Method and system for enhancing signal coverage of terrestrial digital television broadcasting transmitter

Abstract

The invention provides a signal coverage enhancement method and system for a terrestrial digital television broadcasting transmitter, and the method comprises the steps: obtaining the position data of a train, collecting the latitude and longitude information of the train in a high-speed moving scene in real time, matching the latitude and longitude information with a preset moving track database, and determining the coordinate data of the current position of the train; according to the train position coordinate data, transmitter array distribution parameters of the corresponding section are extracted from a pre-established signal coverage database, and initial beam forming parameters of each transmitter in the corresponding section are obtained; according to the change trend of the signal coverage range, acquiring coverage blind area feedback data, determining a section with insufficient signal power density in the tunnel cutting area, and generating a coverage blind area distribution map; and according to the coverage blind area distribution diagram, adjusting parameters for executing beam forming on the transmitter array in real time, obtaining optimized signal power density distribution data, and forming a mobile coverage corridor framework.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method and system for enhancing signal coverage of a terrestrial digital television broadcast transmitter. Background Art

[0002] With the development of high-speed railways, watching digital TV programs smoothly on high-speed trains has become a future development trend. However, high-speed railways are fast and often pass through mountain tunnels where signals are difficult to cover or deep trenches with high slopes on both sides, which poses a huge challenge to the stable reception of TV signals. The rapid development of high-speed railways has become the core pillar of modern transportation. The reliability of its communication and signal coverage technology is directly related to the safety and efficiency of train operation, especially in the key field of digital TV signal coverage enhancement system. With the extension of the high-speed railway network, how to ensure the continuity and stability of signals in high-speed mobile scenarios has become a technical bottleneck that needs to be broken through in the industry. Digital TV signal coverage enhancement is not only an important part of improving passenger experience, but also an indispensable support means to ensure the transmission of operational information. The importance of its research is self-evident. However, existing solutions often expose significant deficiencies when dealing with the complex environment along the high-speed railway. Traditional transmitter deployment mostly adopts static coverage mode, which is difficult to adapt to the dynamic needs brought by high-speed movement of trains, especially in special terrains such as mountain tunnels or deep cuttings, where signal attenuation and coverage blind spots are particularly prominent. The existing adaptive adjustment mechanism usually lags behind the running status of the train, lacks the ability of forward-looking prediction and real-time optimization, resulting in large fluctuations in signal quality, making it difficult to meet the stringent requirements in high-speed scenarios. The core challenges faced in this field are mainly concentrated on the dynamic synchronization and adaptive enhancement capabilities of transmitter signal coverage. Specifically, how to achieve accurate matching between the transmitter array and the train running trajectory, how to adjust the beamforming parameters in real time in complex terrain to optimize coverage, and how to intelligently compensate for blind spots based on on-board receiver feedback. These unsolved technical factors directly lead to the limited performance of the signal enhancement system in high-speed mobile scenarios. Especially when the train passes through special sections, the lack of predictability of the transmitter signal power density and the slow response of the control system further aggravate the difficulty of forming a coverage corridor and form a unique technical problem. Therefore, how to design an intelligent control system based on the precise position data of the train so that it can trigger the signal enhancement mode of a specific section in advance, and form a stable mobile coverage corridor through real-time adjustment of the transmitter beamforming parameters, forward predictive enhancement of the signal power density, and intelligent compensation of the coverage blind spot, has become a key issue that needs to be solved in this study. Summary of the invention

[0003] The present invention provides a method for enhancing signal coverage of a terrestrial digital television broadcast transmitter, which mainly comprises:

[0004] Obtain train position data, collect the longitude and latitude information of the train in the high-speed moving scenario in real time, and match it with the preset operation trajectory database to determine the current position coordinate data of the train;

[0005] According to the train position coordinate data, extract the transmitter array distribution parameters of the corresponding section from the pre-established signal coverage range database, and obtain the initial beamforming parameters of each transmitter in the corresponding section;

[0006] Calculate the moving speed and direction of the train according to the transmitter array distribution parameters, and judge whether the train is approaching the tunnel cut area. If so, adjust the initial beamforming parameters to obtain the signal coverage range change trend;

[0007] According to the signal coverage range change trend, obtain the coverage blind area feedback data, determine the signal power density insufficient section in the tunnel cut area, and generate a coverage blind area distribution map;

[0008] According to the coverage blind area distribution map, adjust the beamforming parameters of the transmitter array in real time to obtain the optimized signal power density distribution data, and form a mobile coverage corridor framework;

[0009] According to the real-time obtained mobile coverage corridor framework data, combined with the real-time obtained train position data and speed information, judge whether the train is about to enter the next tunnel cut area. If so, increase the signal power density of the corresponding transmitter to obtain the pre-adjusted coverage range expansion parameters;

[0010] According to the pre-adjusted coverage range expansion parameters, extract the affected transmitter numbers and adjustment amplitudes from the transmitter array, calibrate the beamforming parameters, and generate a target mobile coverage corridor signal enhancement scheme.

[0011] Further, the train position data is obtained, the latitude and longitude information of the train under the high-speed moving scene is collected in real time, and matched with the preset running track database to determine the coordinate data of the current position of the train, including: collecting the real-time latitude and longitude coordinates of the high-speed train according to the satellite positioning receiving device, and converting the world geographic coordinate system into the line coordinate data based on the railway main line mileage through the national railway coordinate comparison table preset in the coordinate mapping converter. The reference positioning point data set every fifty meters on the railway main line is obtained from the track basic database, and the reference positioning point is interpolated and calculated using the cubic spline function to obtain the continuous track reference point data. The real-time collected train positioning data is median filtered using a 200-meter fixed-length sliding window, and a smooth track curve sequence is generated by Bezier curve fitting. If the lateral deviation value between the positioning point and the track reference point in the smooth track curve sequence is greater than ten meters, the state of the deviation point is estimated using the particle filter algorithm, and the corrected positioning sequence is obtained based on the historical track data training. For the corrected positioning sequence, the similarity score with the reference track is calculated according to the dynamic time warping algorithm in the track matching calculator, and the point with the highest score is selected as the candidate matching position. The actual railway mainline position coordinates of the train are obtained by performing weighted average calculation on the five positioning points before and after the candidate matching position and combining the train running direction vector for spatial interpolation.

[0012] Furthermore, according to the train position coordinate data, the transmitter array distribution parameters of the corresponding section are extracted from the pre-established signal coverage database to obtain the initial beamforming parameters of each transmitter in the corresponding section, including: querying the signal coverage database according to the actual position coordinates of the train, and extracting the longitude and latitude coordinate data and array layout parameters of all transmitters within a radius of two kilometers of the section where the train is located from the database through the geographic information space query to obtain the transmitter distribution parameter table. According to the transmitter distribution parameter table, the antenna height parameters and antenna tilt angle values ​​of each transmitter are obtained, and the transmitter coverage area is spatially divided using a regular hexagonal honeycomb grid algorithm, with each grid having a side length of fifty meters, to generate a gridded beam control parameter table. The position data of each grid node is extracted from the gridded beam control parameter table, and the field strength attenuation value of the electromagnetic wave emitted by each transmitter at each node is calculated based on the free space propagation loss formula, and the superimposed electromagnetic field distribution data is obtained through complex number operations. For the superimposed electromagnetic field distribution data, the signal power density value of each grid point is calculated by least squares fitting, and compared with the preset signal strength reference value to obtain a power difference matrix. According to the numerical values ​​in the power difference matrix, the directional angle and gain coefficient of each transmitter antenna are iteratively optimized using a genetic algorithm. The population size is set to 200 and the number of iterations is set to 50 to obtain the optimized beam steering parameters. By performing spatial interpolation calculations on the optimized beam steering parameters and combining the transmitter position coordinates to generate a continuous beamforming parameter surface, the beamforming parameters and power density distribution data of each transmitter in the segment are obtained.

