Method and system for multi-channel synchronous acquisition and analysis of ultra-wideband signals

Abstract

The application provides a kind of multi-channel synchronous acquisition analysis method and system of ultra-wideband signal, starts the cooperative trigger acquisition of multi-channel acquisition unit to ultra-wideband signal, generates multi-channel acquisition signal and the time mark sequence corresponding to each channel with multiple time node information.The multi-channel acquisition signal and time mark sequence are input into the pre-trained synchronization verification AI model, the time correlation of each channel acquisition signal is analyzed, and the synchronization verification result is obtained.According to the result, the time sequence of the channel acquisition signal with time deviation is adjusted by calling the deviation correction module, and the synchronized multi-channel acquisition signal is obtained.The synchronized signal is input into the signal feature analysis AI model, the frequency band distribution and signal integrity are analyzed, and the multi-channel signal analysis result is obtained.Based on the analysis result, the acquisition analysis report containing the signal quality description and cooperative acquisition suggestion of each channel is generated and sent to the target signal processing terminal.The application improves the accuracy and comprehensiveness of ultra-wideband signal acquisition and analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a multi-channel synchronous acquisition and analysis method and system of an ultra-wideband signal. BACKGROUND

[0002] With the rapid development of communication technology, the ultra-wideband signal has been widely used in radar, wireless communication, imaging and many other fields due to its high bandwidth, high-speed transmission and other characteristics. However, the acquisition of the ultra-wideband signal faces many challenges.

[0003] The traditional single-channel acquisition method cannot meet the demand for multi-dimensional and comprehensive acquisition of the ultra-wideband signal, and multi-channel acquisition becomes an inevitable choice. However, in the multi-channel acquisition process, due to the differences in hardware performance of each channel, different signal transmission paths and other factors, the signals acquired by each channel often have time deviations, which leads to the fact that the acquired signals cannot accurately reflect the real characteristics of the ultra-wideband signal.

[0004] At the same time, the existing multi-channel acquisition and analysis method mostly only focuses on the simple splicing and preliminary processing of the signal, lacks in-depth analysis of the time correlation of the signals acquired by each channel and a precise synchronization adjustment mechanism. The analysis of the frequency band distribution and signal integrity of the acquired signal is also relatively rough, which makes it difficult to provide comprehensive and accurate signal quality evaluation and collaborative acquisition suggestions, and cannot meet the demand for high-quality acquisition and analysis of the ultra-wideband signal in complex application scenarios. SUMMARY

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present application, the embodiments of the present application provide a multi-channel synchronous acquisition and analysis method of an ultra-wideband signal, which comprises:

[0006] starting a multi-channel acquisition unit to collaboratively trigger the acquisition of the ultra-wideband signal, generating a multi-channel acquisition signal and a time mark sequence corresponding to each channel, the time mark sequence containing multiple time node information when each channel acquires the ultra-wideband signal;

[0007] inputting the multi-channel acquisition signal and the time mark sequence into a pre-trained synchronization verification AI model, analyzing the time correlation of the signals acquired by each channel through the synchronization verification AI model, and obtaining a synchronization verification result;

[0008] According to the synchronization verification result, calling a deviation correction module to adjust the time sequence of the channel acquisition signal with time deviation, and obtaining a synchronized multi-channel acquisition signal;

[0009] inputting the synchronized multi-channel acquisition signal into a signal feature analysis AI model, analyzing the frequency band distribution and signal integrity of the synchronized multi-channel acquisition signal through the signal feature analysis AI model, and obtaining a multi-channel signal analysis result;

[0010] Based on the multi-channel signal analysis result, a collection analysis report containing channel signal quality description and cooperative collection suggestion is generated, and the collection analysis report is sent to a target signal processing terminal.

[0011] In another aspect, the embodiment of the present application also provides a multi-channel synchronous collection analysis system for ultra-wideband signals, which comprises a processor and a machine readable storage medium, the machine readable storage medium is connected with the processor, the machine readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the machine readable storage medium to realize the above method.

[0012] Based on the above aspects, the embodiment of the present application triggers cooperative collection by starting a multi-channel collection unit, generates multi-channel collection signals containing time node information and time mark sequences. By using a pre-trained synchronous verification AI model to deeply analyze the time correlation of each channel collection signal, the channel with time deviation and the degree of deviation can be accurately identified, providing a reliable basis for deviation correction. Calling the deviation correction module to adjust the time sequence of the channel collection signal with time deviation effectively solves the problem of time asynchronization of multi-channel collection signals, obtains highly synchronized multi-channel collection signals, and ensures that the collection signals can truly reflect the characteristics of ultra-wideband signals. Inputting the synchronized multi-channel collection signals into a signal feature analysis AI model to analyze the frequency band distribution and signal integrity in detail, the quality status of each channel signal can be comprehensively understood. Based on the multi-channel signal analysis result, a collection analysis report containing channel signal quality description and cooperative collection suggestion is generated and sent to a target signal processing terminal, which significantly improves the accuracy, comprehensiveness and practicality of ultra-wideband signal collection and analysis, and has significant technical advantages and application value in the field of ultra-wideband signal processing. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is an execution flow diagram of the multi-channel synchronous collection analysis method for ultra-wideband signals provided by the embodiment of the present application.

[0014] Figure 2 is a schematic diagram of exemplary hardware and software components of the multi-channel synchronous collection analysis system for ultra-wideband signals provided by the embodiment of the present application. DETAILED DESCRIPTION

[0015] The present application will be described in detail below with reference to the accompanying drawings, Figure 1 is a flow diagram of the multi-channel synchronous collection analysis method for ultra-wideband signals provided by an embodiment of the present application, which will be described in detail below.

[0016] Step S110: Start the multi-channel acquisition unit to cooperatively trigger the acquisition of the ultra-wideband signal, generate a multi-channel acquisition signal and a time marker sequence corresponding to each channel, and the time marker sequence contains multiple time node information of each channel when acquiring the ultra-wideband signal.

[0017] After the central control unit receives the signal acquisition instruction of the ultra-wideband radar system, it first analyzes the sampling frequency, acquisition time, signal gain and other acquisition parameters in the instruction, and then sends a start instruction to the multi-channel acquisition unit. After receiving the instruction, the multi-channel acquisition unit triggers the control module to generate a unified channel trigger signal to ensure that all acquisition channels start ultra-wideband signal acquisition at the same time. During the acquisition process, the analog-to-digital conversion module of each channel converts the ultra-wideband analog signal into a digital ultra-wideband acquisition signal, and the time marker module built-in each channel generates time node information for each sampling point. These time node information are arranged in the order of acquisition to form the time marker sequence of each channel. The ultra-wideband acquisition signals of all channels are combined to form a multi-channel acquisition signal, and the multi-channel acquisition signal and the time marker sequence of each channel are associated through channel identification.

[0018] Step S111: Send a unified channel trigger signal to each acquisition channel in the multi-channel acquisition unit, and the channel trigger signal is used to trigger each acquisition channel to start acquiring the ultra-wideband signal at the same time.

[0019] The trigger control module of the multi-channel acquisition unit includes a signal generator and a signal distribution unit. The signal generator generates an initial trigger signal according to the acquisition parameters analyzed by the central control unit. The amplitude of the initial trigger signal needs to cover the trigger threshold range of all acquisition channels, the pulse width matches the sampling period of the analog-to-digital conversion module, and the frequency is consistent with the carrier frequency of the ultra-wideband radar signal. The signal distribution unit copies the initial trigger signal into channel trigger signals with the same number as the acquisition channels, and ensures that the amplitude, pulse width, frequency and other parameters of all channel trigger signals are completely consistent through the calibration circuit, avoiding the difference in start time caused by the difference in signal parameters of each channel.

