Current transformer detection method, equipment, medium and product for high-speed rail

Abstract

The present invention discloses a current transformer detection method, equipment, medium and product for high-speed rail, which relates to the field of high-speed rail safety detection. The method includes: obtaining the environmental noise of the area where the current transformer is located on the high-speed rail train; adjusting the sensing sensitivity of the acoustic vibration sensor according to the environmental noise, and the sensing sensitivity is used to measure the response ability of the acoustic vibration sensor to the signal generated by the current transformer; after adjusting the sensing sensitivity of the acoustic vibration sensor, obtaining the acoustic vibration sensing signal generated by the current transformer when working; determining the existence of a fault in the current transformer and the current fault type based on the acoustic vibration sensing signal; determining the maintenance plan of the current transformer according to the current fault type. The present invention can detect whether there is a fault in the high-speed rail current transformer in real time, accurately and efficiently when the high-speed rail is running, and can diagnose the fault in time and guide the maintenance, thereby enhancing the safety and reliability of the high-speed rail.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed rail safety detection, and in particular to a current transformer detection method, equipment, medium and product for high-speed rail. Background Art

[0002] In high-speed railway (HSR) systems, current transformers (CTs) are mainly used in traction power supply systems to ensure stable and safe operation of trains. CTs are used to monitor and control the current of traction motors, helping to maintain the balance and efficiency of electrical systems. In addition, CTs are also used in the protection and control systems of HSRs to prevent faults such as overload and short circuit, ensuring that the power supply of trains is not disturbed.

[0003] High-speed rail systems have extremely high requirements for the stability and accuracy of power supply. Current transformers are key components for monitoring and controlling current. If there is a fault, it will lead to unstable power supply, equipment damage or safety accidents. Regular fault detection can ensure the accuracy and stability of current transformers, guarantee the continuity of train operation and the safety of passengers.

[0004] In the related art, the detection method of high-speed rail current transformers is generally to connect the current transformers through special test equipment for testing. In order not to affect the normal operation and driving safety of the high-speed rail, this detection method can only be carried out during the high-speed rail shutdown and maintenance period. Therefore, there is currently a lack of technical solutions for safely and effectively testing current transformers during the operation of high-speed rail. Summary of the invention

[0005] In view of the above-mentioned technical problems and defects, the purpose of the present invention is to provide a current transformer detection method, equipment, medium and product for high-speed railways, which can detect whether there is a fault in the high-speed railway current transformer in real time, accurately and efficiently when the high-speed railway is running, and can diagnose the fault in time and guide maintenance, thereby enhancing the safety and reliability of the high-speed railway.

[0006] To achieve the above-mentioned purpose, in a first aspect, the present invention provides a current transformer detection method for high-speed railways, comprising: obtaining environmental noise in an area where a current transformer is located on a high-speed railway train, the environmental noise being collected by an acoustic vibration sensor, the acoustic vibration sensor being arranged on the current transformer; adjusting a sensing sensitivity of the acoustic vibration sensor according to the environmental noise, the sensing sensitivity being used to measure the response capability of the acoustic vibration sensor to a signal generated by the current transformer; after adjusting the sensing sensitivity of the acoustic vibration sensor, obtaining an acoustic vibration sensing signal generated by the current transformer during operation; determining whether the current transformer has a fault and a current fault type based on the acoustic vibration sensing signal; and determining a maintenance plan for the current transformer according to the current fault type.

[0007] The present invention can realize real-time, non-intrusive monitoring of current transformers in high-speed rail systems through the above method, thereby significantly improving the safety and reliability of train operation. By installing an acoustic vibration sensor on the current transformer and collecting environmental noise, the method can dynamically adjust the sensitivity of the sensor to adapt to different operating environments, ensuring that the working status of the current transformer can be accurately captured under various conditions. In addition, by matching the collected acoustic vibration signal with a preset fault database, it is possible to quickly and accurately identify whether the current transformer has a fault and the type of fault, thereby providing maintenance personnel with timely fault diagnosis information. This method not only reduces unexpected downtime caused by faults, but also reduces maintenance costs because it allows for more targeted maintenance rather than blind inspections.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of determining whether the current transformer has a fault and the current fault type based on the acoustic vibration sensing signal includes: matching the acoustic vibration sensing signal with a preset fault database; if there is a current transformer fault sound wave signal matching the acoustic vibration sensing signal in the fault database, determining that the current transformer has a fault; and determining the current fault type of the current transformer according to the current transformer fault sound wave signal.

[0009] By adopting the technical solution of this embodiment, an efficient and accurate fault detection method is provided by matching the acoustic vibration sensing signal with a preset fault database. This method can quickly identify whether there is a fault in the current transformer and determine the type of fault. By matching the acoustic vibration signal with the fault sound wave signal stored in the database, the detection equipment can respond immediately and provide feedback, thereby reducing interference with high-speed rail operations, while improving the speed of fault response and the timeliness of maintenance. In addition, this method can also provide preliminary information about the cause of the fault, laying the foundation for subsequent maintenance work and ensuring the safe and reliable operation of the high-speed rail system.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the current fault type of the current transformer based on the current transformer fault sound wave signal, it also includes: obtaining the current operating status information of the current transformer, and the current transformer fault status information corresponding to the fault type, and the current transformer fault status information is stored in the fault database; judging whether the current operating status information matches the current transformer fault status information; if so, confirming that the current fault type is identified correctly.

