GSM-R spurious interference identification method and device

Abstract

The invention provides a GSM-R spurious interference identification method and device. The method comprises the following steps: acquiring radio signal data, positioning information and machine account information of a GSM-R frequency band; performing spectrum analysis on the radio signal data to obtain bottom noise data; determining a signal characteristic parameter of the radio signal data based on the floor noise data; channelizing the radio signal data to obtain radio signal data of each frequency point; screening the radio signal data of each frequency point to obtain effective channel radio data; determining GSM-R signal data based on the effective channel radio data; determining GSM-R spurious interference information through signal power symmetry analysis, signal period correlation analysis and signal power correlation analysis; stray interference identification is carried out in combination with radio signal data, the scientificity and accuracy of GSM-R stray interference identification are improved, the misjudgment risk is reduced, and the GSM-R stray interference identification method is suitable for large-scale and high-frequency dynamic detection scenes.

Description

Technical Field

[0001] This invention belongs to the field of dynamic detection technology of railway wireless communication interference, and particularly relates to a method and device for identifying GSM-R spurious interference. Background Technology

[0002] Currently, a large number of network devices on GSM-R lines are gradually entering the long-term service phase. Due to factors such as equipment aging, improper maintenance, or component failure, spectrum waveform distortion problems occur frequently, which not only leads to a decline in network service quality, but may also generate useless transmission signals that encroach on other legitimate frequency resources, posing a potential threat to railway traffic safety.

[0003] Currently, railway communication operation and maintenance mainly relies on fault repair mode, which involves discovering obvious problems through dynamic detection and then carrying out special maintenance. However, this mode is difficult to identify potential hidden dangers caused by early performance degradation of equipment in advance, and the fault response is delayed, which cannot meet the transformation needs of high-quality network operation and maintenance and condition-based maintenance and preventive maintenance.

[0004] In terms of spurious interference identification, current dynamic detection still relies mainly on manual subjective judgment based on spectrum waveforms, lacking a scientific and systematic basis for identification. This method is inefficient, the identification results are highly dependent on the experience of the detection personnel, and the accuracy is prone to low due to individual differences. Furthermore, it has not yet achieved automated identification of spurious interference, making it difficult to adapt to large-scale, high-frequency dynamic detection scenarios. Summary of the Invention

[0005] This invention provides a GSM-R spurious interference identification method. By combining radio signal data, ledger information, and location information to identify spurious interference, the method improves the scientific rigor and accuracy of GSM-R spurious interference identification while reducing the risk of false positives. It is suitable for large-scale, high-frequency dynamic detection scenarios. The GSM-R spurious interference identification method includes: Acquire radio signal data, location information, and ledger information for the GSM-R band; the ledger information includes GSM-R base station information and frequency configuration information. Spectral analysis was performed on GSM-R band radio signal data to obtain GSM-R band noise floor data; Signal characteristic parameters of GSM-R band radio signal data are determined based on GSM-R band noise floor data; the signal characteristic parameters include effective signal center frequency, bandwidth data, and power data; Channelization processing is performed on the radio signal data of the GSM-R band to obtain the radio signal data of each GSM-R frequency point in the GSM-R band. Based on the signal characteristic parameters of the GSM-R band radio signal data, the radio signal data of each GSM-R frequency point in the GSM-R band are filtered to obtain the effective channel radio data; Based on effective channel radio data, determine GSM-R signal data; Based on GSM-R signal data, GSM-R spurious interference information is determined through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis.

[0006] This invention provides a GSM-R spurious interference identification device. By combining radio signal data, ledger information, and location information to identify spurious interference, it improves the scientific rigor and accuracy of GSM-R spurious interference identification while reducing the risk of false positives. It is suitable for large-scale, high-frequency dynamic detection scenarios. The GSM-R spurious interference identification device includes: The data acquisition module is used to acquire radio signal data, positioning information, and ledger information for the GSM-R band; the ledger information includes GSM-R base station information and frequency configuration information. The spectrum analysis module is used to perform spectrum analysis on GSM-R band radio signal data to obtain GSM-R band noise floor data. The signal feature determination module is used to determine the signal feature parameters of GSM-R band radio signal data based on the GSM-R band noise floor data; the signal feature parameters include the effective signal center frequency, bandwidth data, and power data. The channelization processing module is used to perform channelization processing on radio signal data in the GSM-R band to obtain radio signal data at each GSM-R frequency point in the GSM-R band. The channel filtering module is used to filter radio signal data at each GSM-R frequency point in the GSM-R band based on the signal characteristic parameters of the GSM-R band radio signal data, so as to obtain valid channel radio data. The GSM-R signal determination module is used to determine GSM-R signal data based on effective channel radio data. The spurious interference information determination module is used to determine GSM-R spurious interference information based on GSM-R signal data through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis.

