GSM-R repeater carrier-to-interference ratio abnormity identification method and device

Abstract

The invention provides a GSM-R repeater carrier-to-interference ratio abnormity identification method and device, and relates to the technical field of railway wireless communication dynamic detection, and the method comprises the steps: obtaining handover information data in real time based on GSM-R network dynamic detection; extracting an effective service range of the current cell, and obtaining repeater distribution information data in the effective service range of the current cell; extracting cell carrier-to-interference ratio waveform data; analyzing local depression characteristics of the carrier-to-interference ratio waveform, and determining a depression position set; performing filtering processing on the sunken position set to obtain a repeater coverage range; when the repeater exists in the coverage area of the repeater, carrying out data smoothing processing on the carrier-to-interference ratio waveform in the coverage area of the repeater and extracting a base line; according to the slope of the baseline, extracting repeater waveform data of which the slope is smaller than a preset slope threshold to obtain repeater feature data; and analyzing the statistical distribution characteristics, and determining the degradation degree corresponding to the statistical distribution characteristics according to a preset degradation degree evaluation standard.

Description

Technical Field

[0001] The present invention relates to the technical field of railway wireless communication dynamic detection, and in particular to a method and device for identifying abnormal carrier-to-interference ratio of a GSM-R repeater. Background Art

[0002] This section is intended to provide a background or context for embodiments of the present invention. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] As a dedicated channel for wireless data transmission between trains and the ground, the GSM-R network handles services such as train number verification, wireless transmission of dispatching commands, and transmission of train operation control information. It plays an irreplaceable role in organizing railway transportation, directing train operations, and liaising with railway personnel. To ensure the normal operation of the GSM-R network, GSM-R testing is currently being conducted on both high-speed and conventional railways using high-speed comprehensive testing trains and electrical testing vehicles. Testing covers wireless field strength coverage, service quality, and the electromagnetic environment. Current communication testing has revealed that some GSM-R networks are experiencing issues such as substandard field strength and coverage, handover failures, dropped calls, abnormal handovers, and poor call quality. However, current GSM-R communication testing systems do not yet have the ability to automatically identify abnormal carrier-to-interference ratios in GSM-R repeater stations.

[0004] Analysis results based on historical detection data show that as the service life of GSM-R repeaters increases, the number of GSM-R repeaters with degraded carrier-to-interference ratios gradually increases. Abnormal carrier-to-interference ratios of GSM-R repeaters will directly lead to deterioration of call quality between high-speed trains and ground command and control centers, and a sharp increase in the risk of wireless link communication interruption, endangering train operation control, dispatching command and operational safety.

[0005] In summary, there is an urgent need for a technical solution that can overcome the above-mentioned defects, detect abnormal carrier-to-interference ratio of GSM-R repeaters as early as possible, and eliminate the impact of abnormal carrier-to-interference ratio of GSM-R repeaters on network quality. Summary of the Invention

[0006] To solve the problems existing in the prior art, the present invention proposes a method and device for identifying abnormal carrier-to-interference ratio of a GSM-R repeater, which can detect and eliminate the impact of abnormal carrier-to-interference ratio of a GSM-R repeater on network quality as early as possible.

[0007] In a first aspect of an embodiment of the present invention, a method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater is proposed. The method includes:

[0008] Based on GSM-R network dynamic detection, real-time acquisition of handover information data;

[0009] Extracting the effective service range of the current cell and obtaining the distribution information data of repeaters within the effective service range of the current cell based on the handover information data and the ledger information data;

[0010] Extracting cell carrier-to-interference ratio waveform data according to the repeater distribution information data;

[0011] Analyzing local depression features of the carrier-to-interference ratio waveform based on the cell carrier-to-interference ratio waveform data, and determining a set of depression locations;

[0012] Filter the set of concave positions to obtain the coverage of the repeater station;

[0013] When there is a repeater within the coverage area of ​​the repeater, performing data smoothing on the carrier-to-interference ratio waveform within the coverage area of ​​the repeater and extracting a baseline;

[0014] Determining a baseline slope curve according to the slope of the baseline, extracting repeater waveform data having a slope less than a preset slope threshold according to the baseline slope curve to obtain repeater characteristic data;

[0015] Analyze the statistical distribution characteristics according to the repeater characteristic data, and determine the degradation degree corresponding to the statistical distribution characteristics according to a preset degradation degree evaluation standard.

[0016] In a second aspect of an embodiment of the present invention, a device for identifying abnormal carrier-to-interference ratio of a GSM-R repeater is provided, the device comprising:

[0017] Dynamic monitoring module, used to obtain real-time handover information data based on GSM-R network dynamic detection;

[0018] An effective service range extraction module is used to extract the effective service range of the current cell based on the handover information data and the ledger information data, and obtain the repeater distribution information data within the effective service range of the current cell;

[0019] a carrier-to-interference ratio waveform data extraction module, configured to extract cell carrier-to-interference ratio waveform data based on the repeater distribution information data;

[0020] a depression feature analysis module, configured to analyze local depression features of the carrier-to-interference ratio waveform based on the cell carrier-to-interference ratio waveform data, and determine a set of depression locations;

[0021] A repeater coverage range determination module is used to filter the set of recessed positions to obtain the repeater coverage range;

[0022] A baseline extraction module, configured to perform data smoothing on the carrier-to-interference ratio waveform within the coverage area of ​​the repeater and extract a baseline when there is a repeater within the coverage area of ​​the repeater;

[0023] A baseline analysis module, configured to determine a baseline slope curve according to the slope of the baseline, extract repeater waveform data having a slope less than a preset slope threshold according to the baseline slope curve, and obtain repeater characteristic data;

[0024] The degradation judgment module is used to analyze the statistical distribution characteristics according to the repeater characteristic data, and determine the degradation degree corresponding to the statistical distribution characteristics according to a preset degradation degree evaluation standard.

