Power distribution network insulation hidden danger detection method, system, equipment and medium

Abstract

The invention relates to the technical field of insulation hidden danger detection, in particular to a power distribution network insulation hidden danger detection method, system and device and a medium. Preprocessing the original signal to generate a target signal suitable for time-frequency characteristic analysis; carrying out high-speed sampling on the target signal, and analyzing the discharge frequency characteristics of the target signal through time-frequency transformation to identify the type of the insulation hidden danger; and sending the identified insulation hidden danger type to a user terminal. The device has the advantages that weak current signals flowing into the ground in the concrete pole are captured in real time through the Rogowski coil, the phase is restored through the integrating circuit, the signals are enhanced through the amplifying circuit, and interference components are removed through the filtering circuit, so that the signals are suitable for time-frequency characteristic analysis; furthermore, current signal processing and an advanced time-frequency analysis method are combined, the limitation of a traditional detection means is broken through, and comprehensive, accurate and real-time monitoring of the insulation hidden danger of the power distribution network is achieved.

Description

Technical Field

[0001] This invention relates to the field of insulation hazard detection technology, and in particular to a method, system, equipment and medium for detecting insulation hazards in power distribution networks. Background Technology

[0002] Distribution networks play a vital role in modern society's power supply, and their safe and stable operation directly affects the electricity experience of users. The insulation performance of the distribution network is one of the key factors ensuring its stable operation; the insulation properties of insulators, surge arresters, and other equipment are of great importance in maintaining the normal operation of the distribution network.

[0003] However, existing technologies for detecting insulation hazards in power distribution networks have the following shortcomings: (1) Partial discharge monitoring methods are difficult to detect insulation degradation hazards that do not produce partial discharge; (2) Infrared imaging methods are not sensitive to low-heat insulation hazards; (3) UAV methods can only detect hazards with visible traces. These problems lead to incomplete detection of insulation hazards, affecting the safe operation of power distribution networks. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a method for detecting insulation defects in a power distribution network, including acquiring the original signal flowing into the ground from inside a cement pole;

[0006] The original signal is preprocessed to generate a target signal suitable for time-frequency feature analysis;

[0007] The target signal is sampled at high speed, and the discharge frequency characteristics of the target signal are analyzed by time-frequency transformation to identify the type of insulation hazard.

[0008] The identified insulation hazard types are sent to the user terminal.

[0009] In a preferred embodiment of the distribution network insulation defect detection method of the present invention, the original signal is preprocessed to generate a target signal suitable for time-frequency feature analysis, including:

[0010] The original signal is subjected to a first preprocessing to generate a first target signal, wherein the first preprocessing is used to restore the phase of the original signal;

[0011] The first target signal is subjected to a second preprocessing to generate a second target signal, wherein the second preprocessing is used to amplify the amplitude of the first target signal;

[0012] The second target signal is subjected to a third preprocessing step to generate a third target signal, wherein the third preprocessing step is used to filter out interference components of the second target signal.

[0013] As a preferred embodiment of the distribution network insulation hazard detection method of the present invention, the method includes: high-speed sampling of the target signal, and analysis of the discharge frequency characteristics of the target signal through time-frequency transformation to identify the type of insulation hazard, including:

[0014] High-speed instantaneous value sampling is performed on the third target signal;

[0015] Wavelet transform is used to perform time-frequency transformation analysis on the sampled third target signal to determine the discharge frequency characteristics in order to identify the type of insulation hazard.

[0016] As a preferred embodiment of the distribution network insulation hazard detection method of the present invention, the wavelet transform adopts the Haar wavelet and sampling is performed according to a preset sampling frequency;

[0017] By comparing multi-scale and multi-translation transformations and combining them with a preset algorithm, the obtained voltage values ​​are converted into actual current values ​​to determine the corresponding insulation hazard type.

[0018] As a preferred embodiment of the distribution network insulation hazard detection method of the present invention, the method includes: acquiring the original signal flowing into the ground from within the cement pole, including...