[0007] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described GSM-R spurious interference identification method.

[0008] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described GSM-R spurious interference identification method.

[0009] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described GSM-R spurious interference identification method.

[0010] In this embodiment of the invention, the following are obtained: radio signal data, location information, and ledger information for the GSM-R band; the ledger information includes: GSM-R base station information and frequency configuration information; spectrum analysis is performed on the GSM-R band radio signal data to obtain GSM-R band noise floor data; signal characteristic parameters of the GSM-R band radio signal data are determined based on the GSM-R band noise floor data; the signal characteristic parameters include the effective signal center frequency, bandwidth data, and power data; channelization processing is performed on the GSM-R band radio signal data to obtain the various GSM signals in the GSM-R band. -R frequency radio signal data; based on the signal characteristic parameters of the GSM-R band radio signal data, the radio signal data of each GSM-R frequency point in the GSM-R band is filtered to obtain effective channel radio data; based on the effective channel radio data, GSM-R signal data is determined; based on the GSM-R signal data, GSM-R spurious interference information is determined through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis; the embodiments of the present invention improve the scientificity and accuracy of GSM-R spurious interference identification by combining radio signal data, ledger information, and location information, while reducing the risk of misjudgment and adapting to large-scale, high-frequency dynamic detection scenarios. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of the GSM-R spurious interference identification method in an embodiment of the present invention; Figure 2 This is a specific example diagram illustrating the determination of GSM-R band noise floor data in an embodiment of the present invention; Figure 3 This is a specific example diagram illustrating the determination of signal characteristic parameters in an embodiment of the present invention; Figure 4This is a specific example diagram illustrating the determination of GSM-R signal data in an embodiment of the present invention; Figure 5 This is a specific example diagram illustrating the determination of GSM-R spurious interference information in an embodiment of the present invention; Figure 6 This is a structural diagram of the GSM-R spurious interference identification device in an embodiment of the present invention; Figure 7 This is a structural diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0013] As mentioned earlier, in the existing technology, the identification of GSM-R spurious interference during the dynamic detection of railway wireless communication is based solely on manual judgment of the spectrum waveform and adopts a fault repair operation mode. This scheme not only lacks scientific basis for identification, but also suffers from low accuracy of identification results, reliance on human experience, and failure to achieve automatic identification. Furthermore, it is difficult to detect early hidden dangers of equipment, has a lag effect, and cannot guarantee the quality of wireless network services.

[0014] To address this problem, the inventors discovered that it is necessary to achieve automated and accurate identification of stray interference through technical means. Therefore, they proposed a stray interference identification method and device, which utilizes acquired radio signal data, extracts multi-dimensional features of the signal and analyzes their correlations, and integrates location information and ledger data to achieve automatic identification and accurate location of stray interference.

[0015] Figure 1 This is a flowchart of the GSM-R spurious interference identification method in an embodiment of the present invention, as shown below. Figure 1 As shown, the GSM-R spurious interference identification method includes: Step 101: Obtain radio signal data, location information, and ledger information for the GSM-R band; the ledger information includes GSM-R base station information and frequency configuration information. Step 102: Perform spectrum analysis on the GSM-R band radio signal data to obtain the GSM-R band noise floor data; Step 103: Determine the signal characteristic parameters of GSM-R band radio signal data based on the GSM-R band noise floor data; the signal characteristic parameters include the effective signal center frequency, bandwidth data, and power data; Step 104: Perform channelization processing on the GSM-R band radio signal data to obtain radio signal data for each GSM-R frequency point in the GSM-R band; Step 105: Based on the signal characteristic parameters of the GSM-R band radio signal data, filter the radio signal data of each GSM-R frequency point in the GSM-R band to obtain effective channel radio data; Step 106: Determine GSM-R signal data based on effective channel radio data; Step 107: Based on GSM-R signal data, determine GSM-R spurious interference information through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis.

[0016] Depend on Figure 1 As shown in the process, in this embodiment of the invention, the following steps are taken: 1) Obtain radio signal data, location information, and ledger information for the GSM-R band; the ledger information includes GSM-R base station information and frequency configuration information; 2) Perform spectrum analysis on the GSM-R band radio signal data to obtain GSM-R band noise floor data; 3) Determine the signal characteristic parameters of the GSM-R band radio signal data based on the GSM-R band noise floor data; the signal characteristic parameters include the effective signal center frequency, bandwidth data, and power data; 4) Perform channelization processing on the GSM-R band radio signal data to obtain radio signal data for each GSM-R frequency point in the GSM-R band; 5) Based on the signal characteristic parameters of the GSM-R band radio signal data, filter the radio signal data for each GSM-R frequency point in the GSM-R band to obtain effective channel radio data; 6) Determine GSM-R signal data based on the effective channel radio data; 7) Based on the GSM-R signal data, determine GSM-R spurious interference information through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis.

