Urban rail transit communication interference source monitoring and positioning system

Abstract

The invention discloses an urban rail transit communication interference source monitoring and positioning system, and belongs to the technical field of rail transit signals. The urban rail transit communication interference source monitoring and positioning system comprises a signal monitoring module, a signal separation module and a signal positioning module. According to the invention, the problem that wireless communication quality is reduced or communication is interrupted due to the fact that wireless signal transmission is easily interfered by non-cooperative signals and channels and influenced by other unstable factors in a complex electromagnetic environment in the prior art is solved, and the problem of communication interference can be found and solved in time; the communication between the train and the control center is ensured to be unobstructed, abnormal train operation or safety accidents caused by communication faults are avoided, train delay or shutdown caused by communication problems is reduced, the operation efficiency of the train and the travel experience of passengers are improved, the time and cost of manual troubleshooting can be reduced through real-time monitoring and positioning, and the working efficiency of the train is improved. And the fault processing efficiency is improved, so that the overall maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of rail transit signal technology, and in particular to a system for monitoring and locating communication interference sources in urban rail transit. Background Art

[0002] The train-to-ground communication of the rail transit signal system is based on wireless communication. The train needs to communicate with the equipment in the station and the control center through the on-board and trackside wireless systems. The wireless system is closely related to the operation of the train. In the event of an abnormality, the train may be emergency braked at the least, or it may only be operated in manual mode at the most serious. In this scenario, the operation efficiency will be greatly reduced, and there is the possibility of causing injury to personnel and damage to the train.

[0003] A Chinese patent with publication number CN115037773A discloses a rail transit communication system with stable transmission, including a dedicated telephone module, a control center, a wireless subsystem, an anti-interference subsystem, a clock subsystem, a broadcast subsystem, a video monitoring subsystem, a power subsystem, and an encryption module; wherein the power subsystem is used to provide uninterrupted power supply to ensure that each subsystem can still work for a period of time when the city power is interrupted; the control center is used to control the operation of the communication system, to achieve communication and management, including a video management server, a data management server, a client, and a streaming media server; the anti-interference subsystem is connected to the control center, and the signal is filtered by the anti-interference subsystem to ensure that the received signal quality is pure, the sound and image are clear and distinct, and the stability of the transmission is ensured; when the encryption module is used for data transmission, it ensures the integrity and confidentiality of the data, and can authenticate the sender of the data.

[0004] In actual use of the above patent, wireless signal transmission in complex electromagnetic environments is easily affected by interference from non-cooperative signals and channels and other unstable factors, resulting in reduced wireless communication quality or communication interruption; therefore, it does not meet existing needs. In response to this, we have proposed a system for monitoring and locating communication interference sources in urban rail transit. Summary of the invention

[0005] The purpose of the present invention is to provide an urban rail transit communication interference source monitoring and positioning system. By real-time monitoring and positioning of communication interference sources, communication interference problems can be discovered and resolved in a timely manner, which can help to quickly troubleshoot and reduce train delays or suspensions caused by communication problems, thereby improving the operating efficiency of trains and passengers' travel experience. It can reduce the time and cost of manual troubleshooting, improve the efficiency of fault handling, and thus reduce overall maintenance costs, solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a system for monitoring and locating interference sources in urban rail transit communication, comprising:

[0007] The signal monitoring module is used to collect the signal strength and quality of urban rail transit communication in real time and perform preprocessing, extract the real-time signal frequency, amplitude and phase of urban rail transit communication, and obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication after analysis and conversion;

[0008] The signal separation module is used to compare the frequency components of real-time signals of urban rail transit communication with the frequency spectrum in the frequency distribution data, and to separate and extract the useful signal from the interference signal to obtain the frequency components and frequency distribution data of the interference signal;

[0009] The signal positioning module is used to build an interference signal characteristic monitoring model, use the interference signal characteristic monitoring model to analyze the interference signal propagation characteristics, obtain interference signal propagation characteristic data, perform interference source positioning processing on urban rail transit communication interference signal data, and obtain urban rail transit communication interference signal positioning data.

[0010] Preferably, the signal monitoring module includes:

[0011] Real-time monitoring module, which is used to collect the signal strength and quality of urban rail transit communications in real time by using sensors and IoT devices deployed in the urban rail transit system;

[0012] The data acquisition module is used to obtain the signal strength and quality of urban rail transit communications collected in real time and perform preprocessing;

[0013] The data extraction module is used to extract the frequency, amplitude and phase of the real-time signal of urban rail transit communication, and obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication after analysis and conversion.

[0014] Preferably, the signal monitoring module specifically includes:

[0015] Use sensors and IoT devices deployed in urban rail transit systems to collect real-time information on the signal strength and quality of urban rail transit communications;

[0016] Clean and optimize the signal strength and quality of urban rail transit communications collected in real time;

[0017] Remove noise from real-time urban rail transit communication signals, filter real-time urban rail transit communication signals, and adjust the amplitude of the signals;

[0018] After processing, the real-time signal frequency, amplitude and phase of urban rail transit communication are extracted;

[0019] Analyze the frequency, amplitude and phase of the real-time signal in the time domain to obtain the waveform of the frequency, amplitude and phase of the real-time signal;

[0020] Analyze the waveform of the frequency, amplitude and phase of the real-time signal to obtain the data of the change of the frequency, amplitude and phase of the real-time signal over time;

[0021] The obtained change data is converted from the time domain to the frequency domain through Fourier transform, and normalized to obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication.

[0022] Preferably, the data acquisition module includes:

[0023] A data receiving module is used to receive the signal strength and quality of urban rail transit communications collected in real time by sensors and IoT devices deployed in the urban rail transit system;

[0024] The preprocessing module is used to clean and optimize the signal strength and quality of urban rail transit communications collected in real time, remove noise, filter or adjust the amplitude of the signal.

