Charging pile cable fault detection method and system, storage medium and program product

Abstract

The invention discloses a charging pile cable fault detection method and system, a storage medium and a program product, and the method comprises the steps: judging whether a fault is an external fault; if the fault is not the external fault, receiving the conductivity of the two ends of the cable, and judging whether the conductivity is in a preset table; if the conductivity is not in the preset table, receiving the infrared image; judging whether the cable has a high-risk heating point or not; if the high-risk heating point does not exist, receiving a cable resistance data set sent by the insulation resistance tester; judging whether cable resistance data lower than a preset standard resistance value exists or not according to the cable resistance data set; if the cable resistance data does not exist, determining that the cable has no fault; if the cable resistance data exist, determining a damp fault; if the cable has a high-risk heating point, determining a poor contact fault; and if the conductivity is in the preset table, determining that the fault is a conductor fracture fault. According to the invention, the possible fault type of the charging pile cable can be comprehensively detected, so that the accuracy of fault detection of the charging pile cable is improved.

Description

Technical Field

[0001] This application belongs to the field of electronic digital data processing, and particularly relates to a method, system, storage medium, and program product for detecting charging pile cable faults. Background Art

[0002] In recent years, with the improvement of environmental awareness and policy support, the electric vehicle market has grown rapidly, driving the demand for charging infrastructure. The charging power has been continuously increased, evolving from slow charging at the beginning to fast charging and ultra-fast charging, which poses higher requirements for cable performance. Consequently, the requirements for cable fault detection have gradually increased.

[0003] In the related art, if the root cause analysis result indicates that the charging pile cable is abnormal, magnetic flux detection is performed on the charging pile cable to obtain a magnetic flux detection result; if there are defects in the charging pile cable, multiple cable surface images corresponding to the charging pile cable are collected, and cable surface defect detection is performed on the charging pile cable based on the multiple cable surface images to obtain a surface defect detection result; fault location is performed on the charging pile cable according to the surface defect detection result to obtain a fault location result, and the faulty cable corresponding to the charging pile cable is intercepted according to the fault location result, and a cross-sectional image of the faulty cable is collected; the cross-sectional image is input into a preset cable fault detection model for cable fault detection to obtain a cable fault detection result.

[0004] However, the above technical solution only determines cable surface faults through image recognition. In actual applications, cable faults are not only surface defects, but internal faults may also occur in the cable. Detecting only the cable surface will reduce the accuracy of charging pile cable fault detection and is not conducive to the safety detection of charging pile cables. Summary of the Invention

[0005] This application provides a method, system, storage medium, and program product for detecting charging pile cable faults, which is used to comprehensively detect possible fault types of charging pile cables, thereby improving the accuracy of charging pile cable fault detection.

[0006] In a first aspect, this application provides a method for detecting charging pile cable faults. When it is determined that a charging pile issues a fault alarm, it is judged whether it is an external fault; If it is not an external fault, the conductivity of both ends of the cable measured by a multimeter is received, and it is judged whether the conductivity is in a preset table; If the conductivity is not in the preset table, an infrared image captured by an infrared thermal imager is received; According to the infrared image, it is judged whether there are high-risk heating points on the cable. A high-risk heating point is a position point with a temperature higher than a preset temperature in the infrared image; If there are no high-risk heating points, a cable resistance data set sent by an insulation resistance tester is received; Determine whether there is cable resistance data lower than the preset standard resistance value according to the cable resistance data set; If there is no cable resistance data lower than the preset standard resistance value, it is determined that the cable has no fault; If there is cable resistance data lower than the preset standard resistance value, it is determined that the fault type of the cable is moisture ingress fault; If there is a high-risk heating point in the cable, it is determined that the fault type of the cable is poor contact fault; If the conductivity is in the preset table, it is determined that the fault type of the cable is conductor breakage fault.

[0007] By adopting the above technical solutions, when the charging pile issues an alarm, first determine whether it is an external fault. By eliminating the interference of external factors, the scope of fault detection is narrowed and the diagnostic efficiency is improved. If it is determined to be an internal fault, then use a multimeter to measure the cable conductivity and quickly determine whether it is a conductor breakage fault by comparing with the preset table. This preliminary screening based on electrical characteristics can quickly identify a type of typical fault and provide a direction for subsequent detailed diagnosis. For the case where the conductivity is normal, the method further uses an infrared thermal imager to detect the cable temperature distribution and identify high-risk heating points. This non-destructive testing technology based on thermal radiation can intuitively reflect the heating condition of the cable, help to discover local abnormal heating caused by reasons such as poor contact, and prevent serious safety accidents caused by overheating. For the case where there is no high-risk heating point, an insulation resistance tester is used to measure the cable resistance and determine whether there is an insulation moisture ingress fault by comparing with the standard value. This quantitative insulation performance detection can sensitively detect the deterioration or damage of the insulation layer, eliminate potential leakage hazards in time, and ensure the safe operation of the system. Through step-by-step screening and in-depth analysis, different types of faults such as breakage, overheating, and moisture ingress can be accurately located. It realizes the comprehensive detection of the possible fault types of the charging pile cable, and then improves the accuracy of the charging pile cable fault detection.

[0008] Combined with some embodiments of the first aspect, in some embodiments, when it is determined that the charging pile issues a fault alarm, determining whether it is an external fault specifically includes: Obtain a monitoring video containing the cable; Determine whether there is one or more of deformation, bubbles, cracking, and discoloration according to the monitoring video; If there is, it is determined that the cable has an external fault; If not, it is determined that the cable has no external fault.

[0009] By adopting the above technical solution, the cable is observed in real time and continuously using the monitoring video. Through image processing technology, typical defect features such as cable deformation, bubbles, cracks, and color changes are automatically identified, realizing the rapid discovery and positioning of external faults. This data-driven intelligent diagnosis mode can improve the timeliness and accuracy of fault discovery. At the same time, the monitoring video covers the entire area of the cable, enabling a comprehensive observation of its appearance state and effectively reducing the risk of missed detection. Through the analysis of video data, it is also possible to predict and warn of the trend of the cable's poor condition, providing data support for fault prevention. In addition, the video monitoring data can be remotely transmitted and stored, facilitating the establishment of a cable status file and realizing the traceability and big data analysis of fault diagnosis. The vision-based fault diagnosis method has the characteristics of intuitiveness, which helps to improve the efficiency of fault analysis and disposal.

