Method and device for detecting insulation state of motor stator winding

Abstract

The invention discloses a method and device for detecting the insulation state of a motor stator winding, and relates to the field of digital data processing, and the method comprises the steps: testing a first test current and a second test current of a stator winding of a target motor at a first test frequency and a second test frequency respectively; based on the first test current and the second test current, a current change rate used for representing the insulation degradation degree of the stator winding is obtained through calculation; determining whether an insulation deterioration degree of the stator winding is higher than a preset deterioration threshold based on the current change rate; if yes, measuring and acquiring a polarization index curve of the insulation resistance of the stator winding associated with the test time by adopting a static insulation test technology; extracting a characteristic waveform of the polarization index curve and inputting the characteristic waveform into the insulation detection model to obtain a defect type of the stator winding; and displaying the defect type to the user. By implementing the method, the identification precision of the insulation state and the defect type of the stator winding can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of digital data processing, and in particular to a method and device for detecting the insulation status of a motor stator winding. Background Art

[0002] Electric motors are widely used in industrial production and everyday life, with generators and electric motors being typical examples. A motor primarily consists of a stator and a rotor, with windings typically made of insulated copper wire wound around the stator. During motor operation, the windings generate an electromotive force and electromagnetic field, causing the rotor to rotate. The insulation performance of the stator windings directly impacts the motor's safe operation and service life. Over time, the stator winding insulation deteriorates, leading to cracks, spalling, and discharges, which can lead to serious consequences.

[0003] In the related art, the traditional method for detecting the insulation condition of motor stator windings is to measure insulation resistance. The principle of this method is to apply a certain voltage between the winding and the core after the motor is stopped, and then measure the insulation resistance value of the winding to ground. The resistance value is used to determine whether the insulation is normal.

[0004] However, the relevant methods are not precise enough when detecting the insulation status of the motor stator winding, making it difficult to effectively identify and determine the defect type. Summary of the Invention

[0005] The present application provides a method and device for detecting the insulation status of a motor stator winding. The method determines the degree of insulation degradation by testing the test current at two frequencies, and then uses polarization index curve characteristic analysis to determine the type of insulation damage. This can effectively improve the accuracy of identifying the insulation status and defect type of the stator winding.

[0006] In a first aspect, the present application provides a method for detecting the insulation state of a motor stator winding, which is applied to a detection device, and the method includes: testing a first test current and a second test current of the stator winding of a target motor at a first test frequency and a second test frequency, respectively; the second test frequency is N times the first test frequency; based on the first test current and the second test current, calculating a current change rate for characterizing the degree of insulation degradation of the stator winding; determining whether the degree of insulation degradation of the stator winding is higher than a preset degradation threshold based on the current change rate; if so, using a static insulation test technology to measure and obtain a polarization index curve of the insulation resistance of the stator winding associated with the test time; extracting a characteristic waveform of the polarization index curve and inputting it into an insulation detection model to obtain the defect type of the stator winding; and displaying the defect type to a user.

[0007] In the above embodiment, the detection device uses two test current frequencies to calculate the degree of insulation degradation and then uses polarization index curve characteristic analysis to determine the defect type. This enables accurate and automatic detection of the motor's insulation condition, eliminating the risk of manual misjudgment, improving detection efficiency, and making detection more comprehensive and accurate. This also allows for more targeted maintenance, significantly improving motor reliability and service life.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the characteristic waveform of the polarization index curve is extracted and input into the insulation detection model to obtain the defect type of the stator winding, specifically including: preprocessing the polarization index curve, removing the noise signal in the curve, and obtaining the preprocessed polarization index curve; the preprocessing includes smoothing, filtering and normalizing the curve; according to a preset feature extraction algorithm, numerical feature parameters for characterizing the curve characteristics are extracted from the preprocessed polarization index curve; the feature parameters include curve slope, curve convexity, number of inflection points, inflection point slope and inflection point position; the feature parameters are input into the trained insulation detection model to obtain the defect type.

[0009] In the above embodiment, the detection device eliminates noise by preprocessing the curve, extracts characteristic parameters such as slope and convexity, and inputs them into the intelligent model to determine the defect type. This can filter out interference, enhance analysis stability and accuracy, improve the anti-interference ability and judgment accuracy of the detection method, and make the detection results more precise and reliable.

[0010] In combination with some embodiments of the first aspect, in some embodiments, displaying the defect type to the user specifically includes: displaying the defect type on the display interface of the detection device according to preset display rules; the preset display rules include using preset colors and preset highlight marks to display preset defect types; detecting the user's click selection operation on the defect type on the display interface; and displaying the solution corresponding to the defect type on the display interface in response to the user's click selection operation.

[0011] In the above embodiment, the detection device adopts an intuitive graphical display and establishes a user knowledge feedback mechanism. This graphical display can guide standardized maintenance and continuously optimize the test model, greatly improving the usability, comprehensibility and optimization capability of the method, achieving seamless connection between detection and maintenance, and providing strong support for precise maintenance of motors.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of displaying a solution corresponding to the defect type on the display interface in response to the user's click selection operation, the method also includes: locking the motor and cutting off the power supply to the motor in response to the user's selected operation of confirming maintenance; using graphic displays to guide the user to perform corresponding maintenance operations; maintenance operations include disassembling the motor housing, cleaning the stator, inspecting, replacing or repairing the windings; detecting whether the user's maintenance operations meet the standards; if not, displaying a prompt message, which is used to prompt the user to adjust the operation mode.

