A high-speed cable electromagnetic interference signal millimeter wave transmission method and system

Abstract

This invention relates to the technical field of high-speed cable monitoring, and provides a method and system for millimeter-wave transmission of electromagnetic interference signals in high-speed cables. The method includes: continuously monitoring reflected signals inside the cable and processing these signals to obtain reflected signal parameters; analyzing the reflected signals and comparing the reflected signal parameters with signal patterns corresponding to multiple preset physical degradation types to determine the physical degradation type inside the cable, obtaining a physical degradation type determination result; determining the location of the physical degradation point based on the time difference of multiple reflected signals, obtaining the physical degradation location; and assessing the cable's operating status and issuing a fault warning based on the physical degradation type determination result, the physical degradation location, and the changing trend of the reflected signal parameters. This invention improves the efficiency and safety of cable maintenance.

Description

Technical Field

[0001] This invention relates to the technical field of high-speed cable monitoring, and specifically to a method and system for millimeter-wave transmission of electromagnetic interference signals in high-speed cables. Background Technology

[0002] In the deployment of 5G and IoT sensing terminals, high-speed cables serve as crucial interconnection carriers, undertaking the transmission of ultra-wideband signals in the millimeter-wave band. The transmission of these high-frequency signals places extremely high demands on the physical integrity of the cables and the electromagnetic environment. However, in complex industrial environments such as smart manufacturing plants, high-speed cables are exposed to environmental stresses such as mechanical vibration and thermal cycling for extended periods, leading to microscopic physical degradation within the cables and at connector interfaces that is difficult to detect with the naked eye. This includes fatigue cracks in the insulation medium, conductor interface peeling, or fretting wear at connector contact points.

[0003] These microscopic physical degradations create local impedance discontinuities along the cable transmission path, causing some high-frequency electromagnetic energy to leak into the external space of the cable as weak electromagnetic waves, resulting in signal transmission mode distortion. This electromagnetic leakage and mode distortion do not occur continuously and stably, but rather exhibit intermittent, random, or condition-dependent characteristics. More challengingly, smart manufacturing plants themselves are electromagnetic noise sources, with numerous motors, frequency converters, and other equipment radiating broadband electromagnetic interference. When high-speed cables experience intermittent electromagnetic leakage due to internal microscopic degradation, these leakage signals are often very weak, and their spectral characteristics may overlap with existing background electromagnetic interference in the factory environment.

[0004] This leads to a serious diagnostic challenge: traditional electromagnetic compatibility (EMC) monitoring equipment typically treats all received electromagnetic energy as background noise, making it difficult to distinguish between interference from the external environment and weak leakage signals caused by internal cable degradation. Leakage signals are submerged in strong industrial background noise, becoming difficult to identify and trace, making it impossible for maintenance personnel to determine whether the problem is caused by external environmental deterioration or by a failure in the cable itself. This difficult-to-diagnose intermittent signal degradation severely impacts the factory's real-time control system, causing unpredictable performance declines and even production line shutdowns. The dilemma faced by maintenance teams is that they observe system performance degradation but cannot quickly and accurately pinpoint the specific cable and location of the type of micro-degradation.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] This application discloses a millimeter-wave transmission method and system for electromagnetic interference signals in high-speed cables, aiming to solve the technical problem that intermittent electromagnetic leakage signals caused by the internal microscopic physical degradation of high-speed cables are difficult to be effectively identified and tracked in complex industrial background noise, thus making it impossible to accurately assess the cable's operating status and provide timely early warning of faults.

[0007] The technical solution of this application is as follows: In a first aspect, this application discloses a method for millimeter-wave transmission of electromagnetic interference signals over high-speed cables, specifically including: Multiple millimeter-wave micro-sensors are deployed on the cable; the millimeter-wave micro-sensors are used to capture reflected signals inside the cable; the reflected signals are millimeter-wave reflected signals generated after impedance discontinuities are formed at physical degradation points inside the cable under the excitation of continuous wave detection signals; The reflected signals inside the cable are continuously monitored using millimeter-wave micro sensors, and the reflected signals are processed to obtain the reflected signal parameters, including arrival time, reflection amplitude, pulse width, resonant frequency, resonant intensity, quality factor, and bandwidth. The reflected signal is analyzed, and the reflected signal parameters are compared with the signal patterns corresponding to various preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type determination result. The location of the physical degradation point is determined by the time difference of multiple reflected signals, thus obtaining the physical degradation location; Based on the results of the physical degradation type assessment, the location of physical degradation, and the changing trends of reflected signal parameters, the cable operating status is evaluated and a fault warning is issued.

[0008] This technical solution enables early and accurate identification and location of microscopic physical degradation points inside high-speed cables, and provides fault warnings based on their evolution trends. It effectively solves the problem that traditional methods struggle to distinguish between internal cable degradation signals and external environmental noise, significantly improving the accuracy and timeliness of cable operation status monitoring.

[0009] Secondly, this application also discloses a high-speed cable electromagnetic interference signal millimeter-wave transmission system for performing high-speed cable electromagnetic interference signal millimeter-wave transmission, specifically including: A sensor deployment module for deploying multiple millimeter-wave microsensors on a cable; the millimeter-wave microsensors are used to capture reflected signals inside the cable. The reflection signal processing module is used to continuously monitor the reflection signal inside the cable using a millimeter-wave miniature sensor, and process the reflection signal to obtain the reflection signal parameters, including arrival time, reflection amplitude, pulse width, resonant frequency, resonant intensity, quality factor, and bandwidth. The degradation type determination module is used to analyze the reflected signal and compare the reflected signal parameters with the signal patterns corresponding to various preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type determination result. The degradation location determination module is used to determine the location of the physical degradation point based on the time difference of multiple reflected signals, thereby obtaining the physical degradation location; The fault warning execution module is used to assess the cable's operating status and issue fault warnings based on the physical degradation type judgment results, physical degradation location, and the changing trend of reflected signal parameters.

[0010] This technical solution provides a system-level solution for realizing millimeter-wave transmission of electromagnetic interference signals in high-speed cables. Through modular design, it ensures the coordinated operation of each functional unit, thereby achieving comprehensive and efficient monitoring and early warning of internal degradation of cables, and improving the system's integration and practicality.

[0011] Beneficial Effects: This application discloses a millimeter-wave transmission method for electromagnetic interference signals in high-speed cables. It captures millimeter-wave reflected signals generated by physical degradation points within the cable by deploying millimeter-wave miniature sensors on the cable, and continuously monitors and processes these reflected signals to obtain detailed reflected signal parameters. This method further analyzes these parameters and compares them with preset signal patterns for various physical degradation types, thereby accurately determining the type of physical degradation within the cable. Simultaneously, by analyzing the temporal differences of multiple reflected signals, the location of the physical degradation points is precisely determined. Finally, by combining the physical degradation type determination results, the physical degradation location, and the changing trends of the reflected signal parameters, the cable's operating status is assessed, and a fault warning is issued.