[0010] Combined with some embodiments of the first aspect, in some embodiments, determining whether there is one or more of deformation, bubbles, cracks, and color changes according to the monitoring video specifically includes: Decompose the monitoring video into a number of single-frame images; Perform denoising and brightness adjustment operations on each single-frame image to obtain the corresponding preprocessed single-frame image; Use an image segmentation algorithm to identify and segment the cable area in each preprocessed single-frame image; Perform feature extraction operations on the cable area to obtain a number of feature points; Judge whether there is one or more of deformation, bubbles, cracks, and color changes according to a number of feature points.

[0011] By adopting the above technical solutions, reasonable image processing and pattern recognition technologies can be used to accurately detect the defect characteristics of cables in complex environments, improving the robustness and practicality of external fault diagnosis. This method performs frame-by-frame processing on the monitoring video, converting the dynamic video into a sequence of static images. This discretization processing in the time dimension can simplify the subsequent image analysis process and reduce the computational complexity. By denoising and adjusting the brightness of a single-frame image, factors such as image noise and uneven illumination can be eliminated, improving the image quality and laying a foundation for feature extraction. Then, this method uses an image segmentation algorithm to process the preprocessed single-frame image, accurately segmenting the cable area of interest. This object detection and positioning technology can exclude background interference, narrow the feature extraction range, and focus on the defect analysis of the cable itself. Based on the image segmentation results, features such as the shape, texture, and color of the cable area can be further extracted to comprehensively characterize its appearance state. According to the extracted feature points, a comprehensive judgment can be made on whether the cable has typical defects such as deformation, bubbles, cracks, and discoloration. This classification decision method based on multi-feature fusion can make full use of image information to improve the reliability of fault discrimination. By establishing a defect feature model library, the recognition and classification of unknown fault modes can be realized, expanding the diagnostic scope.

[0012] Combined with some embodiments of the first aspect, in some embodiments, after determining that the fault type of the cable is a conductor break fault if the conductivity is in the preset table, the method further includes: Controlling the time domain reflectometer to send a preset short pulse signal into the cable; Receiving the reflection signal detected by the time domain reflectometer; Generating a reflection map based on the reflection signal; Determining the conductor break fault point according to the reflection map and sending the conductor break fault point to the control terminal.

[0013] By adopting the above technical solutions, a time domain reflectometer is used to apply a pulse signal to the cable. When the signal encounters an impedance change point (such as a break position) during transmission in the line, reflection will occur. By detecting the amplitude and delay of the reflection signal and combining the cable characteristic parameters, the distance to the break point can be calculated to achieve fast and accurate positioning. Generating a graphical representation of the reflection signal visually shows the propagation and reflection of the signal in the cable, facilitating visual analysis and break point positioning. Based on the position corresponding to the spike pulse in the map, the interval where the break point is located can be judged, narrowing the troubleshooting range and guiding the repair personnel to quickly lock the faulty cable section. At the same time, this method feeds back the diagnostic results to the control terminal, realizing the automatic upload and centralized management of detection information, facilitating fault analysis, cause tracing, and statistical analysis.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after determining that the fault type of the cable is a poor contact fault if there is a high-risk heating point in the cable, the method further includes: Sending the position point corresponding to the high-risk heating point to the control terminal.

[0015] By adopting the above technical solution, after the infrared thermal imager detects the high-risk heating point, the position information is sent to the control terminal in a timely manner, which can enable the fault to be quickly disposed of, avoid a chain reaction caused by local overheating, and prevent equipment damage or safety accidents.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after determining that the fault type of the cable is a moisture ingress fault if there is cable resistance data lower than the preset standard resistance value, the method further includes: Determining the moisture ingress point of the cable according to the monitoring video; Sending the moisture ingress point to the control terminal.

[0017] By adopting the above technical solution, while detecting an abnormal decrease in the insulation resistance, the moisture ingress position is determined by analyzing the monitoring video, which can comprehensively and deeply grasp the fault situation, provide a reliable basis for reasonably arranging moisture-proof measures and carrying out targeted repairs, and improve the moisture-proof ability of the cable.

[0018] In combination with some embodiments of the first aspect, in some embodiments, before determining whether it is an external fault in the case where the charging pile issues a fault alarm, the method further includes: Obtaining the working environment parameters and power input of the charging pile, where the working environment parameters include temperature and humidity; In the case where the working environment parameters are within the preset environmental parameter range and the power input is within the preset power region, perform the step of determining whether it is an external fault in the case where the charging pile issues a fault alarm.

[0019] By adopting the above technical solution, the ambient temperature and humidity data collected in real time are compared with the preset safety thresholds. If it exceeds the normal working range, it is determined that the environmental conditions are abnormal. Similarly, by evaluating the deviation degree of the power supply index from the standard range, power supply quality problems can be found. Once an abnormality is detected, the early warning mechanism is triggered. By collecting the ambient temperature and humidity parameters and power supply indicators in real time and comparing them with the normal operating condition thresholds, external condition abnormalities can be detected in a timely manner, preventing charging pile faults caused thereby, and ensuring the safe and reliable operation of charging equipment in a wider range.