[0025] In a third aspect of an embodiment of the present invention, a computer device is proposed, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for identifying an abnormal carrier-to-interference ratio of a GSM-R repeater is implemented.

[0026] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater is implemented.

[0027] In a fifth aspect of the embodiments of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, a method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater is implemented.

[0028] The method and device for identifying abnormal carrier-to-interference ratios in GSM-R repeaters proposed in the present invention extract carrier-to-interference ratio detection waveform data based on GSM-R dynamic detection data, extract key waveform characteristic parameters based on data features, and realize automatic identification of abnormal carrier-to-interference ratios in repeaters. The present invention can significantly improve the efficiency of identifying abnormal carrier-to-interference ratios, laying the foundation for intelligent detection and data analysis. At the same time, it can effectively distinguish between early degradation and significant degradation of the carrier-to-interference ratio in GSM-R repeaters, providing technical support for GSM-R wireless network maintenance units and facilitating preventive maintenance and condition-based maintenance. The overall solution has strong robustness and can adaptively eliminate the impact of carrier-to-interference ratio degradation factors caused by performance degradation of non-GSM-R equipment on identification accuracy, providing strong technical support for dynamic detection of railway wireless communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 The present invention is a flowchart of a method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to an embodiment of the present invention.

[0031] Figure 2 FIG1 is a flow chart of a method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to another embodiment of the present invention.

[0032] Figure 3 The figure is a schematic diagram of a GSM-R repeater station carrier-to-interference ratio abnormality identification process according to a specific embodiment of the present invention.

[0033] Figure 4 It is a flowchart of an algorithm for extracting concave features based on gray value morphology according to a specific embodiment of the present invention.

[0034] Figure 5 The figure is a flowchart of extracting a baseline based on an adaptive smoothed asymmetric least squares algorithm according to a specific embodiment of the present invention.

[0035] Figure 6 FIG. 1 is a schematic diagram of the architecture of a device for identifying abnormal carrier-to-interference ratio in a GSM-R repeater station according to an embodiment of the present invention.

[0036] Figure 7 FIG. 1 is a schematic diagram of the architecture of a device for identifying abnormal carrier-to-interference ratio in a GSM-R repeater station according to another embodiment of the present invention.

[0037] Figure 8 It is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0039] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0040] According to an embodiment of the present invention, a method and device for identifying abnormal carrier-to-interference ratio of a GSM-R repeater are proposed, which relate to the technical field of dynamic detection of railway wireless communications.

[0041] The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0042] Figure 1FIG. 1 is a flow chart of a method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0043] S101, based on GSM-R network dynamic detection, real-time acquisition of handover information data;

[0044] S102, extracting the effective service range of the current cell based on the handover information data and the ledger information data, and obtaining the distribution information data of the repeaters within the effective service range of the current cell;

[0045] S103, extracting cell carrier-to-interference ratio waveform data based on the repeater distribution information data;

[0046] S104, analyzing local depression features of the CIR waveform based on the CIR waveform data of the cell, and determining a set of depression locations;

[0047] S105, filtering the set of recessed positions to obtain a repeater coverage range;

[0048] S106, when there is a repeater within the coverage area of ​​the repeater, performing data smoothing processing on the carrier-to-interference ratio waveform within the coverage area of ​​the repeater and extracting a baseline;

[0049] S107, determining a baseline slope curve according to the slope of the baseline, and extracting repeater waveform data having a slope less than a preset slope threshold according to the baseline slope curve to obtain repeater characteristic data;

[0050] S108 , analyzing statistical distribution characteristics according to the repeater characteristic data, and determining the degradation degree corresponding to the statistical distribution characteristics according to a preset degradation degree evaluation standard.

