Method, device and storage medium for determining leakage cable fault

Abstract

The application discloses a method and device for determining a leaky cable fault and a storage medium, and relates to the field of signal detection and processing. The method comprises the following steps: receiving a target echo signal and a reference signal of a target leaky cable; mixing each sub-echo signal in N sub-echo signals with the reference signal respectively to obtain N first signals; performing signal amplitude processing on the N first signals according to the signal propagation distance of each first signal and a preset distance threshold, taking the processed N first signals as N second signals, and performing noise filtering on the N second signals to obtain S target signals; and determining fault information of the target leaky cable according to the S target signals. The application solves the technical problem of low accuracy of leaky cable fault detection in a complex and high-noise environment.

Description

Technical Field

[0001] This application relates to the field of signal detection and processing, and more specifically, to a method, apparatus, and storage medium for determining a leaky cable fault. Background Technology

[0002] With the rapid development of communication technology and the continuous expansion of its application fields, leaky cables, as a critical communication infrastructure, play an increasingly important role in complex environments such as subways, tunnels, and mines. However, fault detection of leaky cables faces severe challenges when operating in these high-noise, high-reflection environments. Traditional detection techniques based on time-domain and frequency-domain reflection methods can locate fault points in leaky cables to a certain extent, but their accuracy and reliability are significantly reduced in complex, high-noise environments, making it difficult to meet the high requirements of stability and security in modern communication systems.

[0003] In complex environments, leaky cable fault detection is constrained by multiple factors. First, background noise, including electromagnetic interference and environmental noise, has an amplitude close to or even exceeding that of the leaky cable echo signal. This makes it difficult for traditional detection techniques to accurately extract effective signals from the noise, reducing the signal-to-noise ratio for fault location. Second, the severe attenuation of the signal due to long-distance propagation in the leaky cable makes it difficult to detect minute faults at distant points, especially at high frequencies, where signal attenuation is even more significant, further affecting the accuracy of detection.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method, apparatus, and storage medium for determining cable leakage faults, in order to at least solve the technical problem of low accuracy in cable leakage fault detection under complex and high-noise environments.

[0006] According to one aspect of this application, a method for determining a leaky cable fault is provided, comprising: receiving a target echo signal and a reference signal from a target leaky cable, wherein the target echo signal is a signal transmitted by a signal source into the target leaky cable and reflected by a fault point in the target leaky cable, the target echo signal including N sub-echo signals, where N is an integer greater than 1, and the reference signal is a signal transmitted by a signal source and not propagated through the target leaky cable; mixing each of the N sub-echo signals with the reference signal to obtain N first signals; performing signal amplitude processing on the N first signals according to the signal propagation distance of each first signal and a preset distance threshold, using the processed N first signals as N second signals, and performing noise filtering on the N second signals to obtain S target signals, where S is an integer less than or equal to N; and determining the fault information of the target leaky cable based on the S target signals.

[0007] Optionally, the signal amplitude of N first signals is processed according to the signal propagation distance of each first signal and a preset distance threshold. The processed N first signals are then used as N second signals, and noise is filtered on the N second signals to obtain S target signals. This includes: Step 1, determining the attenuated i-th first signal based on the signal propagation distance of the i-th first signal among the N first signals and the target model, where i is an integer greater than or equal to 1 and less than or equal to N, with an initial value of 1, and the target model is used to attenuate the signal amplitude of the i-th first signal; Step 2, determining a first gain value based on the signal propagation distance of the i-th first signal and a preset distance threshold, where the first gain value is used to compensate for the signal amplitude of the first signal during propagation. Step 3: Attenuate the amplitude of the first signal; Step 4: Process the amplitude of the attenuated first signal according to the first gain value to obtain the i-th second signal; Step 5: Determine the target noise threshold according to the first gain value, and filter the i-th second signal according to the target noise threshold, wherein the target noise threshold is used to distinguish whether the second signal is a noise signal and filter the noise signal; Step 6: When the amplitude of the i-th second signal is greater than or equal to the target noise threshold, the i-th second signal is used as the target signal; when the amplitude of the i-th second signal is less than the target noise threshold, the i-th second signal is filtered; Step 7: Increment i by 1, and repeat steps one to six until i equals N, to obtain S target signals.

[0008] Optionally, determining the first gain value based on the signal propagation distance of the i-th first signal and a preset distance threshold includes: determining the first gain value as 1 when the signal propagation distance of the i-th first signal is less than or equal to the preset distance threshold; and determining the first gain value based on the signal propagation distance of the i-th first signal, the target model, and the preset distance threshold when the signal propagation distance of the i-th first signal is greater than the preset distance threshold.

[0009] Optionally, after filtering the i-th second signal when the signal amplitude of the i-th second signal is less than the target noise threshold, the method further includes: determining the signal-to-noise ratio of the i-th second signal based on the target noise threshold and the i-th second signal; determining a target value based on the signal-to-noise ratio of the i-th second signal and a first gain value, wherein the target value is used to correct the first gain value of the (i+1)-th first signal.

[0010] Optionally, determining the target noise threshold based on the first gain value includes: acquiring noise data of the target leaky cable; determining a noise reference value based on the noise data, wherein the noise reference value is used to characterize the average noise intensity in the target leaky cable; and determining the target noise threshold based on the noise reference value, the target coefficient, and the first gain value, wherein the target coefficient is used to constrain the magnitude of the target noise threshold.

[0011] Optionally, after mixing each of the N sub-echo signals with a reference signal to obtain N first signals, the process includes: determining the frequency characteristics of each of the N first signals; determining the hardware delay corresponding to the sub-echo signal in each first signal based on the frequency characteristics of each first signal, wherein the hardware delay is used to characterize the time delay of the sub-echo signal during transmission; and correcting the propagation time of the sub-echo signal in each first signal based on the hardware delay corresponding to the sub-echo signal in each first signal.

[0012] Optionally, determining the fault information of the target leaky cable based on the S target signals includes: determining the spectral characteristics of each of the S target signals to obtain S spectral characteristics; and determining the fault information of the target leaky cable based on the S spectral characteristics.

[0013] Optionally, the fault information includes at least the fault location, which is determined by the following steps: determining S target frequencies based on S spectral characteristics, wherein the target frequencies are used to characterize the frequency difference between the reference signal and the sub-echo signal in each target signal; determining the propagation delay corresponding to each of the S target signals based on the S target frequencies; and determining the fault location of the target leaky cable based on the signal propagation speed and propagation delay corresponding to each of the S target signals.