[0011] By adopting the technical solution of this embodiment, the current operating status information of the current transformer is obtained and compared with the fault status information in the fault database to verify the correctness of the fault type identification. This step ensures the accuracy of fault diagnosis and avoids misjudgment and unnecessary maintenance work. When the operating status information matches the fault status information, it can be confirmed that the fault type has been correctly identified, thereby improving the efficiency of maintenance work and the stability of the high-speed rail system. This method also helps to reduce maintenance costs because it ensures that only components that really need maintenance are processed.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining whether the current operating status information matches the current transformer fault status information, it also includes: if the current operating status information does not match the current transformer fault status information, then increasing the signal acquisition frequency and acquisition duration of the acoustic vibration sensor.

[0013] By adopting the technical solution of this embodiment, an adaptive signal acquisition strategy is proposed to improve the accuracy of fault detection. When the current operating status information does not match the fault status information, the detection device will automatically increase the signal acquisition frequency and acquisition duration of the acoustic vibration sensor in order to collect more detailed data for re-analysis. This adaptive adjustment mechanism enables the detection device to maintain high accuracy in complex or uncommon fault conditions, thereby improving the robustness of the system. In this way, even if the initial detection fails to clearly identify the fault, the accuracy of the diagnosis can be improved by increasing the level of detail of data collection.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the step of adjusting the sensing sensitivity of the acoustic vibration sensor according to the ambient noise includes: obtaining noise signal characteristics of the ambient noise; inputting the noise signal characteristics into a preset ambient noise adjustment model to obtain sensitivity setting parameters of the acoustic vibration sensor; and adjusting the sensing sensitivity of the acoustic vibration sensor according to the sensitivity setting parameters.

[0015] The technical solution of this embodiment provides a method for adjusting the sensitivity of the acoustic vibration sensor based on the characteristics of the environmental noise, which helps to optimize the performance of the sensor to adapt to different monitoring environments. By analyzing the spectrum of the environmental noise, determining the type of the main noise source, the peak frequency and the average signal strength, the detection equipment can automatically adjust the sensitivity setting parameters of the sensor to maximize the detection capability of the current transformer signal while suppressing noise interference. This method enables the sensor to maintain optimal monitoring performance under various environmental conditions, improving the accuracy of fault detection and the safety of the high-speed rail system.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the step of obtaining the noise signal characteristics of the ambient noise includes: performing spectral analysis on the ambient noise to determine the type of the main noise source and the peak frequency and average signal strength of the main noise source; determining the type of the main noise source, the peak frequency and the average signal strength as the noise signal characteristics.

[0017] By adopting the technical solution of this embodiment, the sensitivity adjustment process of the acoustic vibration sensor is further optimized by explaining in detail how to obtain the noise signal characteristics of the environmental noise. By performing spectrum analysis, the detection equipment can accurately identify the characteristics of the main noise source, including its type, peak frequency and average signal strength. This information is the key basis for adjusting the sensitivity of the sensor, so that the sensor can respond more effectively to the signal generated by the current transformer while filtering out irrelevant environmental noise. This method improves the adaptability and reliability of the monitoring system and ensures that high-quality monitoring data can be obtained under various operating conditions.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the maintenance plan of the current transformer according to the current fault type includes: determining the fault location and the fault cause according to the current fault type; determining the maintenance plan of the current transformer according to the fault location and the fault cause.

[0019] The technical solution of this embodiment provides a method for determining a maintenance plan based on the fault type, which helps to achieve fast and accurate maintenance decisions. By analyzing the fault type to determine the specific location and cause of the fault, the detection equipment can generate a detailed maintenance plan, including necessary maintenance steps, tools and spare parts. This method not only improves the efficiency of maintenance work and reduces the downtime of equipment, but also helps to improve the stability and safety of the high-speed rail system. In addition, accurate fault analysis and maintenance plans can also reduce maintenance costs because it ensures that maintenance work is highly targeted and avoids unnecessary maintenance activities.

[0020] In a second aspect, an embodiment of the present invention provides an electronic device, comprising: 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 electronic device to execute the method described in the first aspect or the second aspect, and any possible implementation method of the first aspect or the second aspect.

[0021] In a third aspect, the present invention provides a computer-readable storage medium comprising instructions, which, when executed on an electronic device, causes the electronic device to execute the method described in the first aspect or the second aspect, and any possible implementation of the first aspect or the second aspect.

[0022] In a fourth aspect, the present invention provides a computer program product comprising instructions, which, when executed on an electronic device, enables the electronic device to execute the method described in the first aspect or the second aspect, and any possible implementation of the first aspect or the second aspect.

[0023] It is understandable that the electronic device provided in the second aspect, the storage medium provided in the third aspect, and the computer program product provided in the fourth aspect are all used to execute the method provided in the present invention. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0025] 1. Real-time fault detection and diagnosis: By collecting the acoustic and vibration signals of the current transformer in real time and matching them with the fault database, real-time fault detection and diagnosis of the high-speed rail current transformer is achieved. This method can promptly detect and identify the problem at the early stage of the fault, avoid further expansion of the fault, reduce the unexpected downtime caused by the fault, and improve the operating efficiency and safety of the high-speed rail system.

[0026] 2. Adaptive sensitivity adjustment: It can automatically adjust the sensitivity of the acoustic vibration sensor according to the environmental noise. By analyzing the characteristics of the environmental noise, such as frequency, amplitude and duration, the detection equipment can dynamically adjust the sensing sensitivity of the sensor to adapt to different monitoring environments. This adaptive sensitivity adjustment mechanism optimizes the performance of the sensor, improves the quality of signal acquisition, and ensures that the working status of the current transformer can be accurately captured under various environmental conditions, thereby improving the accuracy and reliability of fault detection.