[0013] In the above embodiment, the detection device, through graphic guidance and process detection feedback, can effectively help users correctly perform maintenance operations, avoid the risks caused by incorrect operations, and improve maintenance efficiency and quality. At the same time, this interactive maintenance guidance method can provide a better user experience and enhance the usability of the device.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of displaying the defect type to the user, the method also includes: obtaining maintenance record information corresponding to the defect type input by the user; after determining that the target motor has been repaired based on the maintenance record information, re-testing the first insulation current and the second insulation current of the stator winding of the target motor at the first test frequency and the second test frequency, and calculating the re-measured current change rate; based on the re-measured current change rate, again determining whether the re-measured insulation degradation degree of the stator winding is lower than a preset degradation threshold.

[0015] In the above embodiment, the detection device verifies the maintenance quality through retesting, which can avoid missed maintenance and ensure the safe operation of the motor. This closed-loop detection and maintenance process improves the reliability of motor monitoring and fault handling.

[0016] In combination with some embodiments of the first aspect, in some embodiments, before the steps of testing the first test current and the second test current of the stator winding of the target motor at the first test frequency and the second test frequency respectively, the method also includes: obtaining operating data of the target motor; the operating data includes the motor load current, speed and core temperature; based on the operating data, determining whether the target motor is in a stable operating state; if so, controlling the target motor to enter a no-load operating state.

[0017] In the above embodiment, the detection device ensures that the motor is in a suitable state during the test, avoids interference with the test results, improves the accuracy and reliability of the test, and enhances the scientific nature of the detection method.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of calculating the current change rate used to characterize the degree of insulation degradation of the stator winding based on the first test current and the second test current, the method also includes: analyzing the operating data and the degree of insulation degradation of the target motor to obtain a relationship model between the operating data and the degree of degradation; obtaining real-time operating data of motors of the same specification; determining the predicted degree of insulation degradation of the stator winding of the motor of the same specification based on the relationship model and the real-time operating data; issuing a warning message when the predicted degree of insulation degradation is higher than a preset degradation threshold; the warning message is used to prompt the user to inspect the motor of the same specification.

[0019] In the above embodiment, the detection device makes full use of the data obtained from the test, establishes the degradation model of the motor itself, and applies it to fault prediction of motors of the same type, realizing the transformation from passive detection to predictive maintenance, improving the intelligence level of motor operation and maintenance, and broadening the application scope of detection technology in fields such as predictive maintenance.

[0020] In the second aspect, an embodiment of the present application provides a detection device, which includes: a first measurement module, used to test a first test current and a second test current of the stator winding of the target motor at a first test frequency and a second test frequency, respectively; the second test frequency is N times the first test frequency; a numerical calculation module, used to calculate the current change rate used to characterize the degree of insulation degradation of the stator winding based on the first test current and the second test current; a numerical analysis module, used to determine whether the degree of insulation degradation of the stator winding is higher than a preset degradation threshold based on the current change rate; a second measurement module, used to measure and obtain a polarization index curve associated with the insulation resistance of the stator winding and the test time using a static insulation test technology when the degree of insulation degradation is higher than the preset degradation threshold; a defect confirmation module, used to extract the characteristic waveform of the polarization index curve and input it into the insulation detection model to obtain the defect type of the stator winding; an information display module, used to display the defect type to the user.

[0021] In a third aspect, an embodiment of the present application provides a detection device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the detection device to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when the computer program product is run on a detection device, enables the detection device to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a detection device, the detection device executes the method described in the first aspect and any possible implementation of the first aspect.

[0024] It is understandable that the detection devices provided in the second and third aspects, the computer program product provided in the fourth aspect, and the computer storage medium provided in the fifth aspect are all used to execute the methods provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0025] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By testing the target motor's stator winding with a first test current and a second test current at first and second test frequencies, and determining the degree of insulation degradation by calculating the current change rate based on the test current, the insulation state of the stator winding can be reflected by the current changes at different frequencies. This effectively solves the problem in related technologies where the degree of insulation degradation cannot be accurately determined by measuring insulation resistance alone. This method enables the determination of insulation degradation using changes in variable-frequency test current, improving the accuracy of insulation state detection for motor stator windings. Using variable-frequency test current, subtle changes in current response at different frequencies can be captured. These changes reflect the electrical parameters of the insulation material, and parameter analysis allows for accurate determination of the degree of insulation material degradation.

[0026] 2. When insulation degradation is determined to be above a threshold, the system further utilizes static insulation testing technology to obtain a polarization index curve. This curve's features are then extracted and input into a detection model to identify the defect type. This allows accurate analysis of the insulation defect type based on the characteristic waveform of the polarization index curve after insulation degradation is determined. This effectively addresses the difficulty in accurately determining defect types in related technologies. This system not only detects insulation degradation but also identifies the defect type, providing users with key repair strategies. By combining characteristic parameters from the polarization index curve with a machine learning model, the system can automatically identify a variety of insulation faults and output fault classification results, significantly improving the intelligent detection capabilities.

[0027] 3. By visually displaying the detected defect types and corresponding solutions to guide repairs, the test results can be presented to users in an intuitive graphical manner, effectively solving the problem of visualizing test results in related technologies. This allows users to intuitively read the test report and clarify subsequent maintenance measures. By visually displaying the results, the workload of users in interpreting the report is reduced, and the display solution can directly guide users in targeted equipment repairs. The entire test and repair process is highly automated and intelligent, simplifying the operation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for detecting the insulation status of a motor stator winding according to an embodiment of the present application; Figure 2 is a schematic diagram of a polarization index curve in a method for detecting the insulation state of a motor stator winding in an embodiment of the present application; Figure 3 1 is another flow chart of a method for detecting the insulation status of a motor stator winding according to an embodiment of the present application; Figure 4 This is a schematic diagram of a functional module structure of a detection device in an embodiment of the present application; Figure 5 It is a schematic diagram of the physical device structure of the detection device in the embodiment of the present application. DETAILED DESCRIPTION

[0029] 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 of the present application, the singular expressions "a", "an", "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 the present application refers to any or all possible combinations of one or more of the listed items.