[0012] This technical solution effectively solves the problem in existing technologies where intermittent electromagnetic leakage signals caused by microscopic physical degradation of high-speed cables in complex industrial environments are difficult to identify and track amidst strong industrial background noise. By directly capturing the reflected signals inside the cable using millimeter-wave micro-sensors, the interference of external electromagnetic interference is avoided. Through refined analysis and pattern comparison of the reflected signal parameters, accurate identification and type judgment of weak degradation signals are achieved. Multi-sensor time difference positioning solves the problem of unclear degradation point location. Therefore, this application provides a high-precision, high-reliability high-speed cable operation status monitoring and fault early warning mechanism, significantly improving the efficiency and safety of cable maintenance in application scenarios such as smart manufacturing plants, avoiding system performance degradation and production line downtime caused by cable faults, and possessing significant practical value and technological advancement. Attached Figure Description

[0013] Figure 1This is a flowchart of a method for high-speed cable electromagnetic interference signal millimeter-wave transmission in one embodiment of the present invention; Figure 2 This is a flowchart of a method for high-speed cable electromagnetic interference signal millimeter-wave transmission according to another embodiment of the present invention; Figure 3 This is a system block diagram of a high-speed cable electromagnetic interference signal millimeter-wave transmission system according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. High-speed cable electromagnetic interference signal millimeter-wave transmission system; 11. Sensor deployment module; 12. Reflection signal processing module; 13. Degradation type judgment module; 14. Degradation location determination module; 15. Fault early warning execution module. Detailed Implementation

[0014] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] This application proposes a method for millimeter-wave transmission of electromagnetic interference signals over high-speed cables, combined with... Figure 1 As shown, it includes: S1, Multiple millimeter-wave micro sensors are deployed on the cable; the millimeter-wave micro sensors are used to capture reflected signals inside the cable; the reflected signals are millimeter-wave reflected signals generated after impedance discontinuities are formed at physical degradation points inside the cable under the excitation of continuous wave detection signals; S2 continuously monitors the reflected signals inside the cable using a millimeter-wave micro-sensor and processes the reflected signals to obtain reflected signal parameters, including arrival time, reflection amplitude, pulse width, resonant frequency, resonant intensity, quality factor, and bandwidth. S3, analyze the reflected signal and compare the reflected signal parameters with the signal patterns corresponding to various preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type judgment result; S4. Based on the time difference of multiple reflected signals, determine the location of the physical degradation point to obtain the physical degradation location; S5 assesses the cable's operating status and issues a fault warning based on the physical degradation type, physical degradation location, and the changing trend of reflected signal parameters.

[0017] In order to better understand the technical solution proposed in this application, it is necessary to explain some of the key terms involved.

[0018] A "millimeter-wave micro-sensor" refers to a miniature sensing device that can be non-invasively deployed on or inside the surface of a cable. Operating in the millimeter-wave band, it transmits continuous-wave detection signals into the cable and receives millimeter-wave reflected signals generated by physical degradation points within the cable. Because the millimeter-wave band is highly sensitive to minute changes in impedance, millimeter-wave micro-sensors can detect microscopic degradation phenomena that are difficult to detect using traditional low-frequency detection methods. Furthermore, millimeter-wave micro-sensors are characterized by their small size, low power consumption, and high integration, making them suitable for long-term online monitoring of high-speed cables.

[0019] "Reflected signal" specifically refers to the millimeter-wave reflected signal generated when a physical degradation point inside a cable is excited by a continuous wave probe signal, due to local impedance discontinuities. The reflected signal contains electromagnetic response information corresponding to the physical degradation point. Different types of physical degradation correspond to different impedance changes, thus forming reflected signals with different characteristics.

[0020] "Reflection signal parameters" are a series of quantitative indicators obtained after processing the captured reflected signal, including arrival time, reflection amplitude, pulse width, resonant frequency, resonant intensity, quality factor, and bandwidth. These parameters collectively characterize the electromagnetic properties corresponding to the physical degradation point. Specifically, the arrival time reflects the propagation delay of the reflected signal; the reflection amplitude reflects the degree of impedance abrupt change; the pulse width reflects the spatial diffusion characteristics of the degradation region; and the resonant frequency, resonant intensity, quality factor, and bandwidth reflect the local resonance characteristics and energy loss characteristics of the corresponding degradation region. Therefore, reflection signal parameters are used not only to identify the type of physical degradation but also to assess the degree and trend of its development.

[0021] "Physical degradation type" refers to different forms of microscopic damage that occur inside the cable, including insulation fatigue cracks, conductor interface peeling, connector contact point fretting wear, and localized corrosion. Because different physical degradation types correspond to different structural changes, the resulting reflected signal parameter distribution characteristics also differ.

[0022] A "signal pattern" refers to a set of reflected signal parameter characteristics corresponding to a specific type of physical degradation. A signal pattern is not a single parameter threshold, but rather a composite parameter feature composed of multiple reflected signal parameters. For example, a certain type of physical degradation may simultaneously manifest as resonant frequency drift, resonant intensity attenuation, and a decrease in the quality factor. Signal patterns can be pre-established using experimental data, simulation data, or historical operating data, and used for subsequent determination of the type of physical degradation.

[0023] The "location of the physical degradation point" refers to the specific spatial coordinates of the physical degradation that occurs inside the cable. Since the reflected signal generated by the same physical degradation point will arrive at different millimeter-wave micro sensors at different times, the location of the physical degradation point can be determined by the time difference between multiple reflected signals.

[0024] "Fault warning" refers to the alert information output by the system when a potential fault risk reaches a preset condition after continuous assessment of the physical degradation state of the cable during operation. Fault warnings are used to remind maintenance personnel to take timely maintenance measures, thereby preventing the cable fault from escalating further.

[0025] The core of the millimeter-wave transmission method for electromagnetic interference signals in high-speed cables proposed in this application lies in the identification, location, and fault warning of internal physical degradation of cables through refined analysis of millimeter-wave reflected signals.

[0026] Specifically, the first step is to deploy multiple millimeter-wave micro-sensors on the cable. These sensors can be deployed in various ways. For example, they can be integrated inside the cable sheath or attached to the cable surface using a flexible circuit structure. To improve the accuracy of reflected signal capture and spatial resolution, an array-based deployment is preferred. In applications with long cables or high transmission rates, multiple millimeter-wave micro-sensors can be deployed at preset intervals to form a distributed monitoring structure.

[0027] Millimeter-wave miniature sensors continuously emit continuous wave detection signals into the cable during its operation. As the continuous wave signal propagates within the cable, if physical degradation points exist, energy reflection occurs in the corresponding areas due to impedance discontinuities, generating millimeter-wave reflected signals. The millimeter-wave miniature sensors receive and record these reflected signals, thereby enabling online sensing of the cable's internal condition.

[0028] Subsequently, the reflected signals inside the cable are continuously monitored using millimeter-wave micro sensors, and the reflected signals are processed to obtain the reflected signal parameters.

[0029] To ensure the accuracy of reflected signal parameter extraction, millimeter-wave miniature sensors can be equipped with corresponding signal processing units to perform digital processing, filtering, and demodulation on the raw reflected signal. Since strong industrial electromagnetic interference is typically present in the cable operating environment, preprocessing can reduce the impact of background noise on the extracted reflected signal parameters.

[0030] In the process of extracting reflected signal parameters, the arrival time can be obtained by analyzing the propagation time difference between the continuous wave detection signal and the reflected signal; the reflection amplitude and pulse width can be obtained by time-domain waveform analysis; and the resonant frequency, resonant intensity, quality factor, and bandwidth can be obtained by identifying the resonant peak after performing spectrum analysis on the reflected signal.

[0031] Among them, the resonant frequency reflects the characteristics of the local electromagnetic resonance center corresponding to the physical degradation point; the resonant intensity reflects the strength of the local reflected energy; the quality factor reflects the degree of resonant energy loss; and the bandwidth reflects the degree of resonant response diffusion. Therefore, by jointly analyzing multiple reflection signal parameters, the ability to distinguish the types of physical degradation can be improved.

[0032] After obtaining the reflected signal parameters, the reflected signal is analyzed, and the reflected signal parameters are compared with the signal patterns corresponding to various preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type judgment result.

[0033] Specifically, a database containing signal patterns corresponding to various physical degradation types can be pre-established. The database can store sets of reflected signal parameter characteristics corresponding to physical degradation types such as insulation fatigue cracks, conductor interface peeling, connector contact fretting wear, and localized corrosion.

