A detection method for flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing

Abstract

This invention relates to the fields of electrical engineering and intelligent detection technology, and discloses a detection method for flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing. The scheme sets an excitation end and a response end in a one-dimensional coordinate system, outputting a constant-amplitude single-frequency reference excitation; the response end collects voltage and trims the range according to a given sampling frequency, interval, and number of points; samples are screened using a zero-drift threshold, empty sets are marked as invalid, and for non-empty sets, the standing wave ratio (SWR) is calculated and compared with the reference difference to obtain the SWR deviation; a Hilbert transform is performed on the trimmed sequence to generate an analytical signal and instantaneous phase; the phase difference is expanded across cycles point by point, the average is taken, and compared with the theoretical step size to obtain the phase deviation; the phase deviation is normalized and combined with the SWR deviation to form a joint index; a threshold is set based on the statistical mean and maximum value of healthy reference samples; each cycle is judged, recorded, and reported in a graded manner according to the joint index and threshold.

Description

Technical Field

[0001] This invention relates to the fields of electrical engineering and intelligent testing technology, specifically to a testing method for flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing. Background Technology

[0002] Flame-retardant, low-smoke, halogen-free coaxial cables are widely used in scenarios with high safety and reliability requirements, such as power transmission, rail transportation, data centers, and smart buildings, due to their excellent flame-retardant properties and environmentally friendly characteristics. In these complex electrical systems, online monitoring of cable conditions and early fault warning are crucial for preventing electrical fires and improving system stability. However, current health monitoring of coaxial cables still faces several challenges.

[0003] First, most existing coaxial cable inspection methods rely on periodic manual inspections or centralized management of terminal equipment. These methods suffer from low inspection frequency, limited coverage, significant manual intervention, and slow response. Common techniques include infrared thermal imaging, cable current detection, insulation resistance testing, and offline testing based on instruments such as TDR (Time Domain Reflectometry). These methods either require on-site operation by professionals or can only detect obvious faults, making it difficult to achieve continuous monitoring of early, minor defects. Furthermore, systems based on centralized data acquisition and analysis are susceptible to network latency, data loss, and computational bottlenecks in large-scale application scenarios, making it difficult to meet the real-time and reliability requirements of edge computing environments. Second, current mainstream cable fault diagnosis relies heavily on preset empirical criteria or traditional signal processing algorithms, making it difficult to dynamically and adaptively identify cable damage characteristics under complex and changing operating conditions. Especially in multipath environments with strong electromagnetic interference, signal distortion is severe, and existing models are prone to misjudgment and omission, resulting in insufficient accuracy and robustness in anomaly detection. Furthermore, many detection solutions focus on low-frequency electrical parameters or terminal status detection, lacking in-depth exploration and utilization of high-frequency radio frequency signal characteristics, standing wave response, reflection coefficient, and other physical mechanisms. This fails to fully reflect subtle changes in the internal structural damage, connection anomalies, or latent aging of the cable itself. In terms of data processing and intelligence, traditional centralized processing solutions suffer from drawbacks such as high data transmission pressure, weak real-time decision-making capabilities, and high security risks when facing a large number of multi-point distributed monitoring nodes. These shortcomings make it difficult to meet the urgent needs of industries such as power grids and rail transit for localized, autonomous early warning and multi-level response. Existing edge intelligence applications are relatively rudimentary, struggling to simultaneously achieve efficient data compression, feature extraction, and multi-scale anomaly identification capabilities, thus affecting on-site response speed and system self-healing capabilities.

[0004] Therefore, this case aims to propose a detection method for flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing. It utilizes a single-frequency sinusoidal excitation signal to generate a stable reference, acquires voltage data through high-precision discrete sampling, and constructs a joint anomaly index by combining multiple indicators such as VSWR and phase information. Under healthy conditions, thresholds are set through statistical analysis of multiple reference period samples. Finally, local judgment and anomaly reporting are completed on the edge computing node, reducing the burden of data transmission and central computing. Summary of the Invention

[0005] This invention provides a method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing, which helps to solve the problems mentioned in the background art.

[0006] This invention provides the following technical solution: a method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing, comprising: Establish a one-dimensional coordinate system and set the excitation and response ends. Output a single-frequency sinusoidal reference excitation, configure the amplitude and frequency, and output at the beginning of each detection cycle and keep it stable. Voltage is acquired at the response end, sampling frequency and sampling time interval are set, number of sampling points is set, sampling time and voltage sample sequence are generated, and upper and lower limits are applied to the samples that exceed the range to form a clipped voltage sequence. In the current detection cycle, the maximum and minimum absolute values ​​of the cut voltage samples are obtained, and they are filtered according to the zero drift rejection threshold. If the set is empty, the sampling is marked as invalid. If the set is not empty, the voltage standing wave ratio is calculated and the difference between the voltage standing wave ratio and the reference voltage standing wave ratio is used to obtain the standing wave deviation index. The Hilbert transform is applied to the cut voltage sequence to obtain the imaginary part. The cut voltage sequence is then combined with the real and imaginary parts to form an analytic signal sequence. The instantaneous phase sequence is generated based on the relationship between the real and imaginary parts. The phase difference is calculated point by point and expanded across cycles. The average phase change is taken and compared with the theoretical phase step to obtain the phase deviation index. The dimensionless phase deviation ratio is obtained by normalizing the phase deviation index according to the theoretical phase step size, and then combined with the standing wave deviation index to form a joint anomaly index. During the reference detection period under healthy reference status, the average and maximum values ​​of the combined abnormal indicators are calculated, and a combined judgment threshold is set. For each detection cycle, a status flag is generated based on the joint anomaly index and the joint judgment threshold. During normal detection cycles, the flag is written to the local log and stored in non-volatile storage medium. During anomaly detection cycles, a reporting data unit is generated, and during invalid sampling cycles, an explanation is recorded.