[0030] 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.

[0031] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0032] With the development of society and economy, various types of motors are widely used in industrial production, such as generators in wind farms and electric motors in factories. These motors operate under high loads for a long time, and the stator winding, one of their core components, will gradually deteriorate, such as aging and cracking of the insulation layer, and short circuits due to moisture.

[0033] Failure to promptly detect insulation faults in the stator windings can lead to motor short circuits or even fires and explosions, resulting in casualties, equipment damage, and economic losses. Therefore, a method is needed to accurately detect the insulation condition of motor stator windings to promptly identify insulation faults and guide motor maintenance. The current, widely used method for testing motor stator winding insulation is simply to measure the insulation resistance. However, this method suffers from low detection accuracy and difficulty determining the defect type, making it difficult to meet the requirements for precise detection.

[0034] In related technologies, a simple single-frequency insulation resistance measurement method can be used to detect the insulation condition of a motor's stator windings. This method simply requires using an insulation resistance meter to test the insulation resistance of the stator winding to ground after the motor is shut down. The resistance value is then used to determine whether the insulation condition is normal.

[0035] The following describes a scenario in which a method for detecting the insulation status of a motor stator winding in related technology is used.

[0036] The generator sets at a thermal power plant operate under long-term high-load conditions. To prevent insulation failure in the generator stator windings, plant technicians perform annual inspections. This method uses an insulation resistance meter to measure the insulation resistance between the generator stator windings and ground. If the resistance falls below a threshold, an insulation failure is identified. This method can identify even severe insulation failures, but its accuracy is limited, making it difficult to determine the specific location and type of the fault.

[0037] During testing of Generator Unit 1 this year, its insulation resistance was measured close to the threshold, but not below it. Based on their experience, technicians determined that there might be minor insulation degradation, but they were unable to determine the severity or type of defect. Due to the inaccurate test results, technicians were unable to recommend targeted maintenance measures. Three months later, Generator Unit 1 was forced to shut down for maintenance due to insulation failure, resulting in significant economic losses.

[0038] The method for detecting the insulation condition of a motor stator winding, as described in the present application, measures the insulation current at two different frequencies and calculates the current rate of change. This allows for a more accurate assessment of the degree of stator winding insulation degradation, reflecting not only the overall insulation condition but also the location of deterioration. In severe cases of deterioration, this method further utilizes polarization index curve analysis to determine the defect type.

[0039] The following describes a scenario in which the method for detecting the insulation status of a motor stator winding in this application is used.

[0040] To address the aforementioned issues with power plant generator testing, technicians employed the motor stator winding insulation testing method described in this application to test the stator winding insulation condition of generator sets. This method first tests the insulation current at two different frequencies, then compares the current changes at the two frequencies to accurately calculate the degree of insulation degradation. If insulation degradation is determined to be severe, static insulation testing techniques are further employed to obtain an insulation resistance polarization index curve. The curve's characteristics are then analyzed to determine the specific insulation defect type.

[0041] During an inspection of Generator Unit 1, this method detected a high degree of insulation degradation and identified interturn contamination. Based on the results, technicians located the fault and performed targeted repairs. The generator unit fault was promptly detected and addressed, preventing further deterioration and potentially resulting in downtime, saving significant repair costs. This method offers high accuracy and the ability to identify defect types, making it more reliable and efficient than traditional detection methods.

[0042] It can be seen that the detection method for the insulation status of the motor stator winding in the embodiment of the present application can not only realize the overall judgment of the insulation status of the stator winding, but also effectively solve the problem that the relevant technology cannot accurately determine the defect type, thereby realizing the accurate detection of the insulation status of the stator winding.

[0043] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , is a flow chart of a method for detecting the insulation status of a motor stator winding in an embodiment of the present application.

[0044] S101 : Testing a first test current and a second test current of a stator winding of a target motor at a first test frequency and a second test frequency, respectively.

[0045] The detection device can be connected to a current measurement sensor and installed on the input power line of the target motor to measure the current response of the stator winding under voltage excitation of different frequencies. Based on preset parameters, the detection device sequentially outputs two test voltages of different frequencies to the target motor stator. For example, the first test frequency can be set to the power frequency of 100 Hz, and the second test frequency to 200 Hz. The detection device records the stator winding current values measured at the two frequencies as the first test current and the second test current.

[0046] It should be noted that the second test frequency is N times the first test frequency. Generally, N is 2, that is, when the first test frequency is 100Hz, the second test frequency is 200Hz; similarly, when the first test frequency is 200Hz, the second test frequency is 400Hz; and when the first test frequency is 400Hz, the second test frequency is 800Hz.

[0047] S102 : Calculate a current change rate for characterizing the insulation degradation degree of the stator winding based on the first test current and the second test current.

[0048] Based on the first and second test currents, the detection device calculates the current change rate, which is used to characterize the degree of insulation degradation of the stator winding. This current change rate is also recorded as the I / F value. For ease of understanding, the following description will also use the I / F value. The calculation method can be used using the following formula: I / F value (%) = (first test current - second test current) / first test current * 100% This means subtracting the test current values measured at two different frequencies, dividing by the first test current value, and multiplying by 100% to obtain the percentage change in current. As the stator winding insulation deteriorates, the I / F value changes from -50% to 0%. The I / F deviation can be used to assess the degree of insulation degradation in the motor's stator winding. An I / F value of -50% indicates no degradation, while 0% indicates complete degradation.

[0049] For example, an I / F value range of -50% to -40% indicates mild degradation, an I / F value range of -40% to -20% indicates moderate degradation, and an I / F value range of -20% to 0 indicates severe degradation. This is used to determine the insulation condition of the target motor's stator winding.