[0019] The electromagnetic induction sensor attached to the cement pole captures the current signal flowing into the ground inside the pole;

[0020] The electromagnetic induction sensor is a Rogowski coil, which is 1.5 meters long and has 2000 turns. Its minimum detection current is 0.5mA.

[0021] As a preferred embodiment of the distribution network insulation hazard detection method of the present invention, the insulation hazard types include insulator aging, surge arrester aging, and pollution flashover discharge.

[0022] As a preferred embodiment of the distribution network insulation hazard detection method of the present invention, the identification of insulation hazard types includes,

[0023] When the discharge frequency is the same as the power frequency, it is determined that the insulator or surge arrester is aging.

[0024] When the discharge frequency is equal to 300kHz, it is determined to be a flashover discharge.

[0025] Secondly, the present invention provides a power distribution network insulation hazard detection system, comprising: an acquisition module for acquiring the original signal flowing into the ground from inside a cement pole;

[0026] The processing and generation module is used to preprocess the original signal to generate a target signal suitable for time-frequency feature analysis;

[0027] The sampling and identification module is used to sample the target signal at high speed and analyze the discharge frequency characteristics of the target signal through time-frequency transformation to identify the type of insulation hazard.

[0028] The sending module is used to send the identified insulation hazard types to the user terminal.

[0029] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0030] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described above.

[0031] Compared with existing technologies, the beneficial effects of this invention are as follows: It captures the weak current signal flowing into the ground from within the cement pole in real time using a Rogowski coil, and then restores the phase using an integrator circuit, enhances the signal using an amplifier circuit, and removes interference components using a filter circuit, making the signal suitable for time-frequency characteristic analysis. Furthermore, it achieves accurate identification of insulation hazard types through high-speed instantaneous value sampling and wavelet transform processing using a microcontroller. Even further, by combining current signal processing with advanced time-frequency analysis methods, it overcomes the limitations of traditional detection methods, achieving comprehensive, accurate, and real-time monitoring of insulation hazards in the distribution network, effectively improving the safety and reliability of the distribution network operation. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating the method for detecting insulation defects in power distribution networks. Detailed Implementation

[0034] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0035] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for detecting insulation defects in a power distribution network, comprising:

[0036] S100: Acquire the original signal flowing into the ground from inside the cement pole;

[0037] S200: Preprocesses the original signal to generate a target signal suitable for time-frequency feature analysis;

[0038] S300: High-speed sampling of the target signal and analysis of the discharge frequency characteristics of the target signal through time-frequency transformation to identify the type of insulation hazard;

[0039] S400: Sends the identified insulation hazard type to the user terminal.

[0040] It should be noted that cement poles in power distribution networks are exposed to the complex outdoor environment for extended periods, and their insulation performance is affected by various factors. For example, prolonged exposure to wind and sun can lead to insulator aging, lightning strikes and other overvoltage phenomena can degrade surge arrester performance, and the accumulation of pollutants can cause flashover discharge. During power distribution network operation, insulation defects often develop gradually, initially manifesting as a decrease in insulation impedance or partial discharge. At this stage, the grid can still operate, but if these defects are not detected and addressed promptly, they will gradually worsen, eventually leading to power outages and severely impacting power supply. However, traditional insulation testing methods are mostly conducted when equipment is de-energized, making real-time monitoring during operation difficult. Furthermore, they are ineffective at detecting early, minor insulation defects, resulting in incomplete and untimely detection, failing to meet the demands of modern power distribution networks for real-time monitoring and early warning of insulation conditions.

[0041] Therefore, addressing the aforementioned issues in detecting insulation hazards in power distribution networks, the S100-S400 steps, through real-time acquisition of the raw signals flowing into the ground from within the concrete poles, followed by preprocessing, high-speed sampling, and time-frequency transformation analysis, can comprehensively and accurately identify the types of insulation hazards and promptly send the results to the user terminal. This enables real-time monitoring and early warning of various insulation hazards in the power distribution network, effectively improving the operational safety and reliability of the power distribution network, reducing the risk of power outages, and providing strong support for the stable operation of the power system.