[0014] According to another aspect of this application, a device for determining a leaky cable fault is also provided, comprising: a receiving unit for receiving a target echo signal and a reference signal of a target leaky cable, wherein the target echo signal is a signal emitted by a signal source into the target leaky cable and reflected by a fault point in the target leaky cable, the target echo signal includes N sub-echo signals, where N is an integer greater than 1, and the reference signal is a signal emitted by a signal source and does not propagate through the target leaky cable; a first processing unit for mixing each of the N sub-echo signals with the reference signal to obtain N first signals; a second processing unit for performing signal amplitude processing on the N first signals according to the signal propagation distance of each first signal and a preset distance threshold, using the processed N first signals as N second signals, and performing noise filtering on the N second signals to obtain S target signals, where S is an integer less than or equal to N; and a determining unit for determining the fault information of the target leaky cable based on the S target signals.

[0015] According to another aspect of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the above-described method for determining a leaky cable fault.

[0016] In this application, the target echo signal and reference signal of the target leaky cable are first received. The target echo signal is a signal emitted by a signal source into the target leaky cable and reflected by a fault point in the target leaky cable. The target echo signal includes N sub-echo signals, where N is an integer greater than 1. The reference signal is a signal emitted by the signal source and does not propagate through the target leaky cable. Next, each of the N sub-echo signals is mixed with the reference signal to obtain N first signals. Then, the signal amplitude of the N first signals is processed according to the signal propagation distance of each first signal and a preset distance threshold. The processed N first signals are used as N second signals, and noise filtering is performed on the N second signals to obtain S target signals, where S is an integer less than or equal to N. Finally, the fault information of the target leaky cable is determined based on the S target signals. That is, by adaptively adjusting the signal amplitude and filtering noise, the purpose of signal optimization processing is achieved, namely, signal enhancement and noise suppression, thereby improving the technical effect of leaky cable fault detection accuracy and solving the technical problem of low leaky cable fault detection accuracy in complex and high-noise environments. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a flowchart of an optional method for determining a leaky cable fault according to an embodiment of this application. Figure 1 ;

[0019] Figure 2 This is a schematic diagram of an optional method for determining a leaky cable fault according to an embodiment of this application;

[0020] Figure 3 This is a flowchart of an optional method for determining a leaky cable fault according to an embodiment of this application. Figure 2 ;

[0021] Figure 4 This is a schematic diagram of an optional leaky cable fault determination device according to an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0025] According to an embodiment of this application, a method embodiment for determining a leaky cable fault is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] It should be noted that an intelligent processing system can serve as the execution subject of the leaky cable fault determination method in the embodiments of this application. It is understood that the leaky cable fault determination method provided in the embodiments of this application can also be executed by other systems or devices, and the embodiments of this application do not specifically limit this.

[0027] Figure 1 This is a flowchart of an optional method for determining a leaky cable fault according to an embodiment of this application. Figure 1 ,like Figure 1 As shown, the method includes the following steps:

[0028] Step S101: Receive the target echo signal and reference signal from the target leaky cable.

[0029] In step S101, the target echo signal is a signal emitted by the signal source into the target leaky cable and reflected by the fault point in the target leaky cable. The target echo signal includes N sub-echo signals, where N is an integer greater than 1. The reference signal is a signal sent by the signal source and does not propagate through the target leaky cable.

[0030] Alternatively, the target leaky cable refers to a leaky cable used for signal transmission in a communication system.

[0031] Optionally, the target echo signal: When the signal emitted by the signal source encounters an impedance mismatch fault point (such as a joint, break, or damaged area) in the leaky cable, part of the signal is reflected back to the signal source. These reflected signals carry specific information about the fault point, including its location and type, and are key data for determining the fault.

[0032] Optionally, the reference signal is a signal directly transmitted by the signal source without propagating through the target leaky cable, used for correction and comparison to ensure the accuracy and reliability of signal processing.

[0033] Optionally, the signal emitted by the signal source in this application is an FMCW (Frequency Modulated Continuous Wave). When it encounters a fault point with impedance mismatch (such as an open circuit or loose connection), reflected echoes will be generated because the signal cannot continue to propagate. These echoes carry the location information of the fault point, and their amplitude and delay characteristics are determined by the fault type and distance. The amplitude of the echo signal depends on the reflection coefficient of the fault point (determined by the degree of impedance mismatch) and the attenuation during signal propagation. The delay time of the echo signal is proportional to the propagation distance of the fault point, and the calculation formula is as follows: Where v is the signal propagation speed and Δt is the round-trip time of the echo signal.

[0034] Optionally, the intelligent processing system can ensure that it simultaneously acquires the target echo signal pointing to the fault point and the reference signal used for error correction and signal quality reference by receiving the target echo signal and reference signal of the target leaky cable.

[0035] Step S102: Mix each of the N sub-echo signals with the reference signal to obtain N first signals.

[0036] Optionally, a sub-echo signal refers to the signal portion of the echo signal generated at different distance points (i.e., different fault points in the leaky cable). N represents the number of echo signals, and N sub-echo signals mean that there may be multiple fault points in the leaky cable.

[0037] Optionally, frequency mixing refers to the process of combining two signals of different frequencies to generate a new frequency signal. In this embodiment, the sub-echo signal is mixed with the reference signal to eliminate hardware delay errors and improve the accuracy of signal processing.

[0038] Optionally, the first signal is the signal obtained after frequency mixing, which includes the location and type information of the fault point, and also takes into account the delay introduced by the hardware.

[0039] Optionally, the intelligent processing system compares the echo signal with the reference signal through frequency mixing, which can provide a basis for eliminating the unknown delay introduced by the hardware device response, and at the same time provide more accurate signal data for subsequent signal amplitude processing and noise filtering.

[0040] Step S103: Perform signal amplitude processing on N first signals according to the signal propagation distance of each first signal and a preset distance threshold, take the processed N first signals as N second signals, and perform noise filtering on the N second signals to obtain S target signals.

[0041] In step S103, S is an integer less than or equal to N.

[0042] Optionally, the signal propagation distance refers to the physical distance from the transmission point to the fault point, which directly affects the degree of signal attenuation. The preset distance threshold is a distance value set by the system to distinguish whether the signal needs to be processed. When the signal propagation distance exceeds this threshold, the signal amplitude will be compensated.

[0043] Optionally, signal amplitude processing refers to dynamically adjusting the signal gain according to the signal propagation distance to compensate for signal attenuation during long-distance propagation, thereby ensuring the effectiveness and detection accuracy of long-distance signals.

[0044] Optionally, noise filtering: Based on the signal propagation distance and noise characteristics, a dynamic noise threshold is set to filter out signals below the noise threshold, thereby improving the signal-to-noise ratio and reducing false detections and missed detections.

[0045] Optionally, the second signal refers to the signal after signal amplitude processing, which theoretically contains more accurate fault point information and a higher signal-to-noise ratio; the target signal is the second signal after noise filtering. Only signals with sufficiently high amplitude, i.e., exceeding the set noise threshold, will be retained as the target signal for subsequent fault information determination.