[0050] S103: Determine whether the insulation degradation degree of the stator winding is higher than a preset degradation threshold based on the current change rate.

[0051] The detection device determines whether the stator winding insulation degradation exceeds a preset degradation threshold based on the current rate of change (I / F value). The detection device can be set to different degradation thresholds, such as mild, moderate, and severe. If the calculated current rate of change exceeds the set severe degradation threshold, the target motor's stator winding insulation is considered severely damaged. This method allows the detection device to accurately determine the motor insulation condition using variable-frequency test current, providing a basis for subsequent maintenance.

[0052] S104: Using a static insulation test technology, measure and obtain a polarization index curve of the insulation resistance of the stator winding and the test time.

[0053] When the stator winding insulation is severely degraded, the detection device uses static insulation testing techniques to measure the insulation resistance of the stator winding and obtain a polarization index curve related to the test time. The detection device can be connected to an insulation tester to apply a test voltage between the stator winding and ground. The device measures the change in insulation resistance over time and plots a polarization index curve of resistance value versus time. This curve reflects the charge accumulation and discharge process of the insulating material under the influence of an electric field, and its characteristics can be used to identify the type of insulation defect. The detection device must control the test voltage and application time to obtain an appropriate polarization index curve.

[0054] See also Figure 2 , which is a schematic diagram of the polarization index curve in the method for detecting the insulation status of the motor stator winding in an embodiment of the present application, which respectively describes the 10-minute polarization index curves of four types of motor stator windings. Figure 2 (a) is the characteristic waveform of the curve when there is no fault in the insulation of the motor winding; Figure 2 (b) is the characteristic waveform of the curve when inter-turn contamination occurs in the motor winding; Figure 2 (c) is the characteristic waveform of the motor winding insulation during aging; Figure 2 Figure (d) shows the characteristic waveform of motor winding insulation when it's damp. It's easy to see from these four figures that different defect types display different waveform shapes. Under normal conditions, the curve is smooth and even; when interturn contamination occurs, the curve exhibits dense, wavy patterns; when aged, the curve exhibits a jagged, rising shape; and when damp, the curve exhibits large, wavy patterns.

[0055] S105 , extracting a characteristic waveform of the polarization index curve and inputting it into an insulation detection model to obtain a defect type of the stator winding.

[0056] The detection device extracts the characteristic waveform of the polarization index curve and inputs it into a pre-trained insulation detection model to determine the specific defect type in the stator winding. Characteristic waveform parameters may include information such as the curve slope, convexity, and number of inflection points. The detection device uses digital signal processing technology to analyze the curve and extract these characteristic parameters. These parameters reflect the response characteristics of the insulation material under the test voltage. The detection device inputs the extracted characteristic parameters into the insulation detection model, which then distinguishes defect types such as interturn contamination and moisture.

[0057] It should be noted that the insulation detection model is a machine learning model used in this detection method to identify stator winding defect types. The insulation detection model's input is the characteristic parameters of the polarization index curve, including curve slope, curve convexity, number of inflection points, inflection point slope, and inflection point location, numerical parameters that characterize the curve. These parameters are extracted through preprocessing, filtering, and normalization of the polarization index curve, using a preset feature extraction algorithm. The insulation detection model's internal structure can utilize supervised learning algorithms such as support vector machines and neural networks. The model is trained using a rich set of annotated motor insulation fault samples to learn the distribution patterns of characteristic parameters of curves for different fault types. The insulation detection model's output is a fault type judgment based on the input curve, distinguishing between different fault categories, such as inter-turn short circuits, carbonization, splitting, partial discharge, and moisture. The insulation detection model compares the input parameters with the learned distribution knowledge and, according to the discrimination rules, determines the most likely fault type. This insulation detection model automatically determines the fault type of the motor stator winding insulation based on the characteristic parameters of the polarization index curve, eliminating manual judgment errors and improving the accuracy and intelligence of detection.

[0058] S106. Display the defect type to the user.

[0059] The detection device visually displays the identified stator winding insulation defect types to the user or maintenance personnel. This display can be displayed on the detection device's user interface, with each defect type indicated by a color and explained with text or icons. The detection device can also output a test report detailing the test process, current change rate, defect type, and other information. This intuitive display helps users quickly understand the test results and guide subsequent equipment repair and maintenance.

[0060] In the above embodiment, the dual-frequency test current method compares the test results at two different frequencies to calculate the current change rate, reflecting the degree of insulation degradation. In actual applications, after the motor is repaired and a defect problem is found, a series of repair and confirmation processes are required.

[0061] The following is a supplement to the scenario of this embodiment.

[0062] After the inspection device displays the defect type, the user can click on the interface to select the defect category of interest. The inspection device responds by displaying repair solutions corresponding to the selected defect type. These solutions can be found in a pre-set knowledge base or generated using intelligent analysis models.

[0063] For example, for the insulation moisture problem selected by the user, the detection device can display the following solutions: it is recommended to replace the insulation gaskets and insulation wedges at the insulation location; add insulation reinforcement sheets at locations where discharge is severe to enhance insulation strength; or dry the windings to remove surface leakage stains caused by moisture absorption, etc.

[0064] By presenting users with targeted solutions, they can quickly determine repair strategies, improving the quality and efficiency of subsequent equipment repairs. This interactive approach to defect detection and solution display ensures that the detection device responds to users' actual needs, enabling test results to better serve motor maintenance and servicing.

[0065] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 3 , is another flow chart of the method for detecting the insulation status of the motor stator winding in an embodiment of the present application.

[0066] S301 : Testing a first test current and a second test current of a stator winding of a target motor at a first test frequency and a second test frequency, respectively.

