A real-time communication signal spectrum segmentation detection method and system

Abstract

The present invention provides a method and system for segmenting and detecting a spectrum of a real-time communication signal, which relates to the technical field of channel attribute judgment, including obtaining a broadband signal to be identified, wherein the broadband signal to be identified includes real-time communication signals of all channels; performing signal judgment on the broadband signal to be identified, and obtaining a judgment result of whether each channel in the broadband signal to be identified has a communication signal; performing attribute judgment on the channel having the communication signal, and performing channel splicing based on the attribute of each channel obtained by the judgment, and then judging whether the spliced ​​channels are successfully spliced ​​to obtain a second judgment result; performing channel attribute extraction based on the second judgment result to obtain the attribute type of each spliced ​​channel. The channel attribute discrimination scheme proposed by the present invention has higher accuracy and applicability than traditional methods in signal segmentation and discrimination, and can more effectively extract independent signals from time domain aliasing signals.

Description

Technical Field

[0001] The present invention relates to the technical field of channel attribute determination, and in particular to a method and system for segmenting and detecting a spectrum of a real-time communication signal. Background Art

[0002] In the communication system, due to various interferences and aliasing during signal transmission, time domain aliasing refers to the signal overlap and time domain ambiguity caused by factors such as multipath propagation, multiple interferences, and signal processing in the signal transmission path. This phenomenon brings challenges to the signal detection and identification of the communication system. Existing algorithms often find it difficult to meet the requirements of high-precision channel segmentation, and the effect of judging the attribute type of the channel is not ideal, so it is difficult to complete the task of extracting independent signals from time domain aliased signals. In addition, traditional aliased signal separation methods often cannot segment time domain aliased signals well, nor can they judge the type of each type of signal well, so it is difficult to accurately extract valid signals from aliased signals.

[0003] Therefore, there is an urgent need for a real-time communication signal spectrum segmentation detection method for determining the attribute type of the channel. Summary of the invention

[0004] The purpose of the present invention is to provide a real-time communication signal spectrum segmentation detection method and system to improve the above-mentioned problem. In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides a method for segmenting and detecting a spectrum of a real-time communication signal, comprising:

[0006] Acquire a broadband signal to be identified, wherein the broadband signal to be identified includes real-time communication signals of all channels;

[0007] Performing signal judgment on the broadband signal to be identified to obtain a first judgment result, wherein the first judgment result is a judgment result of whether each channel in the broadband signal to be identified has a communication signal;

[0008] Performing attribute judgment on the channels with communication signals as determined by the first judgment result, performing channel splicing based on the determined attribute of each channel, and then judging whether the spliced ​​channels are successfully spliced ​​to obtain a second judgment result;

[0009] Channel attributes are extracted based on the second judgment result to obtain the attribute type of each spliced ​​channel.

[0010] In a second aspect, the present application also provides a real-time communication signal spectrum segmentation detection system, comprising:

[0011] An acquisition unit, configured to acquire a broadband signal to be identified, wherein the broadband signal to be identified includes real-time communication signals of all channels;

[0012] A first judgment unit, configured to perform signal judgment on the broadband signal to be identified to obtain a first judgment result, wherein the first judgment result is a judgment result of whether each channel in the broadband signal to be identified has a communication signal;

[0013] A second judgment unit is used to judge the attributes of the channels that have the communication signal according to the first judgment result, and to splice the channels based on the attributes of each channel obtained by the judgment, and then to judge whether the spliced ​​channels are successfully spliced ​​to obtain a second judgment result;

[0014] The processing unit is used to extract channel attributes based on the second judgment result to obtain the attribute type of each spliced ​​channel.

[0015] The beneficial effects of the present invention are:

[0016] The present invention proposes an efficient and accurate real-time communication signal spectrum segmentation detection method, which can effectively improve the accuracy and efficiency of spectrum segmentation. Based on this method, effective splicing and attribute extraction of channels are achieved, and the transmission efficiency and performance of the communication system are improved. Through reasonable control and judgment of the channel splicing process, splicing errors can be effectively avoided and the signal transmission quality can be improved. In the application of the communication system, the present invention can improve the transmission efficiency and reliability of the signal, is applicable to various real-time communication scenarios, and has broad application prospects and economic benefits.