[0013] Furthermore, calculate the train moving speed and direction according to the transmitter array distribution parameters, and determine whether the train is approaching the tunnel cutting area. If so, adjust the initial beamforming parameters to obtain the signal coverage range change trend, including: sampling the train position points with a fixed time window of ten seconds according to the transmitter array distribution parameters and the train real-time coordinate data, and calculating the train motion direction angle and instantaneous speed value through cubic polynomial fitting. Extract the line elevation data within five kilometers in front of the train from the terrain feature database, segment and fit the track longitudinal section elevation curve through the terrain feature classifier, and judge the tunnel cutting distribution data according to the slope mutation points. For the tunnel cutting distribution data, use a deep neural network analyzer to extract the features of the tunnel cross-sectional area and cutting depth data to generate a terrain influence prediction parameter sequence. According to the terrain influence prediction parameter sequence, adaptively adjust the antenna beam direction angle and transmit power in the beamforming parameters through the recurrent neural network calculation unit to obtain a beam adjustment instruction sequence. Use a spatial electromagnetic field simulation calculator to verify the adjusted beamforming parameters, and obtain the electromagnetic field distribution data by solving the Maxwell equations. According to the electromagnetic field distribution data and the train motion trajectory, use the electromagnetic wave propagation path calculator to perform dynamic programming on the transmitter power density distribution to obtain the signal coverage range change trend data.

[0014] Further, according to the change trend of signal coverage range, obtain the feedback data of coverage blind spots, determine the sections with insufficient signal power density in the tunnel cut area, and generate a coverage blind spot distribution map, including: According to the real-time running track of the train and the signal coverage range change curve, collect signal data packets once per second through an on-vehicle receiver, and extract the received signal strength value and signal quality index from the data packets to obtain a signal state sequence. For the signal state sequence, use a sliding median filter to eliminate noise from the signal strength value, and calculate the signal strength change curve through least squares fitting. According to the signal strength change curve, set a signal strength threshold, and use a threshold decision maker to mark the time points and corresponding geographical locations where the signal strength is lower than the threshold, generating signal weakening point position data. Extract the interruption occurrence time and recovery time from the communication interruption event log recorded by the on-vehicle receiver, and combine the train positioning data to generate a signal interruption section boundary point sequence. For the signal weakening point position data and the signal interruption section boundary point sequence, use a support vector machine to extract features from the data. The training samples include three feature dimensions: signal strength, interruption duration, and geographical location, obtaining a signal coverage blind spot feature vector. According to the signal coverage blind spot feature vector, use a grid density clustering algorithm to perform spatial clustering on the blind spot position points, and obtain the distribution data of the blind spot core area by calculating the position point density within the grid cells. Use the Kriging spatial interpolation algorithm to perform three-dimensional boundary fitting on the blind spot core area, and combine the terrain elevation data to generate a coverage blind spot boundary surface, obtaining a three-dimensional map of the coverage blind spot distribution.

[0015] Further, according to the coverage blind spot distribution map, adjust the beamforming parameters of the transmitter array in real time to obtain optimized signal power density distribution data, forming a mobile coverage corridor framework, including: According to the coverage blind spot distribution map, use a beam controller to read the beam direction angle, beam width value, and beam gain parameter of each transmitter antenna, and generate a beam control instruction sequence through a multi-beam synthesis calculator. For the beam control instruction sequence, use a phased array antenna matrix to perform vector calculations on the main lobe direction, side lobe level, and null direction of each transmitter, obtaining a beamforming parameter group. Extract the antenna gain coefficient of each transmitter from the beamforming parameter group, set the directivity of the antenna pattern according to the blind spot position density, and use a deep neural network calculation unit to dynamically optimize the transmitter power distribution to obtain a power adjustment command. For the power adjustment command, use an electromagnetic field calculation unit to superimpose the radiation fields of the transmitters, and use a Maxwell's equations solver to calculate the spatial electromagnetic field distribution to obtain power density data. According to the power density data, use a regular hexagonal grid divider to perform honeycomb dissection on the coverage area, with the grid side length changing according to the signal strength gradient, generating an adaptive grid dissection map. Through cubic spline interpolation of the signal strength data in the adaptive grid dissection map and combining the terrain undulation data, generate the spatial contour surface of the mobile coverage corridor.

[0016] Further, analyze the blind area distribution characteristics from the coverage blind area distribution map, determine the beamforming parameters of the transmitter array, obtain the adjusted parameter set, perform beamforming processing, update the signal power to obtain power density data. If the blind area distribution in the power density data exceeds the preset threshold, re-adjust the parameters in combination with the distribution map data to obtain an optimized beamforming scheme, calculate the signal power distribution of the transmitter array, and obtain the optimized signal power density distribution data, including: extracting the blind area position coordinate point set and area data from the coverage blind area distribution map, performing spatial clustering on the blind area position points through a density clustering calculator, and generating a blind area feature sequence based on the cluster center spacing and area. For the blind area feature sequence, use an antenna pattern calculator to generate beamforming control parameters, including four basic parameters: the beam main lobe direction angle, the beam half-power angle, the main lobe gain value, and the null direction angle. According to the beamforming control parameters, adjust the phase factor of each radiation element through the phased array antenna array, and combine the amplitude weighting processor to allocate the feeding current amplitude of each element to obtain the beam pattern data. Use an electromagnetic wave propagation loss calculator to perform spatial attenuation calculation on the beam pattern data, and obtain the initial power density distribution map by superimposing the electromagnetic field distributions generated by each transmitter. For the initial power density distribution map, use a threshold discriminator to mark the areas where the signal intensity is lower than the preset threshold, and generate the updated blind area distribution data. If the total blind area in the updated blind area distribution data exceeds the preset threshold, use a genetic algorithm to iteratively optimize the beamforming control parameters, and the population size is dynamically adjusted according to the blind area area. According to the optimized beamforming control parameters, reconstruct the radiation pattern of the transmitter array through a beam synthesis calculator to generate a new beamforming parameter sequence. Use an electromagnetic field numerical calculator to verify the optimized beamforming parameter sequence, and obtain the optimized signal power density distribution data by solving the Maxwell equations.

[0017] Furthermore, based on the real-time acquired mobile coverage corridor frame data, combined with the real-time acquired train position data and speed information, it is determined whether the train is about to enter the next tunnel cut area. If so, the signal power density of the corresponding transmitter is increased to obtain pre-adjusted coverage range expansion parameters, including: filtering the three state variables of train displacement, speed, and acceleration using a Kalman filter according to the mobile coverage corridor frame data and the real-time position coordinates of the train, and generating a predicted curve of the train movement trajectory through a time series predictor. For the predicted curve of the movement trajectory, the parameters of the front line are extracted using a terrain database, and the projected distance between the train and the tunnel entrance is calculated in combination with the line slope change rate and the curvature change rate. According to the line projected distance and the train speed value, the time interval for the train to reach the tunnel entrance is estimated using a motion prediction calculator to generate predicted time series data. If the predicted time series data is less than a preset threshold, a tunnel attenuation characteristic calculator is used to model the reflection loss and multipath effect of the tunnel wall to generate a path loss compensation curve. For the path loss compensation curve, a beam range optimizer is used to broaden the beam half-power angle, and a new beam envelope parameter is generated through an antenna pattern calculator. According to the beam envelope parameter, a power distribution calculator is used to dynamically adjust the output power of the transmitter, and a target signal strength value is generated in combination with a signal-to-noise ratio predictor. A sector boundary generator is used to reconstruct the beam coverage range, and coverage radius increment data and beam width adjustment values are generated through a spatial interpolation calculator to obtain coverage range expansion parameters.