[0067] Referring to step S101 , the detection device tests a first test current and a second test current of a stator winding of a target motor at a first test frequency and a second test frequency.

[0068] In some embodiments, the detection device obtains the operating data of the target motor; the operating data includes the motor load current, speed and core temperature; based on the operating data, it is determined whether the target motor is in a stable operating state; if so, the target motor is controlled to enter a no-load operating state.

[0069] Specifically, before conducting the dual-frequency test current detection, the detection device connects to sensors to acquire real-time operating data from the target motor, primarily including three parameters: motor load current, speed, and core temperature. The motor load current reflects the current motor output power and load. The speed reflects the mechanical motion of the motor rotor. The core temperature reflects the thermal equilibrium within the motor. These three parameters collectively reflect the motor's stable operating state. After collecting this operating data, the detection device uses preset judgment logic to analyze whether the motor is currently in normal and stable operating conditions. This judgment logic can be configured with multiple threshold parameters, such as load current fluctuations not exceeding 10%, speed fluctuations within a range of plus or minus 2%, and core temperature not exceeding 80% of the rated temperature. Meeting these conditions determines that the motor is in a stable operating state. A stable state is a prerequisite for testing, as it prevents the test from being affected by external factors such as shifting loads and ensures the accuracy of the test current. If the motor is confirmed to be operating stably, the detection device issues a control command to put the motor into a no-load idling state, preparing for the subsequent dual-frequency test. In this way, by judging the operating status of the motor in advance and ensuring that the test is carried out under appropriate working conditions, the accuracy and reliability of the test can be improved, making the test results more valuable for reference.

[0070] S302 : Calculate a current change rate for characterizing the insulation degradation degree of the stator winding based on the first test current and the second test current.

[0071] Referring to step S102 , the detection device calculates the current change rate.

[0072] In some embodiments, the detection device analyzes the operating data and insulation degradation degree of the target motor to obtain a relationship model between the operating data and the degradation degree; obtains real-time operating data of motors of the same specification; determines the predicted insulation degradation degree of the stator winding of motors of the same specification based on the relationship model and the real-time operating data; issues a warning message when the predicted insulation degradation degree is higher than a preset degradation threshold; the warning message is used to prompt the user to inspect motors of the same specification.

[0073] Specifically, after completing the inspection of a target motor, the inspection device can use the data obtained to establish a relationship model between the motor's operating parameters and the degree of insulation degradation. For example, data from multiple inspections of the target motor can be collected, including operating parameters such as load current, speed, and operating time, as well as the measured degree of insulation degradation. A machine learning algorithm is then used to train a prediction model linking the operating parameters and the degree of degradation. This model can be a linear regression model, a neural network, or other algorithmic model. After model training is complete, the inspection device can use real-time operating data from motors of the same type and specification as input to predict the degree of insulation degradation in these motors. If the predicted result exceeds a threshold, indicating a risk of failure, the inspection device will send a warning message to the user. This message includes the motor number exceeding the threshold, the predicted degree of degradation, and a recommended inspection time. Users who receive the warning can then proceed with the inspection of the relevant motor in advance to avoid downtime due to failure. By establishing a relationship model between operating data and degree of degradation, the inspection device can transition from passive detection to active prediction, significantly reducing the probability of equipment failures. This predictive maintenance method can tap into the greater value of detection data, apply the detection results of a single device to the entire motor group, and realize intelligent, digital, and large-scale equipment maintenance.

[0074] S303: Determine whether the insulation degradation degree of the stator winding is higher than a preset degradation threshold based on the current change rate.

[0075] Referring to step S103 , the detection device determines whether the insulation degradation degree is higher than a preset degradation threshold.

[0076] S304: Using a static insulation test technology, measure and obtain a polarization index curve of the insulation resistance of the stator winding and the test time.

[0077] Referring to step S104 , the detection device measures and obtains a polarization index curve.

[0078] S305 , preprocessing the polarization index curve to remove noise signals in the curve, thereby obtaining a preprocessed polarization index curve.

[0079] After the detection device obtains the polarization index curve, it must first preprocess the curve to eliminate the noise signals contained in the curve and obtain a clear characteristic waveform. Preprocessing can include smoothing, which eliminates random noise through algorithms such as averaging or median filtering; filtering, which designs low-pass and band-pass digital filters to filter out noise within a specified frequency range; and normalization, which normalizes the curve to a numerical range of 0-1 or -1-1. These signal processing operations can effectively improve the signal-to-noise ratio of the curve, highlight effective information features, and lay the foundation for subsequent extraction of characteristic parameters. For example, the detection device can use wavelet transform to perform multi-scale decomposition, filter out high-frequency noise components, and then further smooth the curve with a low-pass filter. After a series of preprocessing, the detection device can obtain a clean polarization index curve waveform as input for feature extraction.

[0080] S306 : extracting numerical characteristic parameters for characterizing curve characteristics from the preprocessed polarization index curve according to a preset feature extraction algorithm.

[0081] After obtaining the preprocessed polarization index curve, the detection device uses a pre-configured feature extraction algorithm to extract numerical parameters that effectively characterize the curve. These characteristic parameters may include: the slope of the curve, which reflects the curve shape; the convexity of the curve, which indicates the degree of convexity of the curve; the number of inflection points, which reflects the turning points of the curve; the slope of the inflection point, which indicates the steepness of the curve near the inflection point; and the position of the inflection point, which reflects the time when the inflection point occurs. The detection device can calculate these parameters using methods such as differentiation and curve fitting. For example, the detection device can first detect the maximum and minimum values as inflection points, then determine the number of inflection points based on the change in slope near the maximum value. The second-order derivative is used to determine the convexity of the curve and the convexity of each segment. These characteristic parameters can reflect subtle changes in the curve from different perspectives, providing rich characteristic information for subsequent model identification of faults.