[0027] 3. Accurate fault location and maintenance guidance: It can not only detect the fault of the current transformer, but also accurately locate the fault location and cause, and provide targeted maintenance guidance. By analyzing the characteristics of the acoustic vibration signal and the operating status information of the current transformer, the detection equipment can determine the specific location of the fault, infer the possible cause of the fault, and generate a detailed maintenance plan. This precise fault location and maintenance guidance provides maintenance personnel with a clear work direction, shortens maintenance time, reduces maintenance costs, and improves the maintenance efficiency of the high-speed rail system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings herein are incorporated into and constitute a part of the specification, showing embodiments consistent with the present invention, and together with the specification, are used to explain the principles of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0029] Figure 1 It is a flow chart of a current transformer detection method for high-speed railway provided by an embodiment of the present invention;

[0030] Figure 2 It is a flow chart of another current transformer detection method for high-speed railway provided by an embodiment of the present invention;

[0031] Figure 3 It is a schematic diagram of the architecture of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The terms used in the following embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to be limiting of the present invention. As used in the specification of the present invention, the singular expressions "a", "a", "above", "the" and "this" are intended to also include plural expressions, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present invention refers to any or all possible combinations comprising one or more of the listed items.

[0033] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood as implying relative importance or implicitly indicating the number of the indicated technical features. 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 the present invention, unless otherwise specified, the meaning of "plurality" is two or more.

[0034] It should also be noted that, unless otherwise clearly specified and limited, in the embodiments of the present invention, the terms such as "setting" and "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two components; it can be a wired communication connection or a wireless communication connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The embodiments of the present invention are described in detail below.

[0035] In the related art, a regular offline detection method is usually used to check the status of the high-speed rail current transformer, which includes the use of special test equipment such as insulation resistance testers, voltage withstand testers and thermal imagers to measure the electrical parameters and visually inspect the current transformer during the high-speed rail outage. These methods can evaluate the insulation performance, voltage withstand level and whether there are problems such as overheating of the current transformer. However, these detection methods are limited by the need to be carried out when the train is out of service, and cannot achieve continuous real-time monitoring of the current transformer. Therefore, potential faults may not be discovered and prevented in time, and will cause certain interference to the normal operation of the high-speed rail.

[0036] In order to achieve real-time monitoring and fault diagnosis of the health status of current transformers during the operation of high-speed railways, thereby improving the safety and reliability of the high-speed railway system, an embodiment of the present invention provides a current transformer detection solution for high-speed railways, which detects whether the current transformer is faulty by capturing and analyzing the acoustic vibration signal of the current transformer.

[0037] The technical principle of this current transformer detection solution is to use the difference in sound and vibration signals generated by the current transformer during normal operation and when a fault occurs. Inside the current transformer, if there is a fault, such as a winding short circuit, insulation damage, or mechanical wear, these fault activities will cause slight changes in the structure and generate specific sound waves and vibrations. Therefore, by identifying the differences in these signal characteristics, it is possible to determine whether the current transformer has a fault and further determine the type of fault. This method allows real-time monitoring and fault diagnosis of the health status of the current transformer without direct contact with the equipment, thereby improving the safety and reliability of the high-speed rail system.

[0038] Based on the above technical solution, an embodiment of the present invention provides a current transformer detection method for high-speed rail. By installing an acoustic vibration sensor on the current transformer, it is possible to collect environmental noise and acoustic vibration signals generated by the current transformer during operation in real time during the operation of the high-speed rail. The sensing sensitivity of the acoustic vibration sensor is adjusted so that it can detect weak signals caused by fault activities. Subsequently, through signal processing and analysis technology, features are extracted from the collected acoustic vibration signals and compared with the signal pattern during normal operation. By identifying the differences in these features, it is possible to determine whether the current transformer is faulty, and further determine the type of fault, thereby achieving real-time evaluation of the health status of the current transformer on the high-speed rail.

[0039] Compared with the traditional shutdown detection method, the method of this embodiment improves the timeliness and safety of detection, and can also reduce operational interruptions and economic losses caused by shutdown maintenance.

[0040] In addition, this embodiment can accurately diagnose the fault type and provide maintenance personnel with accurate fault location and maintenance suggestions, thereby optimizing the maintenance process, reducing maintenance costs, and improving the reliability and safety of the high-speed rail system. This embodiment provides strong technical support for the safe operation of high-speed railways through real-time, accurate and efficient current transformer monitoring, and has important practical value and broad application prospects.

[0041] The current transformer detection method of this embodiment can be executed by a detection device, which is communicatively connected to an acoustic vibration sensor. An acoustic vibration sensor, also known as an acoustic emission sensor or a vibration sensor, can be used to capture tiny sound waves or vibrations generated by a mechanical structure or material when it is stressed or fails. An acoustic vibration sensor usually includes a piezoelectric or electromagnetic transducer that can convert the received sound waves or vibration energy into electrical signals. In this embodiment, the acoustic vibration sensor has high sensitivity and a wide frequency response range, and can accurately detect weak signals generated by internal abnormalities of the current transformer, and convert these signals into electrical signals for output.

[0042] Combine the following Figure 1 The current transformer detection method of this embodiment is specifically introduced, and includes the following steps:

[0043] Step 101, obtaining the environmental noise of the area where the current transformer is located on the high-speed train.

[0044] The environmental noise is collected by an acoustic vibration sensor, which is arranged on the current transformer. The environmental noise includes but is not limited to vibration and sound waves generated by train operation, mechanical vibration, air flow, electrical equipment operation, etc.

[0045] The acoustic vibration sensor continuously monitors and records the ambient noise signals, which are then transmitted to the data acquisition system of the detection equipment for storage and preliminary processing. The data acquisition of ambient noise is carried out during the normal operation of the train in order to collect noise data under actual operating conditions. These data are crucial for subsequent signal analysis because they provide a benchmark for distinguishing between the signals generated by the normal operation of the current transformer and the abnormal signals caused by potential faults.