[0046] Optionally, the intelligent processing system enhances the reliability and accuracy of the signal by dynamically adjusting the gain and filtering out noise, ensuring the system's detection capabilities in long-distance and complex environments.

[0047] Step S104: Determine the fault information of the target leaky cable based on the S target signals.

[0048] Optionally, the fault information includes, but is not limited to, the location, type, and severity of the fault point. It is the final output of this embodiment and is used to guide the maintenance and repair of leaky cables.

[0049] Optionally, based on the optimized target signal, the intelligent processing system can accurately identify fault points in the leaky cable, generate detailed fault reports, provide key information for the operation and maintenance of the communication system, and significantly improve maintenance efficiency and accuracy.

[0050] As can be seen from steps S101 to S104, in this application, firstly, the target echo signal and reference signal of the target leaky cable are received. The target echo signal is a signal emitted by a signal source into the target leaky cable and reflected by a fault point in the target leaky cable. The target echo signal includes N sub-echo signals, where N is an integer greater than 1. The reference signal is a signal emitted by the signal source and does not propagate through the target leaky cable. Next, each of the N sub-echo signals is mixed with the reference signal to obtain N first signals. Then, based on the signal propagation distance and pre-... A distance threshold is used to process the signal amplitude of N first signals. The processed N first signals are then used as N second signals, and noise filtering is applied to the N second signals to obtain S target signals, where S is an integer less than or equal to N. Finally, the fault information of the target leaky cable is determined based on the S target signals. That is, by adaptively adjusting the signal amplitude and filtering noise, the purpose of signal optimization processing is achieved, namely, signal enhancement and noise suppression. This improves the technical effect of leaky cable fault detection accuracy and solves the technical problem of low leaky cable fault detection accuracy in complex and high-noise environments.

[0051] In one optional embodiment, step one, the intelligent processing system determines the attenuated i-th first signal based on the signal propagation distance of the i-th first signal among N first signals and the target model, where i is an integer greater than or equal to 1 and less than or equal to N, the initial value of i is 1, and the target model is used to attenuate the signal amplitude of the i-th first signal; step two, a first gain value is determined based on the signal propagation distance of the i-th first signal and a preset distance threshold, where the first gain value is used to compensate for the attenuation of the signal amplitude of the first signal during propagation; step three, the signal amplitude of the attenuated i-th first signal is adjusted according to the first gain value. The process involves several steps: Step 1: Processing to obtain the i-th second signal; Step 4: Determining the target noise threshold based on the first gain value, and filtering the i-th second signal for noise based on the target noise threshold, whereby the target noise threshold is used to distinguish whether the second signal is a noise signal and to filter the noise signal; Step 5: When the signal amplitude of the i-th second signal is greater than or equal to the target noise threshold, the i-th second signal is taken as the target signal; when the signal amplitude of the i-th second signal is less than the target noise threshold, the i-th second signal is filtered; Step 6: Incrementing i by 1, repeating steps 1 to 6 until i equals N, to obtain S target signals.

[0052] Optionally, the intelligent processing system first receives each of the N first signals (i.e., the i-th first signal), where N represents the total number of received signals. The system uses a target model that incorporates the attenuation characteristics of signals propagating in a leaky cable to determine the actual attenuation of the i-th first signal reflected back from a fault point in the target leaky cable during propagation. The target model comprehensively considers factors such as the physical properties of the leaky cable (e.g., length, material, temperature) and signal frequency to accurately simulate the degree of signal attenuation, thereby calculating the signal amplitude of the attenuated i-th first signal. Next, the system compares the actual propagation distance of the i-th first signal with a preset distance threshold to determine whether signal amplitude compensation is needed. The preset distance threshold sets a distance point beyond which the signal will experience more significant attenuation, thus requiring a larger gain for compensation. The first gain value is calculated based on the signal propagation distance to ensure that even after long-distance transmission, the signal amplitude can be restored to a level sufficient for accurate detection.

[0053] Optionally, after determining the first gain value, the intelligent processing system performs signal amplitude processing on the attenuated i-th first signal, that is, uses the first gain value to perform gain compensation on the signal to obtain the i-th second signal. This process particularly emphasizes dynamic gain adjustment to ensure that fault information in the signal can still be effectively extracted even when the signal strength is extremely weakened.

[0054] Optionally, after signal amplitude processing, the system sets a target noise threshold based on a first gain value. The target noise threshold is dynamically adjusted, taking into account the signal propagation distance and the signal-to-noise ratio in the current environment. This threshold is used to distinguish between valid signals and background noise, ensuring that only true echo signals are retained for subsequent analysis.

[0055] Optionally, the intelligent processing system performs noise filtering by comparing the signal amplitude of each i-th second signal with a target noise threshold. If the signal amplitude is greater than or equal to the target noise threshold, the signal is considered valid and marked as a target signal, ready for fault location and analysis. Conversely, if the signal amplitude is lower than the target noise threshold, the system considers the signal a noise signal and filters it to prevent it from interfering with subsequent data analysis.

[0056] Optionally, the above steps are an iterative process, where the variable i starts from 1 and gradually increases to N (N is the total number of sub-echo signals), and the above processing is performed on each first signal (i.e., echo signal). After completing the loop for all values ​​of i, the system will collect S target signals that have undergone signal amplitude processing and noise filtering. These signals have high signal-to-noise ratio and accurate fault information, which are the main basis for fault location.

[0057] Alternatively, traditional leaky cable detection solutions typically require multiple deployments of hardware to cover the entire leaky cable length, increasing the complexity and cost of hardware deployment. This solution uses dynamic gain compensation to enable a single node to effectively detect long-distance leaky cables, reducing overall hardware deployment costs. Furthermore, by dynamically adjusting signal transmission power and gain compensation, continuous transmission of high-power signals is avoided, thereby reducing energy loss. In short, this solution significantly reduces hardware deployment and maintenance costs and minimizes system energy consumption through energy-saving design, making the system more efficient in detection.

[0058] As can be seen from the above, through this series of steps, the intelligent processing system significantly improves the accuracy and reliability of leaky cable fault detection. First, by accurately simulating signal attenuation using a target model, the scientific validity and effectiveness of signal amplitude processing are ensured. Second, the adaptive gain adjustment strategy combined with dynamic noise filtering successfully enhances the detection capability of long-distance signals, effectively eliminating environmental noise interference and greatly reducing the possibility of misjudgment and missed detection. Finally, the collected target signals are not only more numerous but also more reliable in quality, providing a solid foundation for subsequent fault location. This method is particularly suitable for leaky cable detection in complex, high-noise environments, significantly improving the detection effectiveness of existing technologies under these conditions. By precisely controlling signal amplitude and effectively filtering noise, the fault detection system achieves a qualitative leap in addressing the two major challenges of signal strength attenuation and noise interference, greatly improving detection accuracy and fault location efficiency.