[0007] Optionally, the step of establishing a one-dimensional coordinate system and setting the excitation and response ends, outputting a single-frequency sinusoidal reference excitation, configuring the amplitude and frequency, and outputting at the beginning of each detection cycle and maintaining stability specifically includes: Establish a one-dimensional coordinate system along the axis of the coaxial cable, and set the origin of the coordinate system at any ground center point of the cable and mark it as the excitation end; A sampling node is set at the other end of the cable and marked as the response end. The total length of the cable is recorded for endpoint identification. Select a single-frequency sine wave as the reference excitation, and set the amplitude and frequency parameters respectively, ensuring that the frequency parameter is positive; The reference excitation output is activated at the beginning of each detection cycle and remains stable for the predetermined duration of the current detection cycle. After completing the configuration of coordinate system establishment, response end settings, reference excitation parameter settings, and output startup, the sampling and judgment process begins.

[0008] Optionally, the step of acquiring voltage at the response end, setting the sampling frequency and sampling time interval, setting the number of sampling points, generating a sampling time and voltage sample sequence, and performing upper and lower limit pruning on over-range samples to form a pruned voltage sequence specifically includes: At the response end, an RF probe is used to collect voltage signals, and the sampling frequency and corresponding sampling time interval are set synchronously. Within a single detection cycle, the number of discrete sampling points shall be set, and the number shall not be less than two. The total sampling duration for a single period is calculated as the product of the number of sampling points and the sampling time interval. Starting from the beginning of the current detection period, the sampling time interval is sequentially added according to the sampling point index to generate a sampling time sequence. At each sampling time, voltage samples at the response terminal are acquired to form a voltage sample sequence; For each sample, upper and lower limit clipping is performed to restrict the sample values ​​to the rated range of the measurement channel, forming a clipped voltage sequence.

[0009] Optionally, the step of obtaining the maximum and minimum absolute values ​​of the cut voltage samples in the current detection cycle, filtering them according to the zero drift rejection threshold, marking empty sets as invalid samples, and calculating the voltage standing wave ratio (VSWR) for non-empty sets and subtracting it from the reference voltage VSWR to obtain the VSWR deviation index specifically includes: Calculate the maximum absolute value of the shear voltage sample within the current detection cycle; Set the zero-drift rejection threshold to a positive value; Construct a set of valid sampling points, where the absolute value of the samples in the set is greater than the zero drift removal threshold; When the set of valid sampling points is empty, the current detection period is marked as invalid sampling, and the edge computing node records a log that the sampling amplitude is lower than the zero drift rejection threshold. When the set of valid sampling points is not empty, calculate the minimum absolute value of the cut-off voltage within the set of valid sampling points; The voltage standing wave ratio (VSWR) for the current detection cycle is the ratio of the maximum absolute value to the minimum absolute value. The difference between the voltage standing wave ratio (VSWR) of the current detection cycle and the reference voltage VSWR is taken as the absolute value, which is recorded as the VSWR deviation index of the current detection cycle.

[0010] Optionally, the step of performing a Hilbert transform on the clipped voltage sequence to obtain the imaginary part, combining the real and imaginary parts of the clipped voltage sequence to form an analytic signal sequence, and generating an instantaneous phase sequence based on the relationship between the real and imaginary parts, specifically includes: A discrete finite-point Hilbert transform is applied to the cut voltage sequence to generate an imaginary part sequence; The cut voltage sequence is used as the real part and combined with the imaginary part sequence to construct an analytic signal sequence in complex form; When the real part of the analytical signal is not zero, the intermediate phase quantity is calculated according to the arctangent and quadrant criteria. When the real part of the analytical signal is zero, the positive half-cycle is taken according to the positive value of the imaginary part, the negative half-cycle is taken according to the negative value of the imaginary part, and the zero value is taken according to the zero imaginary part. The intermediate phase value calculated based on the real and imaginary parts is combined with the phase value determined based on the sign of the imaginary part to generate the instantaneous phase sequence for each sampling point.

[0011] Optionally, the step of calculating the phase difference point by point and expanding it across cycles, taking the average phase change, and comparing it with the theoretical phase step size to obtain the phase deviation index specifically includes: Calculate the original phase difference between adjacent sampling points one by one according to the sampling order; Perform cross-cycle expansion on each raw phase difference and limit it to the principal value interval to form a normalized phase difference; The arithmetic mean of the normalized phase difference is taken to obtain the average phase change in the current detection cycle. The theoretical phase step size is calculated based on the frequency parameters of the reference excitation and the sampling time interval. The absolute value of the difference between the average phase change and the theoretical phase step is taken as the phase deviation index for the current detection cycle.

[0012] Optionally, the step of normalizing the phase deviation index according to the theoretical phase step size to obtain the dimensionless phase deviation ratio, and synthesizing it with the standing wave deviation index to form a joint anomaly index, specifically includes: The phase deviation index is normalized according to the theoretical phase step size to obtain the dimensionless phase deviation ratio. The standing wave deviation index and the dimensionless phase deviation ratio are arithmetically summed to form a joint anomaly index.

[0013] Optionally, the step of collecting reference detection data during a health reference state, calculating the average and maximum values ​​of the joint abnormal indicators, and setting a joint judgment threshold specifically includes: Several reference testing cycles were collected and numbered under the cable health reference state; For each reference detection cycle, the excitation output, response end sampling, voltage standing wave ratio and standing wave deviation index calculation, phase increment average calculation and phase deviation index calculation, and joint anomaly index calculation are completed sequentially to obtain a reference joint anomaly index sample. The arithmetic mean of the reference joint anomaly index sample is calculated to obtain the average level. Obtain the maximum value in the reference joint anomaly index sample; The arithmetic mean of the average level and the maximum value is taken as the joint judgment threshold.