[0025] Preferably, the signal separation module comprises:

[0026] A comparison module, used to compare the frequency components of real-time signals of urban rail transit communications with the frequency spectrum in the frequency distribution data;

[0027] The identification module identifies the characteristics of the useful signal and the interference signal through the comparison results of the comparison module, and separates and extracts the useful signal and the interference signal according to the identification characteristics to obtain the frequency component and frequency distribution data of the interference signal.

[0028] Preferably, the signal separation module specifically includes:

[0029] Conduct spectrum analysis on the frequency components and frequency distribution data of real-time signals of urban rail transit communications, and compare the spectrum analysis results;

[0030] Identify the characteristics of useful signals and interference signals in the frequency components and frequency distribution data of real-time signals of urban rail transit communication based on the comparison results;

[0031] The useful signal and the interference signal are separated and extracted according to the identification characteristics to obtain the frequency components and frequency distribution data of the interference signal.

[0032] Preferably, the signal positioning module includes:

[0033] A model building module, used to build an interference signal characteristic monitoring model using the frequency components and frequency distribution data of the interference signal;

[0034] An analysis module, used to analyze the propagation characteristics of urban rail transit communication interference signal data to obtain the propagation characteristics data of urban rail transit communication interference signal;

[0035] The processing module is used to perform interference source positioning processing on the urban rail transit communication interference signal data to obtain the urban rail transit communication interference signal positioning data.

[0036] Processing modules, including:

[0037] The interference signal characteristic monitoring model is used to analyze the propagation characteristics of the urban rail transit communication interference signal data, and the propagation characteristic data of the urban rail transit communication interference signal is obtained;

[0038] Determine the frequency range of interference signals based on propagation characteristic data of urban rail transit communication interference signals;

[0039] The frequency range of the interference signal data is drawn into a polygon, and the polygon is grid-divided to obtain a plurality of grids, and the data in each grid corresponds to its frequency range;

[0040] Extract the branch current information of the interference signal from the data in each grid, and use the branch current information of the interference signal in each grid to obtain the interference value of the frequency range in each grid;

[0041] The interference values ​​of the frequency ranges in each grid are compared to determine the maximum interference value, and the interference values ​​of the frequency ranges in each grid are divided by the maximum interference value of the grid in turn to obtain the interference ratio of the frequency range corresponding to the interference signal;

[0042] The number of interference signal frequency ranges corresponding to the interference ratio in each grid is obtained, the grid with the largest number is used as the target grid where the interference source is located, and the location of the interference source is determined according to the location information corresponding to the target grid.

[0043] Preferably, the step of drawing the frequency range of the interference signal data into a polygon and dividing the polygon into a plurality of grids specifically includes:

[0044] Extract the preset grid initial size;

[0045] Dividing the target area corresponding to the polygon into grids according to the preset initial grid size to generate a plurality of initial grids; wherein the grids are squares with equal length and width;

[0046] Extract the interference signal strength contained in each grid;

[0047] Obtaining an interference signal strength coefficient using the interference signal strength contained in each grid;

[0048] The interference signal strength coefficient is obtained by the following formula:

[0049]

[0050] Where R represents the interference signal strength coefficient; n represents the number of interference signals contained in each grid; Q f Indicates the average value of the signal strength variation of the interference signal contained in each grid; Q b represents the signal strength standard deviation of the n interference signals contained in each grid; Q max and Q min Indicates the maximum and minimum signal strength of the interference signal contained in each grid; Q i represents the signal strength of the i-th interference signal contained in each grid; Q Lmax Indicates the signal strength difference between the two interference signals with the largest straight-line distance in each grid; Q Lmin represents the signal strength difference corresponding to the two interference signals with the smallest straight-line distance in each grid; s represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0051]

[0052] Where s represents the adjustment coefficient; θ represents the angle between the line between the two positions of the maximum and minimum signal strength of the interference signal and the grid diagonal; h 01 Indicates the vertical distance between the position corresponding to the maximum signal strength of the interference signal contained in each grid and the diagonal line; h 02 Indicates the vertical distance between the position corresponding to the minimum signal strength of the interference signal contained in each grid and the diagonal line; L indicates the straight-line distance between the two position points of the maximum and minimum signal strength of the interference signal; Case 1 is: when the corresponding positions of the maximum and minimum signal strength of the interference signal are on the same side of the diagonal line; Case 2 is: when the corresponding positions of the maximum and minimum signal strength of the interference signal are on both sides of the diagonal line;

[0053] Extracting the interference signal strength coefficient corresponding to each grid, and using the interference signal strength coefficient corresponding to each grid to obtain the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon;

[0054] Compare the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon with a preset standard deviation threshold;

[0055] When the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, the grid size is adjusted to obtain the adjusted grid size;

[0056] The target area corresponding to the polygon is re-divided into grids according to the adjusted grid size to obtain a plurality of re-divided grids.

[0057] Preferably, when the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, the grid size is adjusted to obtain the adjusted grid size, specifically including:

[0058] When the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, calling the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon;

[0059] Retrieve interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon;

[0060] The grid size adjustment coefficient is obtained by using the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon and the interference signal strength coefficients corresponding to all grids in the target area, wherein the grid size adjustment coefficient is obtained by the following formula:

[0061]

[0062] Among them, K represents the grid size adjustment coefficient; m represents the total number of grids in the target area corresponding to the polygon; R i represents the interference signal strength coefficient corresponding to the i-th grid; R b represents the standard deviation of the interference signal strength coefficient corresponding to m grids; R bc Indicates the preset reference value of the standard deviation of the interference signal strength coefficient;

[0063] The grid size adjustment coefficient is used to adjust the grid side lengths of the initial plurality of grids, wherein the side length size of the adjusted grid is obtained by the following formula:

[0064] D=(1+K)·D0

[0065] Wherein, D represents the side length of the grid after adjustment; D0 represents the side length of the grid before adjustment; and K represents the grid size adjustment coefficient.