[0020] Second aspect, an embodiment of the present application provides a charging pile cable fault detection system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions. The one or more processors call the computer instructions to cause the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] Third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the above instructions run on the system, causing the above system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] Fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on the system, causing the system to execute the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a method for detecting charging pile cable faults. When the charging pile issues an alarm, it first determines whether it is an external fault. By eliminating the interference of external factors, the scope of fault detection is narrowed, and the diagnostic efficiency is improved. If it is determined to be an internal fault, a multimeter is used to measure the cable conductivity, and by comparing with a preset table, it is quickly determined whether it is a conductor break fault. This preliminary screening based on electrical characteristics can quickly identify a type of typical fault and provide a direction for subsequent detailed diagnosis. For the case where the conductivity is normal, the method further uses an infrared thermal imager to detect the cable temperature distribution and identify high-risk heating points. This non-destructive detection technology based on thermal radiation can intuitively reflect the heating condition of the cable, help to discover local abnormal heating caused by reasons such as poor contact, and prevent serious safety accidents caused by overheating. For the case where there are no high-risk heating points, an insulation resistance tester is used to measure the cable resistance, and by comparing with the standard value, it is determined whether there is an insulation moisture absorption fault. This quantitative insulation performance detection can sensitively detect the deterioration or damage of the insulation layer, eliminate potential leakage hazards in time, and ensure the safe operation of the system. Through step-by-step screening and in-depth analysis, different types of faults such as breaks, overheating, and moisture absorption can be accurately located. It realizes a comprehensive detection of the possible fault types of the charging pile cable, and thus improves the accuracy of the charging pile cable fault detection.

[0024] 2. The present application provides a method for detecting faults in charging pile cables. By using monitoring videos to observe the cables in real time and continuously, and through image processing technology, typical defect features such as cable deformation, bubbles, cracks, and color changes can be automatically identified, realizing the rapid discovery and location of external faults. This data-driven intelligent diagnosis mode can improve the timeliness and accuracy of fault discovery. At the same time, the monitoring video covers the entire area of the cable, enabling a comprehensive observation of its appearance state and effectively reducing the risk of missed inspections. Through the analysis of video data, it is also possible to predict and warn of the trend of cable deterioration, providing data support for fault prevention. In addition, video monitoring data can be remotely transmitted and stored, facilitating the establishment of cable status files and realizing the traceability and big data analysis of fault diagnosis. The vision-based fault diagnosis method has the characteristics of intuitiveness, which helps to improve the efficiency of fault analysis and disposal.

[0025] 3. The present application provides a method for detecting faults in charging pile cables. By adopting reasonable image processing and pattern recognition technologies, it can accurately detect the defect features of cables in complex environments, improving the robustness and practicality of external fault diagnosis. This method performs frame-by-frame processing on the monitoring video, converting the dynamic video into a sequence of static images. This discretization processing in the time dimension can simplify the subsequent image analysis process and reduce the computational complexity. By denoising and adjusting the brightness of single-frame images, the interference of factors such as image noise and uneven illumination can be eliminated, improving the image quality and laying a foundation for feature extraction. Then, this method uses an image segmentation algorithm to process the preprocessed single-frame images, accurately segmenting the cable area of interest. This object detection and localization technology can exclude background interference, narrow the feature extraction range, and focus on the defect analysis of the cable itself. Based on the image segmentation results, features such as the shape, texture, and color of the cable area can be further extracted to comprehensively describe its appearance state. According to the extracted feature points, a comprehensive judgment is made on whether the cable has typical defects such as deformation, bubbles, cracks, and color changes. This classification decision method based on multi-feature fusion can make full use of image information and improve the reliability of fault discrimination. By establishing a defect feature model library, the recognition and classification of unknown fault modes can be realized, expanding the diagnostic scope. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart of a method for detecting faults in charging pile cables in an embodiment of the present application.

[0027] Figure 2 is another flowchart of a method for detecting faults in charging pile cables in an embodiment of the present application.

[0028] Figure 3 is a schematic structural diagram of an entity device of a system for detecting faults in charging pile cables provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", "this" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any and all possible combinations of one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0031] The following describes an application scenario of the embodiments of the present application: With the booming development of the new energy vehicle industry, more and more charging stations and charging piles have emerged in cities, providing convenient charging services for electric vehicle owners. A large-scale multi-storey parking garage has been newly built in a certain city, equipped with hundreds of charging piles, providing charging services for thousands of electric vehicles every day, and is the first choice for electric vehicle owners in the surrounding area to charge.

[0032] However, with the increasing frequency of use of the charging piles, fault problems have gradually emerged. One day, the parking garage administrator received reports from multiple vehicle owners, saying that the charging speed had become slower or even unable to charge. The administrator immediately organized technicians to investigate the faulty charging piles.

[0033] Through the analysis of the charging data, the technicians initially determined that it was caused by a cable fault. In order to accurately locate the fault point, they first performed a magnetic flux detection on the suspicious cables, and the results showed that some cables were abnormal. Subsequently, they collected the images of the surfaces of these defective cables, analyzed the surface defect features using an image recognition algorithm, and combined with the magnetic flux detection results to finally determine the positions of several faulty cables.

[0034] The technical personnel immediately cut off the faulty cable section and collected cross-sectional images for further fault diagnosis. They input the images into a pre-trained cable fault detection model, which quickly generated a fault cause analysis report. The report showed that these cables had internal defects such as damaged insulation layers and broken conductors. These defects were difficult to observe with the naked eye but would significantly increase the resistance, resulting in a decrease in charging efficiency and even posing serious safety hazards. The parking garage management realized that relying solely on surface detection could not comprehensively detect hidden cable faults, and a three-dimensional detection system that addressed both internal and external issues had to be established. To solve the above technical problems, this application provides a method for detecting faults in charging pile cables, which is used to comprehensively detect the possible fault types of charging pile cables, thereby improving the accuracy of fault detection for charging pile cables.

[0035] The following will describe, in conjunction with Figure 1 , a method for detecting faults in charging pile cables in an embodiment of this application: Please refer to Figure 1 , which is a schematic flowchart of a method for detecting faults in charging pile cables in an embodiment of this application.

[0036] Before performing the following steps, the system obtains the working environment parameters and power input of the charging pile. The working environment parameters include temperature and humidity; When the working environment parameters are within the preset environmental parameter range and the power input is within the preset power region, step S101 is executed.

[0037] S101: When it is determined that the charging pile issues a fault alarm, determine whether it is an external fault; When it is determined that the charging pile issues a fault alarm, the system determines whether it is an external fault. The external fault can be deformation, bubbles, cracking, discoloration, etc., and is not limited here. For how to specifically implement this step, it will be described in another embodiment and will not be elaborated here.

[0038] S102: Receive the conductivity of both ends of the cable measured by a multimeter and determine whether the conductivity is in a preset table; If it is not an external fault, the system receives the conductivity of both ends of the cable measured by a multimeter and determines whether the conductivity is in a preset table. The preset table is a table of normal cable conductivity.