[0014] Optionally, for each detection cycle, a status flag is generated based on the joint anomaly index and the joint judgment threshold. During normal detection cycles, the flag is written to a local log and stored in a non-volatile storage medium. During anomaly detection cycles, a reporting data unit is generated. Invalid sampling cycles are recorded with explanations, specifically including: On the edge computing node side, a periodic data item is generated for each detection cycle, which includes the cycle number, cycle start time and sampling validity status; When the sampling is valid, write the standing wave deviation index, dimensionless phase deviation ratio and joint anomaly index into the periodic data item; A status flag is generated based on the joint anomaly index and the joint judgment threshold. If the value does not exceed the joint judgment threshold, it is set to normal; if the value exceeds the joint judgment threshold, it is set to abnormal. For normal detection cycles, the cycle number, standing wave deviation index, dimensionless phase deviation ratio, joint anomaly index and cycle start time are written to the local log and stored in non-volatile storage medium without triggering uplink communication. For the anomaly detection cycle, write the cycle number, status mark, standing wave deviation index, dimensionless phase deviation ratio, joint anomaly index and cycle start time, construct the reporting data unit and send it to the management system through the preset communication link; For invalid sampling periods, write the period number, invalid sampling flag, the maximum absolute value of the clipped voltage sample for the invalid sampling period, and a statement that all clipped samples do not exceed the zero drift rejection threshold.

[0015] The present invention has the following beneficial effects:

[0016] 1. In this scheme, a one-dimensional coordinate system is constructed at the excitation end, corresponding the physical endpoints of the cable to the signal source numbers, and outputting a continuous and stable single-frequency sine wave as the reference excitation. First, single-frequency excitation simplifies the design of the signal source and filter, reducing hardware costs and maintenance difficulty. Second, the fixed frequency and constant amplitude excitation helps ensure the stability of subsequent signal processing and feature extraction, reducing fluctuations caused by environmental and hardware drift. Numbering the excitation and response ends separately and registering them in the coordinate system ensures that any subsequent position changes or connection errors can be detected through number verification, thereby guaranteeing the traceability and calibrability of the measurement system. Compared with the extensive deployment method in existing technologies that only rely on signal source output and channel connection, this scheme's deployment strategy has higher field deployment consistency and maintenance controllability, effectively reducing errors and failure rates, while providing a solid foundation for subsequent data calibration and comparison.

[0017] 2. This scheme not only performs high-frequency discrete sampling of the voltage signal at the response end, but also introduces a time-series mapping between the sampling time and the voltage sample, and implements upper and lower limit pruning at the hardware level to eliminate channel overload and outliers. By precisely setting the sampling frequency and sampling time interval, and ensuring that the number of sampling points is no less than two, both the time domain resolution requirements are met and redundant data is avoided. Upper and lower limit pruning of over-range samples can filter out voltage fluctuations caused by hardware or interference in a timely manner, providing higher-quality data input for subsequent algorithms. This design solves the problem of algorithm crashes caused by hardware limiting or sudden spikes in traditional sampling methods. Compared with existing methods that rely solely on single-channel sampling or do not perform pruning preprocessing, this scheme, while ensuring data integrity, achieves local filtering of outliers through pruning, reduces backend computation and storage pressure, and improves the accuracy of edge detection.

[0018] 3. The solution calculates the maximum and minimum absolute values ​​of the trimmed voltage samples to construct the voltage standing wave ratio (VSWR), and subtracts it from the factory-calibrated reference VSWR to obtain the VSWR deviation index. Using the ratio of the minimum to the maximum absolute values ​​better reflects the peak reflected wave caused by impedance mismatch than traditional mean or variance. By differencing with the reference VSWR, the influence of absolute amplitude drift on the judgment is eliminated, achieving relative deviation detection. This method can accurately capture impedance changes caused by cable aging and local damage, solving the problem of simple amplitude monitoring being sensitive to temperature and load fluctuations. Compared with traditional complex algorithms based on spectrum or time-domain reflection wave analysis, this solution's VSWR differential calculation is simpler, more real-time, suitable for running on edge devices, and has a higher false detection rate and lower false alarm rate.

[0019] 4. After applying a finite-point discrete Hilbert transform to the cut voltage sequence, the real part (voltage sequence) and the imaginary part are combined to generate an analytic signal sequence, from which the instantaneous phase sequence is obtained. This processing method can expand single amplitude information into phase information, enabling the capture of subtle phase changes in the cable. Compared to traditional methods that only analyze amplitude in the time domain or spectrum, this analytic signal method can reveal weak phase distortions, providing a highly sensitive input for subsequent phase deviation detection. This innovation solves the problem of difficulty in distinguishing between faults and noise due to amplitude fluctuations in complex environments, compensating for the blind spot of amplitude detection through phase information. Compared to existing fault detection technologies that focus on amplitude, this solution introduces a new dimension through complex signal processing, improving the detection system's ability to warn of early, minor damage.

[0020] 5. This scheme calculates the adjacent phase difference point by point in the instantaneous phase sequence, performs cross-cycle expansion correction, and then averages all phase differences to obtain the average phase change. This average change is then differentially analyzed with the theoretical phase step size to extract the phase deviation index. Cross-cycle expansion eliminates discontinuity errors caused by phase jumps, and the average value suppresses interference from individual sampling point anomalies. Compared with traditional one-time phase difference calculations or FFT phase analysis, the average phase increment method is more robust and has higher noise tolerance. It effectively solves the problem of detection distortion caused by phase signal folding and jumps in high-noise industrial environments, enabling edge nodes to stably obtain phase deviation data representing the overall trend. Compared with existing methods that rely on high-performance computing platforms for phase unpacking, this scheme can complete phase expansion and averaging calculations on edge devices, meeting real-time requirements.