[0017] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic flow chart of a method for segmenting and detecting a real-time communication signal spectrum according to an embodiment of the present invention;

[0020] Figure 2 Schematic diagram of the structure of a real-time communication signal spectrum segmentation detection system according to an embodiment of the present invention.

[0021] In the figure: 701, acquisition unit; 702, first judgment unit; 703, second judgment unit; 704, processing unit; 7021, first processing subunit; 7022, first comparison subunit; 7023, second comparison subunit; 7024, third comparison subunit; 70221, second processing subunit; 70222, third processing subunit; 7031, first calculation subunit; 7032, second calculation subunit; 7033, fourth processing subunit; 7034, fifth processing subunit; 7041, first judgment subunit; 7042, second judgment subunit. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0024] Embodiment 1:

[0025] This embodiment provides a method for segmenting and detecting a spectrum of a real-time communication signal.

[0026] See also Figure 1 , the figure shows that the method includes step S1, step S2, step S3 and step S4.

[0027] Step S1, obtaining a broadband signal to be identified, wherein the broadband signal to be identified includes real-time communication signals of all channels;

[0028] It can be understood that this step obtains the broadband signals to be identified from the communication system, and these signals include real-time communication signals of all channels. These broadband signals come from wireless communication systems, wired communication systems or hybrid communication systems. For example, they can be radio frequency signals from mobile communication base stations or digital signals from wired networks. In actual implementation, the broadband signals to be identified can be obtained through signal acquisition equipment or software radio (SDR) technology, and then the data can be stored in a database. By capturing broadband signals in real time, it can ensure that the system can respond to communication activities on different channels in a timely manner and provide accurate input for subsequent signal processing.

[0029] Step S2, performing signal judgment on the broadband signal to be identified to obtain a first judgment result, wherein the first judgment result is a judgment result of whether each channel in the broadband signal to be identified has a communication signal;

[0030] It can be understood that this step performs signal judgment on the acquired broadband signal to be identified to determine whether each channel has a communication signal. The goal of this step is to classify and distinguish the signal according to the energy value of the signal, so as to further process the channel with the communication signal. In this step, step S2 includes step S21, step S22, step S23 and step S24.

[0031] Step S21, classifying the broadband signals to be identified according to their corresponding channels to obtain broadband signals to be identified corresponding to at least two channels;

[0032] It can be understood that this step divides the broadband signal into frequencies according to the frequency range of each channel. For example, if a base station supports communications in multiple frequency bands at the same time, we can divide the broadband signal according to the frequency range of each frequency band to obtain the broadband signal to be identified corresponding to each frequency band. This step effectively classifies the broadband signal to be identified according to the channel, providing a basis for subsequent signal judgment and processing. By dividing the broadband signal into different channels, the system can more accurately analyze and identify the communication signal in each channel, thereby improving the accuracy and reliability of signal judgment.

[0033] Step S22, sending the broadband signal to be identified corresponding to each channel to threshold calculation, and comparing the calculated threshold with the energy value of the broadband signal to be identified in each channel to obtain a comparison result;

[0034] It can be understood that this step calculates the energy threshold value of the broadband signal to be identified in each channel as the threshold for judging whether the channel has a communication signal. Through energy threshold calculation and comparison, it is possible to effectively judge whether there is a communication signal in each channel, and further screen out channels with communication signals. By dynamically adjusting the threshold to adapt to different channel conditions, the adaptability and robustness of the system can be improved, thereby improving the accuracy and reliability of signal judgment. In this step, step S22 includes step S221 and step S222.

[0035] Step S221: clustering the broadband signal to be identified corresponding to each channel using a distance-based clustering algorithm to obtain at least two clusters, wherein the clusters include at least one energy value of the broadband signal to be identified;

[0036] It can be understood that this step uses the K-means clustering algorithm as a distance-based clustering algorithm to cluster the broadband signals to be identified corresponding to each channel. During the execution of the algorithm, we first set the number of clusters to be at least two, and then cluster the samples according to the distance of the signal energy value to generate at least two clustering clusters. Each clustering cluster includes at least one energy value of the broadband signal to be identified. By clustering the broadband signals to be identified based on the distance-based clustering algorithm, signal samples with similar energy characteristics can be effectively clustered into the same category. This helps to identify potential signal patterns and features, and provide a more accurate and reliable data basis for subsequent signal processing. At the same time, the requirement of generating at least two clustering clusters ensures the diversity and reliability of the clustering results.

