A multi-band signal transmission optimization method for a glass composite cable

Abstract

The present application relates to a kind of glass composite cable multi-band signal transmission optimization method, comprising: by the channel state of cooperation detection and transmission signal, construct timing channel quality curve and timing signal characteristic curve.Based on multi-band transmission standard screening high-quality transmission sample set, using density clustering algorithm extracts channel reliable domain boundary.Through multi-parametric degradation synthesis identification comprehensive degradation trend direction, combined with channel reliable domain dynamic scanning accurately positioning channel degradation point.Using timing channel quality curve and timing signal characteristic curve generates band-performance mapping model, outputs performance attenuation curve, mutual interference gain and bandwidth utilization rate key parameters.Based on three color band state grading, screen uncoupled band combination and realize transmission optimization by dynamic weight distribution.Exponential fade-in fade-out mode is used to switch band combination, significantly improve spectrum utilization, effectively solve multi-band interference and channel degradation problem.

Description

Technical Field

[0001] This invention relates to the field of signal transmission, and more particularly to an optimization method for multi-band signal transmission via glass composite cables. Background Technology

[0002] Current multi-band signal transmission technologies suffer from two main shortcomings: First, existing systems struggle to dynamically track changes in channel conditions. Most solutions employ fixed frequency band configurations, which cannot be adjusted promptly in the event of sudden interference, leading to a significant deterioration in signal quality. Particularly when multiple frequency bands interfere with each other, some bands are often shut down directly to avoid conflicts, resulting in wasted spectrum resources and a widespread problem of low high-frequency band utilization.

[0003] On the other hand, the coordination efficiency of various stages in the transmission process is low. The coordination between channel detection, data analysis, and frequency band switching is not smooth, and the detection delay and switching jitter accumulate errors. Frequent hard handovers not only cause momentary signal interruptions but may also trigger a chain of bit errors, which can easily lead to communication interruptions in scenarios such as high-speed vehicle movement or passing through tunnels.

[0004] To address these shortcomings, two major issues urgently need to be addressed in this field: first, developing methods to detect channel changes in real time and automatically optimize frequency band allocation to improve spectrum utilization efficiency; and second, establishing an end-to-end collaborative control mechanism to achieve seamless coordination from state detection to execution switching, ensuring stable transmission in complex environments. Summary of the Invention

[0005] To address the shortcomings of existing technologies, embodiments of the present invention provide a method for optimizing multi-band signal transmission in glass composite cables, comprising the following steps:

[0006] The channel state detector responds to the received band detection command, scans the full-band channel state of the cable, and sends a first ready command after completing the scan; the multi-band signal generator responds to the received band detection command, loads preset band configuration parameters, and sends a second ready command after completing the loading.

[0007] When the time interval between the first and second ready commands is less than or equal to the time interval threshold, a signal transmission command is sent to the multi-band signal generator; if the time interval is greater than the time interval threshold, the frequency band detection command is resent; in response to receiving the signal transmission command, the multi-band signal generator synchronously transmits test signals in multiple frequency bands.

[0008] The channel state detector collects the channel performance vectors of each frequency band in real time, and the multi-band signal generator collects the signal transmission vectors of each frequency band in real time. Based on the cable propagation delay, the channel performance vectors and signal transmission vectors are spatiotemporally aligned to generate time-series channel quality curves and time-series signal characteristic curves.

[0009] Channel degradation constraints are generated based on the multi-band transmission standards retrieved from the database. Historical transmission datasets are loaded, and historical transmission data that meet the channel degradation constraints are selected to form a high-quality transmission sample set. Each high-quality transmission sample in the high-quality transmission sample set is mapped to a multi-dimensional feature space to obtain several high-quality channel state points.

[0010] Density boundaries are extracted from high-quality channel state points using clustering algorithms to construct a reliable channel domain.

[0011] Multi-parameter degradation synthesis is performed on the timing channel quality curve to identify the direction of the overall degradation trend, and the timing channel quality curve is dynamically scanned along the direction of the overall degradation trend. Then, the channel degradation point is identified based on the channel degradation constraints and the channel reliability domain.

[0012] A frequency band-performance mapping model is constructed based on the time-series channel quality curve and the time-series signal characteristic curve. Based on the frequency band-performance mapping model and the channel degradation point, the frequency band performance failure interval is located, and frequency band correlation analysis is performed to generate the optimal frequency band combination strategy composed of uncoupled frequency bands.

[0013] The frequency band co-optimizer switches the frequency band combination of the multi-band signal generator in an exponentially gradual-in and gradual-out manner according to the optimal frequency band combination strategy.

[0014] According to a preferred embodiment, the frequency band range specified by the frequency band detection command includes: low frequency band: 1-100MHz, mid frequency band: 100MHz-1GHz, and high frequency band: 1-10GHz.

