510 : Obtain a preset multi-operating-condition dynamic feature library, where the multi-operating-condition dynamic feature library includes fault feature matrix templates under different speeds and different loads.

[0073] In this embodiment, the system establishes a fault signature database categorized by type and operating condition. This database contains standard signature templates for motor demagnetization, mechanical jamming, and bearing faults under different operating conditions. The database employs a hierarchical structure, categorizing and storing fault signatures by speed level (e.g., 20%, 50%, and 100% rated speed) and load level (e.g., 0%, 50%, and 100% rated load).

[0074] Specifically, for a 15kW / 1700rpm motor, the system presets three speed points: 340rpm, 850rpm, and 1700rpm. Each speed point contains characteristic data for three load states: no load, half load, and full load. The characteristic matrix template includes the fundamental frequency (28.3Hz) and the standard amplitude ratios of related characteristic frequencies, such as the amplitude ratio of the second harmonic frequency (56.6Hz) of a demagnetization fault and the amplitude ratio of the characteristic frequency (113.2Hz) of a bearing fault, as well as the standard values of torque fluctuation under the corresponding operating conditions.

[0075] S520 : Acquire current spectrum data and torque ripple data during the load gradual change process.

[0076] In this embodiment, the system adopts a dynamic test method with a gradually changing load. A load gradual change curve is preset, and the load is gradually increased from 0 to the rated value while collecting current and torque data.

[0077] Specifically, the system controls the load driver to linearly increase torque from 0 to the rated value over 30 seconds, with a sampling frequency set to 10kHz. A 1024-point FFT transform is performed on the collected current signal to extract spectrum data within the 0-500Hz range. Simultaneously, real-time statistics are collected on the torque signal, calculating the maximum, minimum, and average values every 100ms. The speed is maintained constant during data acquisition to ensure accurate spectrum analysis.

[0078] S530 , calculating the amplitude ratio of the fundamental wave to the characteristic harmonics of the current spectrum data and the fluctuation rate of the torque ripple data to form a characteristic matrix sequence.

[0079] In this embodiment, the system pre-sets a feature calculation template, including an amplitude ratio calculation formula and a volatility statistical method. The system generates a feature matrix sequence through real-time calculation for subsequent fault identification.

[0080] Specifically, for current spectrum data, the amplitude ratio of the characteristic harmonic to the fundamental frequency is calculated as r = A(f_n) / A(f_1), where f_1 is the fundamental frequency and f_n is the characteristic frequency. This includes calculating the amplitude ratios of the second harmonic, fifth harmonic, and bearing fault frequencies. For torque data, the fluctuation rate δ = (T_max - T_min) / T_avg × 100% is calculated. A set of characteristic data is generated every 100 ms, consisting of multiple amplitude ratios and a fluctuation rate, forming a characteristic matrix sequence.

[0081] S540 , extracting characteristic harmonic amplitude ratio change gradients, torque fluctuation rate mutation characteristics, and spectrum energy reconstruction errors from the characteristic matrix sequence to obtain a fault feature vector.

[0082] In this embodiment, the system pre-installs a feature extraction algorithm library, which includes three types of algorithms: gradient calculation, mutation detection, and energy reconstruction. The system extracts key features required for fault diagnosis by analyzing the time domain variation characteristics of the feature matrix sequence.

[0083] Specifically, the amplitude ratio gradient k = Δr / Δt is calculated to reflect the fault development trend. The torque fluctuation rate mutation point is detected, and the mutation index σ = |δ(t) - δ(t-1)| / δ(t-1) is calculated using a sliding window method. The spectral energy distribution is reconstructed through wavelet decomposition, and the energy error e = |E - E_std| / E_std compared to the standard template is calculated. This ultimately forms a feature vector containing the gradient k, the mutation index σ, and the energy error e.

[0084] S550 : Perform pattern matching on the fault feature vector and the multi-operating-condition dynamic feature library to determine the fault type.

[0085] In this embodiment, the system pre-installs a fault pattern matching rule library that contains feature vector discrimination criteria for different fault types. The system quickly determines the fault type by calculating the similarity between the feature vector and the standard template.