[0017] Compared with existing technologies that rely solely on manual determination of GSM-R spurious interference based on spectral waveforms, this new technology collects GSM-R band radio signal data, location information, and ledger information, integrates and extracts multi-dimensional signal features, and combines signal power symmetry analysis, signal periodic correlation analysis, and signal power correlation analysis to achieve automatic identification and accurate location of spurious interference. This significantly improves the accuracy of spurious interference identification, reduces the probability of false alarms and missed detections, and possesses the ability to analyze the time-domain characteristics of wireless signals. It effectively eliminates the impact of special scenarios such as increased noise floor, providing technical support for railway wireless communication status maintenance and preventive maintenance, and ensuring the quality of wireless network service and train operation safety.

[0018] In step 101, the following are obtained for the GSM-R band: radio signal data, positioning information, and ledger information; the ledger information includes GSM-R: base station information and frequency configuration information.

[0019] In a specific embodiment, acquiring radio signal data, location information, and ledger information in the GSM-R band includes: Acquiring radio signal data: A dedicated GSM-R antenna installed on the roof of a dynamic inspection vehicle, such as a railway-specific inspection vehicle (including high-speed integrated inspection trains and electrical inspection vehicles), operates in the frequency range of 870MHz-960MHz, suitable for the high-speed operation environment of trains. This antenna receives radio signals from the GSM-R band and adjacent frequency bands along the railway line and transmits the signals via a feeder to the radio signal acquisition equipment inside the dynamic inspection vehicle. This equipment, based on the configured center frequency and bandwidth parameters, performs radio frequency reception processing on the target frequency band radio signals, converts them into baseband IQ data, and outputs them in a specified format to obtain GSM-R band radio signal data.

[0020] Location information acquisition: The location information is acquired through the spatiotemporal positioning system carried by the dynamic detection vehicle. This information includes the railway line, class, mileage, latitude and longitude information, and operating speed of the dynamic detection train, providing positional support for subsequent stray interference positioning.

[0021] Obtain ledger information: Retrieve the electronic spreadsheet ledger containing GSM-R related information for railway lines. The base station information in the ledger includes the base station name and location, including mileage, latitude and longitude, cell number, and boundary; the frequency configuration information includes the BCCH frequency and TCH frequency of the base station, providing data basis for querying spurious interference cell attributes and accurate positioning.

[0022] Figure 2 This is a specific example diagram illustrating the determination of GSM-R band noise floor data in an embodiment of the present invention, as shown below. Figure 2 As shown, spectral analysis of GSM-R band radio signal data yields GSM-R band noise floor data, which may include: Step 201: Perform spectrum analysis on the GSM-R band radio signal data to obtain level value samples for the entire frequency band; Step 202: Filter the level value samples below the preset threshold from the full frequency band level value samples; Step 203: Based on the selected level value samples, the GSM-R band noise floor data is obtained by root mean square calculation.

[0023] In a specific embodiment, spectrum analysis is performed on GSM-R band radio signal data to obtain GSM-R band noise floor data, including: Obtain full-band level samples: The system receives GSM-R band radio signal data output from radio signal acquisition equipment, performs spectrum analysis on the signal data, converts the signal from the time domain to the frequency domain, and obtains a set of level value samples across the entire GSM-R band and adjacent frequency bands, providing raw data for subsequent noise floor calculation.

[0024] Filter low-level samples: A preset threshold is set, which is based on the standard of eliminating useless occupancy signals. The level value samples in the full frequency band are selected that are lower than the preset threshold. Specifically, the lowest 20% of the level value samples in the full frequency band are selected and the remaining 80% of the samples are discarded. This is to eliminate the interference of useless signal occupancy on the calculation of the noise floor data and ensure that the selected samples can only represent the radio noise characteristics of the frequency band.

[0025] Calculate the noise floor data using the root mean square (RMS) method: Based on the low-level value samples obtained in step 202, the GSM-R band noise floor data is calculated using the root mean square formula, as follows:

[0026] in, This represents the noise floor data, where N is the number of filtered level value samples. This is the i-th filtered level value sample.

[0027] This calculation accurately yields noise floor data that characterizes radio noise in the GSM-R band.