[0059] In one optional embodiment, the intelligent processing system determines a first gain value of 1 when the signal propagation distance of the i-th first signal is less than or equal to a preset distance threshold; and determines a first gain value based on the signal propagation distance of the i-th first signal, the target model, and the preset distance threshold when the signal propagation distance of the i-th first signal is greater than the preset distance threshold.

[0060] Optionally, after receiving the i-th signal, the intelligent processing system first assesses whether its signal propagation distance is less than or equal to a preset distance threshold. This step is crucial because the signal propagation distance directly determines the degree of signal attenuation, significantly impacting subsequent gain adjustment and fault detection. If the signal propagation distance is less than or equal to the preset distance threshold, the system determines that the signal attenuation during transmission is within a controllable range, and no additional gain compensation is needed; the first gain value is set to 1 by default. This means the signal will proceed directly to subsequent processing without being affected by gain adjustment. Conversely, if the signal propagation distance exceeds the preset distance threshold, it indicates that the signal has experienced significant attenuation during long-distance transmission, requiring intervention from the intelligent processing system to calculate an appropriate gain compensation value based on the target model. The intelligent processing system uses the signal propagation distance as input, combined with the target model and the preset distance threshold, to calculate the first gain value to compensate for signal attenuation, ensuring that the signal can recover sufficient strength in subsequent signal amplitude processing.

[0061] As can be seen from the above, the intelligent processing system solves the detection problem caused by long-distance signal attenuation through dynamic gain control, ensuring high-precision signal recovery.

[0062] In one optional embodiment, the intelligent processing system determines the signal-to-noise ratio (SNR) of the i-th second signal based on the target noise threshold and the i-th second signal, and then determines a target value based on the SNR of the i-th second signal and a first gain value, wherein the target value is used to correct the first gain value of the (i+1)-th first signal.

[0063] Optionally, the intelligent processing system calculates the signal-to-noise ratio (SNR) of the i-th second signal by comparing its amplitude with a previously set target noise threshold. SNR is an important indicator of the ratio of effective signal to background noise; a higher SNR indicates better signal quality and higher detection accuracy.

[0064] Optionally, after obtaining the signal-to-noise ratio (SNR), the intelligent processing system analyzes the signal-to-noise ratio of the i-th second signal and the calculated first gain value. If the SNR is lower than expected, indicating poor signal quality, the system adjusts the gain correction value (target value) based on this information. The target value is determined by considering the current signal's SNR and the first gain value, guiding the gain adjustment strategy for the next echo signal (i.e., the (i+1)-th first signal). This dynamic adjustment process can be viewed as a self-calibration mechanism, intelligently predicting and correcting the gain compensation value of the next signal based on the current signal processing effect, aiming to achieve better signal quality and higher detection accuracy.

[0065] Optionally, once the target value is determined, the intelligent processing system applies this correction value to the processing of the next first signal. This means that the system adaptively adjusts the gain compensation for the (i+1)th first signal based on feedback information obtained from the i-th second signal to improve its signal-to-noise ratio. This closed-loop control strategy ensures that the signal processing can be continuously optimized, maintaining good detection performance even in environments with fluctuating signal quality.

[0066] As described above, the intelligent processing system continuously optimizes signal quality and improves the accuracy and reliability of leaky cable fault detection by dynamically adjusting signal gain and combining it with an intelligent feedback mechanism. By calculating the signal-to-noise ratio (SNR), the intelligent processing system can quantify the effectiveness of the signal and the degree of noise impact, and then adaptively adjust the gain to ensure that the signal maintains a high SNR even after long-distance propagation. This combination of dynamic gain adjustment and feedback control enables the system to automatically adjust its signal processing strategy in complex and ever-changing environments, improving its ability to detect leaky cable faults under long-distance and low SNR conditions. Compared to static gain compensation, the dynamic adjustment mechanism of this scheme can more effectively address signal attenuation and noise interference issues, thereby significantly improving the accuracy and efficiency of detection and providing strong technical support for the maintenance and troubleshooting of communication systems. This adaptive adjustment strategy not only improves signal quality but also enhances the overall robustness of the system, enabling it to maintain stable performance when facing various detection challenges.

[0067] In one optional embodiment, the intelligent processing system collects noise data of the target leaky cable, then determines a noise reference value based on the noise data, wherein the noise reference value is used to characterize the average noise intensity in the target leaky cable, and then determines a target noise threshold based on the noise reference value, the target coefficient, and a first gain value, wherein the target coefficient is used to constrain the magnitude of the target noise threshold.

[0068] Optionally, during the initial startup of the intelligent processing system, noise data of the target leaky cable environment is first collected. This process is achieved through built-in sensors or external noise measurement devices, aiming to collect background noise information in the environment where the target leaky cable is located, including but not limited to electromagnetic noise and thermal noise. The collection of noise data provides a basis for subsequent calculation of noise reference values, ensuring that the system can adapt to detection tasks in various complex environments.

[0069] Optionally, based on the collected noise data, the intelligent processing system calculates a noise reference value as a benchmark characterizing the average noise intensity in the target leaky cable. The noise reference value is determined by statistically analyzing the distribution of noise data to find the average noise intensity or a specific percentile, reflecting the most common noise level of the target leaky cable under normal operating conditions. This value plays a crucial role in subsequently setting the target noise threshold and is the core basis for the noise filtering strategy.

[0070] Optionally, after obtaining the noise reference value, the intelligent processing system determines the target noise threshold based on the noise reference value, the target coefficient, and the first gain value. The target coefficient is a pre-set parameter used to control the ratio of the target noise threshold to the noise reference value, ensuring that the target noise threshold is both sensitive and not overly stringent. The first gain value reflects the degree of signal amplitude enhancement in signal processing. Considering the noise amplification effect that may result from gain adjustment, the setting of the target noise threshold also needs to be comprehensively considered in conjunction with the first gain value to ensure that while the signal is enhanced, the noise signal is not misjudged as a valid signal.

[0071] Optionally, the intelligent processing system performs dynamic gain compensation and noise filtering on the received signal through the following steps:

[0072] 1) Initialization: Parameter settings: Set the signal propagation attenuation model parameters: attenuation coefficient α, gain threshold G max Initial gain value G init Set the dynamic adjustment step size ΔG for gradual gain correction. Set the distance threshold d. threshold This is used to distinguish whether gain compensation is performed. Initial state: Records the maximum value d of the signal propagation path length. max and minimum value d min For the region [d] min ,d max The initial amplitude distribution analysis is performed on the signal amplitude A(d) within the time interval.

[0073] 2) Dynamic gain calculation: Based on the signal propagation distance d and the attenuation model, calculate the attenuated signal amplitude A(d), as shown in formula (1):

[0074] A(d)=A0·e -αd (1)

[0075] Where A0 represents the initial signal amplitude of the transmitted signal.