[0066] Preferably, the signal positioning module specifically includes:

[0067] Based on the recurrent neural network, the interference signal characteristic monitoring model is constructed for the frequency components and frequency distribution data of the interference signal, and the interference signal characteristic monitoring model is obtained, which specifically includes:

[0068] Extracting frequency components of interference signals and characteristic data from frequency distribution data;

[0069] Select the number of layers of the recurrent neural network and build an initial model for monitoring interference signal characteristics. The number of layers of the recurrent neural network includes an input layer, a hidden layer, and an output layer.

[0070] The feature data set is divided into a training set and a test set, and the hyperparameters of the initial model for monitoring interference signal characteristics are set, including the learning rate, the number of hidden layer nodes, and the number of training rounds;

[0071] The initial model for monitoring the characteristics of interference signals is trained using the training set, and the parameters of the initial model for monitoring the characteristics of interference signals are updated using the back propagation algorithm;

[0072] Use the test set to evaluate the performance of the initial model for monitoring the characteristics of interference signals;

[0073] According to the evaluation results, the hyper parameters of the initial model for monitoring the characteristics of interference signals are adjusted, and the initial model for monitoring the characteristics of interference signals is optimized to obtain the monitoring model for the characteristics of interference signals.

[0074] According to the interference signal characteristic monitoring model, the propagation characteristic of the urban rail transit communication interference signal data is analyzed to obtain the propagation characteristic data of the urban rail transit communication interference signal;

[0075] According to the propagation characteristic data of the urban rail transit communication interference signal, interference source positioning processing is performed on the urban rail transit communication interference signal data to obtain the urban rail transit communication interference signal positioning data.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] The present invention can timely discover and solve communication interference problems by real-time monitoring and locating communication interference sources, ensure unimpeded communication between the train and the control center, and avoid abnormal train operation or safety accidents caused by communication failures. Accurate interference source positioning can help to quickly troubleshoot and reduce train delays or suspensions caused by communication problems, thereby improving the operating efficiency of the train and the travel experience of passengers. Through real-time monitoring and positioning, the time and cost of manual troubleshooting can be reduced, the efficiency of fault handling can be improved, and the overall maintenance cost can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A schematic diagram of a system for monitoring and locating interference sources in urban rail transit communications according to the present invention;

[0079] Figure 2 It is a schematic diagram of the method of the urban rail transit communication interference source monitoring and positioning system of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In order to solve the problem that in the actual use of existing technologies, wireless signal transmission in complex electromagnetic environments is easily affected by non-cooperative signals and channel interference and other unstable factors, resulting in reduced wireless communication quality or communication interruption, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0082] Urban rail transit communication interference source monitoring and positioning system, including:

[0083] The signal monitoring module is used to collect the signal strength and quality of urban rail transit communication in real time and perform preprocessing, extract the real-time signal frequency, amplitude and phase of urban rail transit communication, and obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication after analysis and conversion;

[0084] The signal separation module is used to compare the frequency components of real-time signals of urban rail transit communication with the frequency spectrum in the frequency distribution data, and to separate and extract the useful signal from the interference signal to obtain the frequency components and frequency distribution data of the interference signal;

[0085] The signal positioning module is used to construct an interference signal characteristic monitoring model, use the interference signal characteristic monitoring model to analyze the interference signal propagation characteristics, obtain interference signal propagation characteristic data, perform interference source positioning processing on urban rail transit communication interference signal data, and obtain urban rail transit communication interference signal positioning data.

[0086] By deploying advanced sensors and IoT devices in the rail transit system to collect signal strength and quality in real time, a basis is provided for the subsequent location and analysis of interference sources. The Kalman filter algorithm is used for preprocessing and filtering to extract useful signal components, which can effectively and quickly locate the interference source.

[0087] Signal monitoring module, including:

[0088] Real-time monitoring module, which is used to collect the signal strength and quality of urban rail transit communications in real time by using sensors and IoT devices deployed in the urban rail transit system;

[0089] The data acquisition module is used to obtain the signal strength and quality of urban rail transit communications collected in real time and perform preprocessing;

[0090] The data extraction module is used to extract the frequency, amplitude and phase of the real-time signal of urban rail transit communication, and obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication after analysis and conversion.

[0091] Signal monitoring module, specifically including:

[0092] Use sensors and IoT devices deployed in urban rail transit systems to collect real-time information on the signal strength and quality of urban rail transit communications;

[0093] Clean and optimize the signal strength and quality of urban rail transit communications collected in real time;

[0094] Remove noise from real-time urban rail transit communication signals, filter real-time urban rail transit communication signals, and adjust the amplitude of the signals;

[0095] After processing, the real-time signal frequency, amplitude and phase of urban rail transit communication are extracted;

[0096] Analyze the frequency, amplitude and phase of the real-time signal in the time domain to obtain the waveform of the frequency, amplitude and phase of the real-time signal;

[0097] Analyze the waveform of the frequency, amplitude and phase of the real-time signal to obtain the data of the change of the frequency, amplitude and phase of the real-time signal over time;

[0098] The obtained change data is converted from the time domain to the frequency domain through Fourier transform, and normalized to obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication.