[0028] Figure 3 This is a specific example diagram illustrating the determination of signal characteristic parameters in an embodiment of the present invention, such as... Figure 3 As shown, determining the signal characteristic parameters of GSM-R band radio signal data based on GSM-R band noise floor data can include: Step 301: Use a baseline extraction algorithm to extract the baseline of the GSM-R band radio signal data; Step 302: Based on the baseline of the GSM-R band radio signal data, separate the noise floor data in the GSM-R band radio signal data to obtain the signal characteristic parameters of the GSM-R band radio signal data.

[0029] In a specific embodiment, determining the signal characteristic parameters of GSM-R band radio signal data based on GSM-R band noise floor data includes: Baseline for extracting GSM-R band radio signal data: The baseline extraction algorithm is used to process the spectral analysis results of GSM-R band radio signal data to extract the baseline; the objective function of this algorithm is:

[0030] Among them, z i w is the baseline estimate corresponding to the i-th sample. i Let y be the weighted vector corresponding to the i-th sample, where N is the number of samples. i Let λ be the original signal value of the i-th sample, λ be the regularization parameter, d be the difference parameter, and α be the difference parameter. i Let be the weighting coefficient corresponding to the i-th difference term, and .

[0031] The objective function can be further expressed as:

[0032] Where W is a weighted diagonal matrix, D d Let Wy be a d-order difference matrix, Wy be the weighted original signal vector, and the residual be... , for Elements less than 0, for The standard deviation of , where k is the asymmetric coefficient.

[0033] The algorithm iteratively executes the above calculation process until the condition is met. The termination condition is that the residual reaches the preset accuracy or the number of iterations exceeds the limit, and finally the spectrum waveform baseline of the GSM-R band radio signal data is obtained.

[0034] Separate the noise floor data and determine the signal characteristic parameters: Based on the spectrum waveform baseline extracted in step 301, and combined with the acquired GSM-R band noise floor data, the noise floor component is separated from the GSM-R band radio signal data. The interference of special scenarios such as noise floor rise on the effective signal is filtered out, and the core feature parameters of the effective signal are accurately extracted, including the center frequency, bandwidth data and power data of the effective signal, providing accurate signal feature support for subsequent channel screening and spurious interference identification.

[0035] In step 104, the radio signal data of the GSM-R band is channelized to obtain the radio signal data of each GSM-R frequency point in the GSM-R band.

[0036] In a specific embodiment, channelization processing is performed on the GSM-R band radio signal data to obtain radio signal data for each GSM-R frequency point, including: The multiphase filtering algorithm is invoked to perform digital signal processing on the GSM-R band radio signal data output by the radio signal acquisition equipment.

[0037] The algorithm's execution process includes prototype filter design, polyphase decomposition, and filtering. Following the frequency distribution patterns of the GSM-R network (e.g., a single frequency bandwidth of 200kHz), frequency reuse technology is used to decompose the broadband radio signal into multiple narrowband signals corresponding to GSM-R standard channels. Through this channelization process, the radio signal data of each GSM-R frequency point is accurately separated from the complex broadband signal, clarifying the radio signal data of each GSM-R frequency point in the GSM-R band, laying the foundation for subsequent effective channel selection and targeted signal analysis.

[0038] In step 105, the radio signal data of each GSM-R frequency point in the GSM-R band is filtered according to the signal characteristic parameters of the GSM-R band radio signal data to obtain effective channel radio data.

[0039] In a specific embodiment, effective channel radio data is obtained by filtering radio signal data at each GSM-R frequency point based on the signal characteristic parameters of the GSM-R band radio signal data, including: First, clarify the standard characteristic threshold range of GSM-R signals. This range is set based on the GSM-R network technical specifications. Among them, the center frequency must fall within the GSM-R dedicated antenna operating frequency band of 870MHz-960MHz, the bandwidth must meet the standard requirement of 200kHz for a single frequency point, and the power must be higher than the noise floor data and meet the effective power threshold of GSM-R signals.

[0040] Subsequently, the radio signal data of each GSM-R frequency point obtained after channelization processing are compared one by one with the standard threshold range of the above signal characteristic parameters, namely the effective signal center frequency, bandwidth data, and power data.

[0041] GSM-R frequency points with center frequencies in the 870MHz-960MHz band, bandwidths conforming to the 200kHz standard, and power exceeding the noise floor and within the effective power threshold range were selected. The radio signal data corresponding to these GSM-R frequency points were identified as valid channel radio data. Invalid channel data with center frequencies deviating from the GSM-R band, bandwidths not conforming to the standard, or power below the effective threshold were removed to ensure that subsequent signal analysis was conducted only on valid data that conformed to the basic characteristics of GSM-R signals.