[0034] After acquiring the real-time reflected signal parameters, the system performs pattern matching between these parameters and signal patterns in the database. During pattern matching, machine learning algorithms, neural network algorithms, or rule-based expert systems can be used for analysis. For example, by calculating the similarity, distance, or correlation coefficient between the real-time reflected signal parameters and each signal pattern, the system can determine the signal pattern whose parameters are closest to the current reflected signal parameters and use the corresponding physical degradation type as the physical degradation type judgment result.

[0035] After determining the type of physical degradation, the location of the physical degradation point is determined based on the time difference between multiple reflection signals, thus obtaining the physical degradation location.

[0036] Since the reflected signal generated by the same physical degradation point will arrive at different millimeter-wave microsensors at different times, the system can calculate the location of the physical degradation point by analyzing the arrival time difference of the reflected signals received by multiple millimeter-wave microsensors, and combining the spatial distance relationship between the millimeter-wave microsensors and the propagation speed of the signal in the cable.

[0037] One approach is to use a triangulation algorithm to determine the physical degradation location; another approach is to use a multi-point positioning algorithm combined with the cable topology to correct the positioning results, thereby improving the positioning accuracy in complex cabling environments.

[0038] Finally, based on the results of the physical degradation type judgment, the physical degradation location, and the changing trend of the reflected signal parameters, the cable operating status is evaluated and a fault warning is issued.

[0039] Specifically, the system can continuously track changes in reflected signal parameters corresponding to specific physical degradation points. For example, it can continuously analyze whether the resonant intensity decreases, whether the resonant frequency drifts, whether the quality factor continues to decline, and whether the bandwidth gradually expands.

[0040] Since different trends in the variation of reflected signal parameters correspond to different states of degradation, analyzing these trends can help determine whether the physical degradation is in a worsening stage.

[0041] For example, when the quality factor continues to decrease and the bandwidth gradually expands, it usually means that the energy loss in the physical degradation region is increasing; when the resonant frequency continues to drift, it may mean that the structural state of the physical degradation region is changing.

[0042] Therefore, the system can not only identify the current type of physical degradation, but also further assess the development trend and failure risk of physical degradation.

[0043] Based on this, a corresponding risk assessment model can be established to jointly analyze the types and locations of physical degradation, as well as the changing trends of reflected signal parameters. When the risk assessment results reach a preset risk threshold, the system automatically issues a fault warning.

[0044] Fault warnings can be output through audible and visual alarms, SMS notifications, or the factory control system's warning interface, thereby reminding maintenance personnel to perform timely maintenance on the corresponding cables to reduce the impact of high-speed cable faults on the stable operation of industrial control systems.

[0045] Optional, combined Figure 2 As shown, the steps for analyzing the reflected signal and comparing its parameters with signal patterns corresponding to various preset physical degradation types to determine the type of physical degradation within the cable and obtain the physical degradation type determination result include: A1 performs spectral analysis on the resonant signal in the reflected signal to extract the corresponding resonant frequency, bandwidth, resonant intensity and quality factor from the reflected signal parameters; A2 compares the resonant intensity, quality factor, resonant frequency, and bandwidth with pre-stored degradation signal patterns to obtain the pattern comparison results; the degradation signal patterns include the inherent resonant frequency range and resonant parameter characteristics corresponding to different physical degradation types. A3. Based on the pattern comparison results, determine the type of physical degradation inside the cable and obtain the physical degradation type judgment result.

[0046] Specifically, when analyzing reflected signals, the first step is to perform spectral analysis on the resonant signals contained within them. Spectral analysis involves converting a time-domain signal into a frequency-domain signal, thereby revealing the frequency components and intensity distribution of the signal. By performing spectral analysis on the resonant signal, key parameters such as its resonant frequency, bandwidth, resonant intensity, and quality factor can be accurately extracted. The resonant frequency characterizes the center frequency at which the resonance phenomenon occurs, the bandwidth reflects the width of the resonant peak, the resonant intensity represents the amplitude of the resonance, and the quality factor measures the sharpness or energy loss of the resonance. These parameters are important indicators characterizing the physical degradation points within the cable.

[0047] Furthermore, after extracting parameters such as resonance intensity, quality factor, resonant frequency, and bandwidth, these parameters are compared with pre-stored degradation signal patterns. These pre-stored degradation signal patterns are established based on extensive experimental data and theoretical analysis, encompassing the inherent resonant frequency ranges and unique resonant parameter characteristics corresponding to different types of physical degradation (e.g., insulation aging, conductor breakage, connector loosening, etc.). By comparing the real-time monitored resonant parameters with these patterns, the best-matching degradation type can be identified.

[0048] Therefore, based on the pattern comparison results, the type of physical degradation inside the cable can be accurately determined, and a physical degradation type judgment result can be obtained. For example, if the comparison results show that the current resonance parameters are highly matched with the degradation signal mode of "insulation layer aging", it can be determined that there is insulation layer aging inside the cable.

[0049] Optionally, the steps for assessing cable operating status and issuing fault warnings based on the physical degradation type, physical degradation location, and the changing trends of reflected signal parameters include: Acquire environmental parameters during cable operation; these parameters include the temperature and humidity of the area where the cable is located, as well as the operating status of surrounding equipment. Continuously monitor the changing trends of reflected signal parameters to obtain the resonance parameters corresponding to the physical degradation location and the changing trends of the resonance parameters; the resonance parameters include resonant frequency, resonant intensity, quality factor, and bandwidth; Correlation analysis between environmental parameters and resonance parameters is performed to identify transient fluctuations in resonance parameters that are related to changes in environmental parameters. By separating transient fluctuations from the trend of resonant parameter variation, the trend of resonant parameter variation after removing environmental influences is obtained; Based on the trend of resonant parameter changes after removing environmental influences, identify persistent degradation characteristics; Based on the rate and extent of the evolution of continuous degradation characteristics, combined with the results of the physical degradation type judgment and the location of physical degradation, the cable operating status is assessed and a fault warning is issued.

[0050] Specifically, acquiring environmental parameters during cable operation refers to collecting information such as temperature, humidity, and the operating status of surrounding equipment in the area where the cable is located, in real-time or near real-time, through corresponding sensors or system interfaces. These environmental parameters are important external factors affecting the physical characteristics of the cable and millimeter-wave signal transmission. Continuously monitoring the changing trends of the aforementioned reflected signal parameters to obtain the resonance parameters corresponding to the physical degradation location and their changing trends means that the system continuously acquires reflected signals captured by millimeter-wave micro-sensors and extracts parameters such as resonant frequency, resonant intensity, quality factor, and bandwidth related to a specific physical degradation point. The time-series changes of these resonance parameters reflect the dynamic evolution process of the physical degradation point.

[0051] The correlation analysis between the aforementioned environmental parameters and the aforementioned resonant parameters, identifying transient fluctuations in the resonant parameters that are related to changes in environmental parameters, can be understood as analyzing the correlation between environmental parameters and resonant parameters using statistical methods, machine learning algorithms, or pre-set physical models. The purpose is to distinguish between resonant parameter fluctuations caused by environmental changes and actual changes caused by the physical degradation of the cable itself. For example, when the ambient temperature rises, the dielectric constant of the cable material may change slightly, resulting in a brief drift in the resonant frequency or intensity; this drift is identified as a transient fluctuation.