[0039] If the alarm is not caused by an external fault, the system receives the conductivity data of both ends of the cable measured by a multimeter and compares it with the preset standard value range. Conductivity can be measured by resistance value, and the unit is ohm (Ω). For example, if the normal range of the cable conduction resistance set by the system is 0.1 - 0.5Ω, and the resistance value measured by the multimeter is 0.3Ω, it indicates good conductivity; if the measured resistance value is 1.2Ω, it exceeds the normal range, meaning there may be internal faults such as conductor damage or looseness. The system can further infer the type and severity of the fault based on the size of the resistance value. For example, the larger the resistance value, the worse the conductivity, and the more serious the fault may be.

[0040] S103. Receive the infrared image captured by the infrared thermal imager and determine whether there are high-risk heating points on the cable; If the conductivity is not in the preset table, the system receives the infrared image captured by the infrared thermal imager. The system determines whether there are high-risk heating points on the cable based on the infrared image. A high-risk heating point is a position point with a temperature higher than the preset temperature in the infrared image.

[0041] If the conductivity test fails, the system will start infrared imaging detection to determine whether there are abnormal heating points on the cable. The system collects the thermal distribution map of the cable through the infrared thermal imager and performs temperature calibration and segmentation processing on the image. The system presets a temperature threshold, such as 80°C. Any pixel point exceeding this temperature will be marked as a high-risk heating point. These heating points often indicate serious defects inside the cable, such as poor contact and insulation damage, which may cause local overheating and pose safety hazards such as fire and short circuit.

[0042] S104. Receive the cable resistance data set sent by the insulation resistance tester; If there are no high-risk heating points, receive the cable resistance data set sent by the insulation resistance tester. If no abnormality is detected in the infrared detection, the system receives the cable resistance data set uploaded by the insulation resistance tester. This instrument can measure the leakage current and insulation resistance of the cable insulation layer at different voltage levels and convert the data into a set of resistance values, such as [1000MΩ, 950MΩ, 800MΩ...]. These data reflect the changing trend of the insulation performance of the cable under different working conditions. The system saves the resistance data set to the fault diagnosis database for subsequent big data analysis.

[0043] S105. Determine whether there is cable resistance data lower than the preset standard resistance value according to the cable resistance data set; The system makes judgments and screenings on the resistance data groups one by one to check whether there are abnormal data with resistance values lower than the preset standard resistance value. The standard value is determined by factors such as the material, length, and usage environment of the cable. For example, the standard insulation resistance of a polyvinyl chloride insulated cable is 10 MΩ·km. If individual values in the resistance data group are much lower than the standard value, such as 0.5 MΩ·km, it indicates that the insulation performance of this section of the cable has seriously deteriorated and may be affected by moisture or chemical corrosion. The system includes the exceeded resistance values and the corresponding cable section positions in the high-risk defect list and conducts cause analysis in combination with environmental monitoring data.

[0044] S106. Determine that the cable has no faults; If there are no cable resistance numbers lower than the preset standard resistance value, the system determines that the cable has no faults. If all indicators such as resistance testing, infrared detection, and conductivity inspection are within the normal range, the system comprehensively determines that the current cable state is stable and there are no potential fault hazards. The system updates the health file of the cable, uploads the diagnosis result to the cloud platform, and sends a patrol task prompt to the operation and maintenance personnel, requiring regular preventive maintenance of the cable. At the same time, the system will also predict its deterioration trend and life cycle based on big data such as the material, service life, and load curve of the cable, and formulate maintenance or replacement plans in advance, so as to realize the intelligent management of the entire life cycle of the equipment.

[0045] S107. Determine that the fault type of the cable is a conductor break fault; If the conductivity is in the preset table, it is determined that the fault type of the cable is a conductor break fault. If the conductivity inspection of the cable fails, but other indicators such as insulation resistance and infrared imaging are normal, the system infers that the cable may have a conductor break fault. Such faults are often caused by factors such as mechanical stress and metal fatigue, manifested as partial or complete disconnection of the conductor strands, resulting in blocked current transmission. The system preliminarily estimates the location and scope of the break according to the magnitude of the conductivity resistance value. For example, if the nominal resistance of the cable is 0.1 Ω / km and the measured resistance value is 0.5 Ω, it is speculated that the break point is about 2 km from the test end. The system submits the preliminary judgment result to the expert system for confirmation, automatically generates a on-site investigation and emergency repair plan, and notifies the operation and maintenance personnel to dispose of it as soon as possible to avoid greater losses caused by the deterioration of the break fault.

[0046] The following is a general description of steps S108 - S111: S108. Control the time domain reflectometer to send a preset short pulse signal into the cable; S109. Receive the reflected signal detected by the time domain reflectometer; S110. Generate a reflection pattern according to the reflected signal; S111. Determine the conductor break fault point according to the reflection pattern and send the conductor break fault point to the control terminal; The time domain reflectometer is controlled to send a preset short pulse signal to the cable, receive the reflected signal and generate a reflection spectrum, and finally determine the location of the conductor break fault point.

[0047] The basic working principle of the time domain reflectometer (TDR) is to inject a high-frequency narrow pulse signal into the cable under test. By analyzing the reflected waveform spectrum, the changes in the cable's characteristic impedance and the location of the defect can be inferred. First, the system sets the test parameters of the TDR, including pulse width, amplitude, repetition frequency, sampling rate, etc. The pulse width determines the spatial resolution of the test, which is generally set to about 10ns; the pulse amplitude must be greater than the insulation withstand voltage of the cable to ensure that the signal can effectively penetrate, such as setting it to 50V; the pulse repetition frequency and sampling rate must match, such as setting them to 500kHz and 2GSa / s respectively, to ensure the integrity and real-time nature of the collected data.