[0082] S307: Input the characteristic parameters into the trained insulation detection model to obtain the defect type.

[0083] After obtaining the numerical parameters characterizing the polarization index curve, the detection device inputs them into an insulation detection model previously trained through sample analysis to determine the specific type of fault. This insulation detection model can be constructed using machine learning algorithms such as SVM and neural networks, and trained and optimized using well-labeled fault samples. The insulation detection model includes the parameter distribution range and discrimination rules for the curve characteristics of different defect types. The detection device inputs the actual extracted feature parameters, and the insulation detection model can determine the fault category according to established rules and output the identification result, that is, the specific type of fault, such as inter-turn contamination, surface moisture, etc. Utilizing this insulation detection model, the detection device can automatically perform intelligent analysis and discrimination of a variety of complex insulation faults.

[0084] S308: Display the defect type on the display interface of the detection device according to a preset display rule.

[0085] After obtaining the specific defect type of the stator winding, the detection device will display the results on its user interaction interface according to pre-set display rules, which is convenient for users to understand intuitively. The display rules may include the use of different color identification, font size, font highlighting, icon symbols, etc. to distinguish various types of faults, making them clear at a glance. For example, a red cross icon can be used to represent severe surface moisture, and an orange exclamation point can be used to represent mild carbonized ash contamination. At the same time, the detection device will use text and data to display the name of the identified defect type, possible location, severity and other information in detail to help users fully understand the status of the equipment. Users can also set a customized display style, and the detection device will display the test results according to the settings. The intuitive display of results allows non-professional users to quickly obtain maintenance directions.

[0086] S309: Detecting a click-select operation of the user on the defect type on the display interface.

[0087] After displaying the test results, the detection device monitors user clicks on specific defect types within the interface. When a user requires more details about a particular defect, they can simply click the icon or text at the corresponding location, and the detection device will detect this selection. For example, if the results indicate two defects, and the user clicks to view the surface moisture information for the first defect, the detection device will be aware of this selection. This interactive method allows users to proactively access test details for the areas of interest, eliminating the need to review all results individually.

[0088] S310 : In response to a click and selection operation by the user, a solution corresponding to the defect type is displayed on a display interface.

[0089] After receiving the user's click selection, the detection device will respond to this action and display the corresponding repair solution for the defect type selected by the user on the display interface. These solutions can be found in the repository or generated through model analysis. For example, in response to the surface moisture problem selected by the user, the detection device can display a solution that recommends replacing the insulation material in the corresponding part and adding an insulation reinforcement sheet. At the same time, it can also prompt subsequent inspection methods and possible downtime maintenance plans. Displaying targeted solutions can help users quickly identify repair ideas and improve maintenance efficiency and quality. This interactive method enables the detection device to respond to user needs, so that the detection results can better serve subsequent equipment maintenance.

[0090] In some embodiments, the detection device will lock the motor and cut off the power supply to the motor in response to the user's selected operation to confirm the maintenance; use graphic displays to guide the user to perform corresponding maintenance operations; maintenance operations include disassembling the motor housing, cleaning the stator, inspecting, replacing or repairing the windings; detecting whether the user's maintenance operations meet the standards; if not, displaying a prompt message, which is used to prompt the user to adjust the operation method.

[0091] Specifically, after the user clicks the confirmation button to initiate maintenance, the detection device remotely issues commands to lock the motor's mechanical motion and cut off the motor's input power, ensuring the safety of the maintenance operation. Next, the detection device displays the standard maintenance process on its own display terminal using multimedia formats such as images, text, and videos, guiding the user through the steps. The demonstrated maintenance steps include: removing the motor casing using appropriate tools; inspecting internal components and cleaning dust from the stator surface; inspecting the winding contact surfaces and wires, and measuring insulation resistance; replacing or repairing any defective windings; and finally, reassembling the motor casing and performing an insulation retest. As the user performs the maintenance operation, the detection device monitors the user's movements using a camera or sensor. Using image recognition technology or sensor detection, it determines whether the user's actions at each step are in accordance with the preset maintenance action images in the standard database. If the user's maintenance operation is detected to be inconsistent with the standard process or contains errors, a voice or text prompt will be provided on the display interface to guide the user to adjust their actions to meet the requirements. Throughout the maintenance process, the inspection device not only eliminates operational risks but also provides guidance and standardized operations through continuous monitoring feedback, ensuring maintenance quality and preventing potential equipment hazards from repair errors. This closed-loop approach to maintenance guidance and process inspection significantly improves the scientific nature and standardization of motor maintenance.

[0092] In some embodiments, the detection device obtains maintenance record information corresponding to the defect type input by the user; after determining that the target motor has been repaired based on the maintenance record information, the first insulation current and the second insulation current of the stator winding of the target motor are retested at the first test frequency and the second test frequency, and the re-measured current change rate is calculated; based on the re-measured current change rate, it is again determined whether the re-measured insulation degradation degree of the stator winding is lower than the preset degradation threshold.

[0093] Specifically, after the user completes the repair, the detection device requires the user to fill out a maintenance report on the device's user interface. The report should include information such as the repair time, repair personnel, repair measures taken, and spare parts used. The detection device analyzes the contents of the maintenance report. If the records are complete and reasonable, the maintenance work is deemed complete. To verify the quality of the repair, a retest is required. Specifically, the detection device re-tests the stator windings of the target motor at the first and second test frequencies using the original method, re-obtaining the first and second insulation currents at the two frequencies. The detection device calculates the retested current change rate. If the retested current change rate is below the preset fault threshold, it can be determined that there is no significant deterioration in the motor insulation, and the repair was successful, ensuring proper insulation. If the retested current change rate is still above the threshold, it indicates that the repair did not fully restore insulation performance, and further inspection is required until the retested data indicates that the insulation condition meets the requirements. In this way, retesting the motor after repair can verify the repair quality and avoid safety hazards caused by inadequate repair. Carrying out a closed-loop process of inspection, repair, and retesting can greatly improve the scientific nature of motor inspection and maintenance.