[0046] In this way, the detection equipment can monitor the health of the current transformer in real time and promptly detect any problems that may affect the safe operation of the train.

[0047] Step 102: Adjust the sensing sensitivity of the acoustic vibration sensor according to the environmental noise.

[0048] The sensing sensitivity is used to measure the response capability of the acoustic vibration sensor to the signal generated by the current transformer.

[0049] Due to the complex operating environment of high-speed rail, there are many factors that may affect the performance of the sensor, such as changes in train speed, mechanical vibration, air flow, etc. If the noise generated by these factors is captured indiscriminately by the acoustic vibration sensor, it will interfere with the accurate monitoring of the current transformer status.

[0050] The detection equipment adjusts the sensitivity of the sensor according to the actual measured environmental noise level, which can ensure that the sensor remains sensitive to the key signals of the current transformer without overloading, while reducing false alarms and missed alarms, and improving the accuracy and reliability of fault detection. In addition, dynamic adjustment of sensitivity can also help adapt to different operating conditions and monitoring needs, ensuring that high-quality monitoring data can be obtained in various situations, thereby providing strong support for the maintenance of current transformers and the safe operation of high-speed railways.

[0051] Specifically, the detection device analyzes the frequency, amplitude, and duration of ambient noise to determine the optimal sensing sensitivity setting to ensure that the sensor can clearly capture the acoustic vibration signal emitted by the current transformer while minimizing the impact of ambient noise. This may involve adjusting the sensor's gain, filter parameters, or sampling rate. After the adjustment, the detection device will update the sensor's configuration so that it continues to monitor the current transformer at the new sensitivity setting.

[0052] In addition, the adjustment of sensing sensitivity can also be dynamically optimized according to the train's operating status and environmental conditions. For example, when the train is running at high speed, more noise may be generated. At this time, the sensitivity of the sensor can be increased accordingly to ensure accurate capture of the current transformer signal. On the contrary, when the train is running at a low speed or stopped, the sensitivity of the sensor can be reduced to reduce the response to irrelevant signals and reduce the amount of invalid data.

[0053] In some embodiments, the detection device can obtain the best sensing sensitivity setting by calling a preset sensitivity database. This sensitivity database contains acoustic vibration sensor data under various environmental noise conditions when the high-speed rail is running, as well as the corresponding optimal sensitivity settings. When the detection device collects the environmental noise in the area where the current transformer is located, the detection device extracts key features such as the frequency, amplitude and duration of the noise signal. These features are then matched with the data stored in the sensitivity database to find records with similar features. Once a matching record is found, the detection device will adopt the sensing sensitivity setting recommended in the record to ensure that the sensor can effectively capture the working signal of the current transformer in the current environment while suppressing unnecessary noise interference. This database matching-based method provides a fast and accurate sensitivity adjustment strategy that helps to improve the adaptability and reliability of the monitoring system.

[0054] Among them, the construction of the sensitivity database is a systematic process, which involves recording and analyzing a large amount of noise data collected by acoustic vibration sensors in a controlled experimental environment and under actual high-speed rail operating conditions.

[0055] First, engineers will simulate different environmental noise conditions in the laboratory, such as temperature changes, humidity, vibration frequency, etc., and collect the acoustic and vibration signals of the current transformer at different stages of high-speed rail operation (such as acceleration, constant speed, and deceleration).

[0056] The data is then analyzed to determine the optimal sensing sensitivity settings for each environment, which ensure that the sensor effectively captures the key signals of the current transformer while suppressing noise. Each set of data, including environmental conditions, noise characteristics, and corresponding sensitivity settings, is stored in a database. Over time, the database will continue to expand to include data under more scenarios and conditions to improve the generalization and adaptability of the model.

[0057] In addition, the database needs to be regularly updated to reflect any changes that may occur in the high-speed rail system, ensuring that the monitoring methods are always kept up to date and effective.

[0058] Step 103: After adjusting the sensing sensitivity of the acoustic vibration sensor, obtaining the acoustic vibration sensing signal generated by the current transformer during operation.

[0059] Specifically, the detection equipment obtains the acoustic and vibration signals generated by the current transformer during operation through the acoustic and vibration sensors installed on the current transformer. The acoustic and vibration sensors use their highly sensitive transducers to convert the detected sound waves and vibrations into electrical signals. During the operation of the high-speed rail, the acoustic and vibration sensors continuously monitor and record the acoustic and vibration signals emitted by the current transformer, which may include various sound waves and vibration patterns caused by normal operating conditions or potential faults. The signals collected by the acoustic and vibration sensors are then transmitted to the data acquisition system of the detection equipment for further processing and analysis.

[0060] Step 104: determine whether the current transformer has a fault and the current fault type based on the acoustic vibration sensing signal.

[0061] Among them, the fault types include but are not limited to mechanical faults, such as bearing damage or mechanical looseness; electrical faults, such as winding short circuit, insulation material aging or breakdown; faults caused by environmental factors, such as performance degradation due to overheating or high humidity; and damage caused by improper operation or external impact. The current fault type is one or more of the above fault types.

[0062] Specifically, the detection device can use signal processing technology and machine learning algorithms to analyze the acoustic vibration sensing signal obtained from the acoustic vibration sensor to identify whether there is a fault in the current transformer and its type.

[0063] First, the detection equipment will pre-process the collected acoustic vibration sensor signal, including filtering, denoising and signal enhancement, to improve the signal quality. Then, through feature extraction technology, such as Fourier transform, key features such as frequency components, amplitude changes, and time-frequency characteristics of the signal are extracted from the acoustic vibration sensor signal.