[0099] Data acquisition module, including:

[0100] A data receiving module is used to receive the signal strength and quality of urban rail transit communications collected in real time by sensors and IoT devices deployed in the urban rail transit system;

[0101] The preprocessing module is used to clean and optimize the signal strength and quality of urban rail transit communications collected in real time, remove noise, filter or adjust the amplitude of the signal to improve the accuracy and reliability of subsequent processing steps.

[0102] Signal separation module, including:

[0103] The comparison module is used to compare the frequency components of real-time signals of urban rail transit communication with the spectrum in the frequency distribution data. Useful signals refer to those signals that meet the communication standards, have moderate strength, and good quality. These signals can transmit information normally. Interference signals refer to those signals that do not meet the communication standards, have too weak or too strong strength, and have poor quality, as well as noise or interference that may be generated by other equipment or environmental factors.

[0104] The identification module identifies the characteristics of the useful signal and the interference signal through the comparison results of the comparison module, and separates and extracts the useful signal and the interference signal according to the identification characteristics to obtain the frequency component and frequency distribution data of the interference signal.

[0105] The signal separation module specifically includes:

[0106] Conduct spectrum analysis on the frequency components and frequency distribution data of real-time signals of urban rail transit communications, and compare the spectrum analysis results;

[0107] Identify the characteristics of useful signals and interference signals in the frequency components and frequency distribution data of real-time signals of urban rail transit communication based on the comparison results, the characteristics include frequency, amplitude and phase;

[0108] The useful signal and the interference signal are separated and extracted according to the identification characteristics to obtain the frequency components and frequency distribution data of the interference signal.

[0109] Signal positioning module, including:

[0110] A model building module, used to build an interference signal characteristic monitoring model using the frequency components and frequency distribution data of the interference signal;

[0111] An analysis module, used to analyze the propagation characteristics of urban rail transit communication interference signal data to obtain the propagation characteristics data of urban rail transit communication interference signal;

[0112] The processing module is used to perform interference source positioning processing on the urban rail transit communication interference signal data to obtain the urban rail transit communication interference signal positioning data.

[0113] Processing modules, including:

[0114] The interference signal characteristic monitoring model is used to analyze the propagation characteristics of the urban rail transit communication interference signal data, and the propagation characteristic data of the urban rail transit communication interference signal is obtained;

[0115] Determine the frequency range of interference signals based on propagation characteristic data of urban rail transit communication interference signals;

[0116] The frequency range of the interference signal data is drawn into a polygon, and the polygon is grid-divided to obtain a plurality of grids, and the data in each grid corresponds to its frequency range;

[0117] Extract the branch current information of the interference signal from the data in each grid, and use the branch current information of the interference signal in each grid to obtain the interference value of the frequency range in each grid;

[0118] The interference values ​​of the frequency ranges in each grid are compared to determine the maximum interference value, and the interference values ​​of the frequency ranges in each grid are divided by the maximum interference value of the grid in turn to obtain the interference ratio of the frequency range corresponding to the interference signal;

[0119] Obtain the number of interference signal frequency ranges corresponding to the interference ratio in each grid, take the grid with the largest number as the target grid where the interference source is located, and determine the location of the interference source according to the location information corresponding to the target grid;

[0120] The frequency range of the interference signal data is divided into multiple small grids using a grid. By checking each small grid one by one, the possible range of the interference source can be narrowed, thereby improving the accuracy of positioning, helping to quickly find and solve communication signal interference problems and ensure the normal operation of the rail transit communication system.

[0121] Specifically, the frequency range of the interference signal data is drawn into a polygon, and the polygon is grid-divided to obtain a plurality of grids, specifically including:

[0122] Extract the preset grid initial size;

[0123] Dividing the target area corresponding to the polygon into grids according to the preset initial grid size to generate a plurality of initial grids; wherein the grids are squares with equal length and width;

[0124] Extract the interference signal strength contained in each grid;

[0125] Obtaining an interference signal strength coefficient using the interference signal strength contained in each grid;

[0126] The interference signal strength coefficient is obtained by the following formula:

[0127]

[0128] Where R represents the interference signal strength coefficient; n represents the number of interference signals contained in each grid; Q f Indicates the average value of the signal strength variation of the interference signal contained in each grid; Q b represents the signal strength standard deviation of the n interference signals contained in each grid; Q max and Q min Indicates the maximum and minimum signal strength of the interference signal contained in each grid; Q i represents the signal strength of the i-th interference signal contained in each grid; Q Lmax Indicates the signal strength difference between the two interference signals with the largest straight-line distance in each grid; Q Lminrepresents the signal strength difference corresponding to the two interference signals with the smallest straight-line distance in each grid; s represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula:

[0129]

[0130] Where s represents the adjustment coefficient; θ represents the angle between the line between the two positions of the maximum and minimum signal strength of the interference signal and the grid diagonal; h 01 Indicates the vertical distance between the position corresponding to the maximum signal strength of the interference signal contained in each grid and the diagonal line; h 02 Indicates the vertical distance between the position corresponding to the minimum signal strength of the interference signal contained in each grid and the diagonal line; L indicates the straight-line distance between the two position points of the maximum and minimum signal strength of the interference signal; Case 1 is: when the corresponding positions of the maximum and minimum signal strength of the interference signal are on the same side of the diagonal line; Case 2 is: when the corresponding positions of the maximum and minimum signal strength of the interference signal are on both sides of the diagonal line;

[0131] Extracting the interference signal strength coefficient corresponding to each grid, and using the interference signal strength coefficient corresponding to each grid to obtain the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon;

[0132] Compare the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon with a preset standard deviation threshold;

[0133] When the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, the grid size is adjusted to obtain the adjusted grid size;

[0134] The target area corresponding to the polygon is re-divided into grids according to the adjusted grid size to obtain a plurality of re-divided grids.