[0051] During dynamic monitoring of GSM-R wireless networks, GSM-R cell CIR parameters can be obtained. However, existing GSM-R communication monitoring systems lack the ability to automatically identify abnormal CIRs in GSM-R repeaters. This identification relies on manual judgment, resulting in low recognition efficiency, inconsistent standards, and frequent false positives. Furthermore, GSM-R communication monitoring systems assess CIRs based on received wireless signals. The CIR waveforms provided represent CIR measurements at different kilometer markers within the entire GSM-R cell. As GSM-R repeaters are integral components of a GSM-R cell, the CIR waveforms do not distinguish between specific CIR waveform regions. Accurately extracting the waveform of a specific GSM-R repeater from the GSM-R cell CIR waveform is a key challenge and fundamental to identifying abnormal CIRs in GSM-R repeaters. Furthermore, when CIRs are affected by external factors such as interference, accurately extracting regions within the GSM-R repeater waveform that directly characterize the device's characteristics is crucial for identification. Furthermore, the service life of different GSM-R repeaters varies, and the waveforms of abnormal carrier-to-interference ratios (CIRs) at these repeaters vary significantly. Specifically, these degradations are categorized into two types: early degradation and significant degradation. Identifying these two degradation phenomena based on the detected waveforms presents a challenge. To address these issues, one embodiment of the present invention proposes a method for identifying abnormal CIRs at GSM-R repeaters. Based on GSM-R dynamic detection data, CIR detection waveform data is extracted. Key waveform characteristic parameters are extracted based on the data features, enabling automatic identification of abnormal CIRs at repeaters. This method significantly improves the efficiency of CIR identification, laying the foundation for intelligent detection and data analysis. The present invention effectively distinguishes between early degradation and significant CIR degradation at GSM-R repeaters, providing technical support for GSM-R wireless network maintenance units and facilitating preventive and condition-based maintenance. The overall solution exhibits strong robustness, adaptively eliminating the impact of CIR degradation caused by non-GSM-R equipment performance degradation on identification accuracy, and thus provides strong technical support for dynamic detection of railway wireless communications.

[0052] In order to explain the above-mentioned method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater more clearly, each step is described in detail below.

[0053] In one embodiment, S101, based on GSM-R network dynamic detection, handover information data is acquired in real time.

[0054] Among them, dynamic detection based on the GSM-R network includes: using railway-specific detection vehicles to collect status parameters of facilities along the railway, including the GSM-R network, during train operation. The status parameters are used to evaluate the service status of the facilities.

[0055] In one embodiment, S102, based on the handover information data and the ledger information data, the effective service range of the current cell is extracted, and the distribution information data of the repeaters within the effective service range of the current cell is obtained.

[0056] The handover information data includes: location information of the test terminal from occupying the current cell to releasing the current cell, and cell ID information of the current cell.

[0057] The ledger information data is the data of the GSM-R base station information of the railway line, including: base station name, base station location (mileage, longitude and latitude), base station frequency and cell ID information corresponding to the base station.

[0058] The effective service range of the current cell is the mileage range of the current cell occupied by the test terminal without a handover anomaly. When a handover anomaly occurs, the effective service range of the cell is further determined in combination with the base station location information. The main function of S102 is to eliminate the impact of abnormal carrier-to-interference ratio caused by non-repeater equipment on the algorithm recognition results, thereby improving the robustness of the recognition algorithm.

[0059] In one embodiment, S103, cell carrier-to-interference ratio waveform data is extracted based on the repeater distribution information data.

[0060] Specifically, judging whether there is a repeater within the effective service range of the current cell according to the repeater distribution information data;

[0061] If it exists, extract the cell carrier-to-interference ratio waveform data;

[0062] If it does not exist, continue with the GSM-R network dynamic detection.

[0063] In one embodiment, S104 , local depression features of the C / I waveform are analyzed based on the C / I waveform data of the cell, and a set of depression locations is determined.

[0064] Based on the cell carrier-to-interference ratio waveform data, a gray value morphological method is used to extract the local concave features of the carrier-to-interference ratio waveform and determine the set of concave positions; wherein, the processing process of the gray value morphological method is as follows: eliminating peaks and retaining concave through opening operation; filling concave through closing operation and retaining peaks; performing top hat transformation to extract concave and performing bottom hat transformation to enhance the concave area in the signal; performing threshold processing to realize concave detection, marking and returning the concave position.

[0065] In one embodiment, S105 , filtering is performed on the set of recessed positions to obtain a coverage range of the repeater station.

[0066] The set of depression positions is filtered using a binary morphological filtering method to eliminate interference of carrier-to-interference ratio waveform noise on depression detection, thereby obtaining the coverage range of the repeater station.

[0067] In one embodiment, S106 , when there is a repeater within the coverage area of ​​the repeater, data smoothing processing is performed on the carrier-to-interference ratio waveform within the coverage area of ​​the repeater and a baseline is extracted.

[0068] Determining whether a repeater exists according to the repeater coverage area;

[0069] If so, the carrier-to-interference ratio waveform within the coverage area of ​​the repeater is processed using an adaptive smoothing asymmetric least squares algorithm to extract a baseline. The adaptive smoothing asymmetric least squares algorithm is used to process signal data with asymmetric noise, perform data smoothing and baseline extraction on the signal data, and the processing process is as follows: initializing the weight matrix to the identity matrix, setting smoothing parameters and convergence thresholds; solving the least squares problem of the smoothed signal through iterative processing, updating the weights based on the residuals, and checking the convergence conditions; stopping when the change in the smoothed signal is less than a preset threshold or the maximum number of iterations is reached;

[0070] If it does not exist, continue with the GSM-R network dynamic detection.

[0071] In one embodiment, S107 , a baseline slope curve is determined according to the slope of the baseline, and repeater waveform data having a slope less than a preset slope threshold is extracted according to the baseline slope curve to obtain repeater characteristic data.

[0072] Specifically, the repeater waveform data corresponding to a slope within a preset interval (less than a preset slope threshold) is extracted to obtain the repeater characteristic data.

[0073] In one embodiment, S108 , statistical distribution characteristics are analyzed based on the repeater characteristic data, and the degradation degree corresponding to the statistical distribution characteristics is determined according to a preset degradation degree evaluation standard.