[0020] Step S1111: Start the trigger control module of the multi-channel acquisition unit to generate an initial trigger signal, and the signal amplitude and pulse width of the initial trigger signal meet the trigger requirements of each acquisition channel.

[0021] After the signal generator of the trigger control module is started, the requirements of the trigger signal in the acquisition parameters issued by the central control unit are read, including the minimum trigger amplitude, the maximum tolerance amplitude and the minimum recognition pulse width of each acquisition channel. The signal generator generates an initial trigger signal through an internal waveform generation circuit, the amplitude value is between a reasonable multiple of the minimum trigger amplitude and a reasonable multiple of the maximum tolerance amplitude of each channel, the pulse width value is a reasonable multiple of the minimum recognition pulse width of each channel, and the waveform is set to square wave to reduce the interference of edge transition on trigger recognition. After generation, the signal detection circuit detects the initial trigger signal parameters in real time, and adjusts the waveform generation circuit parameters if the parameters exceed the preset range until all parameters meet the requirements.

[0022] Step S1112: copying the initial trigger signal into channel trigger signals consistent with the number of acquisition channels through the signal distribution unit of the trigger control module, to ensure that the signal parameters of each channel trigger signal are completely the same.

[0023] The signal distribution unit includes independent signal replication branches consistent with the number of acquisition channels, and each branch corresponds to an acquisition channel. The signal distribution unit inputs the initial trigger signal into all branches at the same time, and each branch replicates the signal through a high-speed operational amplifier and an impedance matching circuit. Before replication, each branch needs to be calibrated: a standard signal with known parameters is input through a standard signal source, the output signal parameters of each branch are measured, and if there is deviation, the branch element parameters are adjusted until the output signal and the standard signal parameters are within a reasonable range of error. During the replication process, the parameter monitoring module compares the parameters of each channel trigger signal and the initial trigger signal in real time, and corrects if the deviation exceeds a reasonable range, to ensure that all channel trigger signal parameters are consistent.

[0024] Step S1113: transmission delay detection is performed on each channel trigger signal, and the transmission time of the channel trigger signal from the trigger control module to each acquisition channel is recorded.

[0025] A delay detection module is arranged on the transmission line between the trigger control module and each acquisition channel, including a signal sending end detector, a signal receiving end detector and a reflected signal detection unit. When the signal distribution unit outputs the channel trigger signal, the sending end detector records the signal output time; during signal transmission, the reflection unit reflects part of the signal back to the trigger control module, and the reflected signal detection unit records the reflected signal receiving time; after the acquisition channel receives the signal, the trigger receiving module sends a confirmation signal to the receiving end detector, and the receiving end detector records the confirmation signal receiving time. Through the difference between the reflected signal receiving time and the output time, combined with the distance between the reflection unit and the acquisition channel and the signal propagation speed, the transmission time of the channel trigger signal from the trigger control module to the acquisition channel is calibrated. This detection is performed on all channels, and a transmission time list is sorted and stored.

[0026] Step S1114: According to the transmission time, the sending time of each channel trigger signal is fine-tuned to ensure that all channel trigger signals can reach the corresponding acquisition channel at the same time.

[0027] The time adjustment unit reads the transmission time list and extracts the maximum transmission time as the reference transmission time. For each acquisition channel, the difference between the reference transmission time and the channel transmission time is calculated, which is the time that the channel trigger signal needs to be sent in advance. The time adjustment unit assigns the sending time to each channel according to the difference and transmits it to the time sequence control module of the signal distribution unit. The time sequence control module controls the output signal of each branch at the set time. After fine-tuning, the arrival time of each channel trigger signal is detected again. If the difference in arrival time exceeds a reasonable range, the fine-tuning is restarted until all signals arrive at the same time.

[0028] Step S1115: The fine-tuned channel trigger signal is sent to each acquisition channel to trigger each acquisition channel to start collecting ultra-wideband signals at the same time.

[0029] The signal distribution unit sends the fine-tuned channel trigger signal to the corresponding acquisition channel through the high-speed transmission line at the time set by the time sequence control module. After the trigger receiving module of the acquisition channel receives the signal, it sends a start instruction to the central control submodule. The central control submodule starts each module in order: first, start the ultra-wideband signal receiving module to receive the analog signal and transmit it to the signal conditioning module; the signal conditioning module filters, amplifies, and denoises the analog signal; finally, start the analog-to-digital conversion module to convert the conditioned analog signal into a digital ultra-wideband acquisition signal, achieving simultaneous acquisition of all channels.

[0030] Step S112: At the same time as each acquisition channel starts collecting ultra-wideband signals, the time marker module built-in each acquisition channel is started to generate a corresponding time marker for each acquisition channel. The time marker corresponds one-to-one to the sampling points of the acquisition signal.

[0031] The time marker module and the analog-to-digital conversion module of each acquisition channel are connected through a synchronous clock line and use a high-precision clock source to ensure clock synchronization. When the central control submodule starts the analog-to-digital conversion module, it also sends a start signal to the time marker module. The high-precision clock unit of the time marker module starts running and provides complete time information containing multiple dimensions. The marker generation unit is synchronized with the sampling clock of the analog-to-digital conversion module. Whenever the analog-to-digital conversion module outputs a sampling point, the sampling clock sends a synchronization pulse to the marker generation unit, which reads the current time information and combines it into a time marker in a pre-set format. To ensure one-to-one correspondence, each time marker is assigned the same identifier as the sampling point number, which is incremented sequentially to avoid misplacement.

[0032] Step S113: Real-time store the ultra-wideband collection signal output by each collection channel through the signal storage module of the multi-channel collection unit to form a single-channel collection signal.

[0033] The signal storage module adopts a distributed storage architecture and contains as many independent storage partitions as the number of collection channels, each of which is configured with a high-speed solid-state disk and a data cache chip. After the analog-to-digital conversion module of each collection channel outputs the ultra-wideband collection signal, the signal is transmitted to the corresponding storage partition through a high-speed serial bus. During transmission, the data cache chip temporarily caches the signal, and when the amount of cached data reaches a preset threshold, sends a write request to the storage control module, which controls the high-speed solid-state disk to write the cached data in sequence. To ensure data integrity, the cyclic redundancy check technology is used: the analog-to-digital conversion module calculates a check value when transmitting the signal, and the storage partition recalculates the check value after receiving it and compares it. If they are not the same, retransmission is required until the check passes. The complete signal stored in each partition is the single-channel collection signal of the corresponding channel.

[0034] Step S114: Organize the time markers corresponding to each single-channel collection signal and arrange them in the order of collection time to form a time marker sequence corresponding to each channel. Each time node information in the time marker sequence is associated with the sampling point position of the single-channel collection signal.

[0035] The data analysis module reads the time markers corresponding to each single-channel collection signal from the signal storage module. Due to transmission or caching, the time markers may be arranged in disorder, so they need to be sorted by time dimension level to ensure that they are arranged in the order of collection time to form a time marker sequence. Then, the sampling points of the single-channel collection signal are assigned position indexes in the order of storage, and each time marker in the time marker sequence is associated with the corresponding sampling point to ensure that the sampling point can be quickly located through the time marker.

[0036] Step S115: Aggregate the single-channel collection signals of all collection channels to form a multi-channel collection signal and store the multi-channel collection signal in association with the time marker sequences corresponding to each channel.