[0042] Figure 4 This is a specific example diagram illustrating the determination of GSM-R signal data in an embodiment of the present invention, as shown below. Figure 4As shown, determining GSM-R signal data based on effective channel radio data may include: Step 401: Calculate the effective channel radio data amplitude to obtain the effective channel radio data time-domain waveform; Step 402: Add Gaussian white noise to the spectral envelope of the time-domain waveform of the effective channel radio data, and perform multi-mode decomposition using the empirical mode decomposition algorithm to obtain the empirical mode decomposition result of the effective channel radio data; Step 403: Based on the empirical mode decomposition results of the effective channel radio data and in conjunction with the preset GSM-R signal modulation scheme, determine the GSM-R signal data.

[0043] In a specific embodiment, determining GSM-R signal data based on effective channel radio data includes: Calculate the effective channel radio data amplitude spectrum to obtain the time-domain waveform: Based on the baseband IQ data in the effective channel radio data, the amplitude of the effective channel radio data is calculated using the amplitude calculation formula, and then converted into a signal time-domain waveform. The calculation formula is as follows:

[0044] Where i is the in-phase component of the baseband signal and q is the quadrature component of the baseband signal.

[0045] Gaussian white noise is added, and multi-mode decomposition is performed using the empirical mode decomposition algorithm: (1) Spectral envelope of time-domain waveform of radio data to effective channel Adding a set of Gaussian white noise to form:

[0046] in, The noise is Gaussian white noise, and N is the amount of Gaussian white noise. The standard deviation of white noise.

[0047] right Perform empirical mode decomposition to obtain a set of first-order modes, and calculate the mean of this set of modes:

[0048] in, This is the first-order mode estimate. The mean of the first-order modes, This represents the first-order mode obtained from the nth decomposition.

[0049] (2) Calculate the first-order residual signal:

[0050] in, s is the first-order residual signal, and s is the original input signal.

[0051] (3) Define operators , indicating that the k-th order mode is generated based on empirical mode decomposition; in Adding Gaussian white noise to obtain .

[0052] The second-order mode is obtained after empirical mode decomposition:

[0053] (4) Calculate the k-th order residual:

[0054] (5) Add Gaussian white noise to the k-th residual signal and perform empirical mode decomposition to obtain the (k+1)-th mode:

[0055] (6) Repeat steps (4) and (5) until the empirical mode decomposition termination condition is met, and finally obtain the original spectral envelope decomposed as follows:

[0056] Where R is the final residual signal; the above decomposition result is the empirical mode decomposition result of the effective channel radio data.

[0057] GSM-R signal data is determined based on empirical mode decomposition results and a preset modulation scheme: The default standard modulation scheme for GSM-R signals is GMSK, and the corresponding standard time slot period is (557±δ)us (δ is the allowable error threshold). Based on the obtained empirical mode decomposition results, the intrinsic mode function (IMFr) of the trend term is extracted, and the derivative waveform is obtained by taking the reciprocal of IMFr. The zeros of the derivative waveform are found, and the distance between the two zeros is calculated, which is the time slot period of the current effective channel signal. If the calculated time slot period is (557±δ)us, the wireless signal modulation method is determined to be GMSK, which meets the characteristics of GSM-R signal, and the effective channel radio data is identified as GSM-R signal data; if the period does not meet the above conditions, it is determined to be non-GSM-R signal data and is discarded.

[0058] Figure 5This is a specific example diagram illustrating the determination of GSM-R spurious interference information in an embodiment of the present invention, as shown below. Figure 5 As shown, based on GSM-R signal data, GSM-R spurious interference information is determined through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis, which may include: Step 501: Based on GSM-R signal data, determine the frequency range in which GSM-R signals exist; Step 502: Determine whether there is a power-symmetrical signal within the frequency range where the GSM-R signal exists; If a power-symmetrical signal exists, perform a signal periodic correlation analysis on the signal, including: Step 503: Based on GSM-R band radio signal data, determine the periodic correlation data between each GSM-R signal within the frequency range where GSM-R signals exist; If a power-symmetric signal exists, perform a signal power correlation analysis on the signal, including: Step 504: In the center frequency range of the frequency range where the GSM-R signal exists, analyze the power correlation data between the signal power and other signals; Step 505: Determine GSM-R spurious interference information based on periodic correlation data and power correlation data, combined with location information and ledger information.

[0059] In this embodiment, the GSM-R spurious interference information includes the location of the spurious interference and the frequency of the spurious interference.