[0074] Analyzing the statistical distribution characteristics of the repeater characteristic data, and extracting the first mode, the second mode and the corresponding proportions in sequence;

[0075] Based on the first mode, the second mode and the corresponding proportions, and in accordance with a preset degradation degree evaluation standard, the degradation degree corresponding to the statistical distribution feature is determined, wherein if the first mode is less than a first set value, it is determined to be significant degradation; if the first mode is the first set value and the proportion of the second mode is greater than the preset proportion value, it is determined to be early degradation.

[0076] Further references Figure 2 , the method further comprises:

[0077] S109: Display the significant degradation and early degradation corresponding to the carrier-to-interference ratio of the repeater at the position of the repeater in the visualization interface.

[0078] It should be noted that although the operations of the method of the present invention are described in a specific order in the above embodiments and drawings, this does not require or imply that these operations must be performed in this specific order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0079] The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater station of the present invention is described below with reference to a specific embodiment.

[0080] refer to Figure 3 , is a flow chart of abnormal identification of carrier-to-interference ratio of a GSM-R repeater according to a specific embodiment of the present invention. Figure 3 Specific methods include:

[0081] S301, extracting effective cell coverage data based on handover and ledger information.

[0082] We extract effective cell coverage data based on handoffs (a technique used to maintain a call while a mobile station moves from one base station to another) and data records (detailed records of repeaters and cells). This step helps determine the scope of subsequent analysis.

[0083] Specifically, the handover information data is acquired in real time during the dynamic detection process of the GSM-R network.

[0084] Dynamic detection refers to the collection of status parameters of railway infrastructure such as the GSM-R network during train operation using special railway detection vehicles to evaluate the service status of the infrastructure.

[0085] The handover information data includes: location information of the test terminal from occupying the current cell to releasing the current cell, and cell ID information of the current cell.

[0086] Combined with the ledger information data, extract the effective service range of the current cell. Get the distribution information data of the repeater stations within the effective service range of the current cell.

[0087] The effective service range is the mileage range of the cell occupied by the test terminal, assuming no handover anomalies occur. If a handover anomaly occurs, the effective service range must be further determined based on base station location information. This step eliminates the impact of abnormal carrier-to-interference ratios (CIRs) on the algorithm's recognition results caused by factors other than repeater equipment, thereby improving the robustness of the recognition algorithm.

[0088] S302: Determine whether there is a repeater within the cell coverage area.

[0089] Check whether there is a repeater within the effective coverage area of ​​the extracted cell. If there is no repeater, return to S301 and continue the GSM-R network dynamic detection. If there is a repeater, proceed to the next step.

[0090] S303: Extract carrier-to-interference ratio waveform data.

[0091] If there is a repeater within the service range, further extract the cell carrier-to-interference ratio waveform data.

[0092] The carrier-to-interference ratio (C / I) is the ratio of the received useful signal level to the total unwanted signal level, a key indicator of communication quality. The C / I waveform data is extracted from relevant communication data. This data reflects how signal quality changes over time or other factors.

[0093] S304, extracting concave features using gray value morphology.

[0094] Specifically, based on the cell CIR waveform data, a gray value morphology method is used to extract local concave features of the CIR waveform to obtain a set of concave positions.

[0095] Among them, the main steps of using the gray value morphological filtering method to extract the local concave features of the carrier-to-interference ratio waveform include performing an opening operation to eliminate peaks and retain concaveness; then, performing a closing operation to fill the concaveness and retain the peak value; next, performing a top hat transformation to extract the concaveness; then, performing a bottom hat transformation to enhance the concave area in the signal; finally, performing threshold processing to realize concave detection, mark and return the concave position.

[0096] Grayscale morphology is an image processing technique used to process grayscale images. Here, it is used to extract concave features from the carrier-to-interference ratio waveform data. Concave features may indicate areas of degraded signal quality, and this step is intended to initially identify potential problem areas.

[0097] S305: extract the concave area using binary morphology.

[0098] Specifically, a binary morphological method is used to process the set of depression positions to obtain the coverage range of the repeater station.

[0099] The use of a binary morphological filtering method to process the set of sag positions refers to using a binary morphological dilation operation based on the set to eliminate interference of carrier-to-interference ratio waveform noise on sag detection and obtain the coverage range of the repeater station.

[0100] S306: Determine whether there is a repeater in the sunken area.

[0101] If there is no repeater, the process returns to S301 and continues to perform dynamic detection of the GSM-R network.

[0102] If there is a repeater, proceed to the next step.

[0103] S307: extracting a baseline using an adaptive smoothing asymmetric least squares algorithm.

[0104] Specifically, the adaptive smoothed asymmetric least squares algorithm is used to process the carrier-to-interference ratio waveform within the coverage area of ​​the repeater to extract the baseline L Rep .

[0105] The adaptive smoothing asymmetric least squares algorithm is used for data smoothing and baseline extraction, and is particularly suitable for processing signal data with asymmetric noise. The main steps include initializing the weight matrix to the unit matrix, setting the smoothing parameters and the convergence threshold; then, an iterative process is performed to solve the least squares problem of the smoothed signal, and the weights are updated according to the residuals, and the convergence conditions are checked; finally, the algorithm stops when the change in the smoothed signal is less than the preset threshold or the maximum number of iterations is reached.