[0021] 6. This scheme normalizes the phase deviation index to a dimensionless ratio based on the theoretical phase step size, and adds it to the standing wave deviation index to form a joint anomaly index, achieving multi-index fusion. Normalization makes phase and amplitude deviations comparable under the same dimension; the simple addition fusion strategy reduces computational complexity while ensuring simultaneous response to both distortion features. This method solves the problem of difficulty in comprehensive judgment due to the separation of amplitude and phase features, and achieves a unified measurement of multi-dimensional fault indications. Compared with existing techniques that require complex weight allocation or multi-model fusion, this scheme simplifies model training and deployment, improves edge computing efficiency, and demonstrates detection accuracy comparable to traditional deep learning models in experiments, while also being more interpretable.

[0022] 7. This solution innovates in parameter setting by collecting samples from multiple reference detection cycles under healthy conditions, calculating the average and maximum values ​​of the joint abnormal indicators, and then taking their arithmetic mean as the joint judgment threshold. This method avoids subjective experience-based threshold setting or complex statistical distribution fitting, allowing the threshold to adapt to the actual operating conditions of the equipment. By balancing the average and maximum values, it takes into account both typical and extreme health fluctuations, ensuring that the judgment threshold is neither too stringent, leading to missed detections, nor too lenient, leading to false alarms. This innovation solves the problem of traditional threshold experience-based setting or one-time calibration gradually becoming ineffective during long-term operation, providing a dynamic and updatable threshold framework. Compared with existing methods that only set the average value or a fixed multiple standard deviation, this solution's dual-value balancing strategy effectively controls the false alarm rate to a low level in field testing and adapts to equipment aging and environmental changes.

[0023] 8. This solution completes joint anomaly indicator judgment and status marker generation at the edge computing node, and distinguishes between three periods: normal, abnormal, and invalid sampling, for local log writing and targeted data reporting. Local log writing and non-volatile storage ensure complete and traceable data for each period; reporting data units are generated only for abnormal periods, effectively saving communication bandwidth; and invalid sampling periods are specifically recorded and explained to ensure diagnostic accuracy. This mode solves the network burden and central storage pressure brought by the transmission of all raw data in traditional cloud monitoring systems, and can continue to complete local decision-making even when the network is interrupted or delayed, improving the reliability and real-time performance of the system. Compared with existing centralized collection and batch uploading methods, this solution reduces the communication volume between the edge and the cloud, speeds up fault alarms, and meets the stringent requirements of industrial sites for network outage self-diagnosis and data traceability. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the process of the present invention.

[0025] Figure 2 This is a schematic diagram of the one-dimensional coordinate system structure of the present invention.

[0026] In the figure: 1-coaxial cable to be tested, 2-origin, 3-one-dimensional coordinate system. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example, refer to Figure 1 A method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing, comprising: Establish a one-dimensional coordinate system and set the excitation and response ends. Output a single-frequency sinusoidal reference excitation, configure the amplitude and frequency, and output at the beginning of each detection cycle and keep it stable. Voltage is acquired at the response end, sampling frequency and sampling time interval are set, number of sampling points is set, sampling time and voltage sample sequence are generated, and upper and lower limits are applied to the samples that exceed the range to form a clipped voltage sequence. In the current detection cycle, the maximum and minimum absolute values ​​of the cut voltage samples are obtained, and they are filtered according to the zero drift rejection threshold. If the set is empty, the sampling is marked as invalid. If the set is not empty, the voltage standing wave ratio is calculated and the difference between the voltage standing wave ratio and the reference voltage standing wave ratio is used to obtain the standing wave deviation index. The Hilbert transform is applied to the cut voltage sequence to obtain the imaginary part. The cut voltage sequence is then combined with the real and imaginary parts to form an analytic signal sequence. The instantaneous phase sequence is generated based on the relationship between the real and imaginary parts. The phase difference is calculated point by point and expanded across cycles. The average phase change is taken and compared with the theoretical phase step to obtain the phase deviation index. The dimensionless phase deviation ratio is obtained by normalizing the phase deviation index according to the theoretical phase step size, and then combined with the standing wave deviation index to form a joint anomaly index. During the reference detection period under healthy reference status, the average and maximum values ​​of the combined abnormal indicators are calculated, and a combined judgment threshold is set. For each detection cycle, a status flag is generated based on the joint anomaly index and the joint judgment threshold. During normal detection cycles, the flag is written to the local log and stored in non-volatile storage medium. During anomaly detection cycles, a reporting data unit is generated, and during invalid sampling cycles, an explanation is recorded.

[0029] By establishing a one-dimensional coordinate system and setting excitation and response ends at both ends of the coaxial cable, the detection system establishes a clear and traceable positioning basis for the physical layout and signal source of the cable under test, solving the problem of measurement inconsistency caused by endpoint confusion in traditional detection deployments. By outputting a single-frequency sinusoidal reference excitation and maintaining a constant amplitude and stable frequency, the excitation signal source for the cable is made repeatable and stable, avoiding the drawbacks of spectral interference and high complexity of subsequent signal processing caused by multi-frequency or random excitation. By setting the sampling frequency and sampling time interval for the high-speed discrete voltage samples collected at the response end and implementing upper and lower limit clipping for samples exceeding the range, the sampled data can reflect the real voltage fluctuations and automatically filter hardware overload and noise mutations, solving the problems of weak anti-interference capability and susceptibility to algorithm abnormalities caused by overloaded samples in traditional high-precision measurements. By calculating the maximum and minimum absolute values ​​of the clipped samples and differentiating them with the reference standing wave ratio, the standing wave deviation index is directly obtained. This step simplifies the signal feature extraction process and overcomes the limitations of traditional methods based on... The spectral analysis method suffers from high computational cost and high latency. By performing Hilbert transform on the cropped voltage sequence and generating analytical signal and instantaneous phase sequences, a leap from single amplitude values ​​to complex domain phase characteristics is achieved, solving the blind spot of relying solely on amplitude information to distinguish minute local faults. By performing cross-cycle expansion and averaging of the phase difference, a stable phase deviation index is obtained, overcoming the problems of complexity and noise sensitivity of traditional phase unpacking algorithms. By normalizing the phase deviation index and fusing it with the standing wave deviation index to construct a joint anomaly index, multi-feature and multi-dimensional fault indication is achieved, solving the problems of low accuracy and high false alarm rate of single feature judgment. By collecting multiple periodic samples under healthy reference conditions and statistically setting thresholds, the judgment threshold is made adaptive to the field conditions, avoiding the problem of easy failure of empirical static thresholds. Finally, by using edge computing nodes for local judgment, status recording, and classification reporting, real-time detection, bandwidth saving, and network outage self-diagnosis are achieved, solving the drawbacks of centralized platforms that are heavily dependent on the network and have poor timeliness.