[0094] In the embodiments of the present application, a dual-frequency test current method is used to detect the insulation condition of the motor stator winding. When severe degradation is detected, the polarization index curve is further analyzed to determine the defect type. Process control and maintenance feedback can also be performed. Therefore, accurate automatic detection and diagnosis of the motor insulation condition are achieved, and subsequent repair and maintenance are guided. This effectively solves the problem of incomplete detection of motor insulation condition and difficulty in determining defect types in related technologies, thereby significantly improving the accuracy, intelligence level, and maintenance quality of motor insulation fault detection and treatment. This solution fully utilizes multiple technical means such as variable frequency test current to determine insulation degradation, polarization index curve to identify defect types, and process control and feedback to improve maintenance quality, making motor condition monitoring more refined, intelligent, and information-based. It has important technological advancement significance and application promotion value.

[0095] The following describes the detection device in the embodiment of the present application from the perspective of modules. Figure 4 , is a schematic diagram of the functional module structure of the detection device in an embodiment of the present application.

[0096] The detection device comprises: A first measurement module 401 is configured to measure a first test current and a second test current of a stator winding of a target motor at a first test frequency and a second test frequency, respectively; the second test frequency is N times the first test frequency; A numerical calculation module 402 is configured to calculate a current change rate for characterizing the insulation degradation degree of the stator winding based on the first test current and the second test current; A numerical analysis module 403 is configured to determine whether the insulation degradation degree of the stator winding is higher than a preset degradation threshold based on the current change rate; The second measurement module 404 is configured to measure and obtain a polarization index curve of the stator winding's insulation resistance and test time using a static insulation test technique when the insulation degradation degree exceeds a preset degradation threshold; Defect confirmation module 405 is used to extract the characteristic waveform of the polarization index curve and input it into the insulation detection model to obtain the defect type of the stator winding; The information display module 406 is used to display the defect type to the user.

[0097] In some embodiments, the defect confirmation module 405 specifically includes: A preprocessing unit is used to preprocess the polarization index curve, remove noise signals in the curve, and obtain a preprocessed polarization index curve; the preprocessing includes smoothing, filtering, and normalizing the curve; A feature calculation unit is used to extract numerical feature parameters for characterizing the curve characteristics from the preprocessed polarization index curve according to a preset feature extraction algorithm; the feature parameters include curve slope, curve convexity, number of inflection points, inflection point slopes, and inflection point positions; The defect calculation unit is used to input the characteristic parameters into the trained insulation detection model to obtain the defect type.

[0098] In some embodiments, the information display module 406 specifically includes: A defect display unit, configured to display the defect type on a display interface of the detection device according to a preset display rule; the preset display rule includes displaying the preset defect type using a preset color and a preset highlight mark; A click detection unit is used to detect the user's click selection operation on the defect type on the display interface; The solution display unit is used to display the solution corresponding to the defect type on the display interface in response to the user's click selection operation.

[0099] In some embodiments, the information display module 406 further includes: a maintenance preparation unit, configured to lock the motor and cut off power to the motor in response to a maintenance confirmation operation selected by a user; Graphic guidance unit, used to guide users to perform corresponding maintenance operations using graphic display; maintenance operations include disassembling the motor housing, cleaning the stator, and inspecting, replacing or repairing the windings; Maintenance monitoring unit, used to detect whether the user's maintenance operations meet the standards; The adjustment prompt unit is used to display a prompt message when the standard is not met. The prompt message is used to prompt the user to adjust the operation method.

[0100] In some embodiments, the detection device further comprises: Record acquisition module, used to obtain maintenance record information corresponding to the defect type input by the user; a current retest module, configured to retest the first insulation current and the second insulation current of the stator winding of the target motor at the first test frequency and the second test frequency after determining that the target motor has been repaired based on the maintenance record information, and calculate the retest current change rate; The retest determination module is used to re-determine whether the retested insulation degradation degree of the stator winding is lower than a preset degradation threshold based on the retested current change rate.

[0101] In some embodiments, the detection device further comprises: A data acquisition module is used to obtain the operating data of the target motor; the operating data includes the motor load current, speed and core temperature; a stability determination module, configured to determine whether the target motor is in a stable operating state based on the operating data; The test preparation module is used to control the target motor to enter a no-load running state when the target motor is in a stable running state.

[0102] In some embodiments, the detection device further comprises: An operation simulation module is used to analyze the operating data and insulation degradation degree of the target motor and obtain a relationship model between the operating data and the degradation degree; Operation monitoring module, used to obtain real-time operation data of motors of the same specifications; Degradation prediction module, used to determine the predicted degree of insulation degradation of stator windings of motors of the same specifications based on the relationship model and real-time operating data; The degradation warning module is used to issue a warning message when the predicted degree of insulation degradation exceeds the preset degradation threshold; the warning message is used to prompt the user to check motors of the same specifications.

[0103] The above describes the detection device in the embodiment of the present application from the perspective of modular functional entities. The following describes the detection device in the embodiment of the present application from the perspective of hardware processing. Figure 5 , is a schematic diagram of the physical device structure of the detection device in an embodiment of the present application.