[0048] After the TDR transmits a pulse signal, when it encounters discontinuities such as open circuit, short circuit, impedance mutation, etc. of the cable, an obvious reflection echo will be generated. A break fault is a typical open circuit defect, and its reflection waveform is usually spike-shaped, with the peak corresponding to the breakpoint position. The system collects reflection waveform data in real time, and generates a reflection waveform spectrum after filtering, amplification, A / D conversion and other processing. In the spectrum, the horizontal axis represents the signal transmission distance (which can be converted to cable length), and the vertical axis represents the normalized amplitude of the reflection signal. The system performs feature extraction and pattern recognition on the spectrum to detect key parameters such as the position, height, and width of the reflection peak.

[0049] Then, combining the propagation velocity factor (VoP) of the cable and the position offset of the test port, the distance D of the break point is accurately calculated through the formula D=VoP*T / 2 (where D is the break point distance and T is the round-trip transmission time corresponding to the reflection peak). Finally, the system generates a break fault diagnosis report, indicating the type, distance, reflection loss and other key information of the break point, and sends the report to the control terminal to guide subsequent maintenance work.

[0050] For example, a fault alarm occurs in a DC charging cable line in a charging station substation. The system determines that it may be a conductor break fault. The maintenance personnel use a time domain reflectometer to detect the cable. The TDR parameters are set as follows: pulse width 8 ns, amplitude 100 V, repetition frequency 1 MHz, and sampling rate 10 GSa / s. The instrument emits a pulse signal and receives a reflected waveform, which is processed to generate a reflection pattern. A sharp reflection peak is observed at 420 ns on the graph, with an amplitude of 0.85 (normalized) and a half-peak width of approximately 6 ns. According to the data provided by the cable manufacturer, the propagation speed factor VoP of this type of cable is 0.82. From the formula for the distance to the break point, we can obtain: D = 0.82×3×10^8×420×10^-9 / 2 = 51.66 m. At the same time, combining the shape characteristics of the reflection peak, it can be determined that this is a complete break, and the loss of the conductor cross-sectional area is greater than 80%. The system generates a fault report and sends it to the control terminal, indicating that the break point is approximately 52 m from the starting end and needs to be processed as soon as possible. Based on this information, the maintenance personnel quickly lock the defect area, cut off the damaged cable and reconnect it, greatly shortening the maintenance time and effectively reducing the operation risk of the charging station.

[0051] S112. Determine that the fault type of the cable is a poor contact fault; If there are high-risk heating points in the cable, then determine that the fault type of the cable is a poor contact fault.

[0052] S113. Send the position point corresponding to the high-risk heating point to the control terminal; Determine that the fault type of the cable is a poor contact fault and send the position point corresponding to the high-risk heating point to the control terminal.

[0053] When the infrared thermal imager detects an abnormal high-temperature point in the cable, the system determines that there is a poor contact fault at that location. Poor contact mostly occurs at parts with small contact areas and concentrated mechanical stress such as cable connectors and intermediate joints, resulting in local heating due to excessive contact resistance.

[0054] The system analyzes the infrared thermal image, determines the pixel coordinates of the high-temperature point, and then combines the optical parameters (such as field of view angle, focal length) and installation position of the thermal imager. Through the principle of triangulation, the pixel coordinates are mapped to actual space coordinates with an accuracy of up to centimeter level. The system establishes a three-dimensional coordinate system with the lens of the thermal imager as the origin, the X-axis to the right, the Y-axis upward, and the Z-axis forward along the optical axis. Given the parameters such as the resolution, field of view angle, focal length, and installation height of the thermal imager, and finding the pixel coordinates (x, y) of the high-temperature point H on the infrared image, the spatial coordinates (X, Y, Z) of H can be calculated through the following formula: X = Z×tan(field of view angle X / 2)×(x - image width / 2) / image width Y = Z×tan(field of view angle Y / 2)×(image height / 2 - y) / image height Z = focal length / [2 * tan (field of view Y / 2)] * image height / (image height / 2-y) Through the above coordinate transformation, the system can accurately map the infrared image position of the fault point to the actual three-dimensional spatial position of the cable. At the same time, combined with the temperature readings of the high-temperature points, the system will also estimate the danger level and impact range of the fault. For example, based on the current carrying capacity and temperature rise rate of the cable, it can infer the time and range of the possible melting of the insulation layer caused by overheating. These fault location and danger information will be pushed to the control terminal in real time to assist the operation and maintenance personnel to take emergency measures such as power outages in time to prevent the fault from expanding.

[0055] For example, at a charging station in a residential area, the operation and maintenance personnel found that the surface temperature of one of the cables was abnormally high during routine inspections of the AC power supply lines in the cable trench. They used an infrared thermal imager to collect thermal images of the area and observed a distinct high temperature point at the image coordinates (300, 400). The maximum temperature reached 85°C, exceeding the warning value of 80°C. The main parameters of the thermal imager are: resolution 640480, field of view 25°19°, focal length 35mm, installation height 1.5m, parallel to the cable, spacing 0.4m.

[0056] After the system obtains the thermal image and parameters, it calculates the spatial coordinates of the high temperature point H as (120mm, 43mm, 3618mm) through the coordinate transformation formula, which is 3.618m from the lens and 0.52m from the center line of the cable, corresponding to the position of the cable joint. This indicates that there may be a poor contact defect at the joint. The system then pulls the historical temperature data of the joint and finds that the temperature has continued to rise since two days ago, with an average daily temperature rise rate of 8°C. It is expected that it will exceed the maximum allowable temperature threshold of 105°C for the insulating material in another day. The system generates a fault warning report, uploads information such as defect type, location, temperature, and development trend to the digital twin system, and pushes it to the control terminal. The supervisor quickly approved the power outage maintenance plan and disconnected the faulty cable from the main power grid to prevent it from happening. The maintenance personnel opened the joint cover and found that there were obvious signs of ablation on the connection surface between the conductor and the terminal, and the contact resistance far exceeded the standard value.

[0057] S114, determining that the fault type of the cable is a moisture fault; If there is cable resistance data lower than the preset standard resistance value, it is determined that the fault type of the cable is a moisture fault.

[0058] S115. Determine the moisture affected point of the cable according to the monitoring video and send the moisture affected point to the control terminal.

[0059] It is determined that the fault type of the cable is a moisture fault, the moisture point of the cable is determined based on the monitoring video and the moisture point is sent to the control terminal.