[0030] Reference Figure 2 The establishment of a one-dimensional coordinate system and setting of the excitation and response ends, outputting a single-frequency sinusoidal reference excitation, configuring the amplitude and frequency, and outputting at the beginning of each detection cycle and maintaining stability specifically includes: Establish a one-dimensional coordinate system along the axis of the coaxial cable, and set the origin of the coordinate system at any ground center point of the cable and mark it as the excitation end; A sampling node is set at the other end of the cable and marked as the response end. The total length of the cable is recorded for endpoint identification. Select a single-frequency sine wave as the reference excitation, and set the amplitude and frequency parameters respectively, ensuring that the frequency parameter is positive; The reference excitation output is activated at the beginning of each detection cycle and remains stable for the predetermined duration of the current detection cycle. After completing the configuration of coordinate system establishment, response end settings, reference excitation parameter settings, and output startup, the sampling and judgment process begins.

[0031] Further specific implementation steps include: Take a section of the coaxial cable to be tested, and establish a one-dimensional coordinate system with the axis of the coaxial cable as the origin, set at the center point of any bottom surface of the coaxial cable, denoted as . ; The coaxial cable under test The excitation signal generation module is connected at point E, and is numbered as excitation terminal E. Set up a sampling node, numbered as response terminal R; where L is the total length of the coaxial cable to be tested; The excitation signal is set to a single-frequency sine wave, specifically: ;in, The reference RF voltage signal applied to the excitation port at a continuous time t; t is a continuous time variable; The amplitude of the reference excitation signal; The frequency of the reference excitation signal; The phase angle of the excitation signal at time t; Keep the excitation module output signal at Continuously stable; among them, This represents the start time of the kth detection cycle; k is the detection cycle number. The duration for which the excitation signal remains output during each detection cycle.

[0032] The process of acquiring voltage at the response end, setting the sampling frequency and sampling time interval, setting the number of sampling points, generating a sampling time and voltage sample sequence, and performing upper and lower limit pruning on over-range samples to form a pruned voltage sequence specifically includes: At the response end, an RF probe is used to collect voltage signals, and the sampling frequency and corresponding sampling time interval are set synchronously. Within a single detection cycle, the number of discrete sampling points shall be set, and the number shall not be less than two. The total sampling duration for a single period is calculated as the product of the number of sampling points and the sampling time interval. Starting from the beginning of the current detection period, the sampling time interval is sequentially added according to the sampling point index to generate a sampling time sequence. At each sampling time, voltage samples at the response terminal are acquired to form a voltage sample sequence; For each sample, upper and lower limit clipping is performed to restrict the sample values ​​to the rated range of the measurement channel, forming a clipped voltage sequence.

[0033] Further specific implementation steps include: At the response end R, an RF probe is used to accurately acquire the received voltage signal. Set the sampling frequency to The corresponding sampling time interval is ;in, The instantaneous voltage signal measured at the response terminal R at a continuous time t; The predetermined number of discrete sampling points within a detection cycle is set as follows: ,and The total sampling time is calculated as follows: Sampling time is , ;in, The total sampling time length within a single detection cycle; n is the sampling point index; The continuous time points corresponding to the nth sampling point; Constructing the response sampling sequence: ;in, To be at the sampling time The voltage sample at the response terminal measured at the location; The function value represents the voltage signal at the response terminal at continuous time intervals. The value of ; By limiting each sample value to the rated range of the measurement module, the clipped voltage sequence is obtained: ;in, To the original voltage sample Voltage value after upper and lower limit clipping; This is the upper limit of the absolute voltage value that the sampling channel can reliably measure.

[0034] The process involves obtaining the maximum and minimum absolute values ​​of the cut voltage samples in the current detection cycle, filtering them according to the zero-drift rejection threshold, marking empty sets as invalid samples, and calculating the voltage standing wave ratio (VSWR) for non-empty sets and subtracting it from the reference VSWR to obtain the VSWR deviation index. Specifically, this includes: Calculate the maximum absolute value of the shear voltage sample within the current detection cycle; Set the zero-drift rejection threshold to a positive value; Construct a set of valid sampling points, where the absolute value of the samples in the set is greater than the zero drift removal threshold; When the set of valid sampling points is empty, the current detection period is marked as invalid sampling, and the edge computing node records a log that the sampling amplitude is lower than the zero drift rejection threshold. When the set of valid sampling points is not empty, calculate the minimum absolute value of the cut-off voltage within the set of valid sampling points; The voltage standing wave ratio (VSWR) for the current detection cycle is the ratio of the maximum absolute value to the minimum absolute value. The difference between the voltage standing wave ratio (VSWR) of the current detection cycle and the reference voltage VSWR is taken as the absolute value, which is recorded as the VSWR deviation index of the current detection cycle.

[0035] Further specific implementation steps include: Calculate the maximum absolute value of all shear voltage samples within the current detection period. ; Set the zero-drift rejection threshold to ,and ; First, construct a set of valid sampling point indices. Specifically: ; when When it is an empty set, that is, there is no set that satisfies If n is less than or equal to n, then the detection period is considered an invalid sampling period, and the current period is directly marked as invalid. Log information for sampling amplitudes below the effective threshold is only recorded at edge nodes. when When the value is not empty, calculate the smallest non-zero absolute value: ;in, To cut off the minimum absolute value of the voltage among all valid sampling points; Calculate the voltage standing wave ratio (VSWR) in the kth detection period. ; Standing wave ratio with reference voltage By performing subtraction, the standing wave deviation index is extracted, and the result is obtained. ;in, The reference voltage standing wave ratio (VSWR) is measured when the cable is calibrated at the factory. is the standing wave deviation index for the k-th detection cycle.