[0086] Specifically, the system uses Euclidean distance to calculate the degree of match between feature vectors and standard templates. For motor demagnetization faults, the system primarily matches the gradient of the double frequency amplitude ratio. For mechanical jamming faults, the system focuses on the sudden change in torque fluctuation rate. For bearing faults, the system analyzes the energy error of the characteristic frequency. When the matching degree d is less than a threshold and a single feature meets the discrimination criteria, the corresponding fault type is determined.

[0087] In one embodiment, referring to Figure 6 In step S550, the pattern matching process includes the following steps: S551. Preset a fault feature weight matrix, where the fault feature weight matrix includes a characteristic harmonic amplitude ratio change gradient weight, a torque fluctuation rate mutation feature weight, and a spectrum energy reconstruction error weight.

[0088] In this embodiment, the system presets feature weight matrices for different fault types and uses the analytic hierarchy process to determine the weight coefficients of each feature parameter. The weight matrix reflects the importance of different feature quantities in fault diagnosis, improving the accuracy of fault identification.

[0089] Specifically, the characteristic harmonic amplitude ratio change gradient weight w1, the torque fluctuation rate mutation feature weight w2, and the spectrum energy reconstruction error weight w3 satisfy w1 + w2 + w3 = 1. For motor demagnetization faults, set w1 = 0.5, w2 = 0.3, and w3 = 0.2; for mechanical blocking faults, set w1 = 0.2, w2 = 0.5, and w3 = 0.3; and for bearing faults, set w1 = 0.3, w2 = 0.2, and w3 = 0.5.

[0090] S552. Construct a fault feature vector, where the fault feature vector includes an amplitude ratio change gradient, a torque fluctuation rate, and a spectrum energy reconstruction error.

[0091] In this embodiment, the system presets a feature normalization processing template to uniformly convert feature parameters of different dimensions into the [0, 1] interval.

[0092] S553. Calculate the weighted matching degree between the fault feature vector and the fault feature weight matrix.

[0093] In this embodiment, the system establishes a weighted distance calculation method, using weighted Euclidean distance to evaluate the similarity between feature vectors and standard templates. The system quantitatively evaluates the degree of consistency of fault features by calculating weighted matching degrees.

[0094] S554: Determine the fault type based on the comparison between the weighted matching degree and the preset fault threshold.

[0095] In this embodiment, the system pre-configures a hierarchical fault threshold table containing thresholds for different fault types. The system compares the weighted matching degree with the preset thresholds, combining the individual feature discrimination criteria to ultimately determine the fault type. When the weighted matching degree for a particular fault type is less than the threshold and the feature discrimination criteria are met, the system determines the corresponding fault type. If multiple fault types meet the criteria simultaneously, the type with the lowest matching degree is selected as the primary fault.

[0096] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0097] In the second aspect, the present application provides a testing system for a GM drive-control integrated machine. The testing system for the GM drive-control integrated machine of the present application is described below in combination with the testing method for the GM drive-control integrated machine mentioned above.

[0098] A GM drive-control integrated machine test system, comprising: Test host, auxiliary test equipment and GM drive control integrated machine to be tested; among them, The test host is connected to the auxiliary test equipment to perform test tasks and process test data; The auxiliary test equipment is connected to the GM drive control integrated machine to be tested to provide the test load; The test hosts include: Communication detection module, used to detect the communication connection status between the GM drive control integrated machine to be tested and the auxiliary test equipment; Functional test module, used to detect the input and output functions of the GM drive control integrated machine to be tested; Servo test module, used to perform servo drag test under rated speed and overload conditions; The version verification module is used to verify the operating system kernel version and servo software version of the GM drive control integrated machine to be tested; A determination module is used to determine whether the test item is qualified based on the communication connection status, function test results, servo operation data and version verification results; The parameter writing module is used to write the factory parameters into the GM drive control integrated machine to be tested when the judgment result is qualified.