[0076] Dynamically calculate the gain value G d As shown in formula (2):

[0077]

[0078] That is, when the propagation distance d is less than or equal to the set distance threshold d threshold At this time, the gain value is 1 (no gain compensation is performed at close range to prevent excessive signal amplification), and the propagation distance d is greater than the set distance threshold d. threshold At that time, the gain value is calculated based on the attenuation model, propagation distance, and set distance threshold.

[0079] 3) Signal enhancement: Apply dynamic gain to the signal amplitude A(d), as shown in formula (3):

[0080] A comp (d)=A(d)·G(d) (3)

[0081] Among them, A comp This represents the amplitude of the signal after gaining.

[0082] 4) Noise Analysis and Threshold Setting: Based on the preprocessed noise data, estimate the noise reference value N. ref As shown in formula (4):

[0083]

[0084] Where T represents the length of the time window, i.e. the signal time range considered when calculating the noise reference value, and N(t) represents the noise signal strength or noise power at time t.

[0085] The noise threshold is dynamically calculated as shown in formula (5):

[0086] T(d)=β·N ref ·G(d) (5)

[0087] Where β is the noise tolerance coefficient, which controls the sensitivity of the threshold.

[0088] 5) Dynamic adjustment: The signal-to-noise ratio (SNR) is calculated as shown in formula (6):

[0089]

[0090] Next, the gain correction value is dynamically adjusted according to the SNR, as shown in formula (7):

[0091] G new (d)=G(d)+ΔG·f(SNR) (7)

[0092] Where f(SNR) is used to control G new The value of (d) is used. When the SNR is low, ΔG is increased to boost the gain and enhance the noise reduction effect; when the SNR is high, ΔG is decreased to retain more signal details. This gain correction value affects the gain calculation of the next echo signal.

[0093] 6) Output Enhanced Signal: Outputs an enhanced and noise-filtered signal A. filtered (d), as shown in formula (8):

[0094]

[0095] Among them, A filtered(d) represents the signal amplitude after enhancement and noise filtering. If the amplitude of the enhanced signal is less than the noise threshold, the signal is filtered. If the amplitude of the enhanced signal is greater than or equal to the noise threshold, the current signal amplitude is retained and used as the final signal amplitude.

[0096] 7) Cyclic Update: Proceed to the next step of signal enhancement and adjustment processing, repeating the above steps until all echo signals have been processed.

[0097] Optionally, the following is a partial example of the relevant code:

[0098]

[0099]

[0100]

[0101]

[0102] Optionally, the intelligent processing system employs dynamic gain compensation to enhance long-distance signals, while simultaneously combining noise thresholding for effective filtering to improve fault detection accuracy. First, based on a signal propagation attenuation model, the signal amplitude at each distance is calculated, and the gain value is dynamically adjusted according to the signal propagation distance to ensure the amplitude of long-distance signals recovers to its initial level. Subsequently, combined with real-time noise analysis, interference signals below the noise threshold are filtered out through dynamic thresholding, further optimizing the signal-to-noise ratio (SNR). Furthermore, the algorithm adaptively adjusts the gain parameter based on the SNR, enabling the system to flexibly adapt to high-noise or complex environments, thereby achieving accurate location and detection of long-distance faults.

[0103] As described above, the intelligent processing system effectively improves the signal-to-noise ratio in signal processing by integrating noise data acquisition, noise reference value calculation, and dynamic target noise threshold setting, ensuring high accuracy and reliability of fault detection under complex conditions. The calculation of the noise reference value paves the way for noise identification and filtering, while the dynamic setting of the target noise threshold cleverly balances signal strength enhancement with the stringency of noise filtering, avoiding the risk of simultaneous enhancement of noise signals due to excessive signal amplification. This technical solution enables the intelligent processing system to accurately distinguish between valid and noise signals in complex environments, greatly improving the accuracy of fault location, reducing false alarms and missed alarms, and providing strong technical support for leaky cable maintenance in communication systems. Compared with solutions that use static threshold settings or ignore noise control, this technical solution demonstrates superior adaptability and robustness, especially in high-noise environments, significantly improving the performance of the detection system and ensuring the long-term stable operation of communication infrastructure.

[0104] In one optional embodiment, after the intelligent processing system performs frequency mixing processing on each of the N sub-echo signals with a reference signal to obtain N first signals, it first determines the frequency characteristics of each of the N first signals. Then, based on the frequency characteristics of each first signal, it determines the hardware delay corresponding to the sub-echo signal in each first signal. The hardware delay is used to characterize the time delay of the sub-echo signal during transmission. Finally, it corrects the propagation time of the sub-echo signal in each first signal based on the hardware delay corresponding to the sub-echo signal in each first signal.

[0105] Optionally, after receiving N first signals, the intelligent processing system first performs frequency analysis on each signal to determine its frequency characteristics. Frequency characteristics refer to the signal's behavior in the frequency domain, including the signal's center frequency, bandwidth, and frequency response. In this embodiment, the frequency characteristics are obtained by performing a Fast Fourier Transform on the first signals. This transform converts the signal in the time domain into a signal in the frequency domain, facilitating the analysis of the signal's frequency components and amplitude information. Through frequency characteristic analysis, the intelligent processing system can identify the frequency markers of the sub-echo signals contained in the signal; these markers are associated with the location and type of the fault point.

[0106] Optionally, based on the frequency characteristics of each first signal, the intelligent processing system further analyzes the hardware delay of the signal. Hardware delay refers to the additional time delay introduced by the system hardware (such as mixers, amplifiers, filters, etc.) during the signal's transmission to reception. In traditional single-channel detection, the unpredictability of hardware delay can cause a shift in the signal's time reference, thus affecting the accuracy of fault location. This embodiment, through a dual-channel receiving mechanism, can compare the frequency characteristics of the reference signal and the sub-echo signal to identify the phase difference or time difference between them; this difference is the hardware delay. Accurate measurement of hardware delay is fundamental to subsequent correction of signal propagation time and is crucial for improving the accuracy of fault location.

[0107] Optionally, after determining the hardware delay, the intelligent processing system corrects the propagation time of the sub-echo signal in each first signal. This is achieved by subtracting the hardware delay from the propagation time of the sub-echo signal, ensuring the true propagation time from signal transmission to reception of the echo. This correction process plays a decisive role in eliminating the time deviation caused by the hardware delay and improving the accuracy of fault location. By correcting the hardware delay, the intelligent processing system can more accurately measure the propagation speed of the signal in the leaky cable, thereby calculating the true location of the fault point and avoiding the location error caused by hardware delay in traditional detection methods.