[0015] According to a preferred embodiment, performing multi-parameter degradation synthesis on the timing channel quality curve to identify the direction of the overall degradation trend includes:

[0016] The timing channel quality curve is discretized according to a preset time window to obtain several channel time windows, and feature decomposition is performed on each channel time window to obtain the dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics of each channel time window.

[0017] A signal-to-noise ratio degradation vector is generated based on the dynamic characteristics of the signal-to-noise ratio of all channel time windows; a phase instability vector is generated based on the phase offset characteristics of all channel time windows; and a bit error mutation vector is generated based on the bit error anomaly characteristics of all channel time windows.

[0018] Define a time reference vector, and calculate the signal-to-noise ratio (SNR) degradation direction based on the SNR degradation vector and the time reference vector; calculate the phase instability direction based on the phase instability vector and the time reference vector; calculate the bit error mutation direction based on the bit error mutation vector and the time reference vector.

[0019] The vector entropy values ​​of the signal-to-noise ratio (SNR) degradation vector, phase instability vector, and bit error mutation vector are calculated respectively. Based on the vector entropy values ​​of the SNR degradation vector, phase instability vector, and bit error mutation vector, the weight coefficients of the SNR degradation direction, phase instability direction, and bit error mutation direction are determined. Based on the weight coefficients, the SNR degradation direction, phase instability direction, and bit error mutation direction are weighted and fused to generate a comprehensive degradation trend direction.

[0020] According to a preferred embodiment, the channel performance vector includes: signal-to-noise ratio, phase offset value, and bit error rate; the signal transmission vector includes: frequency point, power, and modulation method.

[0021] According to a preferred embodiment, generating channel degradation constraints based on a multi-band transmission standard includes:

[0022] The signal-to-noise ratio threshold, phase offset threshold, and bit error rate threshold are obtained based on the multi-band transmission standard.

[0023] The first constraint is generated based on the signal-to-noise ratio threshold, the second constraint is generated based on the phase offset threshold, and the third constraint is generated based on the bit error rate threshold.

[0024] The first, second, and third constraints are combined into channel degradation constraints.

[0025] According to a preferred embodiment, identifying channel degradation points based on channel degradation constraints and channel reliability domain includes:

[0026] The time-series channel quality curve is discretized into several channel time windows according to a preset time window. The dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics in each channel time window are extracted. Then, the feature values ​​of the dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics in each channel time window are encapsulated into channel state points to transform the time-series channel quality curve into a channel state point sequence.

[0027] The center point of the channel reliability domain is used as the scanning starting point, and a dynamic step-size scan is performed on the channel state point sequence along the direction of the comprehensive degradation trend; the step size is adjusted based on the ratio of the boundary distance between the channel state point and the channel reliability domain to the radius of the reliability domain; the boundary distance refers to the Euclidean distance from the channel state point to the nearest boundary of the channel reliability domain;

[0028] When a channel state point exceeds the boundary of the channel reliability domain, the corresponding channel state point is marked as a channel degradation point, and the frequency band identifier and time stamp of the corresponding channel degradation point are recorded simultaneously.

[0029] When the channel state point does not exceed the boundary of the channel reliability domain, the distance between the channel state point and the boundary of the channel reliability domain is detected. If the boundary distance is less than the boundary distance threshold and the channel state point breaks through the channel degradation constraint, the corresponding channel state point is marked as a candidate degradation point. The channel degradation constraint is that the channel state point does not satisfy any one of the first constraint, the second constraint, and the third constraint.

[0030] Perform transient verification on candidate degradation points. When a candidate degradation point passes the transient verification, mark it as a channel degradation point and simultaneously record the frequency band identifier and time stamp of the corresponding channel degradation point.

[0031] The transient verification process includes: detecting whether the signal-to-noise ratio change rate exceeds the signal-to-noise ratio change rate threshold, whether the phase shift change rate exceeds the phase shift change rate threshold, and whether the bit error rate increment exceeds the bit error rate increment threshold. If all three conditions are met, the transient verification is passed.

[0032] According to a preferred embodiment, the signal-to-noise ratio dynamic characteristic is obtained by calculating the signal-to-noise ratio change rate; the phase offset characteristic is obtained by calculating the phase offset change rate; and the bit error anomaly characteristic is obtained by calculating the bit error rate increment within a unit time window.