[0135] The technical effect of the above technical solution is: the target area corresponding to the polygon is grid-divided by a preset grid initial size, so that the target area is subdivided into multiple square grids of equal size. This division method is helpful for more detailed analysis and processing of interference signals in the target area. By extracting the interference signal strength in each grid and calculating the interference signal strength coefficient, the interference signal situation in each grid can be quantitatively evaluated. This quantitative evaluation provides a reliable data basis for subsequent analysis and decision-making. The calculation of the interference signal strength coefficient takes into account multiple factors, including the number of interference signals, the average value of the signal strength change amplitude, the signal strength standard deviation, the maximum and minimum signal strength, and the signal strength difference between the two interference signals with the largest straight-line distance and the two interference signals with the smallest straight-line distance. These factors comprehensively reflect the complexity and diversity of interference signals, making the evaluation results more comprehensive and accurate. By comparing the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area with the preset standard deviation threshold, it can be determined whether the current grid division meets the analysis requirements. When the standard deviation is greater than the threshold, it means that the distribution of interference signals in the grid is quite different, and the grid size needs to be adjusted. This dynamic adjustment mechanism makes grid division more flexible and adaptable. The analysis accuracy can be further optimized by adjusting the grid size and re-dividing the grid. The new grid division can better reflect the distribution of interference signals in the target area and provide stronger support for subsequent analysis and decision-making.

[0136] On the other hand, by presetting the initial grid size and performing grid division, the target area corresponding to the polygon can be preliminarily covered. When it is found that the standard deviation of the interference signal strength coefficient exceeds the preset threshold, the grid size is adjusted and re-divided, which can further improve the grid's ability to capture changes in interference signals in the target area, thereby improving the accuracy and resolution of the analysis. By calculating the interference signal strength coefficient in each grid and using these coefficients to calculate the standard deviation, the distribution of interference signals in the grid can be quantitatively evaluated. Compared with directly analyzing the position and strength of each interference signal, this method can reduce the complexity of data processing and improve the efficiency of data analysis. The technical solution can dynamically adjust the grid size according to the actual situation of the interference signal in the target area. This adaptive adjustment mechanism makes the grid division more flexible and can adapt to interference signal environments with different complexities and distribution characteristics. By comprehensively considering multiple interference signal characteristics (such as signal strength, change amplitude, standard deviation, maximum and minimum values, etc.) and the geometric distribution of interference signals in the grid (such as the difference in signal strength between the two interference signals with the largest straight-line distance and the two interference signals with the smallest straight-line distance, the angle between the maximum and minimum positions of signal strength and the grid diagonal, etc.), the interference signal situation in the grid can be more accurately evaluated. This comprehensive evaluation method helps improve the system's robustness to complex interference environments. Through the preset standard deviation threshold and the formula for adjusting the grid size, the technical solution achieves the standardization of grid division. This allows consistency in grid division and interference signal analysis at different times, by different people or different systems, and improves the repeatability of the analysis. The grid division and interference signal analysis methods in this technical solution are relatively independent and can be easily integrated with other data processing and analysis methods. At the same time, with the advancement of technology and changes in demand, this technical solution is also easy to expand and optimize to adapt to new application scenarios and needs.

[0137] In summary, this technical solution achieves a comprehensive, accurate and efficient analysis of interference signals in the target area corresponding to the polygon through measures such as fine grid division, interference signal strength assessment, comprehensive consideration of interference signal characteristics, dynamic adjustment of grid size and optimization of analysis accuracy. This technical solution has broad application prospects in wireless communications, radar detection, environmental monitoring and other fields.

[0138] Specifically, when the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, the grid size is adjusted to obtain the adjusted grid size, which specifically includes:

[0139] When the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, calling the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon;

[0140] Retrieve interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon;

[0141] The grid size adjustment coefficient is obtained by using the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon and the interference signal strength coefficients corresponding to all grids in the target area, wherein the grid size adjustment coefficient is obtained by the following formula:

[0142]

[0143] Among them, K represents the grid size adjustment coefficient; m represents the total number of grids in the target area corresponding to the polygon; R i represents the interference signal strength coefficient corresponding to the i-th grid; R b represents the standard deviation of the interference signal strength coefficient corresponding to m grids; R bc Indicates the preset reference value of the standard deviation of the interference signal strength coefficient;

[0144] The grid size adjustment coefficient is used to adjust the grid side lengths of the initial plurality of grids, wherein the side length size of the adjusted grid is obtained by the following formula:

[0145] D=(1+K)·D0

[0146] Wherein, D represents the side length of the grid after adjustment; D0 represents the side length of the grid before adjustment; and K represents the grid size adjustment coefficient.

[0147] The technical effect of the above technical solution is: when the standard deviation of the interference signal strength coefficient corresponding to all grids in the target area corresponding to the polygon is greater than the preset standard deviation threshold, it means that the distribution difference of the interference signal in the grid is large. At this time, by calculating the grid size adjustment coefficient and adjusting the grid size, the grid can be adaptively adjusted. This adjustment mechanism makes the grid division more consistent with the actual situation of the interference signal in the target area, and improves the accuracy and reliability of the analysis. By calculating the standard deviation of the interference signal strength coefficient corresponding to all grids in the target area corresponding to the polygon and the interference signal strength coefficient corresponding to all grids in the target area, the distribution of the interference signal in the grid can be quantitatively evaluated. This quantitative evaluation provides data support for the adjustment of the grid size, making the decision more scientific and reasonable. The adjusted grid size is more consistent with the distribution characteristics of the interference signal in the target area, which helps to achieve more refined division and more efficient analysis. Fine grid division can better capture the detailed characteristics of the interference signal and provide more powerful support for subsequent analysis and decision-making. The grid size adjustment mechanism in the technical solution is flexible and can be adjusted according to the actual situation of the interference signal in the target area. At the same time, this technical solution also has good scalability and can adapt to polygonal target areas of different sizes and shapes, as well as interference signal environments of different complexities. Through adaptive grid adjustment, the grid division can be made more reasonable, thereby reducing unnecessary calculation and analysis workload and improving analysis efficiency. At the same time, finer grid division and more accurate interference signal evaluation also help improve the accuracy of analysis.