[0052] Furthermore, separating the aforementioned transient fluctuations from the resonant parameter variation trends to obtain the resonant parameter variation trends after removing environmental influences involves using techniques such as filtering, signal decomposition, or model compensation to remove these transient fluctuations from the original resonant parameter variation trends after identifying environmentally relevant transient fluctuations. This yields a purer and more accurate reflection of the cable's physical degradation process, avoiding interference from environmental noise. Based on this, identifying persistent degradation characteristics according to the resonant parameter variation trends after removing environmental influences involves analyzing the resonant parameter time series after environmental influence separation to find evolution patterns exhibiting long-term, stable, and non-transient characteristics, such as continuous drift of the resonant frequency, stable attenuation of the resonant intensity, or continuous widening of the bandwidth. These persistent changes are important indicators of irreversible degradation of the cable's internal physical structure. Finally, based on the evolution rate and degree of the aforementioned persistent degradation characteristics, combined with the results of the physical degradation type judgment and the location of the physical degradation, the cable's operating status is assessed and a fault warning is issued. This means that the system will comprehensively consider the severity and speed of degradation characteristics, as well as the previously determined degradation type and location, to make a comprehensive judgment on the overall health status of the cable, and trigger a fault warning mechanism when necessary.

[0053] Optionally, the step of continuously monitoring the changing trend of the reflected signal parameters to obtain the resonance parameters corresponding to the physical degradation location and the changing trend of the resonance parameters includes: Continuously monitor the changing trends of reflected signal parameters to obtain the time series of resonance parameters from multiple millimeter-wave micro-sensors; The time series of resonance parameters is separated and processed to decompose the variation trend of composite resonance parameters that are mutually coupled, nonlinearly evolved, or periodically fluctuating into multiple independent resonance parameter evolution components. Feature reconstruction is performed on multiple independent resonant parameter evolution components to extract independent evolution mode features; independent evolution mode features include frequency drift features, intensity variation features, quality factor attenuation features, and bandwidth broadening features; Each independent evolution mode feature is compared with a preset degradation dynamic mode map to distinguish and track the evolution mode of each independent physical degradation point. Based on the evolution mode, evolution rate, and degree of each independent physical degradation point, the resonance parameters and their variation trends are obtained.

[0054] Specifically, continuously monitoring the changing trends of reflected signal parameters refers to using multiple millimeter-wave micro-sensors deployed on the cable to continuously collect reflected signals inside the cable in real time, and extracting resonant parameters such as resonant frequency, resonant intensity, quality factor, and bandwidth from them to form a time series of resonant parameters that change over time. These time series reflect the dynamic changes of physical degradation points inside the cable.

[0055] The separation of the resonance parameter time series can be understood as using signal processing techniques, such as independent component analysis, blind source separation, wavelet decomposition, or empirical mode decomposition, to decouple the composite resonance parameter time series obtained from the millimeter-wave micro-sensor. The aim is to decompose signal components that may superimpose or influence each other due to different physical degradation points or different degradation mechanisms into independent evolutionary components, thereby eliminating coupling effects between signals and revealing the true dynamics of each independent degradation source.

[0056] In practical applications, feature reconstruction of multiple independent resonant parameter evolution components refers to extracting key mode features that characterize the physical degradation points from the separated independent evolution components. For example, frequency drift features refer to the shift of the resonant frequency over time, which is usually related to changes in the dielectric constant or geometric dimensions of the material; intensity variation features refer to the changes in the amplitude of the reflected signal or the resonant intensity over time, which may indicate loose connections or increased losses; quality factor attenuation features reflect the increase in resonant cavity losses, often related to material aging or impaired structural integrity; and bandwidth broadening features may indicate the dispersion of resonant characteristics, which is related to multipath effects or non-uniform degradation. The extraction of these features aims to quantify and identify the unique manifestations of different degradation modes.

[0057] Furthermore, the characteristics of each independent evolution mode are compared with a pre-defined dynamic degradation mode map. The purpose is to accurately distinguish and track the specific evolution mode of each independent physical degradation point by matching it with a pre-established database of typical evolution modes and features containing various known physical degradation types (such as insulation aging, conductor corrosion, and loose connections). This map can be constructed based on experimental data, simulation models, or historical fault data.

[0058] Therefore, based on the evolution mode, rate and extent of each independent physical degradation point, the current state and future trend of each degradation point can be comprehensively evaluated, thereby obtaining more accurate and detailed resonance parameters and the trend of resonance parameter changes, providing a reliable basis for subsequent cable operation status assessment and fault early warning.

[0059] Optionally, the steps of reconstructing features from multiple independent resonant parameter evolution components and extracting independent evolution mode features include: Multi-scale time-frequency decomposition is performed on each independent resonant parameter evolution component to obtain the energy distribution of the independent resonant parameter evolution component under different time resolutions and frequency resolutions, thus obtaining the multi-scale time-frequency decomposition results; The multi-scale time-frequency decomposition results are sparsely represented, and features with energy accumulation in specific time-frequency regions are extracted. Feature enhancement mapping is performed on features with energy accumulation to enhance evolutionary pattern features related to physical degradation type, resulting in enhanced evolutionary pattern features; Dynamic threshold judgment is performed on the enhanced evolution mode features to distinguish between feature changes caused by the physical evolution of the degradation point itself and fluctuations caused by residual environmental noise or sensor drift, and the dynamic threshold judgment result is obtained. Based on the dynamic threshold judgment results, frequency drift characteristics, intensity change characteristics, quality factor attenuation characteristics, and bandwidth broadening characteristics are reconstructed and used as independent evolution mode characteristics.

[0060] Specifically, multi-scale time-frequency decomposition of each independent resonant parameter evolution component refers to using time-frequency analysis methods such as wavelet transform, short-time Fourier transform, or Hilbert-Huang transform to transform the original time-domain signal into the time-frequency domain, thereby observing the energy distribution of the signal at different time and frequency scales. The aim is to reveal the transient, non-stationary, or multi-scale characteristics that may exist in the resonant parameter evolution components, providing richer information for subsequent feature extraction.

[0061] The sparse representation of multi-scale time-frequency decomposition results can be understood as using mathematical transformations (such as dictionary-based learning or compressed sensing methods) to represent the time-frequency decomposition results as a linear combination of a small number of basis functions, thereby highlighting the main components of the signal and removing redundant information. The aim is to accurately identify and extract features with significant energy accumulation in specific time-frequency regions from complex time-frequency maps; these features are often closely related to physical degradation processes.

[0062] In practical applications, feature enhancement mapping for features exhibiting energy accumulation refers to selectively amplifying feature signals associated with specific physical degradation types while suppressing or weakening irrelevant noise by applying specific algorithms or models (such as feature weighting based on machine learning, nonlinear transformations, or deep learning networks). The aim is to improve the signal-to-noise ratio of the target degradation features, making them easier to identify and distinguish in subsequent analysis.

[0063] Furthermore, dynamic threshold judgment of the enhanced evolution pattern features refers to establishing an adaptive judgment criterion to distinguish between true feature changes caused by the evolution of physical degradation points within the cable itself and fluctuations caused by non-degradation factors such as external environmental noise, sensor drift, or system measurement errors. This dynamic threshold can be adjusted based on the statistical characteristics of the real-time data stream, for example, by constructing a judgment interval based on statistical distribution parameters such as mean, variance, skewness, or kurtosis. The purpose is to ensure that the extracted evolution pattern features accurately reflect the true degradation state of the cable, avoiding false alarms or missed alarms.

[0064] Optionally, the step of performing feature enhancement mapping on features with energy accumulation to enhance evolutionary pattern features associated with physical degradation types includes: Read the result of the current cable's physical degradation type determination; Obtain available computing resources from the system; Based on the physical degradation type determination result, candidate mapping strategies that match the current physical degradation type are selected from the preset feature enhancement mapping strategy set; Based on the available computing resources of the system, the computational complexity of the candidate mapping strategies is evaluated, and candidate mapping strategies whose computational resource requirements exceed the available range of the system are eliminated, resulting in the remaining candidate mapping strategies. Based on the remaining candidate mapping strategies, the enhancement effect and computational resource consumption of the energy-gathering features are comprehensively evaluated, and the target mapping strategy is selected. A target mapping strategy is adopted to perform feature enhancement mapping on features with energy accumulation, resulting in enhanced evolutionary pattern features.