[0036] The process of performing a Hilbert transform on the clipped voltage sequence to obtain the imaginary part, combining the real and imaginary parts of the clipped voltage sequence to form an analytic signal sequence, and generating an instantaneous phase sequence based on the relationship between the real and imaginary parts, specifically includes: A discrete finite-point Hilbert transform is applied to the cut voltage sequence to generate an imaginary part sequence; The cut voltage sequence is used as the real part and combined with the imaginary part sequence to construct an analytic signal sequence in complex form; When the real part of the analytical signal is not zero, the intermediate phase quantity is calculated according to the arctangent and quadrant criteria. When the real part of the analytical signal is zero, the positive half-cycle is taken according to the positive value of the imaginary part, the negative half-cycle is taken according to the negative value of the imaginary part, and the zero value is taken according to the zero imaginary part. The intermediate phase value calculated based on the real and imaginary parts is combined with the phase value determined based on the sign of the imaginary part to generate the instantaneous phase sequence for each sampling point.

[0037] Further specific implementation steps include: The cropped sampling sequence Perform a finite-point Hilbert transform to compute the imaginary part of the sequence: , ;in, To cut voltage sequences The imaginary part value corresponding to the nth sample obtained by performing the discrete Hilbert transform; m represents the sampling point index used for summation in the Hilbert transform, which is an integer dummy variable; Constructing analytic signals in complex form: ;in, This is the analytic signal of the nth sampling point; The imaginary unit satisfies ; The instantaneous phase angle at each moment is calculated as follows: S401, when At that time, construct intermediate phase quantity for: ;in, For the nth sampling point, and Under these conditions, the intermediate phase quantity is calculated based on the real and imaginary parts of the analytic signal; S402, when At that time, construct intermediate phase quantity for: ;in, For the nth sampling point, and Intermediate phase quantity constructed under the given conditions; The final instantaneous phase angle is obtained as follows: , ;in, Let be the instantaneous phase angle of the nth sampling point.

[0038] The step of calculating the phase difference point by point and expanding across cycles, taking the average phase change, and comparing it with the theoretical phase step size to obtain the phase deviation index, specifically includes: Calculate the original phase difference between adjacent sampling points one by one according to the sampling order; Perform cross-cycle expansion on each raw phase difference and limit it to the principal value interval to form a normalized phase difference; The arithmetic mean of the normalized phase difference is taken to obtain the average phase change in the current detection cycle. The theoretical phase step size is calculated based on the frequency parameters of the reference excitation and the sampling time interval. The absolute value of the difference between the average phase change and the theoretical phase step is taken as the phase deviation index for the current detection cycle.

[0039] Further specific implementation steps include: Calculate the original phase difference between adjacent sampling points point by point: , ;in, This represents the original phase difference between the nth and (n+1)th sampling points; Each original phase difference value is normalized to obtain the normalized phase difference value: ;in, This represents the normalized phase difference between the nth and (n+1)th sampling points after phase transition correction. The average phase difference across the entire sampling sequence is taken as the average phase change for that period, specifically: ;in, It is the average value of the normalized phase difference between all adjacent sampling points in the k-th detection period; Calculate the theoretical phase step size: ;in, To ensure that the response signal is strictly frequency Discrete sampling interval under pure sine wave The corresponding theoretical phase change; Calculate the phase deviation index for the kth detection period. .

[0040] The phase deviation index, normalized according to the theoretical phase step size, yields a dimensionless phase deviation ratio, which is then combined with the standing wave deviation index to form a joint anomaly index. Specifically, this includes: The phase deviation index is normalized according to the theoretical phase step size to obtain the dimensionless phase deviation ratio. The standing wave deviation index and the dimensionless phase deviation ratio are arithmetically summed to form a joint anomaly index.

[0041] Further specific implementation steps include: The phase deviation is normalized to construct a dimensionless phase deviation ratio, specifically: ;in, The normalized phase deviation ratio for the k-th detection cycle; A unified joint anomaly indicator function is constructed, specifically as follows: ;in, This represents the joint abnormality index for the k-th detection period.

[0042] The process of collecting reference detection data during a healthy reference state, calculating the average and maximum values ​​of the joint abnormal indicators, and setting a joint judgment threshold specifically includes: Several reference testing cycles were collected and numbered under the cable health reference state; For each reference detection cycle, the excitation output, response end sampling, voltage standing wave ratio and standing wave deviation index calculation, phase increment average calculation and phase deviation index calculation, and joint anomaly index calculation are completed sequentially to obtain a reference joint anomaly index sample. The arithmetic mean of the reference joint anomaly index sample is calculated to obtain the average level. Obtain the maximum value in the reference joint anomaly index sample; The arithmetic mean of the average level and the maximum value is taken as the joint judgment threshold.

[0043] Further specific implementation steps include: When the cable is in a completely healthy state, M reference testing cycles are collected, and the testing cycle numbers are k=1,2,...,M; where M is the total number of reference testing cycles collected when the cable is in a healthy state. For each reference period, the excitation signal output, response terminal sampling, voltage standing wave ratio calculation, phase increment and deviation calculation, and joint anomaly index calculation are performed sequentially to obtain the corresponding joint index. ;in, The combined abnormal index value for the kth detection cycle under a healthy reference state; Calculate the arithmetic mean of the joint abnormality indicators of the healthy reference sample. Specifically: ; Calculate the maximum value of the joint abnormality index in the healthy reference sample. Specifically: ; A joint decision threshold is constructed based on the average level of the reference sample and the upper bound of the worst health state. Specifically: .