[0148] In summary, this technical solution achieves a more accurate, reliable and efficient analysis of interference signals in the target area corresponding to the polygon through adaptive grid adjustment mechanism, quantitative evaluation and decision-making, fine division and efficient analysis, flexibility and scalability, and improved analysis efficiency and accuracy. This technical solution has broad application prospects in wireless communications, radar detection, environmental monitoring and other fields, and helps to improve the performance and reliability of the system.

[0149] The signal positioning module specifically includes:

[0150] Based on the recurrent neural network, the interference signal characteristic monitoring model is constructed based on the frequency components and frequency distribution data of the interference signal to obtain the interference signal characteristic monitoring model;

[0151] According to the interference signal characteristic monitoring model, the propagation characteristic of the urban rail transit communication interference signal data is analyzed to obtain the propagation characteristic data of the urban rail transit communication interference signal;

[0152] According to the propagation characteristic data of the urban rail transit communication interference signal, interference source positioning processing is performed on the urban rail transit communication interference signal data to obtain the urban rail transit communication interference signal positioning data.

[0153] By analyzing the propagation characteristics of communication interference signals, such as signal strength, propagation direction, propagation speed, etc., the location of the interference source can be accurately determined. This method is more efficient and accurate than traditional troubleshooting methods, and avoids blindness and uncertainty. Using the interference signal propagation characteristic data for positioning can quickly narrow the scope of the interference source, reduce the troubleshooting time, and minimize the impact on operations. Timely and accurate positioning and elimination of interference sources can avoid train delays and safety accidents caused by communication failures, thereby improving the reliability and safety of the entire rail transit system. It is of great significance to ensure the travel safety of passengers and improve the service quality of urban rail transit, and helps to timely discover potential communication problems and prevent the recurrence of similar interference incidents.

[0154] Based on the recurrent neural network, the interference signal characteristic monitoring model is constructed based on the frequency components and frequency distribution data of the interference signal, including:

[0155] Extracting characteristic data from the frequency components and frequency distribution data of the interference signal, the characteristic data including frequency domain moment kurtosis coefficient, frequency domain moment skewness coefficient, single frequency energy concentration, average spectrum flatness coefficient, frequency domain parameters and time domain moment kurtosis coefficient;

[0156] Select the number of layers of the recurrent neural network and build an initial model for monitoring interference signal characteristics. The number of layers of the recurrent neural network includes input layer, hidden layer and output layer. The input layer is used to control the format of data entering the network. The input data should be the preprocessed interference signal feature sequence. The hidden layer is used to use LSTM or GRU units as hidden layers. These units can remember the information in the sequence and control the flow of information through the gating mechanism. The output layer includes a fully connected layer for outputting monitoring results, such as the classification or characteristic parameters of the interference signal.

[0157] The feature data set is divided into a training set and a test set, and the hyperparameters of the initial model for monitoring interference signal characteristics are set, including the learning rate, the number of hidden layer nodes, and the number of training rounds;

[0158] The initial model for monitoring the characteristics of interference signals is trained using the training set, and the parameters of the initial model for monitoring the characteristics of interference signals are updated using the back propagation algorithm;

[0159] The test set is used to evaluate the performance of the initial model for monitoring the interference signal characteristics. The performance of the initial model for monitoring the interference signal characteristics includes classification accuracy, recall rate, and F1 score.

[0160] According to the evaluation results, the hyperparameters of the initial model for monitoring the characteristics of interference signals are adjusted, the initial model for monitoring the characteristics of interference signals is optimized, and the interference signal characteristics monitoring model is obtained.

[0161] Working principle: When using the urban rail transit communication interference source monitoring and positioning system of the present invention, according to Figure 1 and Figure 2 , including the following steps:

[0162] Step 1: Use sensors and IoT devices deployed in the urban rail transit system to collect the signal strength and quality of urban rail transit communications in real time for preprocessing;

[0163] Step 2: Extract the frequency, amplitude and phase of the real-time signal of urban rail transit communication, and obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication after analysis and conversion;

[0164] Step 3: Perform spectrum analysis on the frequency components and frequency distribution data of the real-time signal of urban rail transit communication, and compare the analysis results of the spectrum, and identify the characteristics of useful signals and interference signals in the frequency components and frequency distribution data of the real-time signal of urban rail transit communication according to the comparison results;

[0165] Step 4: Separate and extract the useful signal and the interference signal according to the identification features to obtain the frequency component and frequency distribution data of the interference signal;

[0166] Step 5: Use the frequency components and frequency distribution data of the interference signal to build an interference signal characteristic monitoring model, and use the interference signal characteristic monitoring model to analyze the propagation characteristics of the urban rail transit communication interference signal data to obtain the urban rail transit communication interference signal propagation characteristic data;

[0167] Step six: Use the propagation characteristic data of the urban rail transit communication interference signal to perform interference source positioning processing on the urban rail transit communication interference signal data to obtain the urban rail transit communication interference signal positioning data.