[0060] When the cable insulation resistance test fails, the system infers that the cable has a moisture ingress fault. Moisture ingress can damage the molecular structure of the insulation material, causing it to absorb water molecules and increase its conductivity, which is manifested as a decrease in insulation resistance.

[0061] To address this defect, the system first reviews historical data to identify the starting time point of the insulation resistance decline. Then, the system retrieves the surveillance videos of areas prone to water accumulation, such as cable trenches or manholes, during that time period, and uses image analysis algorithms to detect any signs of water accumulation. Common water body recognition methods include color feature extraction, texture analysis, edge detection, etc. For example, a blue-green color threshold can be set, pixel blocks below this threshold can be extracted and subjected to morphological processing to obtain candidate water body regions; further analysis of their texture features such as gray mean, contrast, entropy, etc. can be used to filter out false water bodies; finally, the real water body can be confirmed by combining geometric features such as edge smoothness and connectivity.

[0062] After determining the water accumulation location, the system analyzes the cable routing diagram to infer the approximate range of the moisture ingress fault point, generally in the cable section 1 - 3m downstream of the water accumulation. Moisture ingress faults often exhibit progressive evolution, with the insulation resistance value first decreasing slowly and then dropping suddenly when water trees cover the insulation layer. Therefore, the system also evaluates the moisture ingress range and warns of the worsening trend of the fault. All these warning messages will be sent to the control terminal, which can attract the high attention of the operation and maintenance personnel and prompt them to take remedial measures such as waterproofing, drying, replacement, etc. as early as possible to contain the spread of the fault.

[0063] The above embodiments have the following beneficial effects: When the charging pile issues an alarm, it first determines whether it is an external fault. By eliminating the interference of external factors, the scope of fault detection is narrowed and the diagnostic efficiency is improved. If it is determined to be an internal fault, a multimeter is used to measure the cable conductivity, and by comparing with a preset table, it can quickly determine whether it is a conductor break fault. This preliminary screening based on electrical characteristics can quickly identify a type of typical fault and provide a direction for subsequent detailed diagnosis. For the case where the conductivity is normal, the method further uses an infrared thermal imager to detect the cable temperature distribution and identify high-risk heat spots. This non-destructive testing technology based on thermal radiation can intuitively reflect the heating situation of the cable, helping to discover local abnormal heating caused by reasons such as poor contact and preventing serious safety accidents caused by overheating. For the case where there are no high-risk heat spots, an insulation resistance tester is used to measure the cable resistance, and by comparing with the standard value, it can determine whether there is an insulation moisture ingress fault. This quantitative insulation performance detection can sensitively detect the deterioration or damage of the insulation layer, timely eliminate potential leakage hazards, and ensure the safe operation of the system. Through step-by-step screening and in-depth analysis, different types of faults such as breaks, overheating, and moisture ingress can be accurately located. It realizes the comprehensive detection of the possible fault types of the charging pile cable, thereby improving the accuracy of charging pile cable fault detection.

[0064] Using a time domain reflectometer, a pulse signal is applied to the cable. When the signal encounters an impedance change point (such as a break position) during transmission in the line, reflection will occur. By detecting the amplitude and delay of the reflected signal and combining with the cable characteristic parameters, the distance to the break point can be calculated to achieve fast and accurate positioning. Generate a spectral representation of the reflected signal to visually display the propagation and reflection of the signal in the cable, facilitating visual analysis and break point positioning. Based on the position corresponding to the spike pulse in the spectrum, the interval where the break point is located can be judged, narrowing the scope of investigation and guiding the repair personnel to quickly lock the faulty cable section. At the same time, this method feeds back the diagnosis result to the control terminal to realize the automatic upload and centralized management of detection information, facilitating fault analysis, cause tracing and statistical analysis.

[0065] After the infrared thermal imager detects a high-risk heating point, it promptly sends the position information to the control terminal, which can enable the fault to be quickly disposed of, avoiding a chain reaction caused by local overheating and resulting in equipment damage or safety accidents.

[0066] While detecting an abnormal decrease in insulation resistance, by analyzing the monitoring video to determine the moisture ingress position, the fault situation can be grasped more comprehensively and deeply, providing a reliable basis for reasonably arranging moisture-proof measures and carrying out targeted repairs, and improving the moisture-proof ability of the cable.

[0067] The ambient temperature and humidity data collected in real time are compared with the preset safety thresholds. If it exceeds the normal working range, it is determined that the environmental conditions are abnormal. Similarly, by evaluating the deviation degree of the power supply indicators from the standard range, power supply quality problems can be found. Once an abnormality is detected, the warning mechanism is triggered. By collecting the ambient temperature and humidity parameters and power supply indicators in real time and comparing them with the normal operating condition thresholds, external condition abnormalities can be detected in time, preventing charging pile failures caused by this, and ensuring the safe and reliable operation of charging equipment within a wider range.

[0068] The following describes step S101 in detail in combination with another embodiment. The following combination Figure 2 , describes another method for detecting charging pile cable faults in the embodiments of the present application: Please refer to Figure 2 , which is another process schematic diagram of a method for detecting charging pile cable faults in the embodiments of the present application.

[0069] S201. Obtain the monitoring video containing the cable and decompose the monitoring video into a number of single-frame images; The system obtains a video stream containing cables from a monitoring device and divides it into a series of still images at a fixed frame rate (such as 25fps). This is a conventional preprocessing step for video analysis, aiming to convert a temporally continuous dynamic scene into a spatially discrete sequence of static frames, facilitating subsequent operations such as image recognition and feature extraction. The system sets the resolution of the video capture card to 1920*1080, the encoding format to H.264, the bit rate to 8Mbps, and the GOP length to 50 frames. A key frame is extracted every 1 second to ensure that both the subtle changes in the cable can be captured and the data volume will not be too large to affect real-time performance. For example, for a 10-minute monitoring video, 600 single-frame images can be extracted according to this parameter setting. The average size of each frame is about 200KB, and the total data volume is about 120MB. The transmission and storage pressures are within an acceptable range. At the same time, to reduce the interference of external light, the system preferably captures images at noon on sunny days, avoiding adverse conditions such as rain, fog, and haze.