[0099] like Figure 7As shown, in this embodiment, the test system adopts a layered distributed architecture design, dividing the entire test platform into three levels: a host computer test system, middle-tier test equipment, and a bottom-tier device under test. The test system is built on an industrial computer platform, equipped with a Windows operating system and a web server environment, and exchanges data with the test equipment via the standard Modbus TCP protocol. Auxiliary test equipment primarily includes the MC121S high-precision data acquisition instrument, an electrical safety monitoring module, and a GT series load driver, which together form a complete test system platform. Standardized interface connections are used between the system modules. The test system establishes a Modbus TCP communication link with the MC121S via Industrial Ethernet, enabling high-speed data acquisition and control command issuance. The MC121S, as the core test equipment, connects to the GM2XX series integrated drive and control unit under test via multiple interfaces, including standard I / O, AD / DA conversion, RS485 / CAN communication, ABZ encoder signal interface, and PWM control interface. The electrical safety module connects to the device under test via a dedicated power supply line, monitoring safety-related parameters such as start / stop control signals, safety door signals, and emergency stop signals in real time. The GT series load driver connects to the motor shaft of the device under test via a precise mechanical coupling, providing a controllable test load. The communication test module utilizes a configuration-based management solution. The system comes pre-configured with standard communication parameter tables, including detailed parameters such as baud rate, data format, and parity check for various interfaces like RS485 and CAN. The module uses a polling mechanism to continuously monitor the connection status and communication quality of each communication interface, assessing the reliability of the communication link by recording metrics such as communication error counts and response time. The functional test module is implemented based on a comprehensive IO mapping mechanism. A detailed IO mapping table is established, clearly defining the functional attributes and specific test requirements for each input and output port. Digital IO testing, analog IO testing, and encoder interface testing are sequentially performed using the MC121S data acquisition instrument, recording key electrical characteristics of each port, including voltage, current, and frequency. The servo test module utilizes standardized test process control. The system comes pre-configured with detailed test condition configuration tables, including parameter settings for both rated and overload conditions. The module controls the load driver to provide precise load torque while simultaneously collecting key data during motor operation, focusing on analyzing core performance indicators such as speed control accuracy and torque output fluctuation. The version verification module enables precise software version management. The system maintains a complete software version database, including detailed information on the system kernel and application software versions. The module reads the version identification information of the device under test through a standard communication interface to verify version compatibility requirements and functional integrity. The judgment module utilizes a comprehensive, multi-dimensional evaluation approach. The system comes pre-installed with a comprehensive table of test item judgment criteria, detailing the qualification requirements for each indicator.The module generates a final judgment conclusion by comprehensively analyzing the test results of each functional module, and records detailed test process data to support subsequent traceability analysis. The parameter writing module implements standardized configuration management. The system maintains a professional factory parameter configuration library, which contains standard parameter sets for different product models. The module writes configuration information such as motor parameters and control parameters through a reliable communication interface, and ensures the correctness and completeness of parameter writing through readback verification. The system's safety protection mechanism adopts a multiple redundancy design. The electrical safety module monitors potential faults such as overcurrent and overvoltage in real time; the emergency stop circuit ensures the controllability of the test process; the software platform has complete exception handling and fault recovery functions to maximize the safety of the test process.

[0100] Furthermore, the automated testing process is implemented through a web-based human-computer interaction solution. Operators enter test task information through a user-friendly web interface. The system automatically executes the test program and collects data according to the preset process, displaying test progress and results in real time. Finally, it automatically generates a test report in a standard format and archives it.

[0101] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a test method for a GM drive and control integrated machine is implemented.

[0102] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0103] In one embodiment, an electronic device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0104] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0105] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for testing a GM drive-control integrated machine, characterized in that: The steps include: Connect the GM drive control integrated machine to be tested with the auxiliary test equipment to obtain the communication connection status between the devices; The test host detects the input and output functions of the device under test and obtains the function test results; Perform servo drag test under rated speed and overload conditions to obtain servo operation data; The operating system kernel version and servo software version of the device under test are verified by the test host to obtain the version verification result; Based on the communication connection status, the functional test results, the servo operation data and the version verification results, it is determined whether all test items are qualified. When the judgment result is qualified, the factory parameters are written into the device under test.

2. The testing method of the GM drive-control integrated machine according to claim 1, characterized in that: The test host detects the input and output functions of the device under test, which specifically includes the following steps: Obtaining port configuration information of the device under test, wherein the port configuration information includes the number of digital ports and the number of analog channels; generating a test instruction sequence according to the port configuration information; Executing the test instruction sequence and collecting port response data; The port response data is analyzed to determine the working status of each port.