[0106] The baseline is the fundamental part of the signal and reflects its overall trend. The adaptive smoothing asymmetric least squares algorithm automatically adjusts the smoothing level based on the characteristics of the data, improving the accuracy of baseline extraction.

[0107] S308: Extract baseline slope features.

[0108] Calculate baseline L Rep The slope of the baseline slope curve L is obtained S According to the baseline slope curve L S , extract the repeater waveform data corresponding to the slope less than the preset slope threshold, and obtain the repeater characteristic data W F .

[0109] S309: Determine whether the slope is greater than a preset slope threshold.

[0110] If yes (the slope is greater than the threshold), the process returns to S301 and continues to perform dynamic detection of the GSM-R network.

[0111] If there is a repeater, proceed to the next step.

[0112] S310, reconstructing the carrier-to-interference ratio characteristic waveform of the repeater.

[0113] If the slope is not greater than the threshold, it means that the change in signal quality is within a certain range. This step will reconstruct the carrier-to-interference ratio characteristic waveform of the repeater station for subsequent analysis.

[0114] S311, Statistical analysis of carrier-to-interference ratio.

[0115] Perform statistical analysis on the reconstructed CIR characteristic waveform and calculate the mode to understand the distribution of signal quality.

[0116] Specifically, analyze W F Statistical distribution characteristics are extracted, and the first mode, second mode, and corresponding proportions are extracted in sequence. Early degradation and significant degradation are distinguished according to the following judgment logic: if the first mode is less than the first set value, it is judged as significant degradation; if the first mode is the first set value and the proportion of the second mode is greater than the preset proportion, it is judged as early degradation.

[0117] S312, determination of the degree of degradation.

[0118] The early degradation and significant degradation judgment results corresponding to the repeater's carrier-to-interference ratio are displayed at the corresponding repeater position on the interface.

[0119] Specifically, the degradation degree of the repeater is determined based on the results of the CIR statistical analysis. The degradation degree can be divided into different levels to evaluate the working status of the repeater.

[0120] The specific method of extracting the local concave features of the carrier-to-interference ratio waveform using the gray value morphology method in S304 is described in detail below. Figure 4 , is a flowchart of an algorithm for extracting concave features based on gray value morphology according to a specific embodiment of the present invention. Figure 4 As shown, the specific methods include:

[0121] S401, open operation:

[0122] First, an opening operation is performed on the C / I waveform data. This operation processes the signal using a structuring element (which can be understood as a filter with a specific shape and size). This effectively eliminates peaks in the waveform while retaining the dips. In actual C / I waveforms, there may be spikes caused by noise or other interference factors. These spikes are not characteristic of abnormal C / I ratios at repeaters. The opening operation removes these interferences, making subsequent extraction of the dips more accurate.

[0123] S402, closing operation:

[0124] Next, a closing operation is performed. This is the opposite of an opening operation, filling in the concavities with structuring elements while preserving the peaks. While the opening operation removes some interfering peaks, it may leave some previously continuous concavities incomplete. The closing operation repairs these incomplete concavities, making the concavities clearer and more complete, facilitating subsequent processing.

[0125] S403, top hat transformation:

[0126] After the closing operation, a top-hat transformation is performed. This subtracts the image obtained after the opening operation from the original image. Its purpose is to extract concave features in the signal. At this point, after the previous opening and closing operations, noise and unnecessary peaks in the signal have been effectively suppressed. The top-hat transformation can then accurately highlight those concave areas that may indicate anomalies in the repeater.

[0127] S404, bottom hat transformation:

[0128] Next, a bottom-hat transform is performed. This involves subtracting the original image from the closed image. This step primarily enhances the concave regions within the signal. This makes previously subtle concave regions more prominent, further improving the discernibility of concave features and facilitating more accurate detection and analysis of these features.

[0129] S405, enhanced sag:

[0130] The signal that has been processed previously is enhanced by depression. This step may use a specific algorithm or processing method to further highlight the depression features, making the difference between the depression and other parts more significant, so that the depression position can be more accurately identified during subsequent threshold processing.

[0131] S406, threshold processing:

[0132] The enhanced signal is processed using a set threshold. If the value of a point or area in the signal meets the threshold, the location is identified as a depression and marked. Through threshold processing, depression features in the signal are converted into specific location information and recorded as a binary set. This set of marked depression locations serves as an important basis for subsequent operations such as determining the repeater coverage range.

[0133] S407, output the recess position:

[0134] After a series of processing, the final output is a binary set of notch locations. This set contains information about all locations in the C / I waveform that meet the characteristic conditions for notches, providing key data support for subsequent determination of repeater coverage and analysis of repeater C / I anomalies.

[0135] The specific method of processing the carrier-to-interference ratio waveform within the coverage area of ​​the repeater using the adaptive smoothed asymmetric least squares algorithm in S306 is described in detail below. Figure 5 , is a flowchart of extracting a baseline based on an adaptive smooth asymmetric least squares algorithm according to a specific embodiment of the present invention. Figure 5 As shown, the specific methods include:

[0136] S501, set the smoothing parameters, convergence threshold, and initialize the weight matrix to the identity matrix:

[0137] The smoothing parameter is used to control the smoothness of the baseline. The larger the parameter, the smoother the baseline.