[0033] According to a preferred embodiment, the step of extracting the density boundary of high-quality channel state points using a clustering algorithm to construct a channel reliability domain includes:

[0034] Step 1: Input the high-quality channel state points into the density clustering algorithm, set the neighborhood radius to one-fifth of the maximum range difference of the feature values, and the minimum number of neighborhood points to five;

[0035] Step 2: Identify the cluster containing the most high-quality channel state points and use it as the core stable cluster to filter out discrete noise points;

[0036] Step 3: Perform non-convex boundary extraction on the core stable cluster and generate a set of boundary points using the alpha shape algorithm, where the alpha parameter is the reciprocal of the maximum distance between two points in the core stable cluster;

[0037] Step 4: Map the boundary point set into a three-dimensional polyhedron structure, and define the boundary of the polyhedron as the geometric boundary of the channel reliability domain;

[0038] Step 5: Calculate the average coordinates of all boundary points as the center point of the reliable domain, and calculate the distance from the center point to the farthest boundary point as the radius of the reliable domain.

[0039] According to a preferred embodiment, based on the frequency band-performance mapping model and channel degradation point location of frequency band performance failure intervals, performing frequency band correlation analysis to generate an optimal frequency band combination strategy composed of uncoupled frequency bands includes:

[0040] Step 1: Call the frequency band-performance mapping model, input the frequency band identifier and time stamp of the channel degradation point, and output the performance degradation curve of each frequency band;

[0041] Step 2: Mark consecutive time periods where the performance degradation value exceeds the performance degradation threshold as frequency band performance failure intervals, and perform time-domain merging on adjacent failure intervals;

[0042] Step 3: Calculate the cooperative failure coefficient and mutual interference gain of any two frequency bands based on the performance degradation curve output by the frequency band-performance mapping model. When the cooperative failure coefficient is greater than the failure coefficient threshold, the frequency interval is less than the frequency interval threshold, and the mutual interference gain is greater than the mutual interference gain threshold, they are determined to be a coupled frequency band pair.

[0043] Step 4: Construct a three-color frequency band status classification based on performance degradation values:

[0044] A frequency band is defined as green when the performance degradation value is less than or equal to the first degradation threshold; a frequency band is defined as yellow when the performance degradation value is greater than the first degradation threshold but less than the second degradation threshold; a frequency band is defined as red when the performance degradation value is greater than or equal to the second degradation threshold; and a frequency band is defined as red when the first degradation threshold is less than the second degradation threshold.

[0045] Step 5: Select uncoupled frequency band combinations from the available green frequency bands that meet the total bandwidth requirements;

[0046] Step 6: Calculate the bandwidth utilization rate of each frequency band. When the bandwidth of the green frequency band is insufficient, the yellow warning frequency band is added in descending order of bandwidth utilization rate.

[0047] The present invention has the following beneficial effects:

[0048] 1. By constructing a dynamic channel state analysis model and a multi-parameter degradation prediction mechanism, the system accurately identifies and avoids transmission interference areas. Based on real-time frequency band performance mapping and coupling analysis, it automatically generates the optimal frequency band combination strategy, enabling autonomous optimization and scheduling of spectrum resources in complex electromagnetic environments. This breaks through the limitations of traditional fixed frequency band configuration and significantly improves signal transmission quality and anti-interference capabilities.

[0049] 2. Based on dynamic frequency band status classification and intelligent bandwidth compensation strategy, it effectively improves spectrum utilization efficiency and maximizes the activation potential of spectrum resources by automatically selecting uncoupled frequency band combinations, significantly enhancing the effective transmission bandwidth under high load scenarios and solving the problem of spectrum idleness and conflict in traditional solutions. Attached Figure Description

[0050] Figure 1 A flowchart of a method for optimizing multi-band signal transmission in a glass composite cable, provided as an exemplary embodiment. Detailed Implementation

[0051] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0052] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0053] It should be understood that although the terms first, second, third, etc., may be used in this invention to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of this invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0054] See Figure 1 The method for optimizing multi-band signal transmission in glass composite cables according to the present invention includes:

[0055] S1. The channel state detector responds to the received frequency band detection command, scans the full-band channel state of the cable, and sends a first ready command after the scan is completed; the multi-band signal generator responds to the received frequency band detection command, loads preset frequency band configuration parameters, and sends a second ready command after the loading is completed.

[0056] Optionally, the frequency band range specified by the frequency band detection command includes: low frequency band: 1-100MHz, mid frequency band: 100MHz-1GHz, and high frequency band: 1-10GHz.

[0057] Optionally, the preset frequency band configuration parameters refer to the set of initial values ​​of frequency band operating characteristics loaded by the multi-band signal generator when responding to the frequency band detection command. Specifically, these values ​​include: frequency band range definition, basic transmit power of each frequency band, modulation mode configuration, and test signal waveform parameters.

[0058] S2. When the time interval between the first and second ready commands is less than or equal to the time interval threshold, a signal transmission command is sent to the multi-band signal generator; if the time interval is greater than the time interval threshold, the frequency band detection command is resent; in response to receiving the signal transmission command, the multi-band signal generator synchronously transmits test signals in multiple frequency bands.