[0037] Step S222: Based on all the clusters and the Laida criterion, the threshold range corresponding to each cluster is calculated, and the cluster centers of all clusters are calculated. The smallest cluster center is used as the judgment threshold to be compared with the energy value of the broadband signal to be identified in each channel to obtain the comparison result.

[0038] It can be understood that this step determines the energy threshold range of each channel according to the clustering results and cluster centers, thereby performing signal judgment. Selecting the smallest cluster center as the judgment threshold helps to reduce the judgment error and improve the accuracy and reliability of signal judgment. At the same time, by comparing with the energy value of the broadband signal to be identified in each channel, it is possible to effectively distinguish between channels with signals and channels without signals.

[0039] Step S23: If the comparison result is that the threshold is greater than the energy value of the broadband signal to be identified in the channel, it indicates that the channel is a no-signal channel;

[0040] Step S24: If the comparison result is that the threshold is less than or equal to the energy value of the broadband signal to be identified in the channel, it indicates that the channel is a signal channel.

[0041] It can be understood that this step can accurately determine whether there is a communication signal in each channel. When the energy threshold value is greater than the energy value of the broadband signal to be identified in the channel, the signal-free channel can be effectively screened out, thereby avoiding unnecessary processing of the signal-free channel and improving the efficiency of the system. At the same time, the signal channel can also be accurately identified, providing an accurate basis for subsequent signal processing.

[0042] Step S3, performing attribute judgment on the channels with communication signals as determined by the first judgment result, and performing channel splicing based on the attributes of each channel obtained by the judgment, and then judging whether the spliced ​​channels are successfully spliced, to obtain a second judgment result;

[0043] It can be understood that this step can perform a deeper attribute analysis and processing on the channel with the communication signal, thereby extracting more channel attribute information. Based on this attribute information, channel splicing can more accurately merge channels with similar characteristics, thereby improving the accuracy and success rate of channel splicing. The final second judgment result can help us evaluate the effectiveness of the splicing process and improve the performance and stability of the system. In this step, step S3 includes step S31 and step S32.

[0044] Step S31: input the channel with the communication signal as input information into the preset residual neural network to calculate the channel attribute confidence, and obtain the confidence value of each channel attribute;

[0045] It is understandable that since the output layer of the residual neural network is a softmax layer, its output corresponds to the judgment confidence of 5 different attribute channels, that is, the judgment probability. Based on this confidence, this project designs the following channel attribute correction strategy. For the convenience of expression, assume that The softmax layer output of channels is:

[0046] ;

[0047] ;

[0048] in, Indicates The communication signal is input into the residual neural network and the output is The confidence of the channel, It represents the number of communication signals input to the neural network, Include , , , , ,in, , , , , Respectively represent The probability that a channel is a low-connected channel, a high-connected channel, a high-low-connected channel, a high-low-connected channel with cutoff, and an independent channel.

[0049] Step S32: taking the confidence value of each channel attribute as the determination probability of the channel attribute, and performing calculation based on a preset channel attribute determination formula to obtain the attribute category of each channel.

[0050] It can be understood that the channel attribute determination formula preset in this step is as follows:

[0051] ;

[0052] in, represents the properties of the channel, where Indicates the channel attribute category, including 5 channel attribute categories. 0, 1, 2, 3, and 4 respectively represent the channel attribute category, which are the confidence indexes of low-connection channel, high-connection channel, high-low connection channel, high-low connection channel with cut, or independent channel. Indicates The channel is The probability of a channel attribute category.

[0053] It can be understood that step S3 also includes step S33 and step S34.

[0054] Step S33, sorting all channels according to their attributes, wherein the channels are sorted from low-frequency channels to high-frequency channels to obtain sorted channels;

[0055] It can be understood that in this step, by sorting the channels according to their attributes, the order of the channels can be made clearer and more orderly, which is convenient for subsequent processing and analysis. In particular, sorting by frequency can make the frequency distribution of the channels more intuitive and easy to understand, which is helpful for further signal processing and analysis.

[0056] Step S34: based on a sliding window of a preset length, sequentially perform channel splicing on two adjacent channels, and judge whether the spliced ​​channels are logically reasonable based on a preset channel rationality matrix, so as to obtain a judgment result on whether the channels are reasonable.