[0168] In summary, the urban rail transit communication interference source monitoring and positioning system of the present invention can accurately determine the location of the interference source by analyzing the propagation characteristics of the communication interference signal, avoiding blindness and uncertainty, and using the interference signal propagation characteristic data for positioning, which can quickly narrow the scope of the interference source, reduce the troubleshooting time, and minimize the impact on operations. The interference source is located and eliminated in a timely and accurate manner. The frequency range of the interference signal data is divided into multiple small grids using a grid. By checking each small grid one by one, the possible range of the interference source can be narrowed, thereby improving the accuracy of positioning, helping to quickly find and solve the communication signal interference problem, and ensuring the normal operation of the rail transit communication system. By real-time monitoring and positioning of the communication interference source, the communication interference problem can be discovered and solved in time, ensuring that the communication between the train and the control center is unimpeded, avoiding abnormal train operation or safety accidents caused by communication failures, and accurate interference source positioning can help quickly troubleshoot, reduce train delays or suspensions caused by communication problems, thereby improving the operation efficiency of the train and the travel experience of passengers, and through real-time monitoring and positioning, the time and cost of manual troubleshooting can be reduced, and the efficiency of fault handling can be improved, thereby reducing the overall maintenance cost.

[0169] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0170] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. Urban rail transit communication interference source monitoring and positioning system, characterized in that: include: The signal monitoring module is used to collect the signal strength and quality of urban rail transit communication in real time and perform preprocessing, extract the real-time signal frequency, amplitude and phase of urban rail transit communication, and obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication after analysis and conversion; The signal separation module is used to compare the frequency components of real-time signals of urban rail transit communication with the frequency spectrum in the frequency distribution data, and to separate and extract the useful signal from the interference signal to obtain the frequency components and frequency distribution data of the interference signal; The signal positioning module is used to build an interference signal characteristic monitoring model, use the interference signal characteristic monitoring model to analyze the interference signal propagation characteristics, obtain interference signal propagation characteristic data, perform interference source positioning processing on urban rail transit communication interference signal data, and obtain urban rail transit communication interference signal positioning data.

2. The urban rail transit communication interference source monitoring and positioning system according to claim 1 is characterized in that: The signal monitoring module comprises: Real-time monitoring module, which is used to collect the signal strength and quality of urban rail transit communications in real time by using sensors and IoT devices deployed in the urban rail transit system; The data acquisition module is used to obtain the signal strength and quality of urban rail transit communications collected in real time and perform preprocessing; The data extraction module is used to extract the frequency, amplitude and phase of the real-time signal of urban rail transit communication, and obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication after analysis and conversion.

3. The urban rail transit communication interference source monitoring and positioning system according to claim 1 is characterized in that: The signal monitoring module specifically includes: Use sensors and IoT devices deployed in urban rail transit systems to collect real-time information on the signal strength and quality of urban rail transit communications; Clean and optimize the signal strength and quality of urban rail transit communications collected in real time; Remove noise from real-time urban rail transit communication signals, filter real-time urban rail transit communication signals, and adjust the amplitude of the signals; After processing, the real-time signal frequency, amplitude and phase of urban rail transit communication are extracted; Analyze the frequency, amplitude and phase of the real-time signal in the time domain to obtain the waveform of the frequency, amplitude and phase of the real-time signal; Analyze the waveform of the frequency, amplitude and phase of the real-time signal to obtain the data of the change of the frequency, amplitude and phase of the real-time signal over time; The obtained change data is converted from the time domain to the frequency domain through Fourier transform, and normalized to obtain the frequency component and frequency distribution data of the real-time signal of urban rail transit communication.

4. The urban rail transit communication interference source monitoring and positioning system according to claim 2 is characterized in that: The data acquisition module comprises: A data receiving module is used to receive the signal strength and quality of urban rail transit communications collected in real time by sensors and IoT devices deployed in the urban rail transit system; The preprocessing module is used to clean and optimize the signal strength and quality of urban rail transit communications collected in real time, remove noise, filter or adjust the amplitude of the signal.

5. The urban rail transit communication interference source monitoring and positioning system according to claim 1 is characterized in that: The signal separation module comprises: A comparison module, used to compare the frequency components of real-time signals of urban rail transit communications with the frequency spectrum in the frequency distribution data; The identification module identifies the characteristics of the useful signal and the interference signal through the comparison results of the comparison module, and separates and extracts the useful signal and the interference signal according to the identification characteristics to obtain the frequency components and frequency distribution data of the interference signal.

6. The urban rail transit communication interference source monitoring and positioning system according to claim 5 is characterized in that: The signal separation module specifically includes: Conduct spectrum analysis on the frequency components and frequency distribution data of real-time signals of urban rail transit communications, and compare the spectrum analysis results; Identify the characteristics of useful signals and interference signals in the frequency components and frequency distribution data of real-time signals of urban rail transit communication based on the comparison results; The useful signal and the interference signal are separated and extracted according to the identification characteristics to obtain the frequency components and frequency distribution data of the interference signal.

7. The urban rail transit communication interference source monitoring and positioning system according to claim 1 is characterized in that: The signal positioning module comprises: A model building module, used to build an interference signal characteristic monitoring model using the frequency components and frequency distribution data of the interference signal; An analysis module, used to analyze the propagation characteristics of urban rail transit communication interference signal data to obtain the propagation characteristics data of urban rail transit communication interference signal; The processing module is used to perform interference source positioning processing on the urban rail transit communication interference signal data to obtain the urban rail transit communication interference signal positioning data. Processing modules, including: The interference signal characteristic monitoring model is used to analyze the propagation characteristics of the urban rail transit communication interference signal data, and the propagation characteristic data of the urban rail transit communication interference signal is obtained; Determine the frequency range of interference signals based on propagation characteristic data of urban rail transit communication interference signals; The frequency range of the interference signal data is drawn into a polygon, and the polygon is grid-divided to obtain a plurality of grids, and the data in each grid corresponds to its frequency range; Extract the branch current information of the interference signal from the data in each grid, and use the branch current information of the interference signal in each grid to obtain the interference value of the frequency range in each grid; The interference values ​​of the frequency ranges in each grid are compared to determine the maximum interference value, and the interference values ​​of the frequency ranges in each grid are divided by the maximum interference value of the grid in turn to obtain the interference ratio of the frequency range corresponding to the interference signal; The number of interference signal frequency ranges corresponding to the interference ratio in each grid is obtained, the grid with the largest number is used as the target grid where the interference source is located, and the location of the interference source is determined according to the location information corresponding to the target grid.