[0042] Example 2, refer to Figure 1 This is one embodiment of the present invention. Based on the above embodiment, a method for detecting insulation defects in a power distribution network is provided.

[0043] In this embodiment of the application, obtaining the original signal flowing into the ground from the cement pole in step S100 includes the following steps A1-A2:

[0044] A1: By using an electromagnetic induction sensor attached to a cement pole, the current signal flowing into the ground inside the pole is captured;

[0045] A2: The electromagnetic induction sensor is a Rogowski coil, which is 1.5 meters long and has 2000 turns. Its minimum detection current is 0.5mA.

[0046] It should be noted that the Rogowski coil is placed around the concrete pole with insulation defects to be detected, 1-2 meters above the ground. The Rogowski coil captures the insulation signal inside the concrete pole. The Rogowski coil is 1.5 meters long with 2000 turns. The minimum capture current of the Rogowski coil is 0.5mA, and the output of the Rogowski coil is 100mV / 1000A. For a 10kV line, the phase voltage is 5.7kV, and 5.7kV / 0.5mA = 11.4MΩ. That is, if the insulation is lower than 11.4MΩ, a defect needs to be reported. For a 35kV voltage level, the phase voltage is 20kV, and 20kV / 0.5mA = 40MΩ. That is, the highest detectable insulation value for a 35kV line is 40MΩ. It should be noted that lines above 35kV are not within the detection scope of this invention.

[0047] In an optional implementation, the original signal of the cement pole flowing into the ground in step S100 can also be obtained by using an optical fiber current sensor. Based on the Faraday magneto-optical effect, when current flows into the ground through the cement pole, a magnetic field is generated around it. The sensing optical fiber in the optical fiber current sensor is placed in this magnetic field, and the polarization state of the light propagating in the optical fiber will change. The current signal can be obtained by detecting this change.

[0048] In another optional implementation, the original signal of the cement pole flowing into the ground in step S100 can also be obtained by using a fluxgate sensor. The fluxgate sensor is installed around the cement pole to monitor the changes in the magnetic field generated by the current in the cement pole in real time, thereby obtaining the original signal of the cement pole flowing into the ground.

[0049] In this embodiment of the application, step S200 involves preprocessing the original signal to generate a target signal suitable for time-frequency feature analysis, including the following steps B1-B3:

[0050] B1: Perform a first preprocessing on the original signal to generate a first target signal, wherein the first preprocessing is used to restore the phase of the original signal;

[0051] It should be noted that the first preprocessing refers to using an operational amplifier to integrate the Rogowski coil via an integrating circuit to restore the phase of the original current signal. Since the Rogowski coil output is proportional to the derivative of the current within the rod, this signal has a 90-degree phase difference with the current. The integrating circuit's function is to restore this differentiated signal to a phase-consistent signal with the current within the rod, allowing the processed signal to more accurately reflect the actual waveform and phase information of the current within the rod. The Rogowski coil output is proportional to the derivative of the current within the rod; through integration, the output signal can be made proportional to the current within the rod. This eliminates the phase shift caused by differentiation and restores the signal amplitude to a level corresponding to the current within the rod.

[0052] It should be further noted that the operational amplifier uses the MAX44248 high-precision op-amp.

[0053] Ideally, the signal after integration not only has its phase corrected, but its amplitude also better meets the signal requirements of subsequent circuits, providing an accurate signal basis for subsequent insulation hazard analysis.

[0054] In an optional implementation, the phase restoration of the original signal in step B1 can also be achieved using a phase detector. Specifically, after acquiring the original signal, it is input into the phase detector along with a reference signal. The phase detector detects the phase difference between the two signals and outputs an error signal proportional to the phase difference. Based on this error signal, the phase of the original signal is adjusted, thereby restoring the true phase of the original signal.