[0044] The process involves generating a status flag for each detection cycle based on a joint anomaly index and a joint judgment threshold. During normal detection cycles, the flag is written to a local log and stored in non-volatile storage. During anomaly detection cycles, a reporting data unit is generated. Invalid sampling cycles are recorded with explanations, specifically including: On the edge computing node side, a periodic data item is generated for each detection cycle, which includes the cycle number, cycle start time and sampling validity status; When the sampling is valid, write the standing wave deviation index, dimensionless phase deviation ratio and joint anomaly index into the periodic data item; A status flag is generated based on the joint anomaly index and the joint judgment threshold. If the value does not exceed the joint judgment threshold, it is set to normal; if the value exceeds the joint judgment threshold, it is set to abnormal. For normal detection cycles, the cycle number, standing wave deviation index, dimensionless phase deviation ratio, joint anomaly index and cycle start time are written to the local log and stored in non-volatile storage medium without triggering uplink communication. For the anomaly detection cycle, write the cycle number, status mark, standing wave deviation index, dimensionless phase deviation ratio, joint anomaly index and cycle start time, construct the reporting data unit and send it to the management system through the preset communication link; For invalid sampling periods, write the period number, invalid sampling flag, the maximum absolute value of the clipped voltage sample for the invalid sampling period, and a statement that all clipped samples do not exceed the zero drift rejection threshold.

[0045] Further specific implementation steps include: On the edge computing node side, for each detection cycle, after data acquisition and feature calculation are completed in that cycle, the cycle data item includes: cycle number k; cycle start time. ; Sampling validity status: valid sampling or invalid sampling; When sampling is valid, the corresponding standing wave deviation Phase deviation normalization ratio and joint abnormal indicators ; For valid detection periods, edge nodes rely on joint anomaly indicators. Joint Judgment Threshold Perform state determination and generate state flags, specifically as follows: when When, set ; when When, set ; in, This is the state marker for the k-th detection period. This indicates that the k-th detection period is considered normal. This indicates that the k-th detection period is considered abnormal; For sampling valid and satisfying During a normal cycle, edge nodes perform the following local data recording operations, including: S801, Write the period number k and standing wave deviation into the local log. Phase deviation normalization ratio Joint abnormal indicators and the start time of the cycle ; S802. Store the above records in the local non-volatile storage medium of the edge node without triggering any uplink communication process; For sampling valid and satisfying During abnormal periods, edge nodes perform the following operations, including: S803, Write the cycle number k and status flag to the local log. Standing wave deviation Phase deviation normalization ratio Joint abnormal indicators and the start time of the cycle ; S804. Construct a reporting data unit, which includes at least the period number k and a status flag. Standing wave deviation Phase deviation normalization ratio Joint abnormal indicators and the start time of the cycle ; S805. The reporting data unit is sent to the management system through a preset communication link; For detection cycles marked as "invalid sampling" during the zero-drift removal process, the edge nodes perform the following operations, including: S806. Write the period number k, the sampling invalid flag, and the corresponding period to the local log. and all samples No more than Status description.

[0046] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0047] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing, characterized in that, include: Establish a one-dimensional coordinate system and set the excitation and response ends. Output a single-frequency sinusoidal reference excitation, configure the amplitude and frequency, and output at the beginning of each detection cycle and keep it stable. Voltage is acquired at the response end, sampling frequency and sampling time interval are set, number of sampling points is set, sampling time and voltage sample sequence are generated, and upper and lower limits are applied to the samples that exceed the range to form a clipped voltage sequence. In the current detection cycle, the maximum and minimum absolute values ​​of the cut voltage samples are obtained. These samples are then filtered according to the zero-drift rejection threshold. Empty sets are marked as invalid samples. For non-empty sets, the voltage standing wave ratio (VSWR) is calculated and subtracted from the reference VSWR to obtain the VSWR deviation index, which specifically includes: Calculate the maximum absolute value of the shear voltage sample within the current detection cycle; Set the zero-drift rejection threshold to a positive value; Construct a set of valid sampling points, where the absolute value of the samples in the set is greater than the zero drift removal threshold; When the set of valid sampling points is empty, the current detection period is marked as invalid sampling, and the edge computing node records a log that the sampling amplitude is lower than the zero drift rejection threshold. When the set of valid sampling points is not empty, calculate the minimum absolute value of the cut-off voltage within the set of valid sampling points; The voltage standing wave ratio (VSWR) for the current detection cycle is the ratio of the maximum absolute value to the minimum absolute value. The difference between the voltage standing wave ratio of the current detection cycle and the reference voltage standing wave ratio is taken as the absolute value and recorded as the standing wave deviation index of the current detection cycle. The Hilbert transform is applied to the cut voltage sequence to obtain the imaginary part. The cut voltage sequence is then combined with the real and imaginary parts to form an analytic signal sequence. The instantaneous phase sequence is generated based on the relationship between the real and imaginary parts. The phase difference is calculated point by point and expanded across cycles. The average phase change is taken and compared with the theoretical phase step to obtain the phase deviation index. The dimensionless phase deviation ratio is obtained by normalizing the phase deviation index according to the theoretical phase step size, and then combined with the standing wave deviation index to form a joint anomaly index. During the reference detection period under healthy reference status, the average and maximum values ​​of the combined abnormal indicators are calculated, and a combined judgment threshold is set. For each detection cycle, a status flag is generated based on the joint anomaly index and the joint judgment threshold. During normal detection cycles, the flag is written to the local log and stored in non-volatile storage medium. During anomaly detection cycles, a reporting data unit is generated, and during invalid sampling cycles, an explanation is recorded.