[0059] Optionally, if the time interval between the first ready command and the second ready command is too long, it indicates that there may be a time deviation or response delay between the multi-band signal generator and the channel state detector, which will lead to inaccurate subsequent spatiotemporal alignment. Therefore, it is necessary to resend the band detection command to resynchronize and ensure that the channel state detector (receiver) and the multi-band signal generator (transmitter) start the test signal transmission in a strictly synchronized state.

[0060] S3. The channel state detector collects the channel performance vectors of each frequency band in real time, and the multi-band signal generator collects the signal transmission vectors of each frequency band in real time. Based on the cable propagation delay, the channel performance vectors and signal transmission vectors are spatiotemporally aligned to generate time-series channel quality curves and time-series signal characteristic curves.

[0061] Optionally, the channel performance vector includes: signal-to-noise ratio, phase offset, and bit error rate; the signal transmission vector includes: frequency, power, and modulation scheme.

[0062] Preferably, the time-series channel quality curve is a quantified sequence of channel performance collected in real time by the channel state detector. It includes a set of vectors showing the changes in signal-to-noise ratio, phase offset, and bit error rate of each frequency band over time. It is used to characterize the dynamic degradation trend of cable transmission quality and is the core input data for identifying channel degradation points.

[0063] Preferably, the timing signal characteristic curve is a timing sequence of transmission parameters synchronously recorded by a multi-band signal generator, including feature vectors of frequency point, transmission power, and modulation mode index changing over time, which is used to reflect the spatiotemporal attributes of the transmitted signal and provide an input benchmark for constructing a frequency band-performance mapping model.

[0064] Optionally, the timing channel quality curve and the timing signal characteristic curve are aligned by the cable propagation delay to form a spatiotemporal correlated data pair that jointly drives the construction of the frequency band-performance mapping model.

[0065] S4. Generate channel degradation constraints based on the multi-band transmission standards retrieved from the database, load the historical transmission dataset, and select historical transmission data that meet the channel degradation constraints to form a high-quality transmission sample set; map each high-quality transmission sample in the high-quality transmission sample set to a multi-dimensional feature space to obtain several high-quality channel state points.

[0066] In a preferred embodiment, generating channel degradation constraints based on a multi-band transmission standard includes:

[0067] The signal-to-noise ratio threshold, phase offset threshold, and bit error rate threshold are obtained based on the multi-band transmission standard.

[0068] The first constraint is generated based on the signal-to-noise ratio threshold, the second constraint is generated based on the phase offset threshold, and the third constraint is generated based on the bit error rate threshold.

[0069] The first, second, and third constraints are combined into channel degradation constraints.

[0070] Optionally, the signal-to-noise ratio threshold, phase offset threshold, and bit error rate threshold can be preset according to the actual situation.

[0071] Optionally, high-quality channel state points are non-degraded channel feature vectors selected from historical transmission datasets. They are formed by mapping high-quality samples that meet channel degradation constraints (signal-to-noise ratio / phase offset / bit error rate are all better than the threshold) onto a three-dimensional feature space. These points characterize the optimal transmission state of the cable under stable operating conditions and serve as the geometric reference point set for constructing the channel reliability domain.

[0072] S5. Density boundaries are extracted from high-quality channel state points using a clustering algorithm to construct a reliable channel domain.

[0073] In a preferred embodiment, the step of extracting density boundaries from high-quality channel state points using a clustering algorithm to construct a channel reliability domain includes:

[0074] Step 1: Input the high-quality channel state points into the density clustering algorithm, set the neighborhood radius to one-fifth of the maximum range difference of the feature values, and the minimum number of neighborhood points to five;

[0075] Step 2: Identify the cluster containing the most high-quality channel state points and use it as the core stable cluster to filter out discrete noise points;

[0076] Step 3: Perform non-convex boundary extraction on the core stable cluster and generate a set of boundary points using the alpha shape algorithm, where the alpha parameter is the reciprocal of the maximum distance between two points in the core stable cluster;

[0077] Step 4: Map the boundary point set into a three-dimensional polyhedron structure, and define the boundary of the polyhedron as the geometric boundary of the channel reliability domain;

[0078] Step 5: Calculate the average coordinates of all boundary points as the center point of the reliable domain, and calculate the distance from the center point to the farthest boundary point as the radius of the reliable domain.

[0079] S6. Perform multi-parameter degradation synthesis on the timing channel quality curve to identify the direction of the overall degradation trend, and dynamically scan the timing channel quality curve along the direction of the overall degradation trend. Then, identify the channel degradation point based on the channel degradation constraints and the channel reliability domain.

[0080] In a preferred embodiment, performing multi-parameter degradation synthesis on the time-series channel quality curve to identify the direction of the overall degradation trend includes:

[0081] The timing channel quality curve is discretized according to a preset time window to obtain several channel time windows, and feature decomposition is performed on each channel time window to obtain the dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics of each channel time window.