[0057] It can be understood that this step effectively splices adjacent channels, thereby reducing the number of channels and improving the efficiency of signal processing. At the same time, based on the preset channel rationality matrix, the logical rationality judgment can ensure that the spliced ​​new channel meets the actual needs and physical laws, ensuring the accuracy and reliability of channel splicing.

[0058] Step S4: extract channel attributes based on the second judgment result to obtain the attribute type of each spliced ​​channel.

[0059] It can be understood that in this step, step S4 includes step S41 and step S42.

[0060] Step S41: If the judgment result is that the channel splicing is reasonable, the attributes of the reasonably spliced ​​channels are marked and extracted to obtain the attribute types of the reasonably spliced ​​channels;

[0061] It can be understood that this step extracts the attribute information of each spliced ​​channel. This attribute information can be obtained by the previous signal feature extraction, parameters in the channel splicing process, etc. According to the extracted attribute information, the attribute type of each channel is determined. For example, it can be divided into different frequency bands according to the frequency range, divided into different bandwidth types according to the bandwidth size, and divided into different modulation types according to the modulation method.

[0062] Step S42: If the judgment result is that the channel splicing is unreasonable, the channel attribute of the unreasonable splicing is corrected according to a preset channel attribute correction formula, and the corrected channel attribute is marked as the channel attribute of the unreasonable splicing.

[0063] It is understandable that there may be two unreasonable splicing situations in this step. One is the unreasonable channel form of "channel with high connection attribute + channel without low connection attribute", where the attribute correction formula of this form is as follows:

[0064] ;

[0065] ;

[0066] in, Indicates The properties of a channel, Indicates The properties of a channel, Indicates the The channel is The probability of channel attribute categories, represents the modified properties of the channel, Indicates The probability that a channel has the attributes of high connection channel, high-low connection channel and high-low connection channel with cut, Indicates the The probability that a channel has the attributes of high connection channel, high-low connection channel and high-low connection channel with cut, Indicates the channel attribute category, including 5 channel attribute categories.

[0067] It can be understood that another unreasonable form in this step is a channel of "a channel without a high connection attribute + a channel with a low connection attribute", wherein the attribute correction formula of this form is as follows:

[0068] ;

[0069] ;

[0070] in, Indicates The properties of a channel, Indicates The properties of a channel, Indicates the The channel is The probability of channel attribute categories, represents the modified properties of the channel, Indicates The probability that a channel has the attributes of high connection channel, high-low connection channel and high-low connection channel with cut, Indicates the The probability that a channel has the attributes of high connection channel, high-low connection channel and high-low connection channel with cut, Indicates The probability that a channel has the attributes of a low connection channel, a high-low connection channel, and a high-low connection channel with a cut, Indicates the channel attribute category, including 5 channel attribute categories.

[0071] This step can reasonably and effectively detect and correct erroneous channel properties through the channel correction scheme, thereby significantly improving the accuracy of extracting independent signals from aliased signals.

[0072] Embodiment 2:

[0073] like Figure 2 As shown, this embodiment provides a real-time communication signal spectrum segmentation detection system, see Figure 2 The system includes an acquisition unit 701 , a first judgment unit 702 , a second judgment unit 703 and a processing unit 704 .

[0074] An acquisition unit 701 is used to acquire a broadband signal to be identified, where the broadband signal to be identified includes real-time communication signals of all channels;

[0075] The first judgment unit 702 is used to perform signal judgment on the broadband signal to be identified to obtain a first judgment result, where the first judgment result is a judgment result of whether each channel in the broadband signal to be identified has a communication signal;

[0076] The first judgment unit 702 includes a first processing subunit 7021 , a first comparison subunit 7022 , a second comparison subunit 7023 and a third comparison subunit 7024 .

[0077] The first processing subunit 7021 is used to classify the broadband signals to be identified according to their corresponding channels to obtain broadband signals to be identified corresponding to at least two channels;

[0078] The first comparison subunit 7022 is used to send the broadband signal to be identified corresponding to each channel to the threshold calculation, and compare the calculated threshold with the energy value of the broadband signal to be identified in each channel to obtain a comparison result;

[0079] The first comparison subunit 7022 includes a second processing subunit 70221 and a third processing subunit 70222 .