[0108] As described above, this intelligent processing system employs a dual-channel design. One channel directly transmits a reference signal to correct hardware delay errors. Through mixing, the reference signal is compared with the echo signal, eliminating the unknown delay introduced by the hardware. This significantly improves the timing accuracy of fault location and effectively solves the problem of hardware delay-induced location errors in traditional leaky cable detection. Frequency characteristic analysis provides a basis for hardware delay identification, while accurate measurement and correction of hardware delay significantly improves fault location accuracy. Especially in long-distance leaky cable detection, this technology ensures accurate signal time reference, avoiding location deviations caused by differences in hardware response time. Compared to traditional detection techniques using a single channel, fixed gain compensation, and no hardware delay correction, the dual-channel correction mechanism proposed in this solution significantly improves fault location accuracy in complex environments and long-distance detection, reducing the number of on-site inspections by maintenance personnel and lowering maintenance costs. Furthermore, accurate fault location facilitates rapid repair, reduces communication interruption time and potential economic losses, and improves the overall operating efficiency and stability of the communication system.

[0109] In one alternative embodiment, the intelligent processing system determines the spectral characteristics of each of the S target signals to obtain S spectral characteristics, and then determines the fault information of the target leaky cable based on the S spectral characteristics.

[0110] Optionally, the intelligent processing system begins with the collected S target signals and performs spectral characteristic analysis on each one. Spectral characteristic analysis, based on Fast Fourier Transform (FFT) technology, converts the time-domain signal into a frequency-domain signal, thereby extracting the frequency components and amplitude distribution of the signal. This process aims to identify the main frequency components in each target signal, as these frequency components carry specific information about the leaky cable fault.

[0111] Optionally, after acquiring the spectral characteristics of the S target signals, the intelligent processing system uses this information to determine the fault information of the target leaky cable. The fault information includes the fault type (e.g., open circuit, short circuit, connector fault), the precise location of the fault point, and the possible severity of the fault. This analysis process relies on matching spectral characteristics with known fault modes; through comparison and identification, the intelligent processing system can accurately determine the health status of the leaky cable.

[0112] As described above, the intelligent processing system provides strong technical support for the accurate location and identification of leaky cable faults by deeply analyzing the spectral characteristics of the target signal. Spectral analysis technology can not only effectively extract useful information from the signal but also distinguish between different types of faults, improving the targeting and efficiency of detection. Furthermore, the fault information determination strategy based on spectral characteristics can more accurately assess the fault location and severity, reducing the workload of maintenance personnel in on-site troubleshooting, saving maintenance time, and lowering maintenance costs. Compared to traditional methods based solely on time-domain signal analysis, this solution demonstrates significant advantages in complex environments and long-distance detection, significantly improving the accuracy and reliability of fault detection and providing a more reliable guarantee for the maintenance and safe operation of communication infrastructure.

[0113] In one optional embodiment, the fault information includes at least the fault location, which is determined by the following steps: The intelligent processing system first determines S target frequencies based on S spectral characteristics, wherein the target frequencies are used to characterize the frequency difference between the reference signal and the sub-echo signal in each target signal; then, it determines the propagation delay corresponding to each of the S target signals based on the S target frequencies; and then, it determines the fault location of the target leaky cable based on the signal propagation speed and propagation delay corresponding to each of the S target signals.

[0114] Optionally, after receiving S target signals, the intelligent processing system first performs a Fast Fourier Transform (FFT) on each signal to transform it from the time domain to the frequency domain. In the frequency domain, the system determines the target frequency of each target signal by analyzing its spectral characteristics. The target frequency is the frequency difference between the reference signal and the sub-echo signal, reflecting the phase change of the signal during propagation, which is directly related to the signal's propagation delay. This step determines the target frequency by finding peaks in the spectrum, i.e., the maximum points in spectral analysis, because peak points often correspond to the main characteristic frequencies of the signal and are a direct reflection of the signal's phase change.

[0115] Optionally, based on S target frequencies, the intelligent processing system further determines the propagation delay corresponding to each target signal. Propagation delay refers to the total time it takes for a signal to travel from transmission to reception, and it is closely related to the actual physical distance between the fault point and the signal source. The principle of calculating propagation delay utilizes the relationship between the target frequency and the signal modulation slope, because the target frequency is essentially a function of the signal modulation slope and the propagation delay. By comparing the target frequency with a preset modulation slope, the system can calculate the actual propagation time of the signal in the leaky cable, and thus deduce the physical distance of signal propagation, providing key data points for determining the fault location.

[0116] Optionally, after obtaining the propagation delay, the intelligent processing system uses the product of the signal propagation speed and the propagation delay to determine the fault location on the target leaky cable. The signal propagation speed is usually estimated based on the speed of light in a medium, but it can also be fine-tuned according to the characteristics of the leaky cable.

[0117] Optionally, the fault location is calculated as follows:

[0118] Assume that the reference signal and the echo signal are represented as shown in equation (9) and equation (10), respectively:

[0119]

[0120] The reference signal s1(t) and the echo signal s2(t) are represented in the form of sinusoidal signals, and τ is the propagation delay.

[0121] According to formulas (9) and (10), the mixing signal can be obtained as shown in formulas (11)-(12):

[0122] s mix (t)=s1(t)·s2(t) (11)

[0123]

[0124] Wherein, the target frequency (the frequency of the difference frequency component) is f. beat =kτ, which is the key parameter used to calculate the propagation delay. This frequency value can be obtained using the Fast Fourier Transform. Then, the distance d (fault location) can be calculated using the distance formula, as shown in formula (13):

[0125]

[0126] Where v is the speed of signal propagation, τ is the propagation delay, and k is the frequency modulation slope, which is the rate at which the frequency changes with time during the frequency modulation process.

[0127] Optionally, Figure 2 This is a schematic diagram of an optional method for determining a leaky cable fault according to an embodiment of this application, as shown below. Figure 2As shown, the system includes: signal transceiver processing equipment, a data processing module, and a monitoring center. The signal transceiver processing equipment includes a signal transmitting module and a signal receiving module. The signal transmitting module generates a linear frequency modulated continuous wave (FMCW) signal and transmits it to the leaky cable via a dual-channel design. When the FMCW signal encounters a fault point in the leaky cable, it generates an echo. The signal receiving module captures the echo signal from the leaky cable and the reference signal received directly from the transmitting module. Then, a mixing module mixes the received echo signal with the transmitted signal to generate an intermediate frequency (IF) signal, which is then converted to obtain a frequency domain image. The data processing module includes an adaptive gain compensation module and a delay analysis module. The adaptive gain compensation module dynamically adjusts the gain based on the signal propagation distance to effectively enhance long-distance signals and performs filtering using a noise threshold algorithm. The delay analysis module extracts the delay information of the enhanced and filtered signal, calculates the fault location, and identifies the fault type. The monitoring center includes an analysis and display module, which receives relevant data transmitted by the data processing module in real time and further analyzes the data at the monitoring center to generate fault distribution maps and detection reports, forming a complete monitoring of the leaky cable health status. This avoids the blind spot problem existing in traditional single-end detection and improves detection coverage and overall efficiency.