[0060] In a specific embodiment, based on GSM-R signal data, GSM-R spurious interference information is determined through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis, including: Identify the frequency range where GSM-R signals exist: Based on the established GSM-R signal data, the center frequency and bandwidth data are extracted from its signal characteristic parameters. Combined with the operating frequency band standard of the GSM-R system, the frequency range of the existing GSM-R signal is identified.

[0061] This interval is based on the center frequency of the GSM-R signal, covering the bandwidth corresponding to its standard 200kHz adjacent channel spacing, and extends to the adjacent frequency bands on both sides where spurious radiation may exist, defining the target frequency range for spurious interference analysis.

[0062] Determine whether a power-symmetrical signal exists within the frequency range: Extract the power data of all signals within the frequency range determined in step 501. Using the center frequency of the GSM-R main signal within the range as the axis of symmetry, compare the signal power values ​​at the same frequency offset positions on both sides of the axis of symmetry. Set a power symmetry judgment threshold, i.e., the allowable range of power difference. If the difference in signal power values ​​at corresponding positions on both sides is within this threshold range, and the power changes in a consistent trend with the dynamic detection vehicle's trajectory, then a power symmetry relationship is satisfied, and it is determined that a power-symmetric signal exists within this frequency range; otherwise, it is determined that it does not exist. For power-symmetric signals, perform periodic correlation assessment and signal power correlation assessment, specifically: Among them, signal periodic correlation assessment refers to extracting the intrinsic mode function (IMFr) of the trend term based on the empirical mode decomposition results, obtaining the derivative waveform by taking the reciprocal of IMFr, finding the zeros of the derivative waveform, and constructing a sequence z. If the cosine similarity between the remaining sequences and the sequence corresponding to the center frequency point is greater than the threshold thr, then... cos If a periodic correlation exists between the channels, the cosine similarity calculation formula is as follows:

[0063] in, Let be the cosine similarity between the i-th vector and the j-th vector, where cosine_similarity This represents cosine similarity, where M is the number of vector dimensions. Let be the value of the kth element of the i-th vector.

[0064] Signal power correlation assessment refers to analyzing the power correlation between signals based on changes in received signal power. Generally, during dynamic detection, the received signal power changes with the distance from the base station as the train moves, gradually increasing as it approaches the base station and decreasing as it moves further away. If there is a strong correlation between the signal power on the left and right sides and the power at the center frequency, then the signals on both sides are determined to be from the same radiation source as the center frequency.

[0065] Identify potential spurious signals: For the power symmetrical signal obtained through the above steps, combined with the frequency interval characteristics of the GSM-R system and the characteristics that spurious interference mostly originates from radiation from base stations or mobile station equipment and is symmetrically distributed, after excluding signals generated by normal frequency reuse, the power symmetrical signal is identified as a potential spurious signal.

[0066] By combining location information and ledger information, GSM-R spurious interference information is obtained: Based on potential spurious signals, key parameters such as interference frequency, spurious frequency range, and signal power are extracted; combined with the positioning information of the dynamic detection vehicle, the railway line, class, mileage, and latitude and longitude of the spurious interference are determined; the ledger information is retrieved, and the base station name, base station location, cell number, boundary, and frequency configuration information corresponding to the interference frequency are matched to clarify the base station and cell attributes to which the interference belongs.

[0067] By integrating the above information, a complete GSM-R spurious interference information is formed, which includes the location of the interference, interference frequency parameters, signal power, and information about the base station and cell to which it belongs.

[0068] Verification has shown that the GSM-R spurious interference identification method of this invention has the following beneficial effects: 1. By integrating multi-dimensional characteristic judgments such as signal power symmetry analysis, periodic correlation assessment, and modulation mode analysis, it replaces the traditional manual judgment method based solely on spectrum waveforms, reduces reliance on human experience, effectively improves the scientific nature and accuracy of spurious interference identification, and reduces the risk of misjudgment.

[0069] 2. The baseline extraction algorithm is used to process the spectrum waveform to obtain signal feature parameters, which can effectively eliminate the interference of special scenarios such as noise floor rise on parameter extraction, and ensure the stability and reliability of signal feature parameters.

[0070] 3. Supports status-based maintenance and preventative maintenance modes for railway wireless communication; it can detect early stray interference risks caused by aging or malfunctions of GSM-R network equipment in advance, replacing the lagging operation mode of traditional fault maintenance, providing precise technical support for maintenance decisions, and ensuring the quality of wireless network services and train operation safety.

[0071] This invention also provides a GSM-R spurious interference identification device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the GSM-R spurious interference identification method, the implementation of this device can refer to the implementation of the GSM-R spurious interference identification method, and repeated details will not be elaborated further.