[0082] A signal-to-noise ratio degradation vector is generated based on the dynamic characteristics of the signal-to-noise ratio of all channel time windows; a phase instability vector is generated based on the phase offset characteristics of all channel time windows; and a bit error mutation vector is generated based on the bit error anomaly characteristics of all channel time windows.

[0083] Define a time reference vector, and calculate the signal-to-noise ratio (SNR) degradation direction based on the SNR degradation vector and the time reference vector; calculate the phase instability direction based on the phase instability vector and the time reference vector; calculate the bit error mutation direction based on the bit error mutation vector and the time reference vector.

[0084] The vector entropy values ​​of the signal-to-noise ratio (SNR) degradation vector, phase instability vector, and bit error mutation vector are calculated respectively. Based on the vector entropy values ​​of the SNR degradation vector, phase instability vector, and bit error mutation vector, the weight coefficients of the SNR degradation direction, phase instability direction, and bit error mutation direction are determined. Based on the weight coefficients, the SNR degradation direction, phase instability direction, and bit error mutation direction are weighted and fused to generate a comprehensive degradation trend direction.

[0085] Optionally, the signal-to-noise ratio degradation direction refers to the direction in which the dynamic characteristics of the signal-to-noise ratio degrade over time, reflecting the spatiotemporal evolution direction of signal quality attenuation in cable transmission.

[0086] Optionally, the phase instability direction refers to the direction of phase offset characteristic instability, which characterizes the spatiotemporal trajectory of carrier phase synchronization degradation.

[0087] Optionally, the direction of the sudden change in bit error rate refers to the direction of the sudden increase in the abnormal characteristics of bit error rate, marking the time-domain evolution axis of the abnormal increase in bit error rate.

[0088] Preferably, identifying channel degradation points based on channel degradation constraints and the channel reliability domain includes:

[0089] The time-series channel quality curve is discretized into several channel time windows according to a preset time window. The dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics in each channel time window are extracted. Then, the feature values ​​of the dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics in each channel time window are encapsulated into channel state points to transform the time-series channel quality curve into a channel state point sequence.

[0090] The center point of the channel reliability domain is used as the scanning starting point, and a dynamic step-size scan is performed on the channel state point sequence along the direction of the comprehensive degradation trend; the step size is adjusted based on the ratio of the boundary distance between the channel state point and the channel reliability domain to the radius of the reliability domain; the boundary distance refers to the Euclidean distance from the channel state point to the nearest boundary of the channel reliability domain;

[0091] When a channel state point exceeds the boundary of the channel reliability domain, the corresponding channel state point is marked as a channel degradation point, and the frequency band identifier and time stamp of the corresponding channel degradation point are recorded simultaneously.

[0092] When the channel state point does not exceed the boundary of the channel reliability domain, the distance between the channel state point and the boundary of the channel reliability domain is detected. If the boundary distance is less than the boundary distance threshold and the channel state point breaks through the channel degradation constraint, the corresponding channel state point is marked as a candidate degradation point. The channel degradation constraint is that the channel state point does not satisfy any one of the first constraint, the second constraint, and the third constraint.

[0093] Perform transient verification on candidate degradation points. When a candidate degradation point passes the transient verification, mark it as a channel degradation point and simultaneously record the frequency band identifier and time stamp of the corresponding channel degradation point.

[0094] The transient verification process includes: detecting whether the signal-to-noise ratio change rate exceeds the signal-to-noise ratio change rate threshold, whether the phase shift change rate exceeds the phase shift change rate threshold, and whether the bit error rate increment exceeds the bit error rate increment threshold. If all three conditions are met, the transient verification is passed.

[0095] Optionally, the dynamic step size = base step size × (boundary distance / reliable domain radius).

[0096] Optionally, the phase offset change rate threshold, the bit error rate increment threshold, and the bit error rate increment threshold can be preset according to the actual situation.

[0097] Optionally, the signal-to-noise ratio dynamic characteristics are obtained by calculating the signal-to-noise ratio change rate; the phase shift characteristics are obtained by calculating the phase shift change rate; and the bit error anomaly characteristics are obtained by calculating the bit error rate increment within a unit time window.

[0098] Optionally, a channel degradation point refers to a spatiotemporal marker point of an abnormal channel state identified through dynamic scanning.

[0099] S7. Construct a frequency band-performance mapping model based on the time-series channel quality curve and the time-series signal characteristic curve, and locate the frequency band performance failure interval based on the frequency band-performance mapping model and the channel degradation point. Perform frequency band correlation analysis to generate an optimal frequency band combination strategy composed of uncoupled frequency bands. The frequency band co-optimizer switches the frequency band combination of the multi-band signal generator in an exponentially gradual-in and gradual-out manner according to the optimal frequency band combination strategy.