[0065] Specifically, reading the physical degradation type judgment result of the current cable refers to the system obtaining the specific physical degradation type determined by the currently monitored cable, such as corrosion, insulation aging, mechanical damage, etc. This judgment result can be provided by the degradation type judgment module, and its purpose is to provide a basis for subsequently selecting an appropriate feature enhancement mapping strategy.

[0066] In this context, acquiring available system computing resources refers to the system's real-time monitoring and acquisition of the number of processor cores, memory size, graphics processing unit (GPU) resources, or dedicated accelerator resources currently available for feature enhancement mapping operations. The purpose is to ensure that the selected mapping strategy can operate efficiently in the current system environment, avoiding performance bottlenecks caused by insufficient resources.

[0067] In practical applications, based on the physical degradation type determination results, selecting candidate mapping strategies that match the current physical degradation type from a pre-defined set of feature enhancement mapping strategies means that the system maintains a database containing various feature enhancement algorithms or parameter configurations. Each strategy may have a better enhancement effect for a specific physical degradation type. For example, for insulation aging, it may be necessary to enhance the energy distribution in the low-frequency region; for mechanical damage, it may be necessary to enhance the high-frequency transient features. The system will select the subset of strategies most likely to provide good enhancement effects from this set based on the currently identified degradation type.

[0068] Furthermore, based on the available computing resources of the system, the computational complexity of the candidate mapping strategies is evaluated, and those strategies whose computational resource requirements exceed the system's available capacity are eliminated, resulting in the remaining candidate mapping strategies. Specifically, the computational complexity evaluation may include predicting the algorithm's time complexity, space complexity, and actual runtime resource consumption. If the computational requirements of a candidate strategy (e.g., requiring extensive parallel computing or high-bandwidth memory access) exceed the current system's capacity, that strategy will be eliminated to ensure system stability and response speed.

[0069] Based on this, the remaining candidate mapping strategies are comprehensively evaluated for their enhancement effects and computational resource consumption, leading to the selection of the target mapping strategy. This comprehensive evaluation aims to balance the relationship between enhancement effects and resource consumption. For example, a pre-trained model or empirical rules can be used to score the expected enhancement effect of each remaining strategy under the current degradation type, and a weighted evaluation can be performed based on its computational resource consumption. The final selected strategy should be the one with the lowest resource consumption or the best enhancement effect given the available resources, while still meeting the enhancement effect requirements.

[0070] Therefore, by employing the aforementioned target mapping strategy, feature enhancement mapping is performed on features exhibiting energy accumulation, resulting in enhanced evolutionary pattern features. This means that the system will use optimized algorithms and parameter configurations to process the energy-accumulating features extracted from the multi-scale time-frequency decomposition results, thereby more effectively highlighting evolutionary pattern features related to the current physical degradation type and providing high-quality input for subsequent dynamic threshold judgment and independent evolutionary pattern feature reconstruction.

[0071] Optionally, the steps for performing dynamic threshold judgment on the enhanced evolution mode features, distinguishing feature changes caused by the physical evolution of the degradation point itself from fluctuations caused by residual environmental noise or sensor drift, and obtaining the dynamic threshold judgment result include: Acquire real-time data streams of enhanced evolution pattern features; Based on the real-time data stream, the statistical distribution parameters of the enhanced evolution pattern features are calculated over multiple time windows; the statistical distribution parameters include mean, variance, skewness, and kurtosis. Based on statistical distribution parameters, construct a judgment interval that includes an upper and lower limit; When the enhanced evolution pattern features do not exceed the judgment interval, the enhanced evolution pattern features are determined to be in a stable state, and the stable state is used as the dynamic threshold judgment result. When the enhanced evolution pattern features exceed the judgment range, it is marked as a potential feature change event; Determine the duration of potential characteristic change events; When the duration of a potential feature change event exceeds a preset minimum duration threshold, it is determined to be a feature change caused by the physical evolution of the degradation point itself. When the duration of a potential characteristic change event does not exceed a preset minimum duration threshold, it is determined to be a fluctuation caused by residual environmental noise or sensor drift. The results of dynamic threshold judgment are determined as either the characteristic changes caused by the physical evolution of the degradation point itself or the fluctuations caused by residual environmental noise or sensor drift.

[0072] The enhanced evolution pattern feature real-time data stream refers to a continuous data sequence that reflects the evolution pattern of physical degradation points after feature enhancement mapping. Statistical distribution parameters, including mean, variance, skewness, and kurtosis, are used to describe the central tendency, dispersion, symmetry, and tail characteristics of the real-time data stream, comprehensively reflecting its dynamic characteristics. Based on these statistical distribution parameters, a judgment interval containing upper and lower limits can be constructed to define the normal fluctuation range of the feature. When the enhanced evolution pattern feature exceeds this judgment interval, the system marks it as a potential feature change event, indicating that a change may have occurred, but it has not yet been determined whether it is true degradation or noise. The preset minimum duration threshold is a key parameter used to distinguish between transient fluctuations and persistent changes. Transient fluctuations are usually short-lived, while feature changes caused by physical degradation have a certain degree of persistence.

[0073] Optionally, the step of calculating the statistical distribution parameters of the enhanced evolutionary pattern features over multiple time windows based on the real-time data stream includes: The real-time data stream is timestamped and data packets are encapsulated to obtain tagged data packets; The timestamp is designed to assign precise time information to each data unit, ensuring the time-series traceability of the data in subsequent processing. Data packet encapsulation organizes the raw data into a standardized data packet format, facilitating transmission, storage, and processing, while also including verification information to guarantee data integrity.

[0074] The tagged data packets are buffered, and the buffer size and data extraction strategy are adjusted according to the sampling frequency and transmission delay of the real-time data stream to achieve time alignment between each real-time data stream, resulting in a time-aligned data stream. Specifically, since real-time data streams from different sensors or processing stages may have different sampling frequencies and transmission delays, direct fusion and analysis can lead to data mismatch. By dynamically adjusting the buffer size and data extraction strategy, such as adjusting the order and rate of data packet reading, these differences can be compensated for, ensuring that all relevant data streams are precisely aligned in the time dimension, laying the foundation for subsequent synchronous analysis.

[0075] The time-aligned data stream is format-converted and normalized to eliminate differences in data format and units, resulting in a processed data stream. Format conversion aims to unify data from different sources or types into a standardized data format, such as converting binary data into floating-point representation. Data normalization, on the other hand, uses methods such as linear scaling or Z-score normalization to eliminate dimensional differences between different data features, ensuring that all data are compared and analyzed on a uniform scale, and preventing certain features from dominating the analysis results due to excessively large numerical ranges.

[0076] The processed data streams are merged to form a unified time-synchronized composite data stream; Thus, multiple independent data streams, after time alignment, format conversion, and normalization, are integrated into a unified composite data stream. This composite data stream is synchronized in time, and its data format and units are standardized, greatly simplifying the subsequent statistical analysis process.

[0077] On the time-synchronized composite data stream, sliding window processing is performed according to multiple preset time window lengths and step sizes, and the mean, variance, skewness and kurtosis are calculated for the data in each time window; Specifically, sliding window processing is a commonly used time-series data analysis technique. It involves sliding a fixed-length window across a time-synchronized composite data stream and independently calculating statistical parameters within each window. The mean reflects the central tendency of the data, variance measures the dispersion, skewness describes the asymmetry of the data distribution, and kurtosis characterizes the sharpness or tail thickness of the data distribution. These statistical distribution parameters can comprehensively characterize the dynamic changes in the enhanced evolutionary pattern across different time periods.