[0037] The data analysis module reads the single-channel collection signals from each storage partition and aggregates them by channel identifier to form a multi-channel collection signal. In the data structure of the multi-channel collection signal, a time marker sequence field is added to each single-channel collection signal of each channel to store the time marker sequence of the corresponding channel, realizing the association between the two. After the association, the data is stored in the aggregation storage partition of the signal storage module, and an index file is established to record the storage addresses of each channel identifier, single-channel collection signal, and time marker sequence.

[0038] Step S120: input the multi-channel acquisition signal and the time mark sequence into the pre-trained synchronization verification AI model, analyze the time correlation of each channel acquisition signal through the synchronization verification AI model, and obtain a synchronization verification result.

[0039] The AI model processing module reads the multi-channel acquisition signal and the channel time mark sequence from the summary storage partition of the signal storage module, and inputs them into the pre-trained synchronization verification AI model. The synchronization verification AI model is trained based on historical data of multi-channel synchronization acquisition of ultra-wideband signals, and includes a time sequence mapping layer, an association analysis layer, and a deviation determination layer. Each layer is connected through full connection, and the parameters have been adjusted to the optimal. The time sequence mapping layer establishes the association mapping between the sampling points and the time nodes. The association analysis layer calculates the time offset of other channels based on a certain channel. The deviation determination layer determines the deviation and degree according to a preset threshold. Finally, the integrated result forms a synchronization verification result, reflecting the time synchronization state of each channel.

[0040] Step S121: split the multi-channel acquisition signal into multiple single-channel acquisition signals, and extract the corresponding time mark sequence of each channel.

[0041] After the input layer of the synchronization verification AI model receives the multi-channel acquisition signal and the time mark sequence, it splits the multi-channel acquisition signal into multiple independent single-channel acquisition signals according to the channel identifier in the multi-channel acquisition signal data structure. Each single-channel acquisition signal only contains the ultra-wideband acquisition signal data of one channel. At the same time, the input layer extracts the time mark sequence corresponding to each single-channel acquisition signal from the associated time mark sequence according to the channel identifier, ensuring that the split single-channel acquisition signal and the time mark sequence correspond one-to-one, and then transmits them to the time sequence mapping layer.

[0042] Step S122: input each single-channel acquisition signal and the corresponding time mark sequence into the time sequence mapping layer of the synchronization verification AI model, and establish the association mapping relationship between the acquisition signal sampling points and the time node information through the time sequence mapping layer.

[0043] The time sequence mapping layer includes a feature extraction sub-layer and a mapping establishment sub-layer. After receiving the single-channel acquisition signal and the time marker sequence, the feature extraction sub-layer extracts the signal amplitude, signal phase and sample point number of each sample point in the single-channel acquisition signal, and extracts the multi-dimensional time field information of each time marker in the time marker sequence, converts it into a unified format time feature vector, and all time feature vectors are consistent in dimension. The mapping establishment sub-layer takes the sample point number of each sample point as an index, and binds the signal amplitude, signal phase and corresponding time feature vector of the sample point to form a mapping unit containing the sample point number, signal amplitude, signal phase and time feature vector. All mapping units are arranged in order of sample point number to form the correlation mapping relationship of the single-channel acquisition signal. After the time sequence mapping layer establishes the correlation mapping relationship for all single-channel acquisition signals, it is transmitted to the correlation analysis layer.

[0044] Step S123: Through the correlation analysis layer of the synchronization verification AI model, taking the correlation mapping relationship of one of the channels as a reference, calculate the time offset of the correlation mapping relationship of other channels and the reference correlation mapping relationship.

[0045] The correlation analysis layer includes a reference selection sub-layer, a time node extraction sub-layer and an offset calculation sub-layer, and the specific execution process is as follows:

[0046] Step S1231: Randomly select one of the correlation mapping relationships of each channel as a reference correlation mapping relationship, and record the channel identifier corresponding to the reference correlation mapping relationship as the reference channel identifier.

[0047] The reference selection sub-layer traverses the correlation mapping relationships of all channels, obtains the channel identifiers corresponding to each correlation mapping relationship, uses a random selection algorithm based on a random number sequence to randomly select one from the channel identifiers, determines the correlation mapping relationship corresponding to the identifier as the reference correlation mapping relationship, and records the channel identifier as the reference channel identifier. Then, the reference correlation mapping relationship, the reference channel identifier and the correlation mapping relationships of other non-reference channels are transmitted to the time node extraction sub-layer.

[0048] Step S1232: Extract all time node information in the reference correlation mapping relationship to form a reference time node set.

[0049] After receiving the reference correlation mapping relationship, the time node extraction sub-layer traverses each mapping unit in it, extracts the time feature vector in each mapping unit, combines the multi-dimensional time field information contained in the time feature vector into a complete time string, and takes it as the time node information corresponding to the mapping unit. All time node information is arranged in order of the sample point number corresponding to the mapping unit to form a reference time node set, which is transmitted to the offset calculation sub-layer.

[0050] Step S1233: For each non-reference channel, extract the time node information in the association mapping relationship to form a non-reference time node set.

[0051] The time node extraction sub-layer performs the same processing on the association mapping relationship of each non-reference channel as the reference association mapping relationship: traverses the mapping units in the association mapping relationship, extracts the time feature vector of each mapping unit and combines it into time node information, arranges it in order of sampling point number, forms a non-reference time node set corresponding to each non-reference channel, and transmits it to the offset calculation sub-layer.

[0052] Step S1234: Calculate the time difference value of each time node in the non-reference time node set and the corresponding sampling point in the reference time node set through the association analysis layer of the synchronization verification AI model.

[0053] After the offset calculation sub-layer receives the reference time node set and each non-reference time node set, it matches according to the sampling point number, determines the time node in the reference time node set corresponding to each time node in the non-reference time node set (i.e. the time node corresponding to the sampling point with the same number), and then calculates the time difference value of each non-reference time node and the corresponding reference time node. The calculation of the time difference value is based on the multi-dimensional time information contained in the time string, and the difference value is calculated and integrated in order of time level to obtain the time difference value of each corresponding sampling point, and all time difference values are recorded.

[0054] Step S1235: Statistically analyze the time difference value and take the average value of the time difference value as the time offset of the non-reference channel relative to the reference channel.

[0055] The offset calculation sub-layer statistically analyzes all time difference values corresponding to each non-reference channel, removes obviously abnormal time difference values (such as difference values exceeding a reasonable range), calculates the average value of the remaining time difference values, and the average value is the time offset of the non-reference channel relative to the reference channel. The time offset of each non-reference channel is recorded and transmitted to the deviation determination layer.

[0056] Step S124: Determine the channel with time deviation and the corresponding deviation degree according to the preset offset threshold value through the deviation determination layer of the synchronization verification AI model.

[0057] The deviation determination layer calls a preset offset threshold from a built-in parameter library, which is set based on the accuracy requirement of the multi-channel synchronous acquisition of the ultra-wideband signal. The time offset of each non-reference channel is compared with the offset threshold: if the absolute value of the time offset is less than or equal to the offset threshold, it is determined that the non-reference channel has no time deviation; if the absolute value of the time offset is greater than the offset threshold, it is determined that the non-reference channel has time deviation. At the same time, according to the ratio of the time offset to the offset threshold, the degree of deviation (such as mild deviation, moderate deviation, and severe deviation) is determined. The smaller the ratio, the lighter the degree of deviation, and the larger the ratio, the heavier the degree of deviation. The channel identifier and the corresponding degree of deviation are recorded.

[0058] Step S125: integrate the time offset, the channel identifier with time deviation, and the degree of deviation to generate a synchronization check result containing the description of the synchronization state of each channel.