[0128] Optionally, Figure 3 This is a flowchart of an optional method for determining a leaky cable fault according to an embodiment of this application. Figure 2 ,like Figure 3 As shown, the signal transmission module is first initialized, and the frequency range, power, and transmission direction are set. Then, FMCW signals are successively sent to both sides of the leaky cable. The receiving module then captures the echo signal, dynamically enhances the long-distance signal according to the signal propagation distance, and filters out noise. After that, the data processing module calculates the delay time of the echo signal, determines the location of the fault point by combining it with the signal propagation speed, and identifies the fault type (such as open circuit or looseness). Finally, the detection results are transmitted to the monitoring center via Ethernet, and the monitoring center further analyzes and generates a fault distribution map and detection report.

[0129] Optionally, the hardware options are as follows: the signal transmission and reception module can be the AD9361 module; the data processing module can be the Zedboard development board with an FPGA chip (Xilinx Zynq-7000 series); the network transmission module can be the Zedboard's network transmission module. The software categories are: embedded control program for configuring the AD9361 module and controlling signal transmission and reception; signal processing program running on the FPGA and PC; and monitoring center software using open-source programming software for the software interface, which includes a database.

[0130] As described above, the intelligent processing system achieves high-precision location of leaky cable faults through refined spectrum analysis, propagation delay calculation, and fault location determination. Determining the target frequency not only reveals the essence of signal phase changes but also provides direct parameters for subsequent propagation delay calculation. Accurate calculation of propagation delay ensures the precision of fault location, as propagation delay is directly related to the physical distance of signal propagation. Finally, by combining propagation delay with signal propagation speed, the system can transform the results of frequency domain analysis into the fault location in the time domain, achieving a leap from signal characteristics to actual physical location. Compared to traditional fault location methods, such as time-domain reflection, this technical solution has significant advantages in high-noise environments or long-distance detection, improving the reliability and accuracy of fault detection and reducing maintenance costs and time. Especially under conditions of signal attenuation and complex noise, the strategy combining dynamic gain compensation, noise filtering, and frequency domain analysis created in this invention ensures high accuracy in fault detection even in extreme environments, providing an efficient and accurate solution for the maintenance and management of communication infrastructure.

[0131] This application also provides a device for determining cable leakage faults. It should be noted that the device for determining cable leakage faults in this application can be used to execute the method for determining cable leakage faults provided in this application. The following describes the device for determining cable leakage faults provided in this application.

[0132] According to an embodiment of this application, an apparatus for implementing the above-described method for determining cable leakage faults is also provided. Figure 4 This is a schematic diagram of an optional leaky cable fault determination device according to an embodiment of this application, such as... Figure 4 As shown, the device includes: a receiving unit 401, a first processing unit 402, a second processing unit 403, and a determining unit 404.

[0133] Optionally, the receiving unit 401 is used to receive the target echo signal and reference signal of the target leaky cable, wherein the target echo signal is a signal emitted by a signal source into the target leaky cable and reflected by a fault point in the target leaky cable, and the target echo signal includes N sub-echo signals, where N is an integer greater than 1, and the reference signal is a signal sent by the signal source and does not propagate through the target leaky cable; the first processing unit 402 is used to perform frequency mixing processing on each of the N sub-echo signals and the reference signal respectively to obtain N first signals; the second processing unit 403 is used to perform signal amplitude processing on the N first signals according to the signal propagation distance of each first signal and a preset distance threshold, take the processed N first signals as N second signals, and perform noise filtering on the N second signals to obtain S target signals, where S is an integer less than or equal to N; the determining unit 404 is used to determine the fault information of the target leaky cable according to the S target signals.

[0134] Optionally, the second processing unit 403 includes: a first determining subunit, in step one, determining the attenuated i-th first signal based on the signal propagation distance of the i-th first signal among N first signals and a target model, wherein i is an integer greater than or equal to 1 and less than or equal to N, the initial value of i is 1, and the target model is used to attenuate the signal amplitude of the i-th first signal; a second determining subunit, in step two, determining a first gain value based on the signal propagation distance of the i-th first signal and a preset distance threshold, wherein the first gain value is used to compensate for the attenuation of the signal amplitude of the first signal during propagation; and a first processing subunit, in step three, performing signal amplitude calculation on the attenuated i-th first signal based on the first gain value. The first processing unit processes the signal to obtain the i-th second signal. The second processing unit, in step four, determines the target noise threshold based on the first gain value and performs noise filtering on the i-th second signal based on the target noise threshold. The target noise threshold is used to distinguish whether the second signal is a noise signal and to filter the noise signal. The third processing unit, in step five, uses the i-th second signal as the target signal when the signal amplitude of the i-th second signal is greater than or equal to the target noise threshold; and filters the i-th second signal when the signal amplitude of the i-th second signal is less than the target noise threshold. The third processing unit, in step six, increments i by 1 and repeats steps one through six until i equals N, obtaining S target signals.

[0135] Optionally, the second determining subunit includes: a first determining module and a second determining module. The first determining module is used to determine a first gain value of 1 when the signal propagation distance of the i-th first signal is less than or equal to a preset distance threshold; the second determining module is used to determine the first gain value based on the signal propagation distance of the i-th first signal, the target model, and the preset distance threshold when the signal propagation distance of the i-th first signal is greater than the preset distance threshold.

[0136] Optionally, the device for determining a leaky cable fault further includes a first determining unit and a second determining unit. The first determining unit is configured to determine the signal-to-noise ratio (SNR) of the i-th second signal based on a target noise threshold and the i-th second signal; the second determining unit is configured to determine a target value based on the SNR of the i-th second signal and a first gain value, wherein the target value is used to correct the first gain value of the (i+1)-th first signal.

[0137] Optionally, the third determining subunit includes: a first acquisition module, a third determining module, and a fourth determining module. The first acquisition module is used to acquire noise data of the target leaky cable; the third determining module is used to determine a noise reference value based on the noise data, wherein the noise reference value characterizes the average noise intensity in the target leaky cable; and the fourth determining module is used to determine a target noise threshold based on the noise reference value, a target coefficient, and a first gain value, wherein the target coefficient constrains the magnitude of the target noise threshold.

[0138] Optionally, the device for determining a leaky cable fault further includes: a third determining unit, a fourth determining unit, and a first correcting unit. The third determining unit is used to determine the frequency characteristics of each of the N first signals; the fourth determining unit is used to determine the hardware delay corresponding to the sub-echo signal in each first signal based on the frequency characteristics of each first signal, wherein the hardware delay characterizes the time delay of the sub-echo signal during transmission; and the first correcting unit is used to correct the propagation time of the sub-echo signal in each first signal based on the hardware delay corresponding to the sub-echo signal in each first signal.