[0055] In another optional implementation, the phase restoration of the original signal in step B1 can also be based on the Hilbert transform. That is, by performing the Hilbert transform on the original signal, its analytic signal can be obtained, and then the phase information of the signal can be extracted to realize the restoration of the phase of the original signal. This can be implemented by programming in a digital signal processor (DSP) or a computer.

[0056] B2: Perform a second preprocessing on the first target signal and generate a second target signal, wherein the second preprocessing is used to amplify the amplitude of the first target signal;

[0057] It should be noted that the signal strength after processing by the integrator circuit may not be sufficient to meet the processing or analysis requirements of subsequent circuits. Therefore, an amplifier circuit is needed to increase the signal amplitude. However, since the UAF42 active filter has already amplified the signal by 50 times, the amplifier circuit here only needs to amplify the signal by an additional 100 times. This brings the total amplification factor of the signal in the entire system to 5000 times.

[0058] It should be further noted that the amplifier still uses the MAX44248 high-precision op-amp.

[0059] In an optional implementation, the amplification of the first target signal in step B2 can also be performed using digital signal processing software. Specifically, the first target signal is first converted from analog to digital to obtain a digital signal. Then, in a digital signal processor or computer, a digital signal processing algorithm is used to amplify the signal, such as multiplying it by a coefficient greater than 1 to increase the signal amplitude.

[0060] In another optional implementation, the amplitude of the first target signal in step B2 can also be amplified by a programmable gain amplifier. After receiving the first target signal, a suitable gain value is set by software according to the signal strength and subsequent processing requirements to precisely control the degree of signal amplification.

[0061] B3: Perform a third preprocessing on the second target signal to generate a third target signal. The third preprocessing is used to filter out interference components of the second target signal. These interference components typically include power frequency interference, and may also include harmonic interference, high-frequency noise, etc.

[0062] It should be noted that the third preprocessing refers to filtering the amplified second target signal through a filtering circuit to remove interference components from the signal, thereby obtaining a purer and more accurate third target signal, which improves the accuracy and reliability of subsequent analysis.

[0063] It should be further noted that the filter uses an active filter, with a chip UAF42, a center frequency of 50Hz, and a Q value of 275.

[0064] In an optional implementation, the target signal suitable for time-frequency feature analysis in step S200 can also be generated based on Hilbert-Huang Transform (HHT) signal processing. Specifically, empirical mode decomposition (EMD) is performed on the second target signal to decompose it into several intrinsic mode functions (IMFs). Then, a Hilbert transform is applied to each IMF component to obtain the signal's time-frequency characteristics. By filtering and reconstructing IMF components within a specific frequency range, a target signal suitable for time-frequency feature analysis is generated.

[0065] In another optional implementation, the target signal suitable for time-frequency feature analysis in step S200 can also be generated by signal processing using Short-Time Fourier Transform (STFT), that is, the second target signal is divided into multiple short-time windows, and a Fourier transform is performed on the signal within each window. By selecting an appropriate window length and overlap rate, a balance can be achieved between time and frequency. Depending on the analysis requirements, specific frequency bands are extracted or other processing is performed to generate a target signal suitable for time-frequency feature analysis.

[0066] In this embodiment of the application, step S300 involves high-speed sampling of the target signal and analysis of the discharge frequency characteristics of the target signal through time-frequency transformation to identify the type of insulation hazard, including the following steps C1-C4:

[0067] C1: Perform high-speed instantaneous value sampling on the third target signal;

[0068] It should be noted that the microcontroller is connected to the filtering circuit, and the microcontroller performs high-speed instantaneous value sampling based on the third target signal. The microcontroller uses an STM32F407VET6 ARM processor chip and a real-time operating system (RTOS). Furthermore,

[0069] C2: The third target signal after sampling is analyzed by time-frequency transformation using wavelet transform to determine the discharge frequency characteristics in order to identify the type of insulation hazard. Wavelet transform processing is performed by the microcontroller mentioned above.