[0059] The deviation determination layer integrates the time offset of all channels (the time offset of the reference channel is set to zero), the channel identifier with time deviation, and the corresponding degree of deviation, and adds the synchronization state description of each channel (such as “no deviation” and “mild deviation”), and arranges the synchronization check result in a preset format to form a synchronization check result, which is transmitted to the deviation correction module of the system.

[0060] Step S130: according to the synchronization check result, the deviation correction module is called to adjust the time sequence of the channel acquisition signal with time deviation to obtain a synchronized multi-channel acquisition signal.

[0061] After receiving the synchronization check result, the deviation correction module first analyzes the key information in the result, and then generates adjustment parameters based on these information to adjust the time sequence of the channel acquisition signal with deviation. After adjustment, the time marker sequence is regenerated and verified, and finally the synchronized multi-channel acquisition signal is formed to ensure that the channel acquisition signals are aligned in the time dimension.

[0062] Step S131: analyze the synchronization check result to extract the channel identifier with time deviation, the corresponding time offset, and the degree of deviation.

[0063] After receiving the synchronization check result, the analysis unit of the deviation correction module extracts the information in the preset data format: identifies all channel identifiers with time deviation to determine the channels that need to be adjusted; extracts the time offset corresponding to each deviation channel to determine the amount of time adjustment; extracts the degree of deviation of each deviation channel as a reference for accuracy control during adjustment (such as more precise adjustment control for severe deviation); and arranges the extracted information into a deviation adjustment list and transmits it to the parameter generation unit.

[0064] Step S132: According to the time offset, generate the time sequence adjustment parameter corresponding to each deviation channel, and the time sequence adjustment parameter includes the time offset direction and the adjustment amplitude.

[0065] After the parameter generation unit receives the deviation adjustment list, the time offset of each deviation channel is analyzed: if the time offset is positive, it means that the acquisition signal of the deviation channel lags behind the reference channel, and the time offset direction is set to "advance"; if the time offset is negative, it means that the acquisition signal of the deviation channel leads the reference channel, and the time offset direction is set to "delay". The value of the adjustment amplitude is equal to the absolute value of the time offset, and the adjustment amplitude is fine-tuned in combination with the deviation degree (for example, when the deviation is severe, a small compensation amount is added to the adjustment amplitude to ensure the adjustment accuracy), to form the time sequence adjustment parameter of each deviation channel, which is transmitted to the time sequence adjustment unit.

[0066] Step S133: Input the time sequence adjustment parameter and the corresponding deviation channel acquisition signal into the deviation correction module, and perform translation adjustment on the sampling point time sequence of the deviation channel acquisition signal through the deviation correction module.

[0067] The time sequence adjustment unit reads the corresponding deviation channel acquisition signal (i.e. the single-channel acquisition signal of the deviation channel) from the signal storage module, and inputs the time sequence adjustment parameter and the deviation channel acquisition signal into the sampling point adjustment submodule of the deviation correction module. The sampling point adjustment submodule first indexes the sampling points of the deviation channel acquisition signal in time sequence to ensure that the position of each sampling point can be accurately located. If the time offset direction in the time sequence adjustment parameter is "advance", all sampling points of the deviation channel acquisition signal are translated in the direction of advance on the time axis, and the translation time length is equal to the adjustment amplitude. The specific operation is to delete the sampling points corresponding to the adjustment amplitude time length at the starting end of the sampling point sequence, and supplement the same number of sampling points generated based on the trend interpolation of adjacent sampling points at the end of the sampling point sequence, to ensure that the total number of adjusted sampling points is consistent with the total number of original sampling points. If the time offset direction is "delay", all sampling points of the deviation channel acquisition signal are translated in the direction of delay on the time axis, and the translation time length is equal to the adjustment amplitude. The specific operation is to supplement the sampling points corresponding to the adjustment amplitude time length at the starting end of the sampling point sequence, which are generated based on the trend interpolation of adjacent sampling points, and delete the same number of sampling points at the end of the sampling point sequence, to ensure that the total number of adjusted sampling points is consistent with the total number of original sampling points. During the translation adjustment process, the sampling point adjustment submodule will monitor the signal amplitude and phase change of the sampling points in real time to ensure that the deviation between the signal characteristics of the adjusted sampling points and the original signal characteristics is controlled within a reasonable range, and to avoid signal distortion caused by adjustment.

[0068] Step S134: After the adjustment, the adjusted deviation channel acquisition signal and the reference channel acquisition signal are extracted, and the time mark sequence corresponding to each channel is regenerated, and the regenerated time mark sequence is associated with the adjusted acquisition signal sampling point.

[0069] The time mark updating submodule of the deviation correction module first extracts the adjusted deviation channel acquisition signal and the reference channel acquisition signal that has not been adjusted (the reference channel has no time deviation, and its acquisition signal does not need to be adjusted). For the adjusted deviation channel acquisition signal, the time mark updating submodule regenerates the time mark sequence in the order of the adjusted sampling point time: taking the time node corresponding to the first sampling point of the reference channel acquisition signal as the starting time reference, calculating the time interval between each adjusted deviation channel sampling point and the starting time reference according to the sampling frequency, and generating the time mark corresponding to each sampling point in combination with the starting time reference, to ensure that the time mark sequence of the adjusted deviation channel is aligned with the time mark sequence of the reference channel on the time axis. For the reference channel acquisition signal, its original time mark sequence remains unchanged, but it needs to be calibrated with the new time mark sequence of the adjusted deviation channel to ensure that the time dimension reference standards of the two are consistent. In the regenerated time mark sequence, each time mark is associated with the adjusted acquisition signal sampling point through the sampling point number, and the numbering rule remains consistent with the original numbering rule.

[0070] Step S135: Perform consistency check on all adjusted channel acquisition signals and regenerated time mark sequences, and confirm that the time node information of each channel acquisition signal is aligned to form a synchronized multi-channel acquisition signal.

[0071] The consistency checking submodule of the bias correction module first acquires all the adjusted bias channel acquisition signals, the corresponding regenerated time tag sequences, and the reference channel acquisition signal and the original time tag sequence. The checking process is divided into two steps: the first step is time node alignment checking, which selects the sampling points with the same sampling point number in each channel acquisition signal, extracts the corresponding time tags, and judges whether the time difference values of these time tags are controlled within a preset alignment accuracy range (the accuracy range is set based on the requirements of ultra-wideband signal synchronous acquisition), if the time tag difference values of all the same number sampling points are within the accuracy range, the time node alignment checking is passed; the second step is signal feature consistency checking, which compares the signal amplitude and phase trend of each channel acquisition signal at the same time node, judges whether it conforms to the normal feature distribution of the ultra-wideband signal under multi-channel acquisition (such as no obvious amplitude mutation or phase jump), if it conforms, the signal feature consistency checking is passed. If both steps of checking are passed, it means that the channel acquisition signals have realized synchronization in the time dimension, the acquisition signals of all channels are summarized to form a synchronized multi-channel acquisition signal, and the synchronized multi-channel acquisition signal and the corresponding time tag sequence are associated and stored in the synchronization data partition of the signal storage module; if any step of checking is not passed, return to step S132 to regenerate the timing adjustment parameter, and execute the adjustment and checking process again until the checking is passed.

[0072] Step S140: input the synchronized multi-channel acquisition signal into the signal feature analysis AI model, analyze the frequency band distribution and signal integrity of the synchronized multi-channel acquisition signal through the signal feature analysis AI model, and obtain a multi-channel signal analysis result.