[0100] In a preferred embodiment, based on the frequency band-performance mapping model and channel degradation points to locate frequency band performance failure intervals, performing frequency band correlation analysis to generate an optimal frequency band combination strategy composed of uncoupled frequency bands includes:

[0101] Step 1: Call the frequency band-performance mapping model, input the frequency band identifier and time stamp of the channel degradation point, and output the performance degradation curve of each frequency band;

[0102] Optionally, the performance degradation curve refers to the channel quality degradation function curve (time-attenuation relationship) output by the band-performance mapping model. The horizontal axis represents the time-stamped sequence, and the vertical axis represents the normalized performance degradation percentage, which is generated by combining the signal-to-noise ratio decrease rate, phase offset increment, and bit error rate increase. It is used to locate failure intervals and classify frequency band status.

[0103] Step 2: Mark consecutive time periods where the performance degradation value exceeds the performance degradation threshold as frequency band performance failure intervals, and perform time-domain merging on adjacent failure intervals;

[0104] Optionally, the performance degradation threshold can be set to 40%.

[0105] Step 3: Calculate the cooperative failure coefficient and mutual interference gain of any two frequency bands based on the performance degradation curve output by the frequency band-performance mapping model. When the cooperative failure coefficient is greater than the failure coefficient threshold, the frequency interval is less than the frequency interval threshold, and the mutual interference gain is greater than the mutual interference gain threshold, they are determined to be a coupled frequency band pair.

[0106] Optionally, the failure factor threshold can be set to 0.7, the frequency interval threshold can be set to 500MHz, and the mutual interference gain threshold can be set to 3dB.

[0107] Step 4: Construct a three-color frequency band status classification based on performance degradation values:

[0108] A frequency band is defined as green when the performance degradation value is less than or equal to the first degradation threshold; a frequency band is defined as yellow when the performance degradation value is greater than the first degradation threshold but less than the second degradation threshold; a frequency band is defined as red when the performance degradation value is greater than or equal to the second degradation threshold; and a frequency band is defined as red when the first degradation threshold is less than the second degradation threshold.

[0109] Optionally, the first attenuation threshold can be 15%, the second attenuation threshold can be 40%, the performance attenuation of the green available frequency band is ≤15%, the performance attenuation of the yellow warning frequency band is 15%-40%, and the performance attenuation of the red failed frequency band is ≥40%.

[0110] Optionally, the co-failure factor refers to the linear correlation strength of the performance degradation curves of two frequency bands in the time domain, reflecting the synchronous degradation trend of quality between frequency bands due to electromagnetic coupling or environmental interference. When the co-failure factor is greater than the failure factor threshold, it is determined that the coupled frequency bands should avoid being used in the same group.

[0111] Step 5: Select uncoupled frequency band combinations from the available green frequency bands that meet the total bandwidth requirements;

[0112] Step 6: Calculate the bandwidth utilization rate of each frequency band. When the bandwidth of the green frequency band is insufficient, the yellow warning frequency band is added in descending order of bandwidth utilization rate.

[0113] Preferably, the frequency band bandwidth utilization rate = (frequency band bandwidth × effective bandwidth coefficient) / total bandwidth × 100%.

[0114] Optionally, for the green band: effective bandwidth coefficient = 1.0; for the yellow band: effective bandwidth coefficient = (second attenuation threshold - attenuation value) / (second attenuation threshold - first attenuation threshold); and for the red band: effective bandwidth coefficient = 0.

[0115] This application constructs a dynamic channel state analysis model and a multi-parameter degradation prediction mechanism to accurately identify and avoid transmission interference areas. Based on real-time frequency band performance mapping and coupling analysis, it automatically generates optimal frequency band combination strategies, achieving autonomous optimization and scheduling of spectrum resources in complex electromagnetic environments. This breaks through the limitations of traditional fixed frequency band configurations and significantly improves signal transmission quality and anti-interference capabilities. Based on dynamic frequency band state classification and intelligent bandwidth compensation strategies, it effectively improves spectrum utilization efficiency and maximizes the activation potential of spectrum resources by automatically selecting uncoupled frequency band combinations. This significantly enhances the effective transmission bandwidth under high-load scenarios and solves the problem of spectrum idleness and conflict coexisting in traditional solutions.

[0116] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0117] The present invention discloses a non-transitory computer-readable storage medium storing computer instructions, which, when executed by a processor, cause the processor to perform the above-described method.

[0118] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware (e.g., processor, FPGA, ASIC, etc.), and the program can be stored in a readable storage medium, such as a read-only memory, a disk, or an optical disk. All or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module in the above embodiments can be implemented in hardware, such as by using integrated circuits to implement its corresponding function, or it can be implemented as a software functional module, such as by a processor executing a program / instruction stored in memory to implement its corresponding function. The embodiments of the present invention are not limited to any particular combination of hardware and software.