[0018] Further, according to the pre-adjusted coverage range expansion parameter, extract the affected transmitter numbers and adjustment amplitudes from the transmitter array, calibrate the beamforming parameters, and generate a target mobile coverage corridor signal enhancement scheme, including: According to the coverage range expansion parameter, use a spatial position relationship calculator to traverse the transmitter array, and obtain the affected transmitter identification numbers and adjustment amplitude values through the calculation of the signal beam crossing area and the boundary distance. For the affected transmitter identification numbers, use a beam parameter extractor to read the three basic parameters of the beam direction angle, power gain value, and beam half-power angle of each transmitter, and generate a beam feature data sequence. Extract the beam overlap region data between adjacent transmitters from the beam feature data sequence, and iteratively optimize the beam direction and power gain through a recurrent neural network to obtain a beam parameter optimization matrix. According to the beam parameter optimization matrix, use a time synchronization controller to generate the execution timing of the transmitter beam adjustment, and determine the instruction execution order of each transmitter through a clock pulse distributor. For the instruction execution order, use a beamforming synchronizer to constrain the beam direction adjustment rate and power adjustment slope of each transmitter to generate a synchronous execution instruction set. Verify the synchronous execution instruction set through a beam adjustment validator, and generate a signal enhancement coverage map using an electromagnetic field superposition calculator. Use a boundary detector to extract the edge of the signal enhancement coverage map, and generate a mobile coverage corridor enhancement scheme in combination with the signal strength distribution data.

[0019] Further, obtain the time reference of the dynamic synchronization mechanism, calibrate the beamforming parameters to obtain a calibrated parameter set, extract the moving trajectory features from the target movement data, determine the direction change of the trajectory generation, adjust the calibrated parameter set, generate a beamforming configuration covering the corridor, calculate the signal enhancement gain, and if the signal enhancement gain is lower than the preset threshold, optimize the parameters of the enhancement scheme to obtain the signal enhancement result for the target movement covering the corridor, including: Pulse-synchronize the beam parameters of each transmitter using a timing synchronization controller according to the reference clock signal of the dynamic synchronization mechanism, and compensate for the beam direction angle deviation value through a phase difference calculator to obtain a calibrated parameter set. Extract the train position coordinate sequence from the target movement data, generate a trajectory curve equation using a least squares fitter, and mark the inflection point positions of the trajectory curve through a curvature calculator to obtain a motion feature sequence. For the motion feature sequence, use a neural network predictor to perform forward estimation of the train movement direction, and calculate the direction change rate parameter through time window sliding. According to the direction change rate parameter, use a beam pattern generator to adjust the beam pointing of each transmitter, and generate beamforming configuration data through a phased array antenna parameter calculator. For the beamforming configuration data, use an electromagnetic field strength calculator to perform spatial superposition of the radiation fields of the transmitters to obtain the signal enhancement gain value. If the signal enhancement gain value is lower than the preset threshold, use a genetic algorithm to jointly optimize the beam direction angle and power gain, and generate an optimized parameter set through iterative calculation. According to the optimized parameter set, use an electromagnetic field simulation calculator to reconstruct the beam coverage range of each transmitter to obtain the signal enhancement result data.

[0020] The present invention provides a signal coverage enhancement system for a terrestrial digital television broadcast transmitter, mainly including:

[0021] A train position acquisition module, configured to obtain train position data, collect the longitude and latitude information of the train in a high-speed moving scenario in real time, and match it with a preset operation trajectory database to determine the current position coordinate data of the train;

[0022] A beamforming parameter extraction module, configured to extract the transmitter array distribution parameters of the corresponding section from a pre-established signal coverage range database according to the train position coordinate data, and obtain the initial beamforming parameters of each transmitter in the corresponding section;

[0023] A region judgment and parameter adjustment module, configured to calculate the train movement speed and direction according to the transmitter array distribution parameters, judge whether the train is approaching a tunnel cut area, and if so, adjust the initial beamforming parameters to obtain the signal coverage range change trend;

[0024] A blind area analysis module, configured to obtain the coverage blind area feedback data according to the signal coverage range change trend, determine the section with insufficient signal power density in the tunnel cut area, and generate a coverage blind area distribution map;

[0025] A dynamic coverage optimization module, which is used to adjust the beamforming parameters of the transmitter array in real time according to the coverage blind area distribution map, obtain the optimized signal power density distribution data, and form a mobile coverage corridor framework;

[0026] A pre-adjustment module, which is used to judge whether the train is about to enter the next tunnel cut area according to the data of the mobile coverage corridor framework obtained in real time, combined with the train position data and speed information obtained in real time. If so, it increases the signal power density of the corresponding transmitter to obtain the pre-adjusted coverage range expansion parameters;

[0027] A signal enhancement scheme generation module, which is used to extract the affected transmitter numbers and adjustment amplitudes from the transmitter array according to the pre-adjusted coverage range expansion parameters, calibrate the beamforming parameters, and generate a target mobile coverage corridor signal enhancement scheme.

[0028] A digital TV, which includes: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above method and enhance the signal coverage of the terrestrial digital TV broadcast transmitter according to this method.

[0029] The technical solution provided by the embodiment of the present invention may include the following beneficial effects:

[0030] The present invention discloses a method and system for enhancing the signal coverage of a terrestrial digital TV broadcast transmitter. The method dynamically adjusts the beamforming parameters of the transmitter array by collecting train position data in real time and matching it with a preset trajectory, combined with a pre-established signal coverage range database. For special terrains such as tunnels and cuttings, the present invention can predict the change trend of the signal coverage range, generate a distribution map according to the coverage blind area feedback data, and optimize the signal power density distribution in real time. In addition, the present invention can also adjust the signal coverage parameters of the upcoming area in advance according to the train position and speed information, and perform secondary calibration using a dynamic synchronization mechanism, so as to form a targeted mobile coverage corridor signal enhancement scheme. This method effectively solves the communication coverage problem in high-speed mobile scenarios, especially under complex terrain conditions, significantly improves the signal quality and continuity, and provides a strong guarantee for the stable operation of the high-speed train communication system. Description of the Drawings

[0031] Figure 1 It is a flowchart of a method for enhancing the signal coverage of a terrestrial digital TV broadcast transmitter according to the present invention.

[0032] Figure 2 It is a structural diagram of a system for enhancing the signal coverage of a terrestrial digital TV broadcast transmitter according to the present invention. Detailed implementation manners

[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] As Figure 1 , a method for enhancing the signal coverage of a terrestrial digital television broadcast transmitter in this embodiment may specifically include:

[0035] S101. In a high-speed moving scenario, collect train position data in real time and determine its precise coordinates, obtain the current longitude and latitude information of the train through a satellite positioning device, and match it with a preset railway track database to generate position coordinate data of the train on the main railway line.

[0036] Use the satellite positioning device to obtain the longitude and latitude coordinates of the train in real time during high-speed operation to ensure the timeliness of the position data. According to the preset national railway coordinate comparison table, convert the world geographical coordinate system into a line coordinate based on the mileage of the main railway line, which is convenient for the matching calculation with the coverage parameters of the transmitter.

[0037] S1011. Extract the data of the reference positioning points set every 50 meters on the main railway line from the track basic database, use the cubic spline interpolation method to generate a continuous track for the reference points, obtain a smooth track reference point sequence, and optimize the smoothness of the track curve through Bezier curve fitting.

[0038] The interval of the reference points on the main railway line is set to 50 meters, and each reference point contains longitude, latitude and mileage information. By fitting these points with the cubic spline interpolation method, a continuous and smooth track sequence can be generated to ensure the track accuracy. The Bezier curve further optimizes the smoothness of the curve, making the subsequent matching calculation more reliable. Taking the Beijing-Shanghai High-Speed Railway as an example, a total of 26,360 reference points are set within the 1,318-kilometer range of the whole line. After interpolation, a track point sequence with a smaller interval can be generated to meet the high-precision positioning requirements.