[0070] S202. Perform denoising and brightness adjustment operations on each single-frame image to obtain the corresponding preprocessed single-frame image; Since the originally captured single-frame images often have varying degrees of noise and distortion, which will interfere with the subsequent recognition and analysis processes, image enhancement processing is required. Common types of noise include Gaussian noise, salt-and-pepper noise, speckle noise, etc., each presenting different probability distribution characteristics. The system uses an adaptive median filtering algorithm to denoise the image. This algorithm can automatically adjust the filter parameters according to the local statistical characteristics of the noise, maximizing the retention of edge details while effectively removing noise. Taking Gaussian noise as an example, if the original image is f(x, y), the noise mean is μ, and the variance is σ^2, then the denoised image g(x, y) = f(x, y) - μ. On the other hand, due to changes in lighting conditions, the brightness and contrast of the image may not be ideal, affecting the visual effect and recognition accuracy. The system uses histogram equalization to optimize the gray-scale distribution of the image, improving the contrast and dynamic range. This method enhances the layering and texture details of the image by stretching the probability density function of the pixel values to make them evenly distributed across the entire gray-scale range.

[0071] S203. Use an image segmentation algorithm to identify and segment the cable areas in each preprocessed single-frame image; After denoising and brightness adjustment, the image quality has been significantly improved. However, the cable area is still mixed with the background, making it difficult to directly analyze. Therefore, the system needs to use an image segmentation algorithm to extract the cable target of interest from the complex background. Common segmentation methods include threshold segmentation, edge detection, region growing, watershed, etc. Considering the slender strip-shaped morphological characteristics of the cable, the system selects a segmentation algorithm based on Graph Cut. It regards the image as a weighted undirected graph, with pixels as nodes and similarity as edge weights, and then uses the maximum flow / minimum cut algorithm to find the optimal partition of the graph, thus realizing the separation of foreground and background. This algorithm can make full use of the geometric and texture prior knowledge of the cable and has strong adaptability to irregular boundaries and hole defects. For example, for a 640*480 cable image frame, the system constructs a 640*480 pixel grid graph, calculates the similarity weights between pixels according to gray values, gradient values, spatial adjacency relationships, etc., and then iteratively optimizes the cut set (the subgraph containing the source point S) and the non-cut set (the subgraph containing the sink point T) until the cost of cutting is minimized, that is, the optimal segmentation result of the cable foreground area is obtained.

[0072] S204. Perform feature extraction operations on the cable area to obtain several feature points; After extracting the binary mask image of the cable area, the system performs morphological processing on it to repair broken gaps, smooth the contour curve, and filter out discrete small noise points through connected component analysis to obtain a complete and regular region of interest (ROI) of the cable. Then, the system extracts a series of visual features from the ROI to describe its appearance attributes and provide a criterion basis for subsequent defect detection. Common features include gray-scale statistics (such as mean, variance, entropy), texture features (such as LBP, HOG), shape features (such as area, perimeter, moment), etc. The system uses the SIFT (Scale-Invariant Feature Transform) algorithm to extract scale and rotation-invariant key-point features from the cable ROI. This algorithm selects a series of stable extreme points in the scale space as key points through the Difference of Gaussian (DoG) pyramid and local extreme detection, and calculates its 128-dimensional gradient direction histogram as a descriptor to form a scale, rotation, and brightness-invariant feature vector. For example, for a 200*1000 cable ROI, the system detects 50 SIFT key points, and each key point includes its position, scale, direction and other attribute information, which can be used for subsequent feature matching, recognition, tracking and other tasks. At the same time, the system also calculates the global statistics such as the gray-scale mean of the entire ROI as 120, the standard deviation as 28, and the entropy value as 4.2 as the texture features of the cable area.

[0073] S205. Determine whether there is one or more of deformation, bubbles, cracking, and discoloration according to several feature points; Based on the above-extracted SIFT local feature points and global grayscale statistics, the system constructs a cascaded Adaboost classifier to determine whether there are surface defects on the cable. First, the system trains the classifier model offline, prepares a large number of positive samples (defective cable images) and negative samples (non-defective cable images), and extracts features such as SIFT, LBP, mean, and entropy from them as the feature vectors of the samples. Then, the system uses the Adaboost algorithm for iterative training. In each round, the weak classifier with the minimum classification error rate is selected and given a larger weight coefficient to form a strong classifier. In the test stage, the same feature vectors are extracted from the cable ROI to be detected and sent into the trained strong classifier for decision-making, and a binary label indicating whether there are defects is output. For example, the system collects 500 defective (such as deformed, bubbled, cracked, discolored) and 500 non-defective cable images. Each image extracts 60 SIFT feature points and 4 grayscale statistics to form a sample feature matrix of 4500*64. After 10 rounds of Adaboost iterative training, 20 optimal CART decision trees are selected as weak classifiers and weighted combined into a strong classifier. For a newly captured frame of cable image in the current monitored video, the system extracts the feature vector of its ROI and inputs it into the strong classifier for real-time detection. If the output label is 1, it is determined that there are defects in this frame; otherwise, it is regarded as normal. Experiments show that the defect detection rate of this method reaches 95% on 3000 test images, and the false alarm rate is 8%, which can effectively identify common fault types of cables.

[0074] S206. Determine that an external fault has occurred in the cable; If it exists, determine that an external fault has occurred in the cable.

[0075] S207. Determine that no external fault has occurred in the cable.

[0076] If it does not exist, determine that no external fault has occurred in the cable.

[0077] Based on the fault diagnosis report, the system determines whether an external fault has occurred in the cable and reports the result to the monitoring platform. If a fault is diagnosed, the system immediately issues an alarm to notify the operation and maintenance personnel to go to the site for investigation, and at the same time activates emergency plans such as reducing the cable current-carrying capacity and adjusting the cable routing to prevent the fault from deteriorating. If no fault is diagnosed, the system updates the cable appearance status file, records the current characteristic parameters, and predicts the change trend in the future for a period of time as a reference for preventive operation and maintenance.