[0119] Furthermore, the functional units in the various embodiments of this document can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0120] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this paper, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this paper. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0121] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing multi-band signal transmission in glass composite cables, characterized in that, Includes the following steps: The channel state detector responds to the received band detection command, scans the full-band channel state of the cable, and sends a first ready command after completing the scan; the multi-band signal generator responds to the received band detection command, loads preset band configuration parameters, and sends a second ready command after completing the loading. When the time interval between the first and second ready commands is less than or equal to the time interval threshold, a signal transmission command is sent to the multi-band signal generator; if the time interval is greater than the time interval threshold, the frequency band detection command is resent; in response to receiving the signal transmission command, the multi-band signal generator synchronously transmits test signals in multiple frequency bands. The channel state detector collects the channel performance vectors of each frequency band in real time, and the multi-band signal generator collects the signal transmission vectors of each frequency band in real time. Based on the cable propagation delay, the channel performance vectors and signal transmission vectors are spatiotemporally aligned to generate time-series channel quality curves and time-series signal characteristic curves. Channel degradation constraints are generated based on the multi-band transmission standards retrieved from the database. Historical transmission datasets are loaded, and historical transmission data that meet the channel degradation constraints are selected to form a high-quality transmission sample set. Each high-quality transmission sample in the high-quality transmission sample set is mapped to a multi-dimensional feature space to obtain several high-quality channel state points. Density boundaries are extracted from high-quality channel state points using clustering algorithms to construct a reliable channel domain. Multi-parameter degradation synthesis is performed on the timing channel quality curve to identify the direction of the overall degradation trend, and the timing channel quality curve is dynamically scanned along the direction of the overall degradation trend. Then, the channel degradation point is identified based on the channel degradation constraints and the channel reliability domain. A frequency band-performance mapping model is constructed based on the time-series channel quality curve and the time-series signal characteristic curve. Based on the frequency band-performance mapping model and the channel degradation point, the frequency band performance failure interval is located. Frequency band correlation analysis is performed to generate an optimal frequency band combination strategy composed of uncoupled frequency bands. Herein, the uncoupled frequency bands refer to frequency bands that do not constitute a coupled frequency band pair. The frequency band co-optimizer switches the frequency band combination of the multi-band signal generator in an exponentially gradual-in and gradual-out manner according to the optimal frequency band combination strategy.

2. The transmission optimization method according to claim 1, characterized in that, The frequency band range specified by the frequency band detection command includes: low frequency band: 1-100MHz, mid frequency band: 100MHz-1GHz, and high frequency band: 1-10GHz.

3. The transmission optimization method according to claim 2, characterized in that, Multi-parameter degradation synthesis of time-series channel quality curves to identify the overall degradation trend includes: The timing channel quality curve is discretized according to a preset time window to obtain several channel time windows, and feature decomposition is performed on each channel time window to obtain the dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics of each channel time window. A signal-to-noise ratio degradation vector is generated based on the dynamic characteristics of the signal-to-noise ratio of all channel time windows; a phase instability vector is generated based on the phase offset characteristics of all channel time windows; and a bit error mutation vector is generated based on the bit error anomaly characteristics of all channel time windows. Define a time reference vector, and calculate the signal-to-noise ratio (SNR) degradation direction based on the SNR degradation vector and the time reference vector; calculate the phase instability direction based on the phase instability vector and the time reference vector; calculate the bit error mutation direction based on the bit error mutation vector and the time reference vector. The vector entropy values ​​of the signal-to-noise ratio (SNR) degradation vector, phase instability vector, and bit error mutation vector are calculated respectively. Based on the vector entropy values ​​of the SNR degradation vector, phase instability vector, and bit error mutation vector, the weight coefficients of the SNR degradation direction, phase instability direction, and bit error mutation direction are determined. Based on the weight coefficients, the SNR degradation direction, phase instability direction, and bit error mutation direction are weighted and fused to generate a comprehensive degradation trend direction.

4. The transmission optimization method according to claim 3, characterized in that, The channel performance vector includes: signal-to-noise ratio, phase offset, and bit error rate; the signal transmission vector includes: frequency, power, and modulation scheme.

5. The transmission optimization method according to claim 4, characterized in that, The channel degradation constraints generated based on the multi-band transmission standard include: The signal-to-noise ratio threshold, phase offset threshold, and bit error rate threshold are obtained based on the multi-band transmission standard. The first constraint is generated based on the signal-to-noise ratio threshold, the second constraint is generated based on the phase offset threshold, and the third constraint is generated based on the bit error rate threshold. The first, second, and third constraints are combined into channel degradation constraints.