[0039] S1012. Smooth the real-time collected train positioning data and correct the deviation. If the lateral deviation between the positioning points and the reference points in the track curve sequence exceeds 10 meters, use the particle filter algorithm to perform state estimation based on the historical track data to generate a corrected positioning sequence, and calculate the similarity score with the reference track through the dynamic time warping algorithm to determine the actual position coordinates of the train.

[0040] A sliding window with a fixed length of 200 meters is used for median filtering of real-time positioning data to remove the influence of abnormal points. Taking a train with a speed of 350 km / h as an example, sampling is performed once per second, and there are about 40 sampling points in the window to ensure data smoothness. If the deviation between the positioning point and the reference point exceeds ten meters, correction is performed through the particle filter algorithm combined with historical data. Then, the dynamic time warping algorithm calculates the similarity between the measured trajectory and the reference trajectory, selects the highest scoring point as the candidate position, and performs weighted averaging on the five positioning points before and after, and further calibrates in combination with the running direction vector to obtain the accurate coordinates of the train on the railway main line.

[0041] Whether it is a straight section or a curved section, such as the complex line from Changzhou North Station to Nanjing South Station, the problem of trajectory deviation can be effectively addressed through the direction vector and weighted calculation, laying a foundation for signal coverage optimization. It can be understood that the sliding window length and interpolation interval can be adjusted according to the actual scenario to balance accuracy and computational resource occupancy.

[0042] S1013. According to the corrected positioning sequence and the train running direction vector, combined with the line parameters in the national railway infrastructure database, verify the accuracy of the train position coordinates and generate real-time trajectory data, providing a dynamic basis for the adjustment of the transmitter signal coverage.

[0043] The train running direction is calculated from the coordinate differences of adjacent positioning points. Combining the mileage and superelevation parameters in the infrastructure database can further verify the reliability of the positioning results. Taking the section between Xuzhou East Station and Bengbu South Station as an example, after the above processing of the trajectory data of the 203-kilometer line in this section, a positioning accuracy of 5 meters can be achieved, providing real-time support for the dynamic adjustment of signal enhancement.

[0044] Through the above acquisition and processing of position data, accurate input data can be provided for the beam optimization of the transmitter array, ensuring a high degree of synchronization between signal coverage and train movement, and ultimately improving the stability of digital TV signals in the high-speed rail scenario. The specific algorithm implementation details can be set by technical personnel according to actual needs and will not be elaborated.

[0045] S102. Extract the distribution parameters of the transmitter array for the corresponding section from the preset signal coverage range database according to the real-time position coordinate data of the train and generate an initial beamforming configuration, obtain the position, height, and tilt angle information of each transmitter, and then calculate the reference value of the signal power density and the optimized beam parameter distribution.

[0046] Using the train position coordinate data, access the signal coverage range database through the geographic information query module to quickly extract the distribution parameters of the transmitter array within the current section of the train. These parameters include key information such as the longitude and latitude coordinates of the transmitters, the antenna installation height, and the antenna tilt angle, providing a basic basis for subsequent beam optimization. Taking the section from Wuhan to Changsha on the Beijing-Guangzhou High-Speed Railway as an example, the transmitter spacing in this section is about 2000 meters, the antenna height ranges from 18 to 25 meters, and the antenna tilt angle is usually set between 4 and 8 degrees to ensure a match with the terrain along the high-speed railway.

[0047] S1021. Obtain the transmitter distribution parameter table from the signal coverage database and perform grid division. Based on the regular hexagon honeycomb grid algorithm, divide the transmitter coverage area into hexagonal cells with a side length of 50 meters, and generate a beam control parameter table containing the grid node positions to provide a spatial framework for electromagnetic field distribution calculation.

[0048] According to the extracted transmitter distribution parameter table, use the regular hexagon honeycomb grid algorithm to perform spatial division on the coverage area. The side length of each hexagonal cell is set to 50 meters, and the distance between adjacent grid points is fixed to avoid deformation problems of traditional rectangular grids in curved sections. Taking the section from Wuhan to Changsha as an example, within a range of 1000 meters on both sides of the railway, about 750 grid nodes are formed, and the node density gradually decreases with the distance from the transmitter increasing. This grid method can evenly cover complex terrains and provide an accurate spatial reference for subsequent signal calculations. The beam control parameter table records the coordinate data of each grid node to ensure high traceability in the calculation process.

[0049] S1022. Calculate the electromagnetic field distribution and optimize the signal power density for the grid node position data. Use the free space propagation loss formula to calculate the field strength attenuation of each transmitter at the grid nodes, generate electromagnetic field distribution data through complex superposition, then use the least squares method to fit to obtain the signal power density value of each grid point, and iteratively optimize the beam parameters based on the genetic algorithm.

[0050] Extract the node positions from the grid beam control parameter table, and calculate the attenuation value of electromagnetic waves from the transmitter to each node based on the free space propagation loss formula. For example, in the section from Guangzhou South to Shenzhen North, the terrain is flat with few obstacles, and the signal attenuation at a distance of 1000 meters from the transmitter is approximately 85 dB. Superimpose the electromagnetic field contributions of multiple transmitters through complex number operations to generate the field strength distribution map of the coverage area. Then, use the least squares method to fit the field strength data, calculate the signal power density value of each grid point, and form a difference matrix by comparing it with the preset reference value. To optimize the beam parameters, introduce the genetic algorithm, set the population size to 200, the number of iterations to 50, the gene coding length to 32 bits, the crossover probability to 0.8, and the mutation probability to 0.05. By iteratively adjusting the antenna azimuth angle and gain coefficient, obtain the optimal beam configuration. Taking the section from Hefei South to Nanjing South as an example, the reference signal strength is set to -65 dB, and after optimization, the signal strength of approximately 85% of the grid points reaches or exceeds this value.

[0051] S1023. Generate a continuous beamforming parameter surface according to the optimized beam parameters and improve the signal coverage effect. Use the cubic spline interpolation method to perform spatial smoothing processing on the beam control parameters in combination with the transmitter position coordinates, and generate the beamforming parameters and power density distribution data of each transmitter in the section to ensure smooth transition of signal coverage and blind area compensation.

[0052] Further process the beam parameters optimized by the genetic algorithm, and generate a continuous beamforming parameter surface through the cubic spline interpolation method. Taking the section from Nanchang West to Hangzhou East as an example, the terrain in this section has large undulations, the optimization range of the antenna azimuth angle is set to plus or minus 30 degrees, and the adjustment range of the gain coefficient is 6 to 9 dB. After interpolation calculation, the beam parameter surface can smoothly transition the coverage areas between different transmitters, avoiding signal mutations. In the curve section, increase the adjustment range of the azimuth angle and dynamically change the gain coefficient to effectively compensate for the attenuation caused by the terrain and ensure the stability of signal coverage. The optimization results show that fine-tuning the antenna tilt angle can significantly reduce the coverage dead spots, and the dynamic adjustment of the gain coefficient enhances the penetration ability of the signal in complex environments.

[0053] Through the above steps, it is possible to dynamically generate the beamforming parameters and power density distribution data of the transmitter array according to the train position. This method fully utilizes the advantages of grid division and algorithm optimization to ensure that the signal coverage highly matches the actual requirements of the high-speed rail line. It can be understood that the grid side length and algorithm parameters can be adjusted according to specific scenarios to meet the coverage requirements under different terrain conditions.

[0054] The process of obtaining the distribution parameters of the transmitter and optimizing the beam parameters fully considers the topographical features along the high - speed railway. For example, in flat terrain, free - space loss is the main influencing factor, while in areas with large undulations, the dynamic adjustment of the gain coefficient plays a key role. This adaptive adjustment mechanism provides a reliable guarantee for the construction of the coverage corridor, and the specific optimization details can be set by technicians according to the actual application scenario.

[0055] S103. Calculate the moving speed and direction of the train based on the distribution parameters of the transmitter array and the real - time position data of the train, and predict whether it is approaching the tunnel cut - and - cover area. If it is determined to be approaching, dynamically adjust the beamforming parameters, and generate the change trend data of the signal coverage range through terrain analysis and electromagnetic field simulation.