[0080] The second processing subunit 70221 is used to perform clustering processing on the broadband signal to be identified corresponding to each channel based on a distance-based clustering algorithm to obtain at least two clustering clusters, wherein the clustering clusters include at least one energy value of the broadband signal to be identified;

[0081] The third processing sub-unit 70222 is used to calculate the threshold range corresponding to each cluster based on all the cluster clusters and the Laida criterion, and calculate the cluster centers of all cluster clusters, and use the smallest cluster center as the judgment threshold to compare with the energy value of the broadband signal to be identified in each channel to obtain the comparison result.

[0082] The second comparison subunit 7023 is used to indicate that the channel is a no-signal channel if the comparison result is that the threshold is greater than the energy value of the broadband signal to be identified in the channel;

[0083] The third comparison subunit 7024 is configured to indicate that the channel is a signal channel if the comparison result is that the threshold is less than or equal to the energy value of the broadband signal to be identified in the channel.

[0084] The second judgment unit 703 is used to judge the attributes of the channels with communication signals according to the first judgment result, and splice the channels based on the attributes of each channel obtained by the judgment, and then judge whether the spliced ​​channels are successfully spliced ​​to obtain a second judgment result;

[0085] The second judgment unit 703 includes a first calculation subunit 7031 and a second calculation subunit 7032 .

[0086] The first calculation subunit 7031 is used to input the channel with the communication signal as input information into the preset residual neural network to perform channel attribute confidence calculation to obtain the confidence value of each channel attribute;

[0087] The second calculation subunit 7032 is configured to use the confidence value of each channel attribute as the determination probability of the channel attribute, and perform calculation based on a preset channel attribute determination formula to obtain the attribute category of each channel.

[0088] The second judgment unit 703 further includes a fourth processing subunit 7033 and a fifth processing subunit 7034 .

[0089] The fourth processing subunit 7033 is used to sort all the channels according to their attributes, wherein the channels are sorted in order from low-frequency channels to high-frequency channels to obtain sorted channels;

[0090] The fifth processing subunit 7034 is used to perform channel splicing on two adjacent channels in sequence based on a sliding window of a preset length, and judge whether the spliced ​​channels are logically reasonable based on a preset channel rationality matrix, so as to obtain a judgment result on whether the channels are reasonable.

[0091] The processing unit 704 is configured to extract channel attributes based on the second determination result to obtain an attribute type of each spliced ​​channel.

[0092] The processing unit 704 includes a first judgment subunit 7041 and a second judgment subunit 7042 .

[0093] The first judgment subunit 7041 is used for marking and extracting the attributes of the reasonably spliced ​​channels to obtain the attribute type of the reasonably spliced ​​channels if the judgment result is that the channel splicing is reasonable;

[0094] The second judgment subunit 7042 is used for correcting the unreasonable channel attributes according to a preset channel attribute correction formula if the judgment result is that the channel splicing is unreasonable, and marking the corrected channel attributes as unreasonable channel attributes.

[0095] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0097] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for segmenting and detecting a spectrum of a real-time communication signal, characterized in that: include: Acquire a broadband signal to be identified, wherein the broadband signal to be identified includes real-time communication signals of all channels; Performing signal judgment on the broadband signal to be identified to obtain a first judgment result, wherein the first judgment result is a judgment result of whether each channel in the broadband signal to be identified has a communication signal; Performing attribute judgment on the channels with communication signals as determined by the first judgment result, performing channel splicing based on the determined attribute of each channel, and then judging whether the spliced ​​channels are successfully spliced ​​to obtain a second judgment result; Extracting channel attributes based on the second judgment result to obtain the attribute type of each spliced ​​channel; The process of performing signal judgment on the broadband signal to be identified to obtain a first judgment result includes: Classifying the broadband signals to be identified according to their corresponding channels to obtain broadband signals to be identified corresponding to at least two channels; The broadband signal to be identified corresponding to each channel is sent to the threshold calculation, and the calculated threshold is compared with the energy value of the broadband signal to be identified in each channel to obtain a comparison result; If the comparison result is that the judgment threshold is greater than the energy value of the broadband signal to be identified in the channel, it indicates that the channel is a no-signal channel; If the comparison result is that the judgment threshold is less than or equal to the energy value of the broadband signal to be identified in the channel, it indicates that the channel is a signal channel; The broadband signal to be identified corresponding to each channel is sent to the threshold calculation, and the calculated threshold is compared with the energy value of the broadband signal to be identified in each channel, including: Clustering the broadband signal to be identified corresponding to each channel using a distance-based clustering algorithm to obtain at least two clusters, wherein the clusters include at least one energy value of the broadband signal to be identified; Based on all the clusters and the Laida criterion, the threshold range corresponding to each cluster is calculated, and the cluster centers of all clusters are calculated, and the smallest cluster center is used as the judgment threshold to be compared with the energy value of the broadband signal to be identified in each channel to obtain a comparison result; The channel splicing is performed based on the determined attributes of each channel, and then it is determined whether the spliced ​​channels are successfully spliced ​​to obtain a second determination result, including: All channels are sorted according to their attributes, wherein the channels are sorted from low-frequency channels to high-frequency channels, to obtain sorted channels; Based on a sliding window of a preset length, channel splicing is performed on two adjacent channels in turn, and based on a preset channel rationality matrix, it is judged whether the spliced ​​channels are logically reasonable, and a judgment result of whether the channels are reasonable is obtained.