[0072] Figure 6 This is a structural diagram of the GSM-R spurious interference identification device in an embodiment of the present invention, as shown below. Figure 6 As shown, the GSM-R spurious interference identification device includes: The data acquisition module 601 is used to acquire radio signal data, positioning information, and ledger information of the GSM-R band; the ledger information includes GSM-R base station information and frequency configuration information. The spectrum analysis module 602 is used to perform spectrum analysis on GSM-R band radio signal data to obtain GSM-R band noise floor data. The signal feature determination module 603 is used to determine the signal feature parameters of GSM-R band radio signal data based on the GSM-R band noise floor data; the signal feature parameters include the effective signal center frequency, bandwidth data, and power data. The channelization processing module 604 is used to perform channelization processing on the radio signal data of the GSM-R band to obtain radio signal data of each GSM-R frequency point in the GSM-R band. The channel filtering module 605 is used to filter the radio signal data of each GSM-R frequency point in the GSM-R band according to the signal characteristic parameters of the GSM-R band radio signal data, so as to obtain the effective channel radio data. GSM-R signal determination module 606 is used to determine GSM-R signal data based on effective channel radio data; The spurious interference information determination module 607 is used to determine GSM-R spurious interference information based on GSM-R signal data through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis.

[0073] In one embodiment, the spectrum analysis module 602 is specifically used for: Perform spectrum analysis on GSM-R band radio signal data to obtain level value samples across the entire frequency band; Filter the level value samples from the full frequency band that are below a preset threshold; Based on the selected level value samples, the noise floor data of the GSM-R band is obtained by root mean square calculation.

[0074] In one embodiment, the signal feature determination module 603 is specifically used for: The baseline extraction algorithm is used to extract the baseline of GSM-R band radio signal data; Based on the baseline of GSM-R band radio signal data, the noise floor data in the GSM-R band radio signal data is separated to obtain the signal characteristic parameters of the GSM-R band radio signal data.

[0075] In one embodiment, the GSM-R signal determination module 606 is specifically used for: Calculate the effective channel radio data amplitude to obtain the effective channel radio data time-domain waveform; Gaussian white noise is added to the spectral envelope of the time-domain waveform of the effective channel radio data, and multi-mode decomposition is performed by the empirical mode decomposition algorithm to obtain the empirical mode decomposition result of the effective channel radio data; Based on the empirical mode decomposition results of the effective channel radio data and combined with the preset GSM-R signal modulation scheme, the GSM-R signal data is determined.

[0076] In one embodiment, the spurious interference information determination module 607 is specifically used for: Based on GSM-R signal data, determine the frequency range where GSM-R signals exist; Determine whether there is a power-symmetrical signal within the frequency range where GSM-R signals exist; If a power-symmetric signal exists, perform a signal periodic correlation analysis on the signal, including: Based on GSM-R band radio signal data, determine the periodic correlation data between various GSM-R signals in the frequency range where GSM-R signals exist; If a power-symmetric signal exists, perform a signal power correlation analysis on the signal, including: In the center frequency range of the frequency range where GSM-R signals exist, analyze the power correlation data between the signal power and other signals; Based on periodic correlation data and power correlation data, combined with location information and ledger information, GSM-R spurious interference information is determined.

[0077] In one embodiment, the GSM-R spurious interference information includes the location of the spurious interference and the frequency of the spurious interference.

[0078] Based on the aforementioned inventive concept, such as Figure 7 As shown, the present invention also proposes a computer device 700, including a memory 710, a processor 720, and a computer program 730 stored in the memory 710 and executable on the processor 720. When the processor 720 executes the computer program 730, it implements the aforementioned GSM-R spurious interference identification method.

[0079] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described GSM-R spurious interference identification method.

[0080] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described GSM-R spurious interference identification method.

[0081] In this embodiment of the invention, the following are obtained: radio signal data, location information, and ledger information for the GSM-R band; the ledger information includes: GSM-R base station information and frequency configuration information; spectrum analysis is performed on the GSM-R band radio signal data to obtain GSM-R band noise floor data; signal characteristic parameters of the GSM-R band radio signal data are determined based on the GSM-R band noise floor data; the signal characteristic parameters include the effective signal center frequency, bandwidth data, and power data; channelization processing is performed on the GSM-R band radio signal data to obtain the various GSM signals in the GSM-R band. -R frequency radio signal data; based on the signal characteristic parameters of the GSM-R band radio signal data, the radio signal data of each GSM-R frequency point in the GSM-R band is filtered to obtain effective channel radio data; based on the effective channel radio data, GSM-R signal data is determined; based on the GSM-R signal data, GSM-R spurious interference information is determined through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis; the embodiments of the present invention improve the scientificity and accuracy of GSM-R spurious interference identification by combining radio signal data, ledger information, and location information, while reducing the risk of misjudgment and adapting to large-scale, high-frequency dynamic detection scenarios.