[0056] Utilize the distribution parameters of the transmitter array combined with the train coordinate information to analyze the train's motion state in real - time and predict whether its running path will enter a special terrain area. Through comprehensive calculations of speed, direction, and terrain features, ensure that the beam parameters can adapt to the signal coverage requirements in advance. Taking the high - speed railway from Chengdu to Chongqing as an example, when the train speed reaches 350 km / h, it can quickly identify its motion trend and provide a basis for adjusting the transmitter.

[0057] S1031. Sample the train position using a fixed - time window and calculate the motion parameters. Generate the instantaneous speed and direction angle of the train by fitting the position data with a cubic polynomial. At the same time, extract the elevation data of the front - line from the terrain feature database to provide basic support for subsequent terrain judgment.

[0058] Set a 10 - second time window, sample the train position once per second, and collect a total of 10 data points. Fit these points with a cubic polynomial to calculate the instantaneous speed and direction angle of the train. For example, on the Chengdu - Chongqing line, the direction angle changes from 95 degrees to 110 degrees, indicating that the train enters a left - turning section. At the same time, obtain the elevation data within 5 km in front of the train from the terrain feature database, including information such as slope and tunnel location. The elevation data is stored at 20 - meter intervals, which can reflect the subtle changes in the longitudinal section of the line and provide accurate input for terrain classification.

[0059] S1032. Analyze the elevation data through a terrain feature classifier and extract the tunnel cut - and - cover features. Use a deep neural network to extract features from the terrain parameters to generate a prediction sequence, and then adaptively adjust the beam parameters in combination with a recurrent neural network to ensure that the signal coverage adapts to the special terrain requirements.

[0060] Perform piecewise fitting on elevation data, identify slope mutation points through a terrain feature classifier, and generate tunnel cut distribution data. Taking the section from Humen to Shenzhen North of the Guangzhou-Shenzhen-Hong Kong High-Speed Railway as an example, this section contains 3 tunnels and 2 cuts. Among them, the Shiziyang Tunnel is 4,500 meters long and has a cross-sectional area of about 90 square meters. The deep neural network adopts an 8-layer convolutional structure, inputs parameters such as the tunnel cross-sectional area, burial depth, and slope, and extracts terrain influence features. Taking the Oujiang Tunnel on the Wenzhou-Fuzhou Railway as an example, the maximum burial depth is 180 meters, and the slope changes from 2.5% to 3.2%. The network predicts that the signal attenuation at the tunnel entrance reaches 12 dB. Subsequently, the recurrent neural network adjusts the beam parameters according to the prediction sequence. Before the Mawei Tunnel on the Shanghai-Kunming High-Speed Railway, the transmitter beam direction is deflected 15 degrees in advance, and the power is increased by 3 dB to compensate for the attenuation caused by the waveguide effect. This adjustment takes into account the antenna steering speed limit, which does not exceed 3 degrees per second, to ensure mechanical feasibility.

[0061] S1033. Verify the adjusted beam parameters using spatial electromagnetic field simulation and generate coverage trend data. Calculate the electromagnetic field distribution by solving Maxwell's equations, and then optimize the power density distribution based on dynamic programming to ensure the continuity and stability of the signal coverage range.

[0062] Use a spatial electromagnetic field simulation calculator to verify the adjusted beam parameters and solve Maxwell's equations to simulate the electromagnetic wave propagation characteristics. Taking the Qinling Tunnel Group on the Baoji-Lanzhou High-Speed Railway as an example, the simulation shows that the electromagnetic waves in the tunnel propagate in a waveguide mode, and the wall reflection causes periodic fluctuations in the signal intensity. In the Ganxi Tunnel (3,200 meters long), the overlapping area of adjacent transmitter beams expands from 150 meters to 220 meters. Subsequently, through a dynamic programming combined with the train trajectory by an electromagnetic wave propagation path calculator, optimize the power density distribution to ensure seamless connection of the coverage range. The simulation process takes into account the effects of multipath and terrain occlusion, improving the prediction accuracy.

[0063] The above steps achieve dynamic optimization of the beam parameters through real-time analysis of train movement and terrain. Whether it is a flat section or a complex tunnel area, such as the multi-tunnel terrain in the Guizhou section, the system can adjust the transmitter configuration in advance to ensure the stability of signal coverage. It can be understood that the time window length and the number of neural network layers can be adjusted according to actual needs to adapt to the operating conditions of different lines.

[0064] Analysis of historical location data further enhances the prediction ability. For example, when the train approaches a curve or a tunnel, the system can use the change in the direction angle to plan the beam adjustment path in advance. This forward-looking design significantly reduces the risk of signal interruption and lays a solid foundation for the construction of the coverage corridor. The specific adjustment amplitude can be set by technicians according to the terrain complexity.

[0065] S104. Obtain the coverage blind spot data fed back by the in-vehicle receiver according to the changing trend of the signal coverage range, analyze the signal weakening and interruption conditions, and generate a coverage blind spot distribution map.

[0066] Utilize the train operation trajectory and the signal coverage trend to receive the signal data uploaded by the in-vehicle device in real time, and identify the sections with insufficient signal power in areas such as tunnel cuttings. Taking the section from Zhengzhou South to Wuhan on the Beijing-Guangzhou High-Speed Railway as an example, the in-vehicle receiver collects the signal status every second, and the data includes indicators such as strength, bit error rate, and signal-to-noise ratio. The system dynamically draws the blind spot distribution through this information to improve the pertinence of coverage adjustment.

[0067] S1041. Extract the signal status sequence from the in-vehicle receiver data packet and perform noise processing. Smooth the signal strength value through a sliding median filter, and then use the least squares method to fit and generate a change curve, marking the positions of weakening points below the preset threshold to lay the foundation for blind spot positioning.

[0068] Receive the data packets uploaded by the in-vehicle device every second, and extract the received signal strength and quality indicators to form a time series. Taking the section from Hefei to Nanjing as an example, the signal strength fluctuates between -40 and -95 decibels, and the signal-to-noise ratio remains at 15 to 35 decibels. Use a 21-point sliding median filter to process the sequence to remove noise interference. The filtered curve shows periodic characteristics related to the distance from the base station. Then, calculate the changing trend of the signal strength through least squares fitting, and the inflection points often correspond to terrain mutations. Set the threshold to -85 decibels, and the positions below this value are marked as signal weakening points. Combine with the train positioning data to generate a weakening point sequence, providing high-precision input for analysis.

[0069] S1042. Analyze the communication interruption events and extract the boundary point sequence. Combine with the signal weakening points and use the grid density clustering algorithm to calculate the core area of the blind spot, and then generate a three-dimensional blind spot boundary surface through Kriging interpolation to comprehensively reflect the distribution characteristics of the coverage insufficient area.

[0070] Extract the occurrence and recovery times of communication interruptions from the in-vehicle receiver log, and combine with the positioning data to generate a sequence of boundary points for the interrupted section. Taking the Hexi Corridor section of the Lanxin High-Speed Railway as an example, there are about 12 single-day interruption events, with a duration of 0.5 to 8 seconds, mostly concentrated in the deep cuttings in the gobi area. Subsequently, combine with the weakening point data and use the grid density clustering algorithm to analyze the spatial distribution. The side length of the grid cell is set to 100 meters, and the density threshold is 5 points. Taking the section from Bengbu to Xuzhou on the Beijing-Shanghai High-Speed Railway as an example, 8 blind spots are identified by clustering, and the largest blind spot is located in the cutting section near Suzhou East. Further use the Kriging interpolation algorithm to perform three-dimensional fitting on the core area of the blind spot, generate a boundary surface with a resolution of 10 meters, and after integrating the terrain elevation data, visually display the law of the blind spot changing with the terrain.