[0073] The AI model processing module of the system reads the synchronized multi-channel acquisition signal from the synchronization data partition of the signal storage module, and inputs it into the pre-trained signal feature analysis AI model. The signal feature analysis AI model is trained based on the frequency band features and integrity evaluation sample data of the ultra-wideband signal, and includes a frequency band extraction layer, an integrity detection layer, a collaborative analysis layer, and a result integration layer. Each layer is connected through a feature transmission link, and the model parameters have been optimized through a large number of sample debugging, which can accurately extract signal frequency band information, evaluate signal integrity, and analyze channel collaboration. In the model processing process, the frequency band extraction layer first separates different frequency band components of the signal, the integrity detection layer evaluates the signal integrity based on the standard frequency band range, the collaborative analysis layer analyzes the signal collaboration features among multiple channels, and finally the result integration layer integrates all analysis information to form a multi-channel signal analysis result.

[0074] Step S141: input the synchronized multi-channel acquisition signal into the frequency band extraction layer of the signal feature analysis AI model, separate different frequency band components in the synchronized multi-channel acquisition signal through the frequency band extraction layer, and obtain the frequency band distribution information of each channel.

[0075] The frequency band extraction layer of the signal feature analysis AI model includes a signal preprocessing sub-layer and a frequency band separation sub-layer. The signal preprocessing sub-layer first performs denoising processing on each channel of the synchronized multi-channel acquisition signal, removes random noise and interference components in the signal through an adaptive filtering algorithm, and simultaneously performs normalization processing on the denoised signal to adjust the signal amplitude to a unified numerical range, thereby avoiding the influence of amplitude differences on the frequency band analysis result. The frequency band separation sub-layer uses a multi-scale wavelet decomposition algorithm to perform frequency band decomposition on each channel of the preprocessed acquisition signal. During the decomposition process, the signal is separated into multiple preset frequency band intervals (such as low frequency band, medium frequency band, high frequency band, etc.) according to the common frequency band division rules of the ultra-wideband signal, and each frequency band component contains the frequency range, signal amplitude distribution, and signal energy proportion of the frequency band. The frequency band distribution information of each channel is arranged in order of the frequency band interval to form the frequency band distribution information of the channel, and the frequency band extraction layer transmits the frequency band distribution information of all channels to the integrity detection layer.

[0076] Step S142: The integrity detection layer of the signal feature analysis AI model compares the frequency band distribution information of each channel with the standard frequency band range of the ultra-wideband signal, identifies the region of missing frequency bands or signal attenuation, and obtains signal integrity evaluation information.

[0077] The integrity detection layer first retrieves the standard frequency band range of the ultra-wideband signal from the standard parameter library built in the model. The standard frequency band range is set based on the technical specifications of the ultra-wideband signal and contains all frequency band intervals that the signal should cover, as well as the normal reference range of signal amplitude in each frequency band interval (i.e., the signal intensity reference range). To clarify the execution details of this process, step S142 is further broken down into the following sub-steps:

[0078] Step S1421: Retrieve the standard frequency band range of the ultra-wideband signal from the built-in database of the signal feature analysis AI model. The standard frequency band range contains the frequency interval that the ultra-wideband signal should cover and the signal intensity reference range of each interval.

[0079] The standard data retrieval submodule of the integrity detection layer establishes a communication connection with the built-in database of the model, and the database stores standard frequency band range data of the ultra-wideband signal in different application scenarios (the standard data corresponding to the ultra-wideband radar target detection scenario in this embodiment). During the retrieval process, the submodule accurately locates and retrieves the corresponding standard frequency band range based on the scene identifier of the current acquisition task (pre-set in the system parameters). The standard frequency band range explicitly lists all frequency intervals that the ultra-wideband radar signal should cover (such as a continuous interval from the starting frequency of the low frequency band to the ending frequency of the high frequency band), as well as the upper and lower limits of the signal intensity in each frequency interval (i.e., the signal intensity reference range).

[0080] Step S1422: Compare the frequency intervals in the frequency band distribution information of each channel with the frequency intervals in the standard frequency band range, and determine the frequency intervals not covered by each channel as the frequency band missing areas.

[0081] The frequency band coverage comparison submodule of the integrity detection layer receives the frequency band distribution information of each channel and the standard frequency band range, and then performs comparison for each channel: each frequency interval in the standard frequency band range is extracted one by one, and it is checked whether the interval exists in the frequency intervals in the channel frequency band distribution information; if a certain frequency interval of the standard frequency band does not appear in the channel frequency band distribution information, or the signal energy proportion of the interval in the channel frequency band distribution information is lower than a preset coverage threshold (the threshold is set based on the signal integrity requirement), it is determined that the frequency interval is a frequency band missing area of the channel. All frequency band missing areas of each channel are sorted in order of frequency intervals, and the start frequency, end frequency and corresponding standard frequency band requirement of each missing area are recorded.

[0082] Step S1423: Compare the signal strength in the frequency band distribution information of each channel with the signal strength reference range of the standard frequency band range, and determine the frequency intervals with signal strength lower than the lower limit of the reference range as the signal attenuation areas.

[0083] The signal strength comparison submodule of the integrity detection layer extracts the signal strength data (including the signal strength values of each sampling point in the interval) in each frequency interval in the frequency band distribution information of each channel, and calculates the average signal strength of the interval. The average signal strength is compared with the lower limit of the signal strength reference range of the corresponding frequency interval in the standard frequency band range: if the average signal strength is lower than the lower limit of the reference range, it is determined that the frequency interval is a signal attenuation area of the channel; if the average signal strength is within the reference range or higher than the upper limit, it is determined that the signal strength of the interval is normal. At the same time, the start frequency, end frequency, average signal strength value and corresponding reference range lower limit value of the signal attenuation area are recorded to determine the attenuation degree.

[0084] Step S1424: Statistically analyze the coverage ranges of the frequency band missing areas and the signal attenuation areas, and calculate the proportion of each area in the standard frequency band range.

[0085] The area statistics submodule of the integrity detection layer first calculates the total frequency width of the standard frequency band range (i.e. the difference between the highest frequency and the lowest frequency of the standard frequency band). For each channel's frequency band missing area, the sum of the frequency widths of all missing areas (the frequency width of each missing area is the difference between the end frequency and the start frequency) is calculated, and the sum is divided by the total frequency width of the standard frequency band to obtain the proportion of the frequency band missing area in the standard frequency band range; similarly, the sum of the frequency widths of all signal attenuation areas of each channel is calculated, and divided by the total frequency width of the standard frequency band to obtain the proportion of the signal attenuation area in the standard frequency band range. Record the two proportion values of each channel.

[0086] Step S1425: According to the proportions and the frequency positions of the areas, generate signal integrity evaluation information containing missing area description, attenuation area description and integrity score basis.

[0087] The evaluation information generation submodule of the integrity detection layer integrates all the above data: for each channel, describe the frequency band missing area (including start frequency, end frequency, proportion) and the signal attenuation area (including start frequency, end frequency, average signal intensity, proportion) in frequency order; at the same time, combine the preset scoring rules (such as the proportion of frequency band missing increases by a certain value, the integrity score decreases by a corresponding score; the proportion of signal attenuation increases by a certain value, the score also decreases by a corresponding score, and the missing or attenuation of the key frequency band area has a higher weight on the score), and explain the calculation basis of the signal integrity score of the channel. Organize these information according to the channel identifier to form the signal integrity evaluation information of each channel, and transmit it to the collaborative analysis layer.