[0070] Furthermore, the wavelet transform uses the Haar wavelet and is sampled according to a preset sampling frequency of 1MHz;

[0071] By comparing multi-scale and multi-translation transformations and combining them with a preset algorithm, the obtained voltage values ​​are converted into actual current values ​​to determine the corresponding insulation hazard type.

[0072] It should be noted that the microcontroller samples the third target signal at a frequency of 1 trillion times per second to capture high-frequency variation components in the signal. Furthermore, by performing wavelet transforms on the signal at different scales and locations (the voltage range obtained after wavelet transform analysis is 0 interference component - 300mV interference component), the characteristics of the signal at different time periods and frequency bands are obtained. Further, to convert the measured voltage values ​​into actual current values, an exhaustive method is used. The exhaustive method here can be understood as mapping each possible voltage value to an actual current value based on a pre-established correspondence table. For example, through extensive experiments and data accumulation, the corresponding voltage measurements under different current conditions are known. Then, based on this correspondence, the voltage value of the currently measured interference component (0 interference component - 300mV interference component) is converted into the actual current value range (0 interference component - 100mA interference component), so that power maintenance personnel can use the current value to determine the severity of insulation hazards.

[0073] Ideally, wavelet transform analysis of the signal's time-frequency characteristics can determine the presence of specific frequency components, thus identifying the corresponding type of insulation hazard. Converting voltage values ​​to current values ​​helps quantify the severity of the insulation hazard. Combining both methods provides a more comprehensive understanding of the insulation hazard situation, offering stronger support for the maintenance and management of the distribution network.

[0074] C3: Types of insulation hazards include insulator aging, surge arrester aging, and flashover discharge.

[0075] C4: When the discharge frequency is the same as the power frequency, it is determined that the insulator or surge arrester is aging.

[0076] It should be noted that when insulators age, their discharge phenomena will occur at the same frequency as the power frequency; similarly, discharge caused by the aging of surge arresters will occur at the power frequency.

[0077] When the discharge frequency is equal to 300kHz, it is determined to be a flashover discharge.

[0078] It should be noted that flashover discharge refers to the discharge phenomenon that occurs on the surface of an insulator at a relatively low voltage due to the presence of pollutants. Its discharge frequency is relatively high, at 300kHz.

[0079] In this embodiment of the application, step S400, which involves sending the identified insulation hazard type to the user terminal, includes the following steps D1-D2:

[0080] D1: The information sent to the user terminal includes the identified insulation hazard types and related data;

[0081] It should be noted that the data can be sent to the terminal via a WiFi module connected to the microcontroller. The WiFi module is an ESP-F12, and the user terminal can be a mobile terminal, such as a mobile phone.

[0082] D2: Related information includes current value data and time information, etc.

[0083] In summary, this invention uses a Rogowski coil to capture the weak current signal flowing into the ground from within a cement pole in real time. The signal is then restored using an integrator circuit, amplified using an amplifier circuit, and filtered to remove interference, making the signal suitable for time-frequency characteristic analysis. Furthermore, high-speed instantaneous value sampling and wavelet transform processing by a microcontroller enable accurate identification of insulation hazard types. Even further, by combining current signal processing with advanced time-frequency analysis methods, this invention overcomes the limitations of traditional detection methods, achieving comprehensive, accurate, and real-time monitoring of insulation hazards in the distribution network, effectively improving the safety and reliability of the distribution network operation.

[0084] Example 3 illustrates a schematic scheme for a method of detecting insulation defects in a power distribution network. It should be noted that the technical solution of this power distribution network insulation defect detection system belongs to the same concept as the technical solution of the aforementioned power distribution network insulation defect detection method. Details not described in detail in this embodiment can be found in the description of the technical solution of the aforementioned power distribution network insulation defect detection method.