[0071] By smoothing the signal state and spatiotemporally correlating interruption events, the characteristics of coverage blind spots are accurately captured. The actual measurement in the Guizhou section of the Shanghai-Kunming High-Speed Railway shows that the tunnel and cutting areas account for 85% of the weakening points, and the distance of a single weakening point can reach 800 meters. This analysis method effectively reveals the causal relationship between signal attenuation and terrain, providing a reliable basis for transmitter adjustment.

[0072] The support vector machine further enhances the accuracy of blind spot feature extraction. Taking the Changsha-Guangzhou section of the Wuhan-Guangzhou High-Speed Railway as an example, a model is trained using 6,000 groups of historical samples, which include signal strength, interruption duration, and location information, and the Gaussian kernel function is used for calculation. When the signal strength is below -90 dB and the interruption exceeds 2 seconds, the blind spot determination probability reaches 92%. This machine learning method improves the robustness of blind spot identification, especially suitable for dynamic scenarios under complex terrains.

[0073] Whether in flat sections or the multi-tunnel environment of the Guizhou section, the system can accurately locate the signal-deficient areas. The grid side length and threshold can be adjusted according to the line characteristics to ensure the adaptability of the algorithm, and specific parameter optimization can be set by technicians according to actual needs.

[0074] S105. Adjust the beamforming parameters of the transmitter array in real time according to the coverage blind spot distribution map and optimize the signal power density distribution. Through multi-beam synthesis, phased array adjustment, and electromagnetic field calculation, a stable mobile coverage corridor framework is formed.

[0075] Analyze the signal coverage-deficient areas using the blind spot distribution map and dynamically adjust the direction and power parameters of the transmitter antenna. Taking the Xuzhou-Bengbu section of the Beijing-Shanghai High-Speed Railway as an example, the system accurately identifies the blind spot positions, ensuring that the beam coverage highly matches the train operation requirements and significantly improving signal stability.

[0076] S1051. Extract beam parameters from the coverage blind spot distribution map and generate a control instruction sequence. Use a multi-beam synthesis calculator to process the antenna direction angle and width values, and then adjust the main lobe and side lobe characteristics through the phased array antenna matrix to form an optimized beamforming parameter group.

[0077] Read the transmitter antenna beam direction angle and width values in the blind spot distribution map. Taking the south section of Changsha South-Guangzhou South of the Wuhan-Guangzhou High-Speed Railway as an example, the antenna array contains 32 radiation units, with an adjustable range of plus or minus 60 degrees in the horizontal direction and plus or minus 30 degrees in the vertical direction, and the beam width varies between 15 and 45 degrees. The multi-beam synthesis calculator generates a control instruction sequence based on these parameters to ensure that the beam points to the core area of the blind spot. Then, the phased array antenna matrix adjusts the main lobe direction and side lobe level through vector operations. The maximum main lobe gain reaches 15 dB, and the side lobe to main lobe level ratio remains above 20 dB. The null direction is aligned with the interference source. This adjustment method controls the width in the beam overlap area to be 300 to 500 meters, ensuring smooth coverage transition.

[0078] S1052. Optimize the antenna gain coefficient and calculate the radiation field distribution. Use a deep neural network to dynamically adjust the gain parameters to generate power commands. Then use the Maxwell equations solver to superimpose and calculate the spatial electromagnetic field to obtain refined power density data.

[0079] The gain coefficient is extracted from the beamforming parameter group and optimized using a deep neural network calculation unit. Taking the Shanghai-Kunming High-speed Railway from City A to City B as an example, the network adopts an 8-layer fully connected structure, inputs blind spot coordinates, area, terrain slope and other features, and adjusts the power distribution through back propagation iteration. The power variation range of a single transmitter is plus or minus 6 decibels, and the power difference between adjacent transmitters does not exceed 10 decibels. The optimized power command is input into the electromagnetic field calculation unit to superimpose the radiation fields of each transmitter. The Maxwell equations are solved using the finite difference time domain method, with a grid size of 1 / 10 of the wavelength, which meets the Courant condition. In the Urumqi to Hami section of the Lanzhou-Xinjiang High-speed Railway, calculations show that the path loss index is about 2.8, the signal strength attenuates with distance, and the power density data reflects the effect of filling the blind spot.

[0080] The coverage corridor framework is improved through adaptive meshing and interpolation technology. A regular hexagonal mesher is used to divide the coverage area into a honeycomb shape, and the side length is dynamically adjusted according to the signal strength gradient. Along the Chengdu-Chongqing high-speed railway, the side length is set to 50 meters when the gradient is greater than 3 decibels per 100 meters, and 200 meters when it is less than 1 decibel per 100 meters. Subsequently, cubic spline interpolation is used in combination with terrain data to generate the spatial contour surface of the mobile coverage corridor. Taking the Shenzhen North to Hong Kong section of the Guangzhou-Shenzhen-Hong Kong High-Speed ​​Railway as an example, the surface takes into account the diffraction effect of urban buildings, presents a dumbbell-shaped vertical section, and accurately depicts the signal distribution characteristics.

[0081] S1053. Iteratively optimize beam parameters according to blind spot characteristics and verify coverage effect. Extract blind spot distribution characteristics through density clustering to generate control parameters. Then use genetic algorithm to adjust beam direction and power. If the blind spot area exceeds the standard, repeatedly optimize to generate stable power density distribution data.

[0082] Analyze the blind area distribution map through a density clustering calculator, and extract the position coordinates and area data. Taking the section from Bengbu to Nanjing South of the Beijing-Shanghai High-Speed Railway as an example, the clustering radius is set to 300 meters, the minimum number of points is 5, 23 blind areas are identified, and the maximum blind area reaches 0.8 square kilometers. Based on these characteristics, the antenna pattern calculator generates the main lobe direction and half-power angle parameters, and the half-power angle increases to 35 degrees in the tunnel area of the Guizhou section to cope with the multipath effect. After the phased array antenna adjusts the phase factor and feeding amplitude, the electromagnetic wave propagation loss calculator generates the initial power density distribution map. If the threshold decision detector detects that the area of the deep blind area where the signal is lower than -95 dB exceeds 10%, the genetic algorithm is used to optimize the parameters, the population size is dynamically adjusted to 400, the crossover probability is 0.8, and the mutation probability is 0.05. After optimization, the area of the deep blind area of the section from Jiangjin to Chongqing of the Chengdu-Chongqing High-Speed Railway is reduced from 3.2% to 1.8%, verifying the effectiveness of the method.

[0083] Whether it is the Gobi flat area or the urban dense area, such as the severe cold section of the Harbin-Dalian High-Speed Railway, the system can compensate for the environmental impact through power and direction adjustment, and the specific optimization strategy can be flexibly set according to the line characteristics.

[0084] S106. Judge whether it is approaching the next tunnel cut area according to the real-time obtained mobile coverage corridor framework data and train position information, and adjust the transmitter signal power density in advance to generate coverage range expansion parameters.

[0085] Combined with the mobile coverage corridor framework data and the real-time coordinates of the train, use the prediction model to evaluate the train operation trend. Taking the section from Zhengzhou to Wuhan of the Beijing-Guangzhou High-Speed Railway as an example, the system optimizes the transmitter parameters in advance through high-precision trajectory prediction and terrain matching, avoids signal interruption in the tunnel area, and improves the passenger experience.

[0086] S1061. Use the Kalman filter to process the train motion data and predict the trajectory curve, extract the forward line parameters from the terrain database to calculate the projection distance from the tunnel entrance, and generate the arrival prediction value through time series analysis.