6. The transmission optimization method according to claim 5, characterized in that, Identifying channel degradation points based on channel degradation constraints and channel reliability domain includes: The time-series channel quality curve is discretized into several channel time windows according to a preset time window. The dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics in each channel time window are extracted. Then, the feature values ​​of the dynamic characteristics of signal-to-noise ratio, phase offset characteristics and bit error anomaly characteristics in each channel time window are encapsulated into channel state points to transform the time-series channel quality curve into a channel state point sequence. The center point of the channel reliability domain is used as the scanning starting point, and a dynamic step-size scan is performed on the channel state point sequence along the direction of the comprehensive degradation trend; the step size is adjusted based on the ratio of the boundary distance between the channel state point and the channel reliability domain to the radius of the reliability domain; the boundary distance refers to the Euclidean distance from the channel state point to the nearest boundary of the channel reliability domain; When a channel state point exceeds the boundary of the channel reliability domain, the corresponding channel state point is marked as a channel degradation point, and the frequency band identifier and time stamp of the corresponding channel degradation point are recorded simultaneously. When the channel state point does not exceed the boundary of the channel reliability domain, the distance between the channel state point and the boundary of the channel reliability domain is detected. If the boundary distance is less than the boundary distance threshold and the channel state point breaks through the channel degradation constraint, the corresponding channel state point is marked as a candidate degradation point. The channel degradation constraint is that the channel state point does not satisfy any one of the first constraint, the second constraint, and the third constraint. Perform transient verification on candidate degradation points. When a candidate degradation point passes the transient verification, mark it as a channel degradation point and simultaneously record the frequency band identifier and time stamp of the corresponding channel degradation point. The transient verification process includes: detecting whether the signal-to-noise ratio change rate exceeds the signal-to-noise ratio change rate threshold, whether the phase shift change rate exceeds the phase shift change rate threshold, and whether the bit error rate increment exceeds the bit error rate increment threshold. If all three conditions are met, the transient verification is passed.

7. The transmission optimization method according to claim 6, characterized in that, The signal-to-noise ratio dynamic feature is obtained by calculating the signal-to-noise ratio change rate; the phase offset feature is obtained by calculating the phase offset change rate; and the bit error rate anomaly feature is obtained by calculating the bit error rate increment within a unit time window.

8. The transmission optimization method according to claim 7, characterized in that, The step of extracting density boundaries from high-quality channel state points using a clustering algorithm to construct a reliable channel domain includes: Step 1: Input the high-quality channel state points into the density clustering algorithm, set the neighborhood radius to one-fifth of the maximum range difference of the feature values, and the minimum number of neighborhood points to five; Step 2: Identify the cluster containing the most high-quality channel state points and use it as the core stable cluster to filter out discrete noise points; Step 3: Perform non-convex boundary extraction on the core stable cluster and generate a set of boundary points using the alpha shape algorithm, where the alpha parameter is the reciprocal of the maximum distance between two points in the core stable cluster; Step 4: Map the boundary point set to a three-dimensional polyhedron structure, and define the boundary of the three-dimensional polyhedron as the geometric boundary of the channel reliability domain; Step 5: Calculate the average coordinates of all boundary points as the center point of the reliable domain, and calculate the distance from the center point to the farthest boundary point as the radius of the reliable domain.

9. The transmission optimization method according to claim 8, characterized in that, Based on the frequency band-performance mapping model and channel degradation point location of frequency band performance failure intervals, frequency band correlation analysis is performed to generate an optimal frequency band combination strategy composed of uncoupled frequency bands, including: Step 1: Call the frequency band-performance mapping model, input the frequency band identifier and time stamp of the channel degradation point, and output the performance degradation curve of each frequency band; Step 2: Mark consecutive time periods where the performance degradation value exceeds the performance degradation threshold as frequency band performance failure intervals, and perform time-domain merging on adjacent failure intervals; Step 3: Calculate the cooperative failure coefficient and mutual interference gain of any two frequency bands based on the performance degradation curve output by the frequency band-performance mapping model. When the cooperative failure coefficient is greater than the failure coefficient threshold, the frequency interval is less than the frequency interval threshold, and the mutual interference gain is greater than the mutual interference gain threshold, they are determined to be a coupled frequency band pair. Step 4: Construct a three-color frequency band status classification based on performance degradation values: A frequency band is defined as green when the performance degradation value is less than or equal to the first degradation threshold; a frequency band is defined as yellow when the performance degradation value is greater than the first degradation threshold but less than the second degradation threshold; a frequency band is defined as red when the performance degradation value is greater than or equal to the second degradation threshold; and a frequency band is defined as red when the first degradation threshold is less than the second degradation threshold. Step 5: Select uncoupled frequency band combinations from the available green frequency bands that meet the total bandwidth requirements; Step 6: Calculate the bandwidth utilization rate of each frequency band. When the bandwidth of the green frequency band is insufficient, the yellow warning frequency band is added in descending order of bandwidth utilization rate.

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