[0139] Optionally, the determining unit 404 includes: a fourth determining subunit, used to determine the spectral characteristics of each of the S target signals to obtain S spectral characteristics; and a fifth determining subunit, used to determine the fault information of the target leaky cable based on the S spectral characteristics.

[0140] Optionally, the fifth determining subunit includes: a fifth determining module, a sixth determining module, and a seventh determining module. The fifth determining module is used to determine S target frequencies based on S spectral characteristics, where the target frequencies characterize the frequency difference between the reference signal and the sub-echo signal in each target signal; the sixth determining module is used to determine the propagation delay corresponding to each of the S target signals based on the S target frequencies; and the seventh determining module is used to determine the fault location of the target leaky cable based on the signal propagation speed and propagation delay corresponding to each of the S target signals.

[0141] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the above-described method for determining a leaky cable fault.

[0142] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0143] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0144] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0145] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0146] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0147] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0148] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining a leaky cable fault, characterized in that, include: Receive the target echo signal and reference signal of the target leaky cable, wherein the target echo signal is a signal emitted by a signal source into the target leaky cable and reflected by a fault point in the target leaky cable, the target echo signal includes N sub-echo signals, where N is an integer greater than 1, and the reference signal is a signal sent by the signal source and does not propagate through the target leaky cable. Each of the N sub-echo signals is mixed with the reference signal to obtain N first signals; Based on the signal propagation distance of each first signal and a preset distance threshold, the signal amplitude of N first signals is processed, and the processed N first signals are used as N second signals. Noise filtering is performed on the N second signals to obtain S target signals, where S is an integer less than or equal to N. The fault information of the target leaky cable is determined based on the S target signals.

2. The method for determining a leaky cable fault according to claim 1, characterized in that, Based on the signal propagation distance of each first signal and a preset distance threshold, the signal amplitude of N first signals is processed. The processed N first signals are then used as N second signals, and noise filtering is applied to the N second signals to obtain S target signals, including: Step 1: Determine the attenuated i-th first signal based on the signal propagation distance of the i-th first signal among N first signals and the target model, where i is an integer greater than or equal to 1 and less than or equal to N, and the initial value of i is 1. The target model is used to attenuate the signal amplitude of the i-th first signal. Step 2: Determine a first gain value based on the signal propagation distance of the i-th first signal and the preset distance threshold, wherein the first gain value is used to compensate for the attenuation of the signal amplitude of the first signal during propagation; Step 3: Perform the signal amplitude processing on the attenuated i-th first signal according to the first gain value to obtain the i-th second signal; Step 4: Determine the target noise threshold based on the first gain value, and perform noise filtering on the i-th second signal based on the target noise threshold, wherein the target noise threshold is used to distinguish whether the second signal is a noise signal and to filter the noise signal; Step 5: When the amplitude of the i-th second signal is greater than or equal to the target noise threshold, the i-th second signal is taken as the target signal; when the amplitude of the i-th second signal is less than the target noise threshold, the i-th second signal is filtered. Step six: Increment i by 1, and repeat steps one through six until i equals N, to obtain S target signals.

3. The method for determining a leaky cable fault according to claim 2, characterized in that, Determining a first gain value based on the signal propagation distance of the i-th first signal and the preset distance threshold includes: When the propagation distance of the i-th first signal is less than or equal to the preset distance threshold, the first gain value is determined to be 1; When the signal propagation distance of the i-th first signal is greater than the preset distance threshold, the first gain value is determined based on the signal propagation distance of the i-th first signal, the target model, and the preset distance threshold.

4. The method for determining a leaky cable fault according to claim 2, characterized in that, When the amplitude of the i-th second signal is less than the target noise threshold, after filtering the i-th second signal, the method further includes: The signal-to-noise ratio of the i-th second signal is determined based on the target noise threshold and the i-th second signal; A target value is determined based on the signal-to-noise ratio of the i-th second signal and the first gain value, wherein the target value is used to correct the first gain value of the (i+1)-th first signal.

5. The method for determining a leaky cable fault according to claim 2, characterized in that, Determining the target noise threshold based on the first gain value includes: Collect noise data of the target leaky cable; A noise reference value is determined based on the noise data, wherein the noise reference value is used to characterize the average noise intensity in the target leaky cable; The target noise threshold is determined based on the noise reference value, the target coefficient, and the first gain value, wherein the target coefficient is used to constrain the magnitude of the target noise threshold.

6. The method for determining a leaky cable fault according to claim 1, characterized in that, After mixing each of the N sub-echo signals with the reference signal to obtain N first signals, the process includes: The frequency characteristics of the first signal are determined based on each of the N first signals; The hardware delay corresponding to the sub-echo signal in each of the first signals is determined based on the frequency characteristics of each of the first signals, wherein the hardware delay is used to characterize the time delay of the sub-echo signal during transmission; The propagation time of the neutron echo signal in the first signal is corrected according to the hardware delay corresponding to each neutron echo signal in the first signal.

7. The method for determining a leaky cable fault according to claim 1, characterized in that, The fault information of the target leaky cable is determined based on S target signals, including: Determine the spectral characteristics of each of the S target signals to obtain S spectral characteristics; The fault information of the target leaky cable is determined based on the S spectral characteristics.

8. The method for determining a leaky cable fault according to claim 7, characterized in that, The fault information includes at least the fault location, which is determined through the following steps: S target frequencies are determined based on the S spectral characteristics, wherein the target frequencies are used to characterize the frequency difference between the reference signal and the sub-echo signal in each target signal; The propagation delay corresponding to each of the S target signals is determined based on the S target frequencies; The fault location of the target leaky cable is determined based on the signal propagation speed and propagation delay of each of the S target signals.

9. A device for determining a leaky cable fault, characterized in that, include: The receiving unit receives the target echo signal and reference signal of the target leaky cable. The target echo signal is a signal emitted by a signal source into the target leaky cable and reflected by a fault point in the target leaky cable. The target echo signal includes N sub-echo signals, where N is an integer greater than 1. The reference signal is a signal sent by the signal source and does not propagate through the target leaky cable. The first processing unit performs frequency mixing processing on each of the N sub-echo signals with the reference signal to obtain N first signals; The second processing unit performs signal amplitude processing on N first signals based on the signal propagation distance of each first signal and a preset distance threshold, takes the processed N first signals as N second signals, and performs noise filtering on the N second signals to obtain S target signals, where S is an integer less than or equal to N; The determining unit determines the fault information of the target leaky cable based on the S target signals.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed, the device in which the computer-readable storage medium is located performs the method for determining a leaky cable fault as described in any one of claims 1 to 8.

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