[0087] Obtain the train displacement, speed and acceleration data with a sampling period of 0.1 second, perform state estimation through the Kalman filter, set the observation noise variance to 0.1, and the process noise variance to 0.01. After filtering through 20 sampling points, a smooth trajectory curve is generated, and the prediction error is controlled within 0.8 meters. Subsequently, extract the forward line slope and curvature information from the terrain database. Taking the Guizhou section of the Shanghai-Kunming High-Speed Railway as an example, the slope change rate fluctuates between plus and minus 30‰, and the curvature radius is 2000 to 6000 meters. The system calculates the projection distance between the train and the tunnel entrance, and the error is less than 1.5 seconds in front of the Zunyi tunnel group. According to the distance and speed, the motion prediction calculator generates the arrival time series. If it is less than the preset threshold, the subsequent adjustment is triggered. This method ensures the real-time and accuracy of the prediction, and strives for an adjustment window for beam optimization.

[0088] S1062. Model the attenuation characteristics of the tunnel entrance prediction value and optimize the beam parameters. Calculate the path loss using the tunnel wall reflection and multipath effects. Generate new envelope parameters by broadening the beam half-power angle to improve the adaptability of the coverage range.

[0089] According to the prediction time, use the tunnel attenuation characteristic calculator to analyze the signal propagation. Taking the Majitang Tunnel on the Wuhan-Guangzhou High-Speed Railway as an example, the attenuation in the middle of the 3200-meter-long tunnel reaches 15 dB, the reflection loss is about 0.8 dB each time, and the multipath effect causes the intensity to fluctuate periodically. After the calculator models and generates the loss compensation curve, the beam range optimizer broadens the half-power angle from 25 degrees to 40 degrees. The main lobe gain decreases by 2.5 dB, but the coverage range expands by 35%. The antenna pattern calculator then generates new beam envelope parameters. In the Hexi Corridor section of the Lanxin High-Speed Railway, the signal fluctuation at 1500 meters after optimization is reduced to 3 dB. This adjustment effectively deals with the tunnel environment and improves the signal penetration ability.

[0090] Improve the coverage extension parameters through power distribution and sector reconstruction. Taking the section from Bengbu to Nanjing on the Beijing-Shanghai High-Speed Railway as an example, when the distance from the tunnel entrance is 1000 meters, the transmitter power linearly increases at a slope of 0.01 dB per meter, and the intensity at the entrance increases by 12 dB, compensating for 80% of the loss. The power distribution calculator combines with the signal-to-noise ratio predictor to generate the target signal intensity value, and the sector boundary generator reconstructs the coverage area through adaptive grid division. In the section from Jiangjin to Chongqing West on the Chengdu-Chongqing High-Speed Railway, the grid side length changes with the signal gradient, decreasing to 50 meters in the severe area and increasing to 200 meters in the flat area. The coverage radius expands from 200 meters to 350 meters, and the beam width adjustment value fluctuates within plus or minus 15 degrees, and the coverage area increases by about 45%.

[0091] The collaborative optimization of the above steps is particularly prominent in the section from Shenzhen North to Hong Kong West Kowloon on the Guangzhou-Shenzhen-Hong Kong High-Speed Railway. The tunnel ratio in this section is 93%. By jointly adjusting the prediction threshold, power slope, and beam width, the signal uniformity in the tunnel is significantly improved, and the intensity standard deviation is reduced to 4.2 dB. This forward-looking adjustment ensures the signal continuity under complex terrain, and the specific parameters can be flexibly configured according to the line characteristics.

[0092] S107. Identify the affected transmitters according to the pre-adjusted coverage extension parameters and perform secondary calibration of the beam parameters. Generate the target mobile coverage corridor signal enhancement scheme through the dynamic synchronization mechanism and the optimization algorithm.

[0093] Use the coverage extension parameters to traverse the transmitter array to determine the transmitters that need to be adjusted and their amplitudes. Taking the section from City A to City B on the Beijing-Guangzhou High-Speed Railway as an example, the system accurately calculates the beam crossing area, quickly locates the affected transmitters, and optimizes their parameters to improve the signal enhancement effect.

[0094] S1071. Calculate the affected transmitters and their adjustment amplitudes according to the coverage extension parameters, extract the beam characteristics, optimize the beam direction and power gain using a recurrent neural network, and generate an execution instruction sequence through time synchronization to ensure the consistency of the adjustment process.

[0095] Use a spatial position relationship calculator to analyze the transmitter array, calculate the beam crossing area and the boundary distance, and identify the affected transmitters and the adjustment amplitudes. Taking the section from Wuhan to Changsha as an example, the crossing area accounts for 25% to 35% of the single-beam coverage, the boundary distance is between 300 and 500 meters, and usually 2 to 3 adjacent transmitters are involved. The beam parameter extractor reads the direction angles, power gains, and half-power angles of these transmitters to generate a feature sequence. In the Guizhou section of the Shanghai-Kunming High-Speed Railway, the adjustable range of the direction angle is plus or minus 60 degrees, the gain is between 12 and 18 dB, the half-power angle is 15 to 45 degrees, and the sequence shows a negative correlation between the direction and the gain. The recurrent neural network optimizes the parameters with an 8-layer fully connected structure, sets the number of hidden layer neurons to 64, controls the direction angle difference within 30 degrees, and the gain difference does not exceed 6 dB. The time synchronization controller generates the adjustment timing, with a unit of 5 ms in the Guangzhou-Shenzhen-Hong Kong High-Speed Railway section, the interval is not less than 15 ms, and the total duration is controlled within 100 ms. The beamforming synchronizer limits the direction adjustment rate to 3 degrees per second and the power slope does not exceed 0.5 dB per millisecond, generating a synchronization instruction set to ensure the smoothness of the adjustment.

[0096] S1072. Adjust the beam pointing and optimize the configuration through calibration and trajectory prediction, use the Kalman filter and neural network to predict the train direction change, combine the genetic algorithm to iteratively optimize the parameters, generate an enhanced coverage scheme, and improve the signal strength and boundary clarity.

[0097] Taking a 10 MHz rubidium atomic clock as the reference, compensate for the beam parameter deviation through a phase difference calculator, with a sampling rate of 1 MHz, the synchronization accuracy is better than 50 ns, the direction angle calibration reaches 0.1 degree, and a calibration parameter set is generated. Extract the coordinate sequence from the train position data, sample at 10 Hz, and use the least squares method to fit a cubic polynomial to generate a trajectory curve. Taking the section from Zunyi to the northern part of Guiyang as an example, the curvature radius is 2000 to 6000 meters, 12 inflection points are marked, and the maximum change rate is 0.15 per kilometer. The neural network predictor has an 8-layer structure, inputs the position, speed, and acceleration, with a time window of 2 seconds, predicts 3 seconds in advance at a speed of 350 km / h, and the error is less than 0.5 degree. The beam direction pattern generator adjusts the pointing according to the change rate, and the phased array antenna is controlled by 32 units, with a horizontal adjustment of plus or minus 60 degrees and a vertical adjustment of plus or minus 30 degrees, and the gain range is 12 dB. If the gain is lower than the 15 dB threshold, the genetic algorithm iterates 50 generations with a population size of 200, a crossover probability of 0.8, and a mutation probability of 0.05, and the optimization accuracy reaches 0.2 degree and 0.1 dB, and finally an enhanced scheme is generated.

[0098] Verify the effectiveness of the solution through electromagnetic field simulation. In the Jiangjin to Chongqing West Section of the Chengyu High-Speed Railway, the three-dimensional finite difference method is used for calculation, with the grid size being 1 / 10 of the wavelength. Considering the terrain undulation, the signal enhancement coverage map shows that the intensity is increased by 12 dB. The boundary detector extracts the edge with an adaptive threshold. In the Hexi Corridor Section of the Lanxin High-Speed Railway, the ambiguity is reduced from 150 m to 50 m, and the coverage corridor profile is clearer, ensuring the signal distribution uniformity and boundary sharpness.