[0078] The calculated mean, variance, skewness, and kurtosis are used as statistical distribution parameters.

[0079] Therefore, these statistical distribution parameters provide a quantitative basis for the subsequent construction of dynamic threshold judgment intervals, making the judgment of feature changes more accurate and robust.

[0080] Optionally, the marked data packets are buffered, and the buffer size and data extraction strategy are adjusted according to the sampling frequency and transmission delay of the real-time data stream to achieve time alignment between each real-time data stream. The steps to obtain the time-aligned data stream include: Obtain the current operating status of each cable; the current operating status includes cable load, physical degradation type judgment result, and degradation degree; Based on the current operating status and combined with historical data, predict the short-term trend of the sampling frequency and transmission delay of each data stream in the real-time data stream; Based on the predicted short-term trends of sampling frequency and transmission delay, and based on the system's currently available cache resources, the cache size of each data stream is dynamically adjusted to obtain the adjusted cache size. Based on the predicted short-term trends of sampling frequency and transmission delay, and based on the time alignment accuracy requirements of each data stream, the data extraction strategy for each data stream is dynamically adjusted to obtain the adjusted data extraction strategy; the data extraction strategy includes the data packet extraction order and extraction rate. Based on the adjusted buffer size and adjusted data extraction strategy, the marked data packets are cached and extracted to obtain a time-aligned data stream.

[0081] Specifically, obtaining the current operating status of each cable refers to the system collecting and analyzing the cable's operating parameters in real time, such as cable load, the physical degradation type judgment result obtained by the degradation type judgment module, and the current degradation level. This information is an important basis for assessing cable health and predicting the dynamic behavior of data flow, and can provide comprehensive contextual information for subsequent prediction and adjustment.

[0082] This involves predicting short-term trends in the sampling frequency and transmission delay of each data stream in the real-time data stream based on the current operating status and historical data. This can be understood as using machine learning models (such as Recurrent Neural Networks (RNNs) or Long Short-Term Memory Networks (LSTMs)) or statistical prediction methods (such as the ARIMA model) to learn from and analyze historical data and the current operating status, thereby predicting potential fluctuations in the sampling frequency and transmission delay of each data stream over a future period. The aim is to provide a forward-looking basis for subsequent dynamic adjustments to address instantaneous changes in the data stream, thus achieving more refined control.

[0083] In practical applications, the buffer size for each data stream is dynamically adjusted based on the predicted short-term trends of sampling frequency and transmission delay, as well as the system's currently available buffer resources. For example, the buffer can be pre-expanded to prevent data overflow if the predicted sampling frequency or transmission delay increases; conversely, the buffer can be reduced to conserve system resources if the predicted sampling frequency or transmission delay increases. The aim is to optimize the allocation and utilization efficiency of buffer resources while ensuring no data loss, thus ensuring stable system operation under various conditions.

[0084] Furthermore, based on the predicted short-term trends of sampling frequency and transmission delay, and the time alignment accuracy requirements of each data stream, the data extraction strategy for each data stream is dynamically adjusted. This strategy includes the packet extraction order and extraction rate. For example, for data streams with high time alignment accuracy requirements, extraction can be prioritized or the extraction rate adjusted to ensure synchronization with other data streams; for data streams where increased transmission delay is predicted, the extraction rate can be appropriately reduced to wait for data arrival. The aim is to flexibly control the data extraction process according to the characteristics of different data streams and system requirements, thereby achieving more accurate and reliable time alignment.

[0085] Therefore, based on the adjusted buffer size and the adjusted data extraction strategy, the marked data packets are cached and extracted, ultimately resulting in a time-aligned data stream.

[0086] This application also proposes a high-speed cable electromagnetic interference signal millimeter-wave transmission system for performing high-speed cable electromagnetic interference signal millimeter-wave transmission, combined with... Figure 3 As shown, the high-speed cable electromagnetic interference signal millimeter-wave transmission system 1 includes: The sensor deployment module 11 is used to deploy multiple millimeter-wave micro sensors on the cable; the millimeter-wave micro sensors are used to capture reflected signals inside the cable. The reflection signal processing module 12 is used to continuously monitor the reflection signal inside the cable through a millimeter-wave micro sensor, and process the reflection signal to obtain the reflection signal parameters, including arrival time, reflection amplitude, pulse width, resonant frequency, resonant intensity, quality factor and bandwidth. The degradation type judgment module 13 is used to analyze the reflected signal and compare the reflected signal parameters with the signal modes corresponding to various preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type judgment result. The degradation location determination module 14 is used to determine the location of the physical degradation point based on the time difference of multiple reflected signals, and thus obtain the physical degradation location. The fault warning execution module 15 is used to assess the cable's operating status and issue fault warnings based on the physical degradation type judgment result, physical degradation location, and the changing trend of reflected signal parameters.

[0087] In order to better understand the technical solution proposed in this application, it is necessary to further explain the collaborative relationship between the modules within the system and their working process.

[0088] The sensor deployment module is used to deploy multiple millimeter-wave micro-sensors on the cable to construct a millimeter-wave sensing network distributed along the cable. The millimeter-wave micro-sensors are used to transmit millimeter-wave detection signals into the cable and receive reflected signals generated by physical degradation points within the cable.

[0089] Specifically, millimeter-wave microsensors can employ flexible packaging structures to adapt to high-speed cable bending or vibration environments. As one implementation, the millimeter-wave microsensor can be integrated inside the cable sheath, forming an isolation structure with the cable shielding layer to reduce the impact of external electromagnetic interference. In another implementation, the millimeter-wave microsensor can also be attached to the outside of the cable in the form of a flexible circuit board, and the millimeter-wave detection signal can be injected into the cable through a coupling structure.

[0090] Furthermore, multiple millimeter-wave microsensors are preferably deployed in an array. For example, multiple millimeter-wave microsensors can be arranged along the length of the high-speed cable at preset intervals, allowing internal degradation in different areas to be detected independently. Since reflected signals generated at the same degradation point will propagate to different sensors, array deployment not only improves the ability to capture reflected signals but also improves the accuracy of degradation location.

[0091] When millimeter-wave detection signals encounter physical degradation points such as insulation cracks, conductor corrosion, loose connectors, or local structural deformation during propagation inside the cable, some millimeter-wave energy is reflected back due to changes in the impedance characteristics at the corresponding locations. Millimeter-wave miniature sensors receive and record these reflected signals for subsequent analysis.

[0092] The reflection signal processing module is connected to the sensor deployment module and is used to continuously monitor the reflected signals inside the cable using millimeter-wave miniature sensors, and process the reflected signals to obtain the reflected signal parameters. The reflected signal parameters include at least the time of arrival, reflection amplitude, pulse width, resonant frequency, resonant intensity, quality factor, and bandwidth.

[0093] Specifically, the reflection signal processing module may include a signal sampling unit, a digital filtering unit, and a spectrum analysis unit. The signal sampling unit is used to sample the analog reflection signal output by the millimeter-wave micro-sensor at high speed; the digital filtering unit is used to filter out environmental noise and background interference; and the spectrum analysis unit is used to perform frequency domain analysis or time-frequency joint analysis on the reflection signal.

[0094] For example, the arrival time of the reflected signal can be obtained by calculating the time difference between the transmission time of the probe signal and the reception time of the reflected signal; the reflection amplitude and pulse width can be obtained by analyzing the time-domain waveform; and the resonant frequency, resonant intensity, quality factor, and bandwidth can be obtained by performing a fast Fourier transform or resonant peak extraction on the reflected signal. Since different types of physical degradation lead to different resonance characteristics, the above parameters can form a feature set characterizing the degradation state.