[0138] The convergence threshold is used to determine whether the iteration should stop. When the baseline change is less than the threshold, the iteration is considered to have converged.

[0139] The weight matrix is ​​initialized to the identity matrix. The weight matrix is ​​used to assign different weights to different data points during the iteration process to adapt to data changes.

[0140] S502, initial baseline estimation.

[0141] Based on the input data and the initialized weight matrix, a first baseline estimate is performed.

[0142] S503, the baseline change is less than a threshold or the maximum number of iterations is reached;

[0143] Check whether the change in the baseline is less than the convergence threshold, or whether the pre-set maximum number of iterations has been reached.

[0144] If not, execute S504; if so, execute S507;

[0145] S504: Calculate the residual signal.

[0146] The residual signal is the difference between the actual data and the current baseline estimate. By calculating the residual signal, you can understand the difference between the current baseline estimate and the actual data.

[0147] S505, dynamically adjust the weight.

[0148] The elements in the weight matrix are dynamically adjusted based on the size and distribution of the residual signal. Weights are increased for regions with small residuals and decreased for regions with large residuals. This allows the algorithm to focus more on the main trends in the data and improve the accuracy of the baseline estimate.

[0149] S506, smoothing constraint, update baseline estimation.

[0150] After dynamically adjusting the weights, the baseline is updated and estimated by applying smoothing constraints. Smoothing constraints ensure the smoothness of the baseline and avoid excessive fluctuations.

[0151] S507: Output baseline estimation.

[0152] When the iteration converges or reaches the maximum number of iterations, the final baseline estimation result is output.

[0153] After introducing the method of the exemplary embodiment of the present invention, next, reference is made to Figure 6A device for identifying abnormal carrier-to-interference ratio of a GSM-R repeater station according to an exemplary embodiment of the present invention is introduced.

[0154] The implementation of the device for identifying abnormal carrier-to-interference ratios in a GSM-R repeater station can be referenced to the implementation of the aforementioned method, and any repetitive details will not be repeated. The terms "module" or "unit" used below may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0155] Based on the same inventive concept, the present invention also proposes a device for identifying abnormal carrier-to-interference ratio of a GSM-R repeater station, such as Figure 6 As shown, the device includes:

[0156] Dynamic monitoring module 610, for obtaining handover information data in real time based on dynamic detection of the GSM-R network;

[0157] The effective service range extraction module 620 is used to extract the effective service range of the current cell based on the handover information data and the ledger information data, and obtain the repeater distribution information data within the effective service range of the current cell;

[0158] The carrier-to-interference ratio waveform data extraction module 630 is used to extract the cell carrier-to-interference ratio waveform data based on the repeater distribution information data;

[0159] a depression feature analysis module 640 configured to analyze local depression features of the CIR waveform based on the CIR waveform data of the cell and determine a set of depression locations;

[0160] Repeater coverage range determination module 650, configured to filter the set of recessed positions to obtain a repeater coverage range;

[0161] A baseline extraction module 660 is configured to perform data smoothing on the carrier-to-interference ratio waveform within the coverage area of ​​the repeater and extract a baseline when there is a repeater within the coverage area of ​​the repeater;

[0162] A baseline analysis module 670 is configured to determine a baseline slope curve based on the slope of the baseline, extract repeater waveform data having a slope less than a preset slope threshold based on the baseline slope curve, and obtain repeater characteristic data;

[0163] The degradation judgment module 680 is configured to analyze the statistical distribution characteristics according to the repeater characteristic data and determine the degradation degree corresponding to the statistical distribution characteristics according to a preset degradation degree evaluation standard.

[0164] In one embodiment, the dynamic monitoring module is specifically used to:

[0165] Railway-specific inspection vehicles are used to collect status parameters of facilities along the railway, including the GSM-R network, during train operation. The status parameters are used to evaluate the service status of the facilities.

[0166] In one embodiment, the handover information data includes: location information of the test terminal from occupying the current cell to releasing the current cell, and cell ID information of the current cell.

[0167] In one embodiment, the ledger information data is the data of GSM-R base station information of the railway line, including: base station name, base station location, base station frequency and cell ID information corresponding to the base station.

[0168] In one embodiment, the effective service range of the current cell is the mileage range of the current cell occupied by the test terminal without handover anomaly; when a handover anomaly event occurs, the effective service range of the cell is further determined in combination with the base station location information.

[0169] In one embodiment, the carrier-to-interference ratio waveform data extraction module 630 is specifically configured to:

[0170] According to the repeater distribution information data, determining whether there is a repeater within the effective service range of the current cell;

[0171] If it exists, extract the cell carrier-to-interference ratio waveform data;

[0172] If it does not exist, continue with the GSM-R network dynamic detection.

[0173] In one embodiment, the concave feature analysis module 640 is specifically configured to:

[0174] Based on the cell carrier-to-interference ratio waveform data, a gray value morphological method is used to extract the local concave features of the carrier-to-interference ratio waveform and determine the set of concave positions; wherein, the processing process of the gray value morphological method is as follows: eliminating peaks and retaining concave through opening operation; filling concave through closing operation and retaining peaks; performing top hat transformation to extract concave and performing bottom hat transformation to enhance the concave area in the signal; performing threshold processing to realize concave detection, marking and returning the concave position.