8. The urban rail transit communication interference source monitoring and positioning system according to claim 7 is characterized in that: The step of drawing the frequency range of the interference signal data into a polygon and dividing the polygon into a plurality of grids specifically includes: Extract the preset grid initial size; Dividing the target area corresponding to the polygon into grids according to the preset initial grid size to generate a plurality of initial grids; wherein the grids are squares with equal length and width; Extract the interference signal strength contained in each grid; Obtaining an interference signal strength coefficient using the interference signal strength contained in each grid; The interference signal strength coefficient is obtained by the following formula: Where R represents the interference signal strength coefficient; n represents the number of interference signals contained in each grid; Q f Indicates the average value of the signal strength variation of the interference signal contained in each grid; Q b represents the signal strength standard deviation of the n interference signals contained in each grid; Q max and Q min Indicates the maximum and minimum signal strength of the interference signal contained in each grid; Q i represents the signal strength of the i-th interference signal contained in each grid; Q Lmax Indicates the signal strength difference between the two interference signals with the largest straight-line distance in each grid; Q Lmin represents the signal strength difference corresponding to the two interference signals with the smallest straight-line distance in each grid; s represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula: Where s represents the adjustment coefficient; θ represents the angle between the line between the two positions of the maximum and minimum signal strength of the interference signal and the grid diagonal; h 01 Indicates the vertical distance between the position corresponding to the maximum signal strength of the interference signal contained in each grid and the diagonal line; h 02 Indicates the vertical distance between the position corresponding to the minimum signal strength of the interference signal contained in each grid and the diagonal line; L indicates the straight-line distance between the two position points of the maximum and minimum signal strength of the interference signal; Case 1 is: when the corresponding positions of the maximum and minimum signal strength of the interference signal are on the same side of the diagonal line; Case 2 is: when the corresponding positions of the maximum and minimum signal strength of the interference signal are on both sides of the diagonal line; Extracting the interference signal strength coefficient corresponding to each grid, and using the interference signal strength coefficient corresponding to each grid to obtain the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon; Compare the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon with a preset standard deviation threshold; When the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, the grid size is adjusted to obtain the adjusted grid size; The target area corresponding to the polygon is re-divided into grids according to the adjusted grid size to obtain a plurality of re-divided grids.

9. The urban rail transit communication interference source monitoring and positioning system according to claim 8 is characterized in that: When the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, the grid size is adjusted to obtain the adjusted grid size, specifically including: When the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon is greater than a preset standard deviation threshold, calling the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon; Retrieve interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon; The grid size adjustment coefficient is obtained by using the standard deviation of the interference signal strength coefficients corresponding to all grids in the target area corresponding to the polygon and the interference signal strength coefficients corresponding to all grids in the target area, wherein the grid size adjustment coefficient is obtained by the following formula: Among them, K represents the grid size adjustment coefficient; m represents the total number of grids in the target area corresponding to the polygon; R i represents the interference signal strength coefficient corresponding to the i-th grid; R b represents the standard deviation of the interference signal strength coefficient corresponding to m grids; R bc Indicates the preset reference value of the standard deviation of the interference signal strength coefficient; The grid size adjustment coefficient is used to adjust the grid side lengths of the initial plurality of grids, wherein the side length size of the adjusted grid is obtained by the following formula: D=(1+K)·D0 Wherein, D represents the side length of the grid after adjustment; D0 represents the side length of the grid before adjustment; and K represents the grid size adjustment coefficient.

10. The urban rail transit communication interference source monitoring and positioning system according to claim 7, characterized in that: The signal positioning module specifically includes: Based on the recurrent neural network, the interference signal characteristic monitoring model is constructed for the frequency components and frequency distribution data of the interference signal, and the interference signal characteristic monitoring model is obtained, which specifically includes: Based on the recurrent neural network, the interference signal characteristic monitoring model is constructed based on the frequency components and frequency distribution data of the interference signal, including: Extracting frequency components of interference signals and characteristic data from frequency distribution data; Select the number of layers of the recurrent neural network and build an initial model for monitoring interference signal characteristics. The number of layers of the recurrent neural network includes an input layer, a hidden layer, and an output layer. The feature data set is divided into a training set and a test set, and the hyperparameters of the initial model for monitoring interference signal characteristics are set, including the learning rate, the number of hidden layer nodes, and the number of training rounds; The initial model for monitoring the characteristics of interference signals is trained using the training set, and the parameters of the initial model for monitoring the characteristics of interference signals are updated using the back propagation algorithm; Use the test set to evaluate the performance of the initial model for monitoring the characteristics of interference signals; According to the evaluation results, the hyper parameters of the initial model for monitoring the characteristics of interference signals are adjusted, and the initial model for monitoring the characteristics of interference signals is optimized to obtain the monitoring model for the characteristics of interference signals; According to the interference signal characteristic monitoring model, the propagation characteristic of the urban rail transit communication interference signal data is analyzed to obtain the propagation characteristic data of the urban rail transit communication interference signal; According to the propagation characteristic data of the urban rail transit communication interference signal, interference source positioning processing is performed on the urban rail transit communication interference signal data to obtain the urban rail transit communication interference signal positioning data.

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