[0095] The degradation type determination module is connected to the reflection signal processing module. It is used to analyze the reflection signal parameters and compare the reflection signal parameters with the signal modes corresponding to various preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type determination result.

[0096] The preset signal pattern library can store reflection signal patterns corresponding to different physical degradation types, such as insulation cracks, conductor corrosion, localized ablation, poor connector contact, and shielding layer damage. Each signal pattern includes at least the corresponding resonant frequency range, resonant intensity variation law, quality factor variation trend, and bandwidth characteristics.

[0097] In practice, the degradation type determination module can employ pattern matching algorithms, machine learning classification algorithms, or rule-based expert judgment logic. For example, it can calculate the similarity between real-time reflection signal parameters and each degradation pattern, and take the pattern with the highest similarity as the corresponding degradation type. In another embodiment, the degradation type determination module can also employ a neural network classification model, training the model with historical degradation samples to improve the recognition ability under complex degradation states.

[0098] The degradation type determination module can transform weak reflection changes that were originally considered random background noise into degradation type results with clear physical meaning, thereby improving the interpretability of cable fault diagnosis.

[0099] The degradation location determination module is used to determine the location of the physical degradation point based on the time difference between multiple reflected signals, thus obtaining the physical degradation location.

[0100] Specifically, since the reflected signal generated by the same physical degradation point will arrive at different millimeter-wave micro sensors at different times, the degradation location determination module can calculate the location of the degradation point based on the arrival time difference of the reflected signals received by multiple sensors, combined with the known distance between the sensors and the propagation speed of the millimeter-wave signal in the cable.

[0101] In one implementation, the degradation location determination module can employ a multi-point positioning algorithm or a time-difference positioning algorithm to determine the spatial location of the degradation point within the cable by establishing a propagation path model. In another implementation, the degradation location determination module can also incorporate cable structure topology information to constrain and correct the positioning results, thereby improving positioning accuracy in complex cabling environments.

[0102] The fault warning execution module is used to assess the cable's operating status and issue fault warnings based on the physical degradation type judgment result, physical degradation location, and the changing trend of reflected signal parameters.

[0103] Specifically, the fault early warning execution module continuously tracks the changes in the reflected signal parameters at the corresponding degradation point. For example, the system continuously monitors whether the resonant frequency drifts, whether the resonant intensity continues to decrease, whether the quality factor changes abnormally, and whether the bandwidth expands. When the trend of the corresponding parameter changes meets the preset risk conditions, the fault early warning execution module determines the corresponding degradation state as a risk expansion state.

[0104] Furthermore, the fault early warning execution module can establish a degradation trend model to quantitatively analyze the degradation rate. For example, when the resonant frequency drift rate continues to increase and the quality factor continues to decrease, the system can determine that the corresponding degradation point may enter a rapid deterioration stage and increase the risk level.

[0105] In some implementations, the fault warning execution module can also assess the scope of risk impact by combining the degradation type and degradation location. For example, when the degradation location is in a critical area of ​​high-speed differential signal transmission, the warning level can be increased even if the degree of degradation is low; while for minor degradation located in a low-load area, an observation-based warning strategy can be adopted.

[0106] Finally, when the comprehensive assessment results reach the preset risk threshold, the fault early warning execution module issues a fault warning. Fault warnings can include audible and visual alarms, system log recordings, remote maintenance notifications, or factory control system linkage prompts. This module allows for the early identification of potential risks before cables completely fail, thus enabling predictive maintenance.

[0107] The millimeter-wave transmission system for electromagnetic interference signals of high-speed cables proposed in this application, compared with traditional cable detection methods based on external electromagnetic field monitoring or manual inspection, can directly obtain millimeter-wave reflection characteristics from the physical degradation inside the cable. Through the collaborative processing of the degradation type judgment module, the degradation location determination module, and the fault early warning execution module, it can realize the identification, location, and trend prediction of the physical degradation inside the high-speed cable.

[0108] Especially in industrial environments with significant background electromagnetic noise, this application uses the characteristic analysis of millimeter-wave reflected signals to separate the subtle reflection changes caused by internal cable degradation from complex background interference, thus avoiding the problem of "internal degradation signals being treated as background noise" in traditional systems. Furthermore, by continuously tracking the changing trends of reflected signal parameters, early warnings can be issued before degradation evolves into actual faults, improving the stability and reliability of high-speed cable operation.

[0109] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for high-speed cable electromagnetic interference signal millimeter wave transmission, characterized in that, include: Deploying multiple millimeter-wave miniature sensors on a cable; The millimeter-wave micro-sensor is used to capture reflected signals inside the cable; the reflected signals are millimeter-wave reflected signals generated after impedance discontinuities are formed at physical degradation points inside the cable under the excitation of a continuous wave detection signal. The millimeter-wave micro-sensor continuously monitors the reflected signals inside the cable and processes them to obtain reflected signal parameters, including arrival time, reflection amplitude, pulse width, resonant frequency, resonant intensity, quality factor, and bandwidth. The reflected signal is analyzed, and the parameters of the reflected signal are compared with the signal patterns corresponding to a variety of preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type determination result. Based on the time differences of the multiple reflected signals, the location of the physical degradation point is determined, and the physical degradation location is obtained; Based on the results of the physical degradation type determination, the physical degradation location, and the changing trend of the reflected signal parameters, the cable operating status is assessed and a fault warning is issued.

2. The method of claim 1, wherein the electromagnetic interference signal is a millimeter wave signal. The steps of analyzing the reflected signal and comparing the reflected signal parameters with signal patterns corresponding to multiple preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type determination result include: Spectral analysis is performed on the resonant signal in the reflected signal to extract the corresponding resonant frequency, bandwidth, resonant intensity, and quality factor from the parameters of the reflected signal; Based on the resonance intensity, quality factor, resonance frequency, and bandwidth, the degraded signal patterns are compared with pre-stored degraded signal patterns to obtain the pattern comparison results; the degraded signal patterns include the inherent resonance frequency range and resonance parameter characteristics corresponding to different physical degradation types. Based on the pattern comparison results, the physical degradation type inside the cable is determined, and the physical degradation type judgment result is obtained.

3. The method for millimeter-wave transmission of electromagnetic interference signals via high-speed cables according to claim 1, characterized in that, The step of assessing the cable's operating status and issuing a fault warning based on the physical degradation type determination result, the physical degradation location, and the changing trend of the reflected signal parameters includes: Acquire environmental parameters during cable operation; these environmental parameters include the temperature and humidity of the area where the cable is located, as well as the operating status of surrounding equipment. The changing trends of the reflected signal parameters are continuously monitored to obtain the resonance parameters corresponding to the physical degradation location and the changing trends of the resonance parameters; the resonance parameters include resonance frequency, resonance intensity, quality factor, and bandwidth; The environmental parameters and the resonance parameters are correlated to identify transient fluctuations in the resonance parameters that are related to changes in the environmental parameters. The transient fluctuations are separated from the trend of resonance parameter changes to obtain the trend of resonance parameter changes after removing environmental influences; Based on the trend of resonant parameter changes after removing environmental influences, identify persistent degradation characteristics; Based on the rate and extent of the evolution of the persistent degradation characteristics, combined with the results of the physical degradation type judgment and the location of the physical degradation, the cable operating status is assessed and a fault warning is issued.