[0088] Step S143: Call the collaborative analysis layer of the signal feature analysis AI model to analyze the signal intensity and phase consistency of the same frequency band components among multiple channels, and generate inter-channel collaborative feature information.

[0089] The cooperative analysis layer first performs band matching on the band distribution information of all channels to ensure that the same band components of each channel (i.e., band components with consistent frequency intervals) are accurately identified. Subsequently, for each same band component, two analyses are performed: the first is signal intensity consistency analysis, which extracts the average signal intensity of each channel for the band component, calculates the standard deviation of these average signal intensities, and the smaller the standard deviation, the better the signal intensity consistency of each channel at the band. At the same time, the deviation rate of the average signal intensity of each channel from the average of the average signal intensities of all channels is calculated, and the channels with a deviation rate exceeding a preset consistency threshold are recorded. The second is phase consistency analysis, which extracts the phase data of each channel for the band component, calculates the phase difference between the phase of each channel and the phase of a reference channel (the reference channel in step S1231), and calculates the proportion of the number of channels with a phase difference within a preset phase consistency range, and the higher the proportion, the better the phase consistency. The signal intensity consistency data and the phase consistency data of each same band component are sorted and arranged in order of the band interval to form inter-channel cooperative feature information, which is transmitted to the result integration layer.

[0090] Step S144: input the band distribution information, the signal integrity evaluation information, and the inter-channel cooperative feature information into the result integration layer of the signal feature analysis AI model.

[0091] The result integration layer receives the band distribution information of each channel transmitted by the band extraction layer, the signal integrity evaluation information of each channel transmitted by the integrity detection layer, and the inter-channel cooperative feature information transmitted by the cooperative analysis layer, and performs data format unification processing on these information to ensure that the field definitions and data units (such as frequency units and signal intensity units) of all information are consistent, avoiding information integration confusion caused by format differences. After unification processing, the result integration layer classifies the information according to a two-dimensional structure of "channel-band": taking each channel as the first dimension and each band component under each channel as the second dimension, the band distribution details, integrity evaluation results (such as whether it is a missing / attenuation area, proportion), and the cooperative features (such as signal intensity standard deviation and phase consistency proportion) of the band component among multiple channels are correspondingly associated to form a structured information set.

[0092] Step S145: classify and arrange the above information through the result integration layer to generate a multi-channel signal analysis result containing channel band description, signal integrity level, and channel cooperation evaluation.

[0093] The result integration layer further processes the structured information set: for each channel, a channel frequency band description (including the covered frequency band interval, the signal energy proportion of each frequency band) is generated based on the frequency band distribution information thereof; the signal integrity level of the channel is determined according to a preset level division rule (such as excellent, good, qualified, unqualified) based on the missing / attenuation area proportion and the scoring basis in the signal integrity evaluation information; the multi-channel collaborative performance in each frequency band is evaluated based on the inter-channel collaborative feature information (such as “the signal intensity and phase consistency of each channel in the medium frequency band are good, and there is a deviation in the consistency of some channels in the high frequency band”). The frequency band description, the signal integrity level of all channels, and the multi-channel collaboration evaluation are integrated, arranged according to a preset report format (including information title, channel identification, analysis item, and result content), and a multi-channel signal analysis result is formed and transmitted to the report generation module of the system.

[0094] Step S150: Based on the multi-channel signal analysis result, a collection analysis report containing signal quality description of each channel and collaborative collection suggestion is generated, and the collection analysis report is sent to the target signal processing terminal.

[0095] After the report generation module receives the multi-channel signal analysis result, it first analyzes and extracts key information, then generates signal quality description and collaborative collection suggestion based on the information, integrates it into a standard collection analysis report, and finally sends it to the target signal processing terminal through a preset communication method, ensuring that the terminal can timely obtain and apply the analysis result.

[0096] Step S151: Analyzing the multi-channel signal analysis result, extracting the frequency band distribution information, signal integrity evaluation information, and inter-channel collaborative feature information of each channel.

[0097] The information analysis submodule of the report generation module receives the multi-channel signal analysis result and extracts the information in a structured format: for each channel, the covered frequency band interval and the signal energy proportion of each frequency band in the frequency band distribution information thereof are extracted; the specific frequency range and proportion of the missing area and the signal attenuation area in the signal integrity evaluation information, and the corresponding integrity level are extracted; the signal intensity consistency data (such as standard deviation, channel with deviation rate exceeding threshold) and phase consistency data (such as phase consistency proportion) in each frequency band in the inter-channel collaborative feature information are extracted. The extracted information is stored in a clear information list classified by channel identification and analysis item.

[0098] Step S152: Generating a signal quality description of each channel according to the frequency band distribution information and the signal integrity evaluation information, the signal quality description containing the effective frequency band range and the signal attenuation degree of each channel.

[0099] The quality description generation submodule of the report generation module generates a signal quality description for each channel based on the information list: the determination of the effective frequency band range is based on the frequency band distribution information, after excluding the missing frequency band area, the frequency band interval with remaining coverage and signal energy proportion higher than the preset effective threshold is defined as the effective frequency band range, and the start and end frequencies are clearly marked; the description of the signal attenuation degree is based on the proportion and position of the signal attenuation area, such as "there is signal attenuation in the high frequency band of this channel, the attenuation area accounts for X proportion of the standard high frequency band range, and the average signal strength is lower than the lower limit Y of the reference range", combined with the signal integrity level (such as "the signal integrity level is good, and the overall attenuation degree is controllable"), to form the signal quality description of each channel, to ensure that the description accurately reflects the quality condition of the channel collected signal.

[0100] Step S153: Based on the inter-channel coordination feature information, analyze the coordination deficiency points of multi-channel in the acquisition process, and generate coordination acquisition suggestions combined with the optimization criteria of ultra-wideband signal acquisition.

[0101] The suggestion generation submodule of the report generation module first analyzes the inter-channel coordination feature information to identify coordination deficiency points: if there are multiple channel signal strength deviation rates exceeding the threshold or phase consistency proportions below the preset coordination threshold in a certain frequency band, it is determined that the frequency band is a coordination deficient frequency band, and the corresponding channel and specific deviation data are recorded. Then, combined with the optimization criteria of ultra-wideband signal acquisition (such as "ensure that the parameters of each channel acquisition module are consistent to improve signal strength consistency" "adjust the layout of multi-channel antenna to optimize phase coordination" "increase the acquisition gain of coordination deficient frequency band to improve signal quality"), specific optimization suggestions are proposed for each coordination deficiency point, supplemented by general coordination acquisition optimization suggestions (such as "periodically calibrate the multi-channel time synchronization accuracy" "check the impedance matching state of the transmission line"), to form a complete coordination acquisition suggestion, ensuring that the suggestion is operable and targeted.

[0102] Step S154: Integrate the signal quality description of each channel and the coordination acquisition suggestion according to the preset report structure, add the report generation time and the multi-channel acquisition unit identifier, and form the acquisition analysis report.

[0103] The report integration submodule of the report generation module integrates the information according to a preset report structure (including a cover, a table of contents, an executive summary, analysis of the quality of each channel signal, suggestions for multi-channel coordinated acquisition, and an appendix): the cover marks the name of the report (such as "Ultrawideband radar signal multi-channel synchronous acquisition analysis report"), the time of report generation (accurate to the minute, obtained from the system clock), and the identification of the multi-channel acquisition unit (such as the equipment number of the acquisition unit); the executive summary briefly summarizes the acquisition task and analysis conclusion (such as "This acquisition has X channels, Y channels have good signal quality, and Z frequency bands have insufficient coordination"); the analysis of the quality of each channel signal presents the signal quality description of each channel in turn according to the channel identification; the suggestions for multi-channel coordinated acquisition lists all the suggestions for coordinated acquisition; and the appendix supplements the original data summary of the multi-channel signal analysis result. During the integration process, the text expression is standardized to ensure that the language is simple, professional, and the logic is clear, and finally a complete acquisition analysis report is formed and stored in the report cache partition of the system.