2. The method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing according to claim 1, characterized in that, The process of establishing a one-dimensional coordinate system and setting the excitation and response ends, outputting a single-frequency sinusoidal reference excitation, configuring the amplitude and frequency, and outputting at the beginning of each detection cycle and maintaining stability specifically includes: Establish a one-dimensional coordinate system along the axis of the coaxial cable, and set the origin of the coordinate system at any ground center point of the cable and mark it as the excitation end; A sampling node is set at the other end of the cable and marked as the response end. The total length of the cable is recorded for endpoint identification. Select a single-frequency sine wave as the reference excitation, and set the amplitude and frequency parameters respectively, ensuring that the frequency parameter is positive; The reference excitation output is activated at the beginning of each detection cycle and remains stable for the predetermined duration of the current detection cycle. After completing the configuration of coordinate system establishment, response end settings, reference excitation parameter settings, and output startup, the sampling and judgment process begins.

3. The method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing according to claim 2, characterized in that, The process of acquiring voltage at the response end, setting the sampling frequency and sampling time interval, setting the number of sampling points, generating a sampling time and voltage sample sequence, and performing upper and lower limit pruning on over-range samples to form a pruned voltage sequence specifically includes: At the response end, an RF probe is used to collect voltage signals, and the sampling frequency and corresponding sampling time interval are set synchronously. Within a single detection cycle, the number of discrete sampling points shall be set, and the number shall not be less than two. The total sampling duration for a single period is calculated as the product of the number of sampling points and the sampling time interval. Starting from the beginning of the current detection period, the sampling time interval is sequentially added according to the sampling point index to generate a sampling time sequence. At each sampling time, voltage samples at the response terminal are acquired to form a voltage sample sequence; For each sample, upper and lower limit clipping is performed to restrict the sample values ​​to the rated range of the measurement channel, forming a clipped voltage sequence.

4. The method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing according to claim 3, characterized in that, The process of performing a Hilbert transform on the clipped voltage sequence to obtain the imaginary part, combining the real and imaginary parts of the clipped voltage sequence to form an analytic signal sequence, and generating an instantaneous phase sequence based on the relationship between the real and imaginary parts, specifically includes: A discrete finite-point Hilbert transform is applied to the cut voltage sequence to generate an imaginary part sequence; The cut voltage sequence is used as the real part and combined with the imaginary part sequence to construct an analytic signal sequence in complex form; When the real part of the analytical signal is not zero, the intermediate phase quantity is calculated according to the arctangent and quadrant criteria. When the real part of the analytical signal is zero, the positive half-cycle is taken according to the positive value of the imaginary part, the negative half-cycle is taken according to the negative value of the imaginary part, and the zero value is taken according to the zero imaginary part. The intermediate phase value calculated based on the real and imaginary parts is combined with the phase value determined based on the sign of the imaginary part to generate the instantaneous phase sequence for each sampling point.

5. The method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing according to claim 4, characterized in that, The step of calculating the phase difference point by point and expanding across cycles, taking the average phase change, and comparing it with the theoretical phase step size to obtain the phase deviation index, specifically includes: Calculate the original phase difference between adjacent sampling points one by one according to the sampling order; Perform cross-cycle expansion on each raw phase difference and limit it to the principal value interval to form a normalized phase difference; The arithmetic mean of the normalized phase difference is taken to obtain the average phase change in the current detection cycle. The theoretical phase step size is calculated based on the frequency parameters of the reference excitation and the sampling time interval. The absolute value of the difference between the average phase change and the theoretical phase step is taken as the phase deviation index for the current detection cycle.

6. The method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing according to claim 5, characterized in that, The phase deviation index, normalized according to the theoretical phase step size, yields a dimensionless phase deviation ratio, which is then combined with the standing wave deviation index to form a joint anomaly index. Specifically, this includes: The phase deviation index is normalized according to the theoretical phase step size to obtain the dimensionless phase deviation ratio. The standing wave deviation index and the dimensionless phase deviation ratio are arithmetically summed to form a joint anomaly index.

7. The method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing according to claim 6, characterized in that, The process of collecting reference detection data during a healthy reference state, calculating the average and maximum values ​​of the joint abnormal indicators, and setting a joint judgment threshold specifically includes: Several reference testing cycles were collected and numbered under the cable health reference state; For each reference detection cycle, the excitation output, response end sampling, voltage standing wave ratio and standing wave deviation index calculation, phase increment average calculation and phase deviation index calculation, and joint anomaly index calculation are completed sequentially to obtain a reference joint anomaly index sample. The arithmetic mean of the reference joint anomaly index sample is calculated to obtain the average level. Obtain the maximum value in the reference joint anomaly index sample; The arithmetic mean of the average level and the maximum value is taken as the joint judgment threshold.

8. The method for detecting flame-retardant, low-smoke, halogen-free coaxial cables based on edge computing according to claim 7, characterized in that, The process involves generating a status flag for each detection cycle based on a joint anomaly index and a joint judgment threshold. During normal detection cycles, the flag is written to a local log and stored in non-volatile storage. During anomaly detection cycles, a reporting data unit is generated. Invalid sampling cycles are recorded with explanations, specifically including: On the edge computing node side, a periodic data item is generated for each detection cycle, which includes the cycle number, cycle start time and sampling validity status; When the sampling is valid, write the standing wave deviation index, dimensionless phase deviation ratio and joint anomaly index into the periodic data item; A status flag is generated based on the joint anomaly index and the joint judgment threshold. If the value does not exceed the joint judgment threshold, it is set to normal; if the value exceeds the joint judgment threshold, it is set to abnormal. For normal detection cycles, the cycle number, standing wave deviation index, dimensionless phase deviation ratio, joint anomaly index and cycle start time are written to the local log and stored in non-volatile storage medium without triggering uplink communication. For the anomaly detection cycle, write the cycle number, status mark, standing wave deviation index, dimensionless phase deviation ratio, joint anomaly index and cycle start time, construct the reporting data unit and send it to the management system through the preset communication link; For invalid sampling periods, write the period number, invalid sampling flag, the maximum absolute value of the clipped voltage sample for the invalid sampling period, and a statement that all clipped samples do not exceed the zero drift rejection threshold.

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