[0104] It should be noted that Figure 5 The structure of the detection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0105] like Figure 5 As shown, the detection device includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 502 or programs loaded from a storage unit 508 into a random access memory (RAM) 503. RAM 503 also stores various programs and data required for system operation. CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0106] The following components are connected to the I / O interface 505: an input section 506 including an audio input device, push button switches, and the like; an output section 507 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. Removable media 511, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 510 as needed, so that computer programs read from the removable media can be installed in the storage section 508 as needed.

[0107] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 509 and / or installed from removable media 511. When executed by the central processing unit (CPU) 501, the computer program performs the various functions defined in the present invention.

[0108] It should be noted that specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0110] Specifically, the detection device of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the detection method for the insulation state of the motor stator winding provided in the above embodiment is implemented.

[0111] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the detection device described in the above embodiments, or may exist independently and not incorporated into the detection device. The storage medium carries one or more computer programs, which, when executed by a processor of the detection device, enable the detection device to implement the method for detecting the insulation status of a motor stator winding as provided in the above embodiments.

[0112] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0113] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0114] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for detecting the insulation state of a motor stator winding, applied to a detection device, characterized in that: The method comprises: Testing a first test current and a second test current of a stator winding of a target motor at a first test frequency and a second test frequency, respectively; the second test frequency is N times the first test frequency; Calculating a current change rate for characterizing a degree of insulation degradation of the stator winding based on the first test current and the second test current; determining whether the insulation degradation degree of the stator winding is higher than a preset degradation threshold based on the current change rate; If yes, a static insulation test technique is used to measure and obtain a polarization index curve of the insulation resistance of the stator winding associated with the test time; Extracting a characteristic waveform of the polarization index curve and inputting it into an insulation detection model to obtain a defect type of the stator winding; The defect type is displayed to the user.

2. The method according to claim 1, characterized in that The step of extracting the characteristic waveform of the polarization index curve and inputting it into an insulation detection model to obtain the defect type of the stator winding specifically includes: Preprocessing the polarization index curve to remove noise signals in the curve to obtain a preprocessed polarization index curve; the preprocessing includes smoothing, filtering, and normalizing the curve; According to a preset feature extraction algorithm, numerical characteristic parameters for characterizing curve characteristics are extracted from the preprocessed polarization index curve; the characteristic parameters include curve slope, curve convexity, number of inflection points, inflection point slopes, and inflection point positions; The characteristic parameters are input into a trained insulation detection model to obtain the defect type.

3. The method according to claim 1, characterized in that The displaying of the defect type to the user specifically includes: Displaying the defect type on a display interface of the detection device according to a preset display rule; the preset display rule includes using a preset color and a preset highlight mark to display the preset defect type; Detecting a user's click-select operation on a defect type on the display interface; In response to the click and selection operation of the user, a solution corresponding to the defect type is displayed on the display interface.

4. The method according to claim 3, characterized in that After the step of displaying a solution corresponding to the defect type on the display interface in response to the user's click-select operation, the method further includes: In response to the user selecting an operation to confirm maintenance, locking the motor and cutting off the power supply to the motor; Using graphic displays to guide the user to perform corresponding maintenance operations; the maintenance operations include disassembling the motor housing, cleaning the stator, and inspecting, replacing or repairing the windings; Detecting whether the user's maintenance operation meets the standards; If the standard is not met, a prompt message is displayed, and the prompt message is used to prompt the user to adjust the operation mode.

5. The method according to claim 1, wherein After the step of displaying the defect type to the user, the method further includes: Obtaining maintenance record information corresponding to the defect type input by the user; After determining that the target motor has been repaired based on the maintenance record information, retesting the first insulation current and the second insulation current of the stator winding of the target motor at the first test frequency and the second test frequency, and calculating the retested current change rate; It is determined again based on the remeasured current change rate whether the remeasured insulation degradation degree of the stator winding is lower than a preset degradation threshold.

6. The method according to claim 1, characterized in that Before the step of testing the first test current and the second test current of the stator winding of the target motor at the first test frequency and the second test frequency respectively, the method further includes: Acquiring operating data of the target motor; the operating data including motor load current, speed and core temperature; determining, based on the operating data, whether the target motor is in a stable operating state; If so, the target motor is controlled to enter a no-load running state.

7. The method according to claim 6, characterized in that After the step of calculating a current change rate for characterizing the insulation degradation degree of the stator winding based on the first test current and the second test current, the method further includes: Analyzing the operating data and the insulation degradation degree of the target motor to obtain a relationship model between the operating data and the degradation degree; Obtain real-time operating data of motors with the same specifications; Determining a predicted degree of insulation degradation of the stator winding of the motor of the same specification according to the relationship model and the real-time operation data; When the predicted degree of insulation degradation is higher than a preset degradation threshold, an early warning message is issued; the early warning message is used to prompt the user to inspect the motor of the same specification.

8. A detection device, characterized in that: include: a first measuring module, configured to measure a first test current and a second test current of a stator winding of a target motor at a first test frequency and a second test frequency, respectively; The second test frequency is N times the first test frequency; a numerical calculation module, configured to calculate a current change rate for characterizing a degree of insulation degradation of the stator winding based on the first test current and the second test current; a numerical analysis module, configured to determine whether the insulation degradation degree of the stator winding is higher than a preset degradation threshold based on the current change rate; a second measurement module, configured to, when the insulation degradation degree is higher than a preset degradation threshold, use a static insulation test technology to measure and obtain a polarization index curve of the insulation resistance of the stator winding associated with the test time; a defect confirmation module, configured to extract a characteristic waveform of the polarization index curve and input the characteristic waveform into an insulation detection model to obtain a defect type of the stator winding; The information display module is used to display the defect type to the user.

9. A detection device, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the detection device to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a detection device, the detection device is caused to execute the method according to any one of claims 1 to 7.

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