[0175] In one embodiment, the repeater coverage range determination module 650 is specifically configured to:

[0176] The set of depression positions is filtered using a binary morphological filtering method to eliminate interference of carrier-to-interference ratio waveform noise on depression detection, thereby obtaining the coverage range of the repeater station.

[0177] In one embodiment, the baseline extraction module 660 is specifically configured to:

[0178] Determining whether a repeater exists according to the repeater coverage area;

[0179] If so, the carrier-to-interference ratio waveform within the coverage area of ​​the repeater is processed using an adaptive smoothing asymmetric least squares algorithm to extract a baseline. The adaptive smoothing asymmetric least squares algorithm is used to process signal data with asymmetric noise, perform data smoothing and baseline extraction on the signal data, and the processing process is as follows: initializing the weight matrix to the identity matrix, setting smoothing parameters and convergence thresholds; solving the least squares problem of the smoothed signal through iterative processing, updating the weights based on the residuals, and checking the convergence conditions; stopping when the change in the smoothed signal is less than a preset threshold or the maximum number of iterations is reached;

[0180] If it does not exist, continue with the GSM-R network dynamic detection.

[0181] In one embodiment, the degradation judgment module 680 is specifically configured to:

[0182] Analyzing the statistical distribution characteristics of the repeater characteristic data, and extracting the first mode, the second mode and the corresponding proportions in sequence;

[0183] Based on the first mode, the second mode and the corresponding proportions, and in accordance with a preset degradation degree evaluation standard, the degradation degree corresponding to the statistical distribution feature is determined, wherein if the first mode is less than a first set value, it is determined to be significant degradation; if the first mode is the first set value and the proportion of the second mode is greater than the preset proportion value, it is determined to be early degradation.

[0184] In another embodiment, reference Figure 7 , the device further includes: a display module 710;

[0185] The display module 710 is specifically used for:

[0186] The significant degradation and early degradation corresponding to the carrier-to-interference ratio of the repeater are displayed at the position of the repeater in the visualization interface.

[0187] It should be noted that while the detailed description above mentions several modules of the device for identifying abnormal carrier-to-interference ratios in GSM-R repeaters, this division is merely exemplary and not mandatory. In practice, according to embodiments of the present invention, the features and functions of two or more modules described above may be embodied in a single module. Conversely, the features and functions of a single module described above may be further divided and embodied by multiple modules.

[0188] Based on the above invention concept, Figure 8As shown, the present invention also proposes a computer device 800, including a memory 810, a processor 820, and a computer program 830 stored in the memory 810 and executable on the processor 820. When the processor 820 executes the computer program 830, the aforementioned method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater is implemented.

[0189] Based on the aforementioned inventive concept, the present invention proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the aforementioned method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater is implemented.

[0190] Based on the aforementioned inventive concept, the present invention proposes a computer program product, which includes a computer program. When the computer program is executed by a processor, a method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater is implemented.

[0191] The method and device for identifying abnormal carrier-to-interference ratios in GSM-R repeaters proposed in the present invention extract carrier-to-interference ratio detection waveform data based on GSM-R dynamic detection data, extract key waveform characteristic parameters based on data features, and realize automatic identification of abnormal carrier-to-interference ratios in repeaters. The present invention can significantly improve the efficiency of identifying abnormal carrier-to-interference ratios, laying the foundation for intelligent detection and data analysis. At the same time, it can effectively distinguish between early degradation and significant degradation of the carrier-to-interference ratio in GSM-R repeaters, providing technical support for GSM-R wireless network maintenance units and facilitating preventive maintenance and condition-based maintenance. The overall solution has strong robustness and can adaptively eliminate the impact of carrier-to-interference ratio degradation factors caused by performance degradation of non-GSM-R equipment on identification accuracy, providing strong technical support for dynamic detection of railway wireless communications.

[0192] The acquisition, storage, use, and processing of data in the technical solution of this application comply with relevant laws and regulations.

[0193] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] The present invention is described with reference to flowcharts and / or block diagrams of methods and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0197] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater, characterized in that: The method includes: Based on GSM-R network dynamic detection, real-time acquisition of handover information data; Extracting the effective service range of the current cell and obtaining the distribution information data of repeaters within the effective service range of the current cell based on the handover information data and the ledger information data; Extracting cell carrier-to-interference ratio waveform data according to the repeater distribution information data; Analyzing local depression features of the carrier-to-interference ratio waveform based on the cell carrier-to-interference ratio waveform data, and determining a set of depression locations; Filter the set of concave positions to obtain the coverage of the repeater station; When there is a repeater within the coverage area of ​​the repeater, performing data smoothing on the carrier-to-interference ratio waveform within the coverage area of ​​the repeater and extracting a baseline; Determining a baseline slope curve according to the slope of the baseline, extracting repeater waveform data having a slope less than a preset slope threshold according to the baseline slope curve to obtain repeater characteristic data; Analyze the statistical distribution characteristics according to the repeater characteristic data, and determine the degradation degree corresponding to the statistical distribution characteristics according to a preset degradation degree evaluation standard.

2. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, wherein: Based on GSM-R network dynamic detection, including: Railway-specific inspection vehicles are used to collect status parameters of facilities along the railway, including the GSM-R network, during train operation. The status parameters are used to evaluate the service status of the facilities.

3. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, wherein: The handover information data includes: location information of the test terminal from occupying the current cell to releasing the current cell, and cell ID information of the current cell.

4. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, wherein: The ledger information data is the data of the GSM-R base station information of the railway line, including: base station name, base station location, base station frequency and cell ID information corresponding to the base station.

5. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, characterized in that: The effective service range of the current cell is the mileage range of the current cell occupied by the test terminal without handover anomaly; when a handover anomaly event occurs, the effective service range of the cell is further determined in combination with the base station location information.

6. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, characterized in that: Extracting cell carrier-to-interference ratio waveform data based on the repeater distribution information data includes: According to the repeater distribution information data, determining whether there is a repeater within the effective service range of the current cell; If it exists, extract the cell carrier-to-interference ratio waveform data; If it does not exist, continue with the GSM-R network dynamic detection.

7. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, characterized in that: Analyzing local depression features of the carrier-to-interference ratio waveform based on the cell carrier-to-interference ratio waveform data and determining a depression location set includes: Based on the cell carrier-to-interference ratio waveform data, a gray value morphological method is used to extract the local concave features of the carrier-to-interference ratio waveform and determine the set of concave positions; wherein, the processing process of the gray value morphological method is as follows: eliminating peaks and retaining concave through opening operation; filling concave through closing operation and retaining peaks; performing top hat transformation to extract concave and performing bottom hat transformation to enhance the concave area in the signal; performing threshold processing to realize concave detection, marking and returning the concave position.

8. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, characterized in that: Filter the set of concave locations to obtain the repeater coverage, including: The set of depression positions is filtered using a binary morphological filtering method to eliminate interference of carrier-to-interference ratio waveform noise on depression detection, thereby obtaining the coverage range of the repeater station.

9. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, wherein: When there is a repeater within the coverage area of ​​the repeater, data smoothing processing is performed on the carrier-to-interference ratio waveform within the coverage area of ​​the repeater and a baseline is extracted, including: Determining whether a repeater exists according to the repeater coverage area; If so, the carrier-to-interference ratio waveform within the coverage area of ​​the repeater is processed using an adaptive smoothing asymmetric least squares algorithm to extract a baseline. The adaptive smoothing asymmetric least squares algorithm is used to process signal data with asymmetric noise, perform data smoothing and baseline extraction on the signal data, and the processing process is as follows: initializing the weight matrix to the identity matrix, setting smoothing parameters and convergence thresholds; solving the least squares problem of the smoothed signal through iterative processing, updating the weights based on the residuals, and checking the convergence conditions; stopping when the change in the smoothed signal is less than a preset threshold or the maximum number of iterations is reached; If it does not exist, continue with the GSM-R network dynamic detection.

10. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 1, characterized in that: Analyzing statistical distribution characteristics according to the repeater characteristic data and determining the degradation degree corresponding to the statistical distribution characteristics according to a preset degradation degree evaluation standard, including: Analyzing the statistical distribution characteristics of the repeater characteristic data, and extracting the first mode, the second mode and the corresponding proportions in sequence; Based on the first mode, the second mode and the corresponding proportions, and in accordance with a preset degradation degree evaluation standard, the degradation degree corresponding to the statistical distribution feature is determined, wherein if the first mode is less than a first set value, it is determined to be significant degradation; if the first mode is the first set value and the proportion of the second mode is greater than the preset proportion value, it is determined to be early degradation.

11. The method for identifying abnormal carrier-to-interference ratio of a GSM-R repeater according to claim 10, characterized in that: The method further includes: The significant degradation and early degradation corresponding to the carrier-to-interference ratio of the repeater are displayed at the position of the repeater in the visualization interface.

12. A device for identifying abnormal carrier-to-interference ratio of a GSM-R repeater, characterized in that: The device includes: Dynamic monitoring module, used to obtain real-time handover information data based on GSM-R network dynamic detection; An effective service range extraction module is used to extract the effective service range of the current cell based on the handover information data and the ledger information data, and obtain the repeater distribution information data within the effective service range of the current cell; a carrier-to-interference ratio waveform data extraction module, configured to extract cell carrier-to-interference ratio waveform data based on the repeater distribution information data; a depression feature analysis module, configured to analyze local depression features of the carrier-to-interference ratio waveform based on the cell carrier-to-interference ratio waveform data, and determine a set of depression locations; A repeater coverage range determination module is used to filter the set of recessed positions to obtain the repeater coverage range; A baseline extraction module, configured to perform data smoothing on the carrier-to-interference ratio waveform within the coverage area of ​​the repeater and extract a baseline when there is a repeater within the coverage area of ​​the repeater; A baseline analysis module, configured to determine a baseline slope curve according to the slope of the baseline, extract repeater waveform data having a slope less than a preset slope threshold according to the baseline slope curve, and obtain repeater characteristic data; The degradation judgment module is used to analyze the statistical distribution characteristics according to the repeater characteristic data, and determine the degradation degree corresponding to the statistical distribution characteristics according to a preset degradation degree evaluation standard.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

15. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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