3. The testing method of the GM drive-control integrated machine according to claim 1, characterized in that: The servo drag test is carried out under rated speed and overload conditions, including the following steps: Read the nominal parameters of the motor of the device under test; Calculate the set values of rated working condition and overload working condition according to the nominal parameters of the motor; Collecting motor operating parameters under rated working conditions and overload working conditions respectively according to the set values; The steady-state performance and dynamic response characteristics of the motor are analyzed based on the operating parameters.

4. The testing method of the GM drive-control integrated machine according to claim 3, characterized in that: Analyzing the steady-state performance and dynamic response characteristics of the motor according to the operating parameters specifically includes the following steps: Monitor the operating status of the drive end and the load end and obtain error code information; Collect feedback current, torque and speed of each test point on the drive end to obtain test data; Calculate the effective value of the three-phase current UVW at the drive end to obtain the current change characteristics; Obtain temperature data of IPM power module and motor, and establish temperature monitoring curve; Based on the error code information, the test data, the current variation characteristics and the temperature monitoring curve, the steady-state performance and dynamic response characteristics of the motor are determined.

5. The testing method of the GM drive-control integrated machine according to claim 1, characterized in that: The method further comprises the steps of: Monitor the real-time data collected by the test host to determine whether there are any abnormal parameters; If there are abnormal parameters, the fault type is determined according to the abnormal parameters, and the fault type includes overcurrent fault, overtemperature fault, communication fault and encoder fault; Obtaining a fault level based on the fault type and generating a test alarm log, wherein the fault level includes an emergency stop level, a rapid stop level, and a warning prompt level; Matching emergency response strategies according to the test alarm log, wherein the emergency response strategies include immediate power off, deceleration and shutdown, and parameter adjustment; Execute corresponding protection actions according to the emergency handling strategy and record the fault handling results.

6. The testing method of the GM drive-control integrated machine according to claim 5, characterized in that: The fault types also include motor demagnetization fault, mechanical blocking fault and bearing fault. The real-time data collected by the test host is monitored and the fault type is determined, which also includes the following steps: Obtaining a preset multi-operating-condition dynamic feature library, wherein the multi-operating-condition dynamic feature library includes fault feature matrix templates under different speeds and different loads; Acquire current spectrum data and torque ripple data during load gradient; Calculating the amplitude ratio of the fundamental wave to the characteristic harmonics of the current spectrum data and the fluctuation rate of the torque pulsation data to form a characteristic matrix sequence; Extracting characteristic harmonic amplitude ratio change gradient, torque fluctuation rate mutation characteristics and spectrum energy reconstruction error from the characteristic matrix sequence to obtain a fault feature vector; The fault feature vector is pattern matched with the multi-operating-condition dynamic feature library to determine the fault type.

7. The GM drive-control integrated machine testing method according to claim 6, characterized in that: The pattern matching process includes the following steps: Presetting a fault feature weight matrix, wherein the fault feature weight matrix includes a characteristic harmonic amplitude ratio change gradient weight, a torque fluctuation rate mutation feature weight, and a spectrum energy reconstruction error weight; Constructing a fault feature vector, wherein the fault feature vector includes an amplitude ratio change gradient, a torque fluctuation rate, and a spectrum energy reconstruction error; Calculating a weighted matching degree between the fault feature vector and the fault feature weight matrix; The fault type is determined by comparing the weighted matching degree with a preset fault threshold.

8. A GM drive-control integrated machine test system, characterized in that: include: Test host, auxiliary test equipment and GM drive control integrated machine to be tested; among them, The test host is connected to the auxiliary test equipment and is used to perform test tasks and process test data; The auxiliary test equipment is connected to the GM drive and control integrated machine to be tested, and is used to provide a test load; The test host includes: A communication detection module is used to detect the communication connection status between the GM drive and control integrated machine to be tested and the auxiliary test equipment; Functional testing module, used to detect the input and output functions of the GM drive-control integrated machine to be tested; Servo test module, used to perform servo drag test under rated speed and overload conditions; A version verification module is used to verify the operating system kernel version and servo software version of the GM drive-control integrated machine to be tested; A determination module, configured to determine whether a test item is qualified based on the communication connection status, function test results, servo operation data, and version verification results; The parameter writing module is used to write the factory parameters into the GM drive-control integrated machine to be tested when the judgment result is qualified.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for testing the GM drive-control integrated machine according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the test method of the GM drive-control integrated machine according to any one of claims 1 to 7 are implemented.

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