[0085] This embodiment also provides a power distribution network insulation defect detection system, including:

[0086] The acquisition module is used to acquire the original signal flowing into the ground from inside the cement pole;

[0087] The processing and generation module is used to preprocess the original signal to generate a target signal suitable for time-frequency feature analysis;

[0088] The sampling and identification module is used to sample the target signal at high speed and analyze the discharge frequency characteristics of the target signal through time-frequency transformation to identify the type of insulation hazard.

[0089] The sending module is used to send the identified insulation hazard types to the user terminal.

[0090] This embodiment also provides an electronic device suitable for detecting insulation defects in power distribution networks, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for detecting insulation defects in power distribution networks as proposed in the above embodiment.

[0091] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for detecting insulation hazards in a power distribution network as proposed in the above embodiments.

[0092] The storage medium proposed in this embodiment and the method for detecting insulation hazards in power distribution networks proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0093] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting insulation defects in a power distribution network, characterized in that: include, Obtain the original signal flowing into the ground from inside the cement pole; The original signal is preprocessed to generate a target signal suitable for time-frequency feature analysis; The target signal is sampled at high speed, and the discharge frequency characteristics of the target signal are analyzed by time-frequency transformation to identify the type of insulation hazard. The identified insulation hazard types are sent to the user terminal.

2. The method for detecting insulation defects in a power distribution network as described in claim 1, characterized in that: The preprocessing of the original signal to generate a target signal suitable for time-frequency feature analysis includes, The original signal is subjected to a first preprocessing to generate a first target signal, wherein the first preprocessing is used to restore the phase of the original signal; The first target signal is subjected to a second preprocessing to generate a second target signal, wherein the second preprocessing is used to amplify the amplitude of the first target signal; The second target signal is subjected to a third preprocessing to generate a third target signal, wherein the third preprocessing is used to filter out interference components of the second target signal.

3. The method for detecting insulation defects in a power distribution network as described in claim 2, characterized in that: The process of high-speed sampling of the target signal and analyzing its discharge frequency characteristics through time-frequency transformation to identify insulation hazard types includes: The third target signal is sampled at high speed instantaneously; Wavelet transform is used to perform time-frequency transformation analysis on the sampled third target signal to determine the discharge frequency characteristics in order to identify the type of insulation hazard.

4. The method for detecting insulation defects in a power distribution network as described in claim 3, characterized in that: The wavelet transform uses Haar wavelets and is sampled according to a preset sampling frequency; By comparing multi-scale and multi-translation transformations and combining them with a preset algorithm, the obtained voltage values ​​are converted into actual current values ​​to determine the corresponding insulation hazard type.

5. The method for detecting insulation defects in a power distribution network as described in claim 4, characterized in that: The acquisition of the original signal flowing into the ground from within the cement pole includes, The electromagnetic induction sensor attached to the cement pole captures the current signal flowing into the ground inside the pole; The electromagnetic induction sensor is a Rogowski coil, which is 1.5 meters long, has 2000 turns, and has a minimum detection current of 0.5 mA.

6. A method for detecting insulation defects in a power distribution network as described in any one of claims 1-5, characterized in that: The types of insulation hazards include insulator aging, surge arrester aging, and flashover discharge.

7. The method for detecting insulation defects in a power distribution network as described in claim 6, characterized in that: The types of insulation hazard identification include, When the discharge frequency is the same as the power frequency, it is determined that the insulator or surge arrester is aging. When the discharge frequency is equal to 300kHz, it is determined to be a flashover discharge.

8. A power distribution network insulation defect detection system, using the method described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire the original signal flowing into the ground from inside the cement pole; The processing and generation module is used to preprocess the original signal to generate a target signal suitable for time-frequency feature analysis; The sampling and identification module is used to sample the target signal at high speed and analyze the discharge frequency characteristics of the target signal through time-frequency transformation to identify the type of insulation hazard. The sending module is used to send the identified insulation hazard types to the user terminal.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images