[0104] Step S155: A communication link with the target signal processing terminal is established, and the acquisition analysis report is sent to the target signal processing terminal through a preset transmission protocol, so that the target signal processing terminal can accurately receive and analyze the acquisition analysis report.

[0105] The communication submodule of the report generation module first reads the preset communication address (such as the IP address or the physical address of the equipment) and the communication mode (such as Ethernet or wireless local area network) of the target signal processing terminal in the system parameters, initializes the corresponding communication module (such as the Ethernet module or the wireless communication module) according to the communication mode. After initialization, the communication submodule sends a link establishment request to the target signal processing terminal, which contains the identification information of the multi-channel acquisition unit and the purpose of the report transmission, so that the target terminal can verify the legality of the request. After receiving the request, if the verification is passed, the target signal processing terminal returns a link establishment confirmation signal, and the communication submodule receives the confirmation signal, successfully establishing a communication link with the target terminal.

[0106] Subsequently, the communication submodule reads the acquisition analysis report from the report cache partition, encapsulates the report data according to the preset transmission protocol, and adds a data verification field during the encapsulation process to verify the integrity of the data after the target terminal receives the data. After encapsulation, the communication submodule sends the report data to the target signal processing terminal at a preset transmission rate, monitors the transmission state in real time during the sending process, and if there is a transmission interruption or an error prompt (such as a timeout without receiving an acknowledgement frame), the retransmission mechanism is immediately started to resend the interrupted or erroneous data segment until the data segment is confirmed to be received by the target terminal.

[0107] After the target signal processing terminal receives the complete acquisition analysis report data, the built-in check module calculates the data check code and compares it with the check field in the encapsulated data. If the comparison is consistent, it means that the data is complete, and the communication submodule returns a successful reception signal. If the comparison is inconsistent, it returns an error reception signal, and the communication submodule re-sends the report data after receiving the error signal. After the communication submodule receives the successful reception signal, it records the time and status of the report sending completion, closes the communication link, and completes the sending process of the acquisition analysis report. At the same time, the report generation module stores a copy of the acquisition analysis report in the system's historical report database, adds a unique report number to the report during storage (including acquisition date, acquisition task identification, etc.), and ensures that each report can be accurately located.

[0108] Figure 2 An exemplary hardware and software components of the multi-channel synchronous acquisition and analysis system 100 for ultra-wideband signals provided by some embodiments of the present application are shown in the schematic diagram. For example, the processor 120 can be used in the multi-channel synchronous acquisition and analysis system 100 for ultra-wideband signals, and used to execute the functions in the present application.

[0109] The multi-channel synchronous acquisition and analysis system 100 for ultra-wideband signals can be a general server or a special-purpose server, both of which can be used to implement the multi-channel synchronous acquisition and analysis method for ultra-wideband signals of the present application. Although only one server is shown in the present application, for the sake of convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0110] For example, the multi-channel synchronous acquisition and analysis system 100 for ultra-wideband signals can include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as a disk, a ROM, or a RAM, or any combination thereof. Exemplarily, the multi-channel synchronous acquisition and analysis system 100 for ultra-wideband signals can also include program instructions stored in a ROM, a RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The multi-channel synchronous acquisition and analysis system 100 for ultra-wideband signals also includes an I / O interface 150 between the computer and other input / output devices.

[0111] For the convenience of description, only one processor is described in the multi-channel synchronous acquisition and analysis system 100 of ultra-wideband signals. However, it should be noted that the multi-channel synchronous acquisition and analysis system 100 of ultra-wideband signals in the present application can also include multiple processors, and thus the steps performed by one processor described in the present application can also be jointly performed or separately performed by multiple processors. For example, if the processor of the multi-channel synchronous acquisition and analysis system 100 of ultra-wideband signals performs steps A and B, it should be understood that steps A and B can also be jointly performed by two different processors or separately performed in one processor. For example, a first processor performs step A, a second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0112] In addition, the embodiment of the present application further provides a readable storage medium, in which computer executable instructions are preset, and when a processor executes the computer executable instructions, the multi-channel synchronous acquisition and analysis method of ultra-wideband signals is realized.

[0113] It should be noted that, in order to simplify the description of the present application and to help the understanding of one or more embodiments of the present application, in the foregoing description of the embodiments of the present application, various features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A method for multi-channel synchronous acquisition and analysis of ultra-wideband signals, characterized in that, The method includes: The multi-channel acquisition unit is activated to collaboratively trigger the acquisition of ultra-wideband signals, generating multi-channel acquisition signals and time stamp sequences corresponding to each channel. The time stamp sequences contain multiple time node information when each channel acquires ultra-wideband signals. The multi-channel acquired signals and the time-stamped sequence are input into a pre-trained synchronization verification AI model. The synchronization verification AI model analyzes the temporal correlation of the acquired signals from each channel to obtain the synchronization verification results, which specifically include: The multi-channel acquisition signal is split into multiple single-channel acquisition signals, and the time stamp sequence corresponding to each channel is extracted. Each single-channel acquisition signal and its corresponding time stamp sequence are input into the timing mapping layer of the synchronous verification AI model, and the timing mapping layer establishes the association mapping relationship between the acquisition signal sampling points and time node information. Through the correlation analysis layer of the synchronous verification AI model, the time offset between the correlation mapping relationship of one channel and the reference correlation mapping relationship of the other channels is calculated using the correlation mapping relationship of one channel as a reference. The deviation determination layer of the synchronous verification AI model determines the time deviation based on a preset offset threshold, thereby identifying the channel with time deviation and the corresponding degree of deviation. By integrating the time offset, the channel identifier with time deviation, and the degree of deviation, a synchronization verification result containing a description of the synchronization status of each channel is generated; Based on the synchronization verification result, the deviation correction module is invoked to adjust the timing of the channel acquisition signals with time deviations, thereby obtaining synchronized multi-channel acquisition signals. The synchronized multi-channel acquired signal is input into the signal feature analysis AI model. The AI ​​model analyzes the frequency band distribution and signal integrity of the synchronized multi-channel acquired signal to obtain multi-channel signal analysis results, specifically including: The synchronized multi-channel acquisition signal is input into the frequency band extraction layer of the signal feature analysis AI model. The frequency band extraction layer separates the different frequency band components in the synchronized multi-channel acquisition signal to obtain the frequency band distribution information of each channel. The integrity detection layer of the signal feature analysis AI model compares the frequency band distribution information of each channel with the standard frequency band range of the ultra-wideband signal to identify areas with missing frequency bands or signal attenuation, thereby obtaining signal integrity assessment information. The collaborative analysis layer of the signal feature analysis AI model is invoked to analyze the signal strength and phase consistency of components with the same frequency band across multiple channels, generating collaborative feature information between channels; The frequency band distribution information, the signal integrity assessment information, and the inter-channel collaborative feature information are input into the result integration layer of the signal feature analysis AI model; The results integration layer categorizes and organizes the above information to generate multi-channel signal analysis results that include frequency band descriptions for each channel, signal integrity levels, and channel synergy evaluations. Based on the multi-channel signal analysis results, an acquisition and analysis report containing a description of the signal quality of each channel and suggestions for collaborative acquisition is generated, and the acquisition and analysis report is sent to the target signal processing terminal.