[0078] The above embodiments have the following beneficial effects: Use surveillance videos to conduct real-time and continuous observation of cables. Through image processing technology, automatically identify typical defect features such as cable deformation, bubbles, cracks, and color changes, and achieve rapid detection and location of external faults. This data-driven intelligent diagnosis mode can improve the timeliness and accuracy of fault detection. At the same time, the surveillance videos cover the entire area of the cable, enabling comprehensive observation of its appearance state and effectively reducing the risk of undetected faults. Through the analysis of video data, it is also possible to predict and warn of the trend of cable deterioration, providing data support for fault prevention. In addition, video surveillance data can be remotely transmitted and stored, facilitating the establishment of cable status files and realizing the traceability and big data analysis of fault diagnosis. The vision-based fault diagnosis method has the characteristics of intuitiveness, which helps to improve the efficiency of fault analysis and handling.

[0079] By adopting reasonable image processing and pattern recognition technologies, it is possible to accurately detect the defect features of cables in complex environments, improving the robustness and practicality of external fault diagnosis. This method performs frame-by-frame processing on the surveillance videos, converting the dynamic videos into static image sequences. This discretization processing in the time dimension can simplify the subsequent image analysis process and reduce the computational complexity. By denoising and adjusting the brightness of single-frame images, it is possible to eliminate the interference of factors such as image noise and uneven illumination, improve the image quality, and lay a foundation for feature extraction. Then, this method uses an image segmentation algorithm to process the preprocessed single-frame images, accurately segmenting the cable area of interest. This object detection and location technology can exclude background interference, narrow the feature extraction range, and focus on the defect analysis of the cable itself. Based on the image segmentation results, it is possible to further extract features such as the shape, texture, and color of the cable area, comprehensively depicting its appearance state. According to the extracted feature points, comprehensively judge whether the cable has typical defects such as deformation, bubbles, cracks, and color changes. This classification decision method based on multi-feature fusion can make full use of image information and improve the reliability of fault discrimination. By establishing a defect feature model library, it is possible to identify and classify unknown fault patterns, expanding the scope of diagnosis.

[0080] The following describes the system in the embodiments of the present invention application from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of the physical device of a charging pile cable fault detection system provided in the embodiments of the present application.

[0081] It should be noted that Figure 3 The structure of the system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0082] As Figure 3As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 302 or the program loaded from the storage section 308 into the Random Access Memory (RAM) 303, such as executing the method in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0083] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a Liquid Crystal Display (LCD), a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0084] Specifically, 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 contains a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the Central Processing Unit (CPU) 301, various functions defined in the present invention are executed.

[0085] It should be noted that the computer-readable medium shown in the embodiments of the present invention may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0087] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or may exist separately without being assembled into the system. The above storage medium carries one or more computer programs, and when the one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiments.

[0088] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0089] As used in the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0090] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc.

[0091] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disk, or optical disc that can store program codes.

Claims

1. A charging pile cable fault detection method, characterized in that: include: If it is determined that the charging pile issues a fault alarm, determine whether it is an external fault; If it is not the external fault, receiving the conductivity of both ends of the cable measured by the multimeter, and determining whether the conductivity is in a preset table; If the conductivity is not in the preset table, receiving an infrared image taken by an infrared thermal imager; Determine whether there is a high-risk heating point in the cable according to the infrared image, wherein the high-risk heating point is a position point in the infrared image where the temperature is higher than a preset temperature; If the high-risk heating point does not exist, receiving the cable resistance data group sent by the insulation resistance tester; Determining whether there is cable resistance data lower than a preset standard resistance value according to the cable resistance data group; If there is no cable resistance value lower than the preset standard resistance value, it is determined that there is no fault in the cable; If there is cable resistance data lower than the preset standard resistance value, determining that the fault type of the cable is a moisture fault; If the cable has the high-risk hot spot, determining that the fault type of the cable is a poor contact fault; If the conductivity is in the preset table, it is determined that the fault type of the cable is a conductor break fault.

2. The method according to claim 1, characterized in that When it is determined that the charging pile issues a fault alarm, determining whether it is an external fault specifically includes: obtaining a surveillance video containing the cable; Determining whether there is one or more of deformation, bubbles, cracks, and discoloration according to the monitoring video; If so, it is determined that an external fault occurs in the cable; If not, it is determined that the cable has not experienced the external fault.

3. The method according to claim 2, characterized in that Determining whether there is one or more of deformation, bubbles, cracks and discoloration according to the monitoring video specifically includes: Decomposing the surveillance video into a plurality of single-frame images; Performing denoising and brightness adjustment operations on each of the single-frame images to obtain a corresponding preprocessed single-frame image; Using an image segmentation algorithm to identify and segment the cable area in each of the pre-processed single-frame images; Performing a feature extraction operation on the cable area to obtain a number of feature points; It is determined whether there is one or more of deformation, bubbles, cracks and discoloration based on the characteristic points.

4. The method according to claim 1, characterized in that: After determining that the fault type of the cable is a conductor break fault, the method further includes: Controlling the time domain reflectometer to send a preset short pulse signal to the cable; Receiving a reflection signal detected by the time domain reflectometer; generating a reflection spectrum according to the reflection signal; The conductor break fault point is determined according to the reflection spectrum, and the conductor break fault point is sent to the control terminal.

5. The method according to claim 1, characterized in that After determining that the fault type of the cable is a poor contact fault if the cable has the high-risk heating point, the method further includes: The location point corresponding to the high-risk heating point is sent to the control terminal.

6. The method according to claim 1 or 2, characterized in that: After determining that the fault type of the cable is a moisture fault if there is cable resistance data lower than the preset standard resistance value, the method further includes: Determine the moisture affected point of the cable according to the monitoring video; The dampened point is sent to a control terminal.

7. The method according to claim 1, characterized in that In the case where it is determined that the charging pile issues a fault alarm, before determining whether it is an external fault, the method further includes: Obtain working environment parameters and power input of the charging pile, wherein the working environment parameters include temperature and humidity; When the working environment parameter is within the preset environment parameter range and the power input is within the preset power area, the step of determining whether it is an external fault when it is determined that the charging pile issues a fault alarm is performed.

8. A charging pile cable fault detection system, characterized in that: The system comprises: 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 cause the system to execute the method as described in any one of claims 1-7.

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

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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