[0064] These key features are then fed into a trained fault identification model that, based on a pre-trained data set, is able to identify specific signal patterns associated with different fault types. For example, a specific frequency peak may indicate a winding fault, while an irregular vibration pattern may suggest mechanical looseness. By comparing the features of the real-time signal with the features of known fault types, the detection equipment can determine whether the current transformer is faulty and determine the type of fault, thereby providing accurate guidance for repair and maintenance.

[0065] The training process of the fault identification model involves collecting and annotating a large amount of acoustic and vibration sensor data, including acoustic and vibration signals obtained from current transformers in normal operation and known fault states. In the data preparation stage, technicians will obtain signal samples from different working conditions and environments, and accurately annotate them, clearly indicating the current transformer state corresponding to each signal pattern, such as normal, overload, short circuit or mechanical failure. Subsequently, these annotated data are used to train machine learning models, such as random forests, support vector machines or deep neural networks. During the training process, the model gradually optimizes its internal parameters by learning the characteristic differences between normal and abnormal signals to improve the accuracy of fault signal recognition. In order to improve the generalization ability of the model, cross-validation and hyperparameter tuning techniques are also used. In addition, model training also includes a validation phase, in which a part of the data that is not involved in the training is used to test the performance of the model to ensure its effectiveness and reliability in practical applications. In this way, the machine learning model can learn how to accurately identify the health status and fault type of the current transformer from the acoustic and vibration sensor signals.

[0066] Step 105: Determine a maintenance plan for the current transformer according to the current fault type.

[0067] Specifically, after the detection equipment identifies the current fault type of the current transformer, it will automatically determine the corresponding maintenance plan based on the preset fault handling rules and historical maintenance data. This process involves comparing the detected fault type with the fault cases stored in the maintenance database to match the most similar fault repair cases. Once the match is successful, the device will retrieve the maintenance plan associated with the fault type, which includes replacing specific parts, adjusting equipment settings, performing specific tests, or thoroughly checking related systems. The detection equipment may also provide precise positioning of the fault site, as well as the safety procedures and operating steps that need to be followed during the maintenance process to ensure the efficiency and safety of the maintenance work.

[0068] In addition, the detection equipment may also be integrated with an expert advice system to provide real-time guidance and advice to the maintenance team, helping them to solve problems quickly and effectively, reduce the downtime of the high-speed rail system, and improve overall maintenance efficiency.

[0069] This embodiment uses the above method to make the fault detection of current transformers on high-speed railways no longer rely on regular manual inspections, but instead transform it into a continuous and automated process. This method not only increases the frequency of detection, but also can detect potential faults earlier through real-time data analysis, thereby avoiding further expansion of the fault and ensuring the safe operation of high-speed railway trains. At the same time, this method can also reduce unexpected downtime caused by faults and improve the operating efficiency of high-speed railways.

[0070] At the same time, this embodiment also helps to extend the service life of the current transformer. By timely detecting and handling small faults and anomalies, these small problems can be prevented from turning into large faults, thereby reducing the frequency and cost of maintenance. This method is also highly scalable and can be easily integrated into an existing high-speed rail monitoring system or added to the system as a new monitoring module.

[0071] Combine the following Figure 2 , to further and more specifically introduce the current transformer detection method of this embodiment, comprising the following steps:

[0072] Step 201, obtaining the environmental noise of the area where the current transformer is located on the high-speed train.

[0073] This step refers to step 101 and will not be repeated here.

[0074] Step 202: Acquire noise signal characteristics of the environmental noise.

[0075] After the detection equipment obtains the environmental noise signal through the acoustic vibration sensor, it uses the built-in signal processing algorithm to analyze these signals in detail to extract the characteristics of the noise signal. This includes spectral analysis of the signal to determine the frequency components of the noise, calculating the statistical parameters of the signal such as mean, variance and peak value to evaluate its amplitude characteristics, and observing the duration and change pattern of the signal through time domain analysis. These characteristics together constitute the identification of environmental noise, enabling the detection equipment to identify and distinguish noise generated by different sources and causes, and provide important information for subsequent signal processing and fault diagnosis.

[0076] In some implementations of this embodiment, this step may further include:

[0077] (1) Perform spectrum analysis on the environmental noise to determine the type of the main noise source as well as the peak frequency and average signal strength of the main noise source.

[0078] Among them, the main noise sources include various acoustic and vibration interferences generated by the running environment and operating conditions of high-speed trains, such as vibration noise generated when the train contacts the track, aerodynamic noise generated by friction with the air when the train is running at high speed, electromagnetic noise generated when electrical equipment is running, mechanical noise generated by the movement of mechanical parts inside the vehicle, and noise caused by external environmental factors such as wind noise or other nearby traffic activities. These main noise sources are of various types, and their characteristics and intensity will change according to the different running states and environmental conditions of the train, which constitutes a potential interference to the monitoring and diagnosis of current transformers.

[0079] In this embodiment, the detection device first performs spectrum analysis on the collected environmental noise, which involves converting the noise signal in the time domain to the frequency domain in order to identify and quantify the presence and intensity of different frequency components. Through this analysis, the detection device can identify which frequency ranges have the most significant noise energy, thereby determining the type of the main noise source.

[0080] In addition, the detection equipment will look for peaks in the spectrum, which correspond to the strongest noise frequencies, that is, peak frequencies, which indicate the most significant energy concentration points in the main noise sources. At the same time, the detection equipment will also calculate the average strength of the signal at each frequency, which provides information about the overall energy level of the noise source and helps to evaluate the possible impact of noise on current transformer monitoring.

[0081] (2) The type of the main noise source, the peak frequency and the average signal strength are determined as noise signal characteristics.