2. The multi-channel synchronous acquisition and analysis method for ultra-wideband signals according to claim 1, characterized in that, The multi-channel acquisition unit is activated to collaboratively trigger the acquisition of ultra-wideband signals, generating multi-channel acquisition signals and a time stamp sequence corresponding to each channel. The time stamp sequence contains multiple time node information when each channel acquires ultra-wideband signals, including: A unified channel trigger signal is sent to each acquisition channel in the multi-channel acquisition unit. The channel trigger signal is used to trigger each acquisition channel to start acquiring ultra-wideband signals simultaneously. While each acquisition channel begins acquiring ultra-wideband signals, the built-in time stamp module of each acquisition channel is activated to generate a time stamp corresponding to each acquisition channel. The time stamp corresponds one-to-one with the sampling point of the acquired signal. The signal storage module of the multi-channel acquisition unit stores the ultra-wideband acquisition signal output by each acquisition channel in real time, forming a single-channel acquisition signal. The time stamps corresponding to each single-channel acquisition signal are organized and arranged in the order of acquisition time to form a time stamp sequence for each channel. The information of each time node in the time stamp sequence is associated with the sampling point position of the single-channel acquisition signal. The single-channel acquisition signals from all acquisition channels are aggregated to form a multi-channel acquisition signal, and the multi-channel acquisition signal is associated with and stored with the time stamp sequence corresponding to each channel.

3. The multi-channel synchronous acquisition and analysis method for ultra-wideband signals according to claim 1, characterized in that, The correlation analysis layer of the synchronously verified AI model, using the correlation mapping relationship of one channel as a benchmark, calculates the time offset between the correlation mapping relationship of other channels and the benchmark correlation mapping relationship, including: Randomly select one of the association mapping relationships from each channel as the baseline association mapping relationship, and record the channel identifier corresponding to the baseline association mapping relationship as the baseline channel identifier; Extract all time node information from the benchmark association mapping relationship to form a benchmark time node set; For each non-reference channel, the associated mapping relationship is used to extract the time node information to form a non-reference time node set; The time difference between each time node in the non-baseline time node set and the corresponding sampling point in the baseline time node set is calculated through the correlation analysis layer of the synchronous verification AI model. Statistical analysis is performed on the time difference values, and the average value of the time difference values ​​is taken as the time offset of the non-reference channel relative to the reference channel.

4. The multi-channel synchronous acquisition and analysis method for ultra-wideband signals according to claim 1, characterized in that, Based on the synchronization verification result, the deviation correction module is invoked to adjust the timing of the channel acquisition signals with time deviations, thereby obtaining synchronized multi-channel acquisition signals, including: Analyze the synchronization verification results to extract the channel identifiers with time deviations, the corresponding time offsets, and the degree of deviation; Based on the time offset, timing adjustment parameters corresponding to each deviation channel are generated, and the timing adjustment parameters include the time offset direction and adjustment magnitude. The timing adjustment parameters and the corresponding deviation channel acquisition signal are input into the deviation correction module, and the timing of the sampling points of the deviation channel acquisition signal is shifted and adjusted by the deviation correction module. After the adjustment is completed, the adjusted deviation channel acquisition signal and the reference channel acquisition signal are extracted, and the time stamp sequence corresponding to each channel is regenerated. The regenerated time stamp sequence is associated with the sampling points of the adjusted acquisition signal. A consistency check is performed on all adjusted channel acquisition signals and the regenerated time stamp sequence. After confirming that the time node information of each channel acquisition signal is aligned, a synchronized multi-channel acquisition signal is formed.

5. The multi-channel synchronous acquisition and analysis method for ultra-wideband signals according to claim 1, characterized in that, The integrity detection layer of the signal feature analysis AI model compares the frequency band distribution information of each channel with the standard frequency band range of the ultra-wideband signal to identify areas with missing frequency bands or signal attenuation, thereby obtaining signal integrity assessment information, including: The standard frequency band range of the ultra-wideband signal is retrieved from the built-in database of the signal feature analysis AI model. The standard frequency band range includes the frequency range that the ultra-wideband signal should cover and the signal strength reference range of each range. The frequency range in the frequency band distribution information of each channel is compared with the frequency range of the standard frequency band range to determine the frequency range not covered by each channel, which is taken as the frequency band missing area. The signal strength in the frequency band distribution information of each channel is compared with the signal strength reference range of the standard frequency band range to determine the frequency range where the signal strength is lower than the lower limit of the reference range, which is used as the signal attenuation region. The coverage areas of the frequency band missing region and the signal attenuation region are statistically analyzed, and the proportion of each region to the standard frequency band range is calculated. Based on the aforementioned proportions and the frequency positions of each region, signal integrity assessment information is generated, including descriptions of missing regions, descriptions of attenuated regions, and criteria for integrity scoring.

6. The multi-channel synchronous acquisition and analysis method for ultra-wideband signals according to claim 1, characterized in that, Based on the multi-channel signal analysis results, a data acquisition and analysis report is generated, including a description of the signal quality of each channel and suggestions for collaborative acquisition. This report is then sent to the target signal processing terminal. The multi-channel signal analysis results are analyzed to extract the frequency band distribution information, signal integrity assessment information, and inter-channel cooperative feature information of each channel; Based on the frequency band distribution information and the signal integrity assessment information, a signal quality description for each channel is generated, which includes the effective frequency band range and signal attenuation degree of each channel. Based on the inter-channel collaborative feature information, the shortcomings of multi-channel collaboration during the acquisition process are analyzed, and collaborative acquisition suggestions are generated in combination with the optimization criteria for ultra-wideband signal acquisition. The signal quality descriptions of each channel and the collaborative acquisition suggestions are integrated according to a preset report structure, and the report generation time and multi-channel acquisition unit identifier are added to form an acquisition analysis report; Establish a communication link with the target signal processing terminal, and send the acquisition and analysis report to the target signal processing terminal through a preset transmission protocol to ensure that the target signal processing terminal can accurately receive and parse the acquisition and analysis report.

7. The multi-channel synchronous acquisition and analysis method for ultra-wideband signals according to claim 2, characterized in that, The step of sending a unified channel trigger signal to each acquisition channel in the multi-channel acquisition unit, wherein the channel trigger signal is used to trigger each acquisition channel to simultaneously start acquiring ultra-wideband signals, includes: The trigger control module of the multi-channel acquisition unit is activated to generate an initial trigger signal. The signal amplitude and pulse width of the initial trigger signal meet the trigger requirements of each acquisition channel. The initial trigger signal is copied to a channel trigger signal with the same number of acquisition channels by the signal distribution unit of the trigger control module, ensuring that the signal parameters of each channel trigger signal are exactly the same. For each of the channel trigger signals, a transmission delay is detected, and the transmission time of the channel trigger signal from the trigger control module to each acquisition channel is recorded; Based on the transmission time, the transmission time of each channel trigger signal is finely adjusted to ensure that all channel trigger signals can arrive at the corresponding acquisition channel simultaneously. The fine-tuned channel trigger signal is sent to each acquisition channel, triggering each acquisition channel to start acquiring ultra-wideband signals at the same time.

8. A multi-channel synchronous acquisition and analysis system for ultra-wideband signals, characterized in that, The multi-channel synchronous acquisition and analysis system for ultra-wideband signals includes a processor and a memory, the memory and the processor being connected. The memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to implement the multi-channel synchronous acquisition and analysis method for ultra-wideband signals as described in any one of claims 1-7.

Images