4. The method for millimeter-wave transmission of electromagnetic interference signals via high-speed cables according to claim 3, characterized in that, The step of continuously monitoring the changing trend of the reflected signal parameters to obtain the resonance parameters corresponding to the physical degradation location and the changing trend of the resonance parameters includes: The changing trend of the reflected signal parameters is continuously monitored to obtain the time series of resonance parameters from multiple millimeter-wave micro sensors; The time series of the resonance parameters is separated to decompose the variation trend of the composite resonance parameters that are mutually coupled, nonlinearly evolving, or periodically fluctuating into multiple independent resonance parameter evolution components. Feature reconstruction is performed on multiple independent resonant parameter evolution components to extract independent evolution mode features; the independent evolution mode features include frequency drift features, intensity change features, quality factor attenuation features, and bandwidth broadening features; Each independent evolution mode feature is compared with a preset degradation dynamic mode map to distinguish and track the evolution mode of each independent physical degradation point. Based on the evolution mode, evolution rate, and degree of each independent physical degradation point, the resonance parameters and their variation trends are obtained.

5. A method for millimeter-wave transmission of electromagnetic interference signals via high-speed cables according to claim 4, characterized in that, The step of reconstructing features from multiple independent resonant parameter evolution components and extracting independent evolution mode features includes: Multi-scale time-frequency decomposition is performed on each independent resonant parameter evolution component to obtain the energy distribution of the independent resonant parameter evolution component under different time resolutions and frequency resolutions, thus obtaining the multi-scale time-frequency decomposition results; The multi-scale time-frequency decomposition results are sparsely represented, and features with energy accumulation in specific time-frequency regions are extracted. Feature enhancement mapping is performed on features with energy accumulation to enhance evolutionary pattern features related to physical degradation type, resulting in enhanced evolutionary pattern features; Dynamic threshold judgment is performed on the enhanced evolution mode features to distinguish between feature changes caused by the physical evolution of the degradation point itself and fluctuations caused by residual environmental noise or sensor drift, and the dynamic threshold judgment result is obtained. Based on the dynamic threshold judgment results, frequency drift characteristics, intensity change characteristics, quality factor attenuation characteristics, and bandwidth broadening characteristics are reconstructed and used as independent evolution mode characteristics.

6. The method for millimeter-wave transmission of electromagnetic interference signals via high-speed cables according to claim 5, characterized in that, The step of performing feature enhancement mapping on features with energy accumulation to enhance evolutionary pattern features related to physical degradation types, and obtaining enhanced evolutionary pattern features, includes: Read the result of the current cable's physical degradation type determination; Obtain available computing resources from the system; Based on the physical degradation type determination result, candidate mapping strategies that match the current physical degradation type are selected from the preset feature enhancement mapping strategy set; Based on the available computing resources of the system, the computational complexity of the candidate mapping strategies is evaluated, and candidate mapping strategies whose computational resource requirements exceed the available range of the system are eliminated to obtain the remaining candidate mapping strategies. Based on the remaining candidate mapping strategies, the enhancement effect and computational resource consumption of the energy-gathering features are comprehensively evaluated, and the target mapping strategy is selected. Using the target mapping strategy, feature enhancement mapping is performed on features with energy accumulation to obtain enhanced evolutionary pattern features.

7. A method for millimeter-wave transmission of electromagnetic interference signals via high-speed cables according to claim 5, characterized in that, The step of performing dynamic threshold judgment on the enhanced evolution mode features, distinguishing feature changes caused by the physical evolution of the degradation point itself from fluctuations caused by residual environmental noise or sensor drift, and obtaining the dynamic threshold judgment result includes: Acquire real-time data streams of enhanced evolution pattern features; Based on the real-time data stream, the statistical distribution parameters of the enhanced evolution pattern features within multiple time windows are calculated; the statistical distribution parameters include mean, variance, skewness, and kurtosis. Based on the statistical distribution parameters, a judgment interval containing an upper and lower limit is constructed; When the enhanced evolution mode feature does not exceed the judgment interval, it is determined that the enhanced evolution mode feature is in a stable state, and the stable state is used as the dynamic threshold judgment result. When the enhanced evolution pattern features exceed the judgment interval, it is marked as a potential feature change event; Determine the duration of the potential feature change event; When the duration of the potential feature change event exceeds a preset minimum duration threshold, it is determined to be a feature change caused by the physical evolution of the degradation point itself. When the duration of the potential feature change event does not exceed the preset minimum duration threshold, it is determined to be a fluctuation caused by residual environmental noise or sensor drift; The results of dynamic threshold judgment are determined as either the characteristic changes caused by the physical evolution of the degradation point itself or the fluctuations caused by residual environmental noise or sensor drift.

8. A method for millimeter-wave transmission of electromagnetic interference signals via high-speed cables according to claim 7, characterized in that, The step of calculating the statistical distribution parameters of the enhanced evolutionary pattern features over multiple time windows based on the real-time data stream includes: The real-time data stream is timestamped and data packets are encapsulated to obtain tagged data packets; The tagged data packets are buffered, and the buffer size and data extraction strategy are adjusted according to the sampling frequency and transmission delay of the real-time data stream to achieve time alignment between each real-time data stream, resulting in a time-aligned data stream. The time-aligned data stream is format-converted and normalized to eliminate differences in data format and units, resulting in a processed data stream. The processed data streams are merged to form a unified time-synchronized composite data stream; On the time-synchronized composite data stream, sliding window processing is performed according to multiple preset time window lengths and step sizes, and the mean, variance, skewness, and kurtosis are calculated for the data within each time window. The calculated mean, variance, skewness, and kurtosis are used as statistical distribution parameters.

9. A method for millimeter-wave transmission of electromagnetic interference signals via high-speed cables according to claim 8, characterized in that, The steps of caching the marked data packets and adjusting the buffer size and data extraction strategy according to the sampling frequency and transmission delay of the real-time data stream to achieve time alignment between each real-time data stream, and obtaining a time-aligned data stream, include: Obtain the current operating status of each cable; the current operating status includes cable load, physical degradation type judgment result, and degradation degree; Based on the current operating status and combined with historical data, predict the short-term trend of the sampling frequency and transmission delay of each data stream in the real-time data stream; Based on the predicted short-term trends of sampling frequency and transmission delay, and based on the system's currently available cache resources, the cache size of each data stream is dynamically adjusted to obtain the adjusted cache size. Based on the predicted short-term trends of sampling frequency and transmission delay, and based on the time alignment accuracy requirements of each data stream, the data extraction strategy for each data stream is dynamically adjusted to obtain the adjusted data extraction strategy; the data extraction strategy includes the data packet extraction order and extraction rate. Based on the adjusted buffer size and adjusted data extraction strategy, the marked data packets are cached and extracted to obtain a time-aligned data stream.

10. A high-speed cable electromagnetic interference signal millimeter-wave transmission system, used for performing high-speed cable electromagnetic interference signal millimeter-wave transmission, characterized in that, include: A sensor deployment module for deploying multiple millimeter-wave miniature sensors on a cable; The millimeter-wave micro-sensor is used to capture reflected signals inside the cable; The reflection signal processing module is used to continuously monitor the reflection signal inside the cable through the millimeter-wave micro sensor, and process the reflection signal to obtain reflection signal parameters; the reflection signal parameters include arrival time, reflection amplitude, pulse width, resonant frequency, resonant intensity, quality factor, and bandwidth. The degradation type determination module is used to analyze the reflected signal and compare the reflected signal parameters with the signal patterns corresponding to a variety of preset physical degradation types to determine the physical degradation type inside the cable and obtain the physical degradation type determination result. The degradation location determination module is used to determine the location of the physical degradation point based on the time difference of multiple reflected signals, thereby obtaining the physical degradation location; The fault warning execution module is used to assess the cable's operating status and issue a fault warning based on the physical degradation type judgment result, the physical degradation location, and the changing trend of the reflected signal parameters.

Images