[0082] Specifically, after completing the spectrum analysis, the detection equipment combines the type of the main noise source identified, the calculated peak frequency, and the average strength of the signal to determine the characteristics of the noise signal. These noise signal characteristics are crucial for adjusting the sensing sensitivity of the acoustic vibration sensor because they directly affect the sensor's ability to respond to the current transformer signal and the monitoring system's accuracy in identifying fault signals. By accurately identifying the characteristics of the noise signal, the detection equipment can more effectively filter out noise interference, improve the reliability of fault detection, and improve the safety of the high-speed rail system.

[0083] Step 203: input the noise signal feature into a preset environmental noise adjustment model to obtain a sensitivity setting parameter of the acoustic vibration sensor.

[0084] Specifically, after acquiring and analyzing the characteristics of the environmental noise signal, the detection device uses these characteristics as input data, including the type of the main noise source, the peak frequency and the average signal strength, and sends them to a pre-set environmental noise adjustment model. This environmental noise adjustment model is an algorithm or a collection of algorithms that outputs the sensitivity setting parameters of the acoustic vibration sensor that are most suitable for the current noise environment through calculation and reasoning based on the input noise characteristics. These parameters are designed to optimize the performance of the sensor to ensure that it can effectively suppress the interference of environmental noise while maintaining high sensitivity to the key signals of the current transformer. The environmental noise adjustment model may use a variety of mathematical and statistical methods, including but not limited to regression analysis, neural networks or optimization algorithms, to achieve precise adjustment of the sensitivity setting parameters, thereby improving the accuracy and reliability of the monitoring system.

[0085] In some implementations of this embodiment, the environmental noise adjustment model may adopt a neural network model, and its training process generally involves the following steps:

[0086] First, a large amount of environmental noise data and corresponding acoustic vibration sensor outputs are collected, covering various operating conditions and environmental conditions in high-speed rail operation. Then, the data is preprocessed, including denoising, normalization, and feature extraction, to ensure data quality and highlight information useful for the model. Next, the dataset is divided into a training set, a validation set, and a test set, where the training set is used for model learning, the validation set is used for model tuning, and the test set is used to evaluate model performance.

[0087] Later in the training phase, the neural network processes the training data through the forward propagation algorithm, and evaluates the difference between the predicted results and the actual data through the loss function, and then adjusts the network weights through the back-propagation algorithm to minimize the loss function. This process is carried out in multiple iterations or epochs until the performance of the model on the validation set reaches a satisfactory level.

[0088] Finally, the trained model is evaluated using the test set to ensure that the model has good generalization capabilities. In addition, in order to improve the robustness and adaptability of the model, data enhancement, regularization and other techniques may be used, as well as hyperparameter optimization, such as the learning rate, the number of network layers and the number of neurons. Through such a training process, the environmental noise adjustment model can learn how to automatically adjust the sensitivity of the acoustic vibration sensor according to the input environmental noise characteristics to achieve the best monitoring effect.

[0089] Step 204: adjusting the sensing sensitivity of the acoustic vibration sensor according to the sensitivity setting parameter.

[0090] This step refers to step 102 and will not be described in detail here.

[0091] Step 205: After adjusting the sensing sensitivity of the acoustic vibration sensor, obtaining the acoustic vibration sensing signal generated by the current transformer during operation.

[0092] This step refers to step 103 and will not be described again here.

[0093] Step 206: Match the acoustic vibration sensor signal with a preset fault database.

[0094] Among them, the fault database contains the acoustic wave signal characteristics of current transformers of various known fault types, which are established based on historical data collection and expert analysis.

[0095] Specifically, the detection equipment uses signal processing technology to extract key features from the collected acoustic vibration signal. These features may include frequency distribution, amplitude, time-frequency characteristics of the signal, etc. The equipment then compares these features with the features of known fault types stored in the fault database. This comparison can be achieved through pattern recognition technology to evaluate the similarity of signal features with each fault type in the database.

[0096] The detection equipment will calculate a matching score or similarity index to quantify the degree of matching between the signal and each fault type class feature in the fault database. If the matching degree of a fault type feature exceeds the preset threshold, it indicates that the currently collected acoustic vibration signal matches the fault type, thereby determining that the current transformer may have a corresponding fault.

[0097] In addition, in order to improve the accuracy of matching, the detection equipment may also use multi-feature fusion technology and comprehensive scoring mechanism to ensure that the existence of a fault is confirmed only when multiple independent features support the same fault diagnosis. This matching mechanism enables the detection equipment to efficiently and accurately identify the fault status of the current transformer.

[0098] In this embodiment, the construction of the fault database is a systematic process, which first needs to collect and record the acoustic and vibration signals from the current transformer under different working conditions. These signals should be obtained under normal operation and various known fault conditions. Then, these signals are analyzed and labeled in detail, and the correspondence between each signal feature and the specific fault type is clearly pointed out. These labeled data are then used to establish a database containing various fault type features. The database construction process may also involve classification and cluster analysis of signals to identify and distinguish different fault types. In addition, in order to improve the accuracy and coverage of the database, new fault cases and signal features will be continuously added to the database, and existing data will be verified and updated. In this way, the fault database can provide rich reference information for the detection equipment, enabling it to effectively identify and match the fault signals of the current transformer.

[0099] Step 207: If there is a current transformer fault acoustic wave signal matching the acoustic vibration sensor signal in the fault database, it is determined that the current transformer is faulty.