[0082] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying spurious interference in GSM-R, characterized in that, include: Acquire radio signal data, location information, and ledger information for the GSM-R band; the ledger information includes GSM-R base station information and frequency configuration information. Spectral analysis was performed on GSM-R band radio signal data to obtain GSM-R band noise floor data; Signal characteristic parameters of GSM-R band radio signal data are determined based on GSM-R band noise floor data; the signal characteristic parameters include effective signal center frequency, bandwidth data, and power data; Channelization processing is performed on the radio signal data of the GSM-R band to obtain the radio signal data of each GSM-R frequency point in the GSM-R band. Based on the signal characteristic parameters of the GSM-R band radio signal data, the radio signal data of each GSM-R frequency point in the GSM-R band are filtered to obtain the effective channel radio data; Based on effective channel radio data, determine GSM-R signal data; Based on GSM-R signal data, GSM-R spurious interference information is determined through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis.

2. The method as described in claim 1, characterized in that, Spectral analysis of GSM-R band radio signal data yields GSM-R band noise floor data, including: Perform spectrum analysis on GSM-R band radio signal data to obtain level value samples across the entire frequency band; Filter the level value samples from the full frequency band that are below a preset threshold; Based on the selected level value samples, the noise floor data of the GSM-R band is obtained by root mean square calculation.

3. The method as described in claim 1, characterized in that, Based on the noise floor data of the GSM-R band, the signal characteristic parameters of the GSM-R band radio signal data are determined, including: The baseline extraction algorithm is used to extract the baseline of GSM-R band radio signal data; Based on the baseline of GSM-R band radio signal data, the noise floor data in the GSM-R band radio signal data is separated to obtain the signal characteristic parameters of the GSM-R band radio signal data.

4. The method as described in claim 1, characterized in that, Based on effective channel radio data, determine GSM-R signal data, including: Calculate the effective channel radio data amplitude to obtain the effective channel radio data time-domain waveform; Gaussian white noise is added to the spectral envelope of the time-domain waveform of the effective channel radio data, and multi-mode decomposition is performed by the empirical mode decomposition algorithm to obtain the empirical mode decomposition result of the effective channel radio data; Based on the empirical mode decomposition results of the effective channel radio data and combined with the preset GSM-R signal modulation scheme, the GSM-R signal data is determined.

5. The method as described in claim 1, characterized in that, Based on GSM-R signal data, GSM-R spurious interference information is determined through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis, including: Based on GSM-R signal data, determine the frequency range where GSM-R signals exist; Determine whether there is a power-symmetrical signal within the frequency range where GSM-R signals exist; If a power-symmetric signal exists, perform a signal periodic correlation analysis on the signal, including: Based on GSM-R band radio signal data, determine the periodic correlation data between various GSM-R signals in the frequency range where GSM-R signals exist; If a power-symmetric signal exists, perform a signal power correlation analysis on the signal, including: In the center frequency range of the frequency range where GSM-R signals exist, analyze the power correlation data between the signal power and other signals; Based on periodic correlation data and power correlation data, combined with location information and ledger information, GSM-R spurious interference information is determined.

6. The method as described in claim 1, characterized in that, The GSM-R spurious interference information includes the location and frequency of the spurious interference.

7. A GSM-R spurious interference identification device, characterized in that, include: The data acquisition module is used to acquire radio signal data, location information, and ledger information in the GSM-R band. The ledger information includes GSM-R's: base station information and frequency configuration information; The spectrum analysis module is used to perform spectrum analysis on GSM-R band radio signal data to obtain GSM-R band noise floor data. The signal feature determination module is used to determine the signal feature parameters of GSM-R band radio signal data based on the GSM-R band noise floor data; the signal feature parameters include the effective signal center frequency, bandwidth data, and power data. The channelization processing module is used to perform channelization processing on radio signal data in the GSM-R band to obtain radio signal data at each GSM-R frequency point in the GSM-R band. The channel filtering module is used to filter radio signal data at each GSM-R frequency point in the GSM-R band based on the signal characteristic parameters of the GSM-R band radio signal data, so as to obtain valid channel radio data. The GSM-R signal determination module is used to determine GSM-R signal data based on effective channel radio data. The spurious interference information determination module is used to determine GSM-R spurious interference information based on GSM-R signal data through signal power symmetry analysis, signal period correlation analysis, and signal power correlation analysis.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

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