2. The method for detecting the spectrum of a real-time communication signal according to claim 1, characterized in that , performing attribute judgment on the channel with the first judgment result that has a communication signal, including: The channel with the communication signal is input as input information into a preset residual neural network to calculate the channel attribute confidence, and the confidence value of each channel attribute is obtained; The confidence value of each channel attribute is used as the judgment probability of the channel attribute, and is calculated based on a preset channel attribute judgment formula to obtain the attribute category of each channel.

3. A real-time communication signal spectrum segmentation detection system, characterized in that: include: An acquisition unit, configured to acquire a broadband signal to be identified, wherein the broadband signal to be identified includes real-time communication signals of all channels; A first judgment unit, configured to perform signal judgment on the broadband signal to be identified to obtain a first judgment result, wherein the first judgment result is a judgment result of whether each channel in the broadband signal to be identified has a communication signal; A second judgment unit is used to judge the attributes of the channels that have the communication signal according to the first judgment result, and to splice the channels based on the attributes of each channel obtained by the judgment, and then to judge whether the spliced ​​channels are successfully spliced ​​to obtain a second judgment result; A processing unit, configured to extract channel attributes based on the second judgment result to obtain an attribute type of each spliced ​​channel; Wherein, the first judgment unit includes: A first processing subunit, configured to classify the broadband signals to be identified according to their corresponding channels, to obtain broadband signals to be identified corresponding to at least two channels; A first comparison subunit is used to send the broadband signal to be identified corresponding to each channel to the threshold calculation, and compare the calculated threshold with the energy value of the broadband signal to be identified in each channel to obtain a comparison result; A second comparison subunit, configured to indicate that the channel is a no-signal channel if the comparison result is that the judgment threshold is greater than the energy value of the broadband signal to be identified in the channel; A third comparison subunit, configured to indicate that the channel is a signal channel if the comparison result is that the judgment threshold is less than or equal to the energy value of the broadband signal to be identified in the channel; Wherein, the first comparison subunit includes: A second processing subunit is used to perform clustering processing on the broadband signal to be identified corresponding to each channel based on a distance-based clustering algorithm to obtain at least two clustering clusters, wherein the clustering clusters include at least one energy value of the broadband signal to be identified; The third processing subunit is used to calculate the threshold range corresponding to each cluster based on all the clusters and the Laida criterion, and calculate the cluster centers of all clusters, and use the smallest cluster center as the judgment threshold to compare with the energy value of the broadband signal to be identified in each channel to obtain a comparison result; Wherein, the second judgment unit further includes: A fourth processing subunit is used to sort all the channels according to their attributes, wherein the channels are sorted in order from low-frequency channels to high-frequency channels to obtain sorted channels; The fifth processing subunit is used to perform channel splicing on two adjacent channels in sequence based on a sliding window of a preset length, and judge whether the spliced ​​channels are logically reasonable based on a preset channel rationality matrix, so as to obtain a judgment result on whether the channels are reasonable.

4. The real-time communication signal spectrum segmentation detection system according to claim 3, characterized in that: The second judging unit includes: A first calculation subunit is used to input a channel having a communication signal as input information into a preset residual neural network to perform channel attribute confidence calculation to obtain a confidence value of each channel attribute; The second calculation subunit is used to use the confidence value of each channel attribute as the determination probability of the channel attribute, and perform calculation based on a preset channel attribute determination formula to obtain the attribute category of each channel.

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