[0100] Once the detection device finds a fault acoustic wave signal in the fault database that matches the real-time acoustic vibration signal, this indicates that the current transformer may have a similar fault to that recorded in the fault database. The matching process may involve a similarity assessment of signal features, where a specific threshold is used to determine the accuracy of the match. If the similarity of the match exceeds a preset threshold, or multiple features match at the same time, the detection device will confirm that a fault exists. This confirmation is based on the high consistency between the acoustic vibration signal and the acoustic wave signal recorded in the fault database, which allows the detection device to determine the health status of the current transformer with a high degree of confidence.

[0101] After confirming the existence of a fault, the detection equipment can further analyze the type of fault and provide corresponding maintenance suggestions or warnings so that timely measures can be taken to ensure the safe operation of the high-speed rail system.

[0102] If there is no current transformer fault acoustic wave signal matching the acoustic vibration sensor signal in the fault database, it is considered that the current transformer is not currently faulty, and the process returns to step 205 to continue monitoring.

[0103] Step 208: determining the current fault type of the current transformer according to the fault acoustic wave signal of the current transformer.

[0104] Specifically, the detection device determines the fault type corresponding to the fault sound wave signal as the current fault type. For example, if the fault type corresponding to the fault sound wave signal is "winding loose", the detection device will determine the current fault type of the current transformer as "winding loose" and then issue a corresponding warning.

[0105] Step 209: Acquire current operating status information of the current transformer and fault status information of the current transformer corresponding to the fault type.

[0106] The fault state information of the current transformer is stored in the fault database. The fault database contains the acoustic wave signal characteristics of the current transformer under various fault states, and these data are pre-defined and stored through historical fault cases and expert knowledge.

[0107] Specifically, the detection equipment obtains the current operating status information of the current transformer through relevant sensors and data acquisition systems. These sensors monitor key parameters of the current transformer such as temperature, pressure, voltage, current, etc., and transmit the data to the detection equipment in real time.

[0108] Step 210, determining whether the current operating state information matches the current transformer fault state information.

[0109] After obtaining the current operating status information of the current transformer, the detection equipment will compare it with the current transformer fault status information stored in the fault database to verify the accuracy of the current fault type identification. This comparison process involves analyzing the real-time parameters of the current transformer, such as voltage, current, temperature, etc., as well as the characteristics of the acoustic wave signal collected by the acoustic vibration sensor, and matching these data with the specific fault type recorded in the database.

[0110] If the current operating status information and the current transformer fault status information corresponding to the fault type in the fault database are similar to each other to a set standard, that is, the two match, it indicates that the current fault type is correctly identified, and the detection device will confirm this identification result. Then proceed to step 211.

[0111] If the current operating status information is similar to the current transformer fault status information corresponding to the fault type in the fault database at a lower level than the set standard and the comparison results are inconsistent, it indicates that the current fault type is incorrectly identified and the acoustic vibration sensor signal needs to be re-collected and identified, and the process returns to step 205.

[0112] After that, the detection equipment increases the frequency and time of signal acquisition to collect more detailed data for re-analysis. This adaptive adjustment strategy helps improve the accuracy of fault diagnosis and ensures that the fault type can be accurately identified even in complex or uncommon fault situations.

[0113] Step 211, determining the fault location and fault cause according to the current fault type.

[0114] After confirming the current fault type of the current transformer, the detection equipment will use historical fault data to further analyze the specific location and cause of the fault. This process involves a detailed analysis of the acoustic vibration sensor signal and a deep understanding of the structure and working principle of the current transformer.

[0115] For example, if the current fault type is identified as a poor winding connection, the detection equipment will refer to the physical layout of the winding and the signal propagation path, combined with the characteristics of the signal, such as changes in frequency and amplitude, to accurately locate the specific area where the short circuit occurs. At the same time, the detection equipment will also consider the operation history, maintenance records and environmental factors to infer the possible cause of the fault, such as material fatigue, overload or environmental erosion. Through this comprehensive analysis, the detection equipment is able to provide the maintenance team with detailed information on the location and cause of the fault, thereby guiding them to perform effective repairs.

[0116] Step 212: Determine a maintenance plan for the current transformer according to the fault location and the fault cause.

[0117] Once the detection equipment determines the fault location and cause of the current transformer, it will automatically generate a maintenance plan based on this information and the preset maintenance strategy. The maintenance plan will detail the required maintenance steps, necessary tools and spare parts, estimated maintenance time and possible risks.

[0118] For example, if the cause of the fault is due to poor winding connection, the maintenance plan may include inspection of the winding, replacement of damaged parts, re-insulation and electrical testing. In addition, the maintenance plan will also take into account safety measures to ensure the safety of maintenance personnel and the normal operation of the equipment. The detection equipment may provide troubleshooting guides, maintenance flowcharts and failure case analysis to help the maintenance team perform maintenance work efficiently, reduce equipment downtime, and ensure that the repaired current transformer can operate stably and reliably.

[0119] This embodiment can detect the operating status and health of the current transformer in the high-speed railway system in real time through the above current transformer detection method. By installing an acoustic vibration sensor on the current transformer, the acoustic emission technology is used to capture the sound and vibration signals generated by the internal fault activity of the current transformer. These signals contain important information about the health status of the current transformer, and through advanced signal processing and analysis technology, the fault can be accurately identified and located.

[0120] During the implementation process, the environmental noise is first collected and analyzed to establish a noise baseline and adjust the sensitivity of the acoustic vibration sensor. This step is critical because it ensures that the sensor can work effectively under various environmental conditions while suppressing unnecessary noise interference. Through spectrum analysis and feature extraction, the main sources and characteristics of environmental noise can be identified, providing a basis for adjusting the sensitivity of the sensor.

[0121] Subsequently, the acoustic vibration sensor begins to monitor the working status of the current transformer in real time, collecting the acoustic vibration signals generated under normal and abnormal conditions. These signals are transmitted to the detection equipment and matched and analyzed through the preset fault database. The fault database contains the acoustic vibration signal characteristics of various known fault types, which are pre-defined and stored based on historical fault cases and expert knowledge. The detection equipment uses pattern recognition and machine learning algorithms to compare the real-time signal with the fault type in the database to determine whether the current transformer has a fault and the type of fault.

[0122] Once the fault type is identified, the detection equipment will further analyze the specific location and cause of the fault. This process involves in-depth analysis of the acoustic and vibration signals, as well as an understanding of the structure and working principle of the current transformer. By comprehensively considering signal characteristics, operating history, maintenance records and environmental factors, the detection equipment can accurately locate the fault area and infer the possible cause of the fault.

[0123] Finally, based on the fault location and cause, the detection equipment will generate a detailed maintenance plan. This plan includes maintenance steps, required tools and spare parts, estimated maintenance time, and safety measures. The troubleshooting guide and maintenance flowchart provided by the detection equipment can help the maintenance team perform maintenance work efficiently, reduce equipment downtime, and ensure that the repaired current transformer can operate stably and reliably.

[0124] This embodiment provides an efficient, accurate and reliable solution for monitoring and maintaining current transformers in high-speed railway systems through real-time monitoring, intelligent diagnosis and automatic maintenance suggestions. It not only improves the timeliness and accuracy of fault detection, but also reduces maintenance costs and improves the safety and reliability of high-speed railway systems by reducing unexpected downtime. In addition, the solution is highly scalable and adaptable, and can be easily integrated into existing high-speed railway monitoring systems or added to the system as a new monitoring module, providing high-speed railway operators with a valuable tool to ensure the stable operation of the high-speed railway system and the safety of passengers.

[0125] The method provided in the above embodiment can be executed by a detection device, which is an electronic device. The following describes the electronic device in the embodiment of the present invention from the perspective of hardware processing. Figure 3, is a schematic diagram of a physical device structure of an electronic device in an embodiment of the present invention.

[0126] It should be noted that Figure 3 The structure of the electronic 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.

[0127] like Figure 3 As shown, the electronic device includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In RAM 403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0128] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a button switch, etc.; an output section 407 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0129] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the present invention are performed.

[0130] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a 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 may be used by or in combination with an instruction execution system, apparatus, or device.

[0131] 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 may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a 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 may also occur in an order different from that marked in the accompanying drawings.

[0132] Specifically, the electronic device of this embodiment includes a processor and a memory, the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the electronic device to execute the method provided by the above embodiment.

[0133] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the electronic device, the electronic device implements the method provided in the above embodiment.

[0134] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

[0135] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0136] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A current transformer detection method for high-speed rail, characterized in that: include: Acquire the environmental noise of the area where the current transformer is located on the high-speed train, wherein the environmental noise is collected by an acoustic vibration sensor, and the acoustic vibration sensor is arranged on the current transformer; Acquiring noise signal characteristics of the environmental noise; The noise signal characteristics are input into a preset environmental noise adjustment model to obtain the sensitivity setting parameters of the acoustic vibration sensor; the environmental noise adjustment model adopts a neural network model, and the neural network model is trained based on environmental noise data under various working conditions and environmental conditions during high-speed rail operation and corresponding acoustic vibration sensor outputs; Adjusting the sensing sensitivity of the acoustic vibration sensor according to the sensitivity setting parameter, wherein the sensing sensitivity is used to measure the responsiveness of the acoustic vibration sensor to the signal generated by the current transformer; After adjusting the sensing sensitivity of the acoustic vibration sensor, obtaining the acoustic vibration sensing signal generated by the current transformer when it is working; Determine whether the current transformer has a fault and the current fault type based on the acoustic vibration sensor signal; A maintenance plan for the current transformer is determined according to the current fault type.

2. The method according to claim 1, characterized in that The step of determining whether the current transformer has a fault and the current fault type based on the acoustic vibration sensing signal comprises: Matching the acoustic vibration sensor signal with a preset fault database; If there is a current transformer fault acoustic wave signal matching the acoustic vibration sensor signal in the fault database, it is determined that the current transformer is faulty; The current fault type of the current transformer is determined according to the current transformer fault acoustic wave signal.

3. The method according to claim 2, characterized in that After the step of determining the current fault type of the current transformer according to the fault acoustic wave signal of the current transformer, the method further includes: Acquire current operation status information of the current transformer and fault status information of the current transformer corresponding to the fault type, wherein the fault status information of the current transformer is stored in the fault database; Determining whether the current operating state information matches the current transformer fault state information; If so, it is confirmed that the current fault type is identified correctly.

4. The method according to claim 3, characterized in that After the step of determining whether the current operating state information matches the current transformer fault state information, the method further includes: If the current operating state information does not match the current transformer fault state information, the signal acquisition frequency and acquisition duration of the acoustic vibration sensor are increased.

5. The method according to claim 1, characterized in that The step of obtaining the noise signal characteristics of the environmental noise includes: Performing spectrum analysis on the environmental noise to determine the type of main noise source and the peak frequency and signal average strength of the main noise source; the type of the main noise source includes various acoustic and vibration interferences generated by the running environment and operating conditions of the high-speed train; The type of the main noise source, the peak frequency and the average signal strength are determined as noise signal characteristics.

6. The method according to claim 1, characterized in that The step of determining the maintenance plan of the current transformer according to the current fault type includes: Determine the fault location and fault cause according to the current fault type; A maintenance plan for the current transformer is determined according to the fault location and the fault cause.

7. An electronic device, characterized in that: including one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the electronic device to execute the method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed on an electronic device, the electronic device is caused to execute the method as claimed in any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program product is executed on an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 6.

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