[0099] The above steps perform excellently in complex environments. Taking the cold region of the Harbin-Dalian High-Speed Railway as an example, the simulation considers the influence of snow accumulation, and the attenuation increases by 4 to 7 dB. The loss is compensated by reconstructing the coverage range, maintaining the boundary clarity and intensity improvement. The specific timing and optimization parameters can be adjusted according to the line characteristics, enhancing the flexibility of the solution to adapt to different scenario requirements.

[0100] Such as Figure 2 , the present invention provides a ground digital television broadcast transmitter signal coverage enhancement system, mainly including:

[0101] A train position acquisition module, which is used to obtain train position data, collect the longitude and latitude information of the train in the high-speed moving scenario in real time, and match it with the preset operation trajectory database to determine the coordinate data of the current position of the train;

[0102] A beamforming parameter extraction module, which is used to extract the transmitter array distribution parameters of the corresponding section from the pre-established signal coverage range database according to the train position coordinate data, and obtain the initial beamforming parameters of each transmitter in the corresponding section;

[0103] A region judgment and parameter adjustment module, which is used to calculate the train moving speed and direction according to the transmitter array distribution parameters, judge whether the train is approaching the tunnel cut area. If so, adjust the initial beamforming parameters to obtain the signal coverage range change trend;

[0104] A blind area analysis module, which is used to obtain the coverage blind area feedback data according to the signal coverage range change trend, determine the signal power density insufficient section in the tunnel cut area, and generate a coverage blind area distribution map;

[0105] A dynamic coverage optimization module, which is used to adjust the beamforming parameters of the transmitter array in real time according to the coverage blind area distribution map, obtain the optimized signal power density distribution data, and form a mobile coverage corridor framework;

[0106] A pre-adjustment module, which is used to judge whether the train is about to enter the next tunnel cut area according to the real-time obtained mobile coverage corridor framework data, combined with the real-time obtained train position data and speed information. If so, increase the signal power density of the corresponding transmitter to obtain the pre-adjusted coverage range expansion parameters;

[0107] A signal enhancement scheme generation module, which is configured to extract the affected transmitter numbers and adjustment amplitudes from the transmitter array according to the pre-adjusted coverage extension parameters, calibrate the beamforming parameters, and generate a target mobile coverage corridor signal enhancement scheme.

[0108] A digital TV, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above method and perform signal coverage enhancement for a terrestrial digital TV broadcast transmitter according to the method.

[0109] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A method for enhancing the signal coverage of a terrestrial digital television broadcast transmitter, characterized in that The method includes: Obtaining train position data, collecting the longitude and latitude information of the train in a high-speed moving scenario in real time, and matching it with a preset operation trajectory database to determine the current position coordinate data of the train; According to the train position coordinate data, extracting the transmitter array distribution parameters of the corresponding section from a pre-established signal coverage range database to obtain the initial beamforming parameters of each transmitter in the corresponding section; Calculating the moving speed and direction of the train according to the transmitter array distribution parameters, and judging whether the train is approaching a tunnel cut area. If so, adjusting the initial beamforming parameters to obtain the signal coverage range change trend; According to the signal coverage range change trend, obtaining the coverage blind area feedback data, determining the signal power density insufficient section in the tunnel cut area, and generating a coverage blind area distribution map; According to the coverage blind area distribution map, adjusting the beamforming parameters executed on the transmitter array in real time to obtain the optimized signal power density distribution data and form a mobile coverage corridor framework; According to the mobile coverage corridor framework data obtained in real time, combined with the train position data and speed information obtained in real time, judging whether the train is about to enter the next tunnel cut area. If so, increasing the signal power density of the corresponding transmitter to obtain the pre-adjusted coverage range expansion parameters; According to the pre-adjusted coverage range expansion parameters, extracting the affected transmitter numbers and adjustment amplitudes from the transmitter array, calibrating the beamforming parameters, and generating a target mobile coverage corridor signal enhancement scheme.

2. The method according to claim 1, characterized in that, The obtaining train position data, collecting the longitude and latitude information of the train in a high-speed moving scenario in real time, and matching it with a preset operation trajectory database to determine the current position coordinate data of the train includes: Obtaining the real-time longitude and latitude coordinate data of the high-speed train, and using the railway coordinate comparison table preset in the coordinate mapping converter to convert the longitude and latitude coordinate data into line coordinate data based on the railway main line mileage; Obtaining the reference positioning point data set at fixed intervals along the railway main line from the track basic database, and performing interpolation calculation on the reference positioning point data using a cubic spline function to obtain continuous trajectory reference point data; For the line coordinate data, performing median filtering through a fixed-length sliding window and using Bezier curve fitting to obtain a smooth trajectory curve sequence; If the lateral deviation value between the positioning points in the smooth trajectory curve sequence and the continuous trajectory reference points is greater than a preset threshold, using a particle filter algorithm to perform state estimation on the positioning points and training according to historical trajectory data to obtain the current position coordinate data of the train.

3. The method according to claim 1, wherein The extracting the transmitter array distribution parameters of the corresponding section from a pre-established signal coverage range database according to the train position coordinate data to obtain the initial beamforming parameters of each transmitter in the corresponding section includes: Receiving the train position coordinate information, and obtaining the transmitter distribution parameter table from the signal coverage database according to the position coordinate information. The transmitter distribution parameter table includes the longitude and latitude coordinates and array arrangement parameters of the transmitter; Using the regular hexagonal cellular grid algorithm based on the transmitter distribution parameter table to perform spatial partitioning on the coverage area of the transmitter, obtaining a grid-based beam control parameter table, where the grid-based beam control parameter table contains grid node position data; For the grid node position data, calculating the superimposed electromagnetic field distribution data through the free space propagation loss formula, and using the least squares method to fit to obtain the signal power density value at the grid points; According to the difference matrix between the signal power density value at the grid points and the preset signal strength reference value, using the genetic algorithm to iteratively optimize the transmitter antenna direction angle and gain coefficient to obtain the beamforming parameter surface.

4. The method according to claim 1, wherein Calculating the train moving speed and direction according to the transmitter array distribution parameters, and determining whether the train is approaching the tunnel cut area. If so, adjusting the initial beamforming parameters to obtain the signal coverage range change trend, including: Sampling the train position points using a fixed time window, and calculating the train movement direction angle and instantaneous speed value through cubic polynomial fitting; Extracting the line elevation data from the terrain feature database according to the train movement direction angle, and performing piecewise fitting on the elevation data through a terrain feature classifier to obtain the tunnel cut distribution data; For the tunnel cut distribution data, using a deep neural network analyzer to perform feature extraction to generate a terrain influence prediction parameter sequence; Adjusting the beamforming parameters according to the terrain influence prediction parameter sequence, and solving the Maxwell equations through a spatial electromagnetic field simulation calculator to obtain the electromagnetic field distribution data; According to the electromagnetic field distribution data and the train movement trajectory, performing dynamic programming on the transmitter power density distribution to obtain the signal coverage range change trend data.

5. The method according to claim 1, wherein According to the signal coverage range change trend, obtaining the coverage blind area feedback data, determining the signal power density insufficient section in the tunnel cut area, and generating a coverage blind area distribution map, including: Obtaining the data packet collected by the vehicle-mounted receiver, extracting the received signal strength value and signal quality index from the data packet to obtain the signal state sequence, and using a sliding median filter to eliminate noise from the signal state sequence to obtain the signal strength change curve; According to the signal strength change curve and the preset signal strength threshold, marking the time points and corresponding geographical locations where the signal strength is lower than the preset signal strength threshold to obtain the signal weakening point position data; For the communication interruption event log recorded by the vehicle-mounted receiver, extracting the interruption occurrence time and recovery time, and generating a signal interruption interval boundary point sequence in combination with the train positioning data; Using a grid density clustering algorithm to perform spatial clustering on the signal weakening point position data and the signal interruption interval boundary point sequence, and generating a coverage blind area distribution map by calculating the position point density within the grid cells.

6. The method according to claim 1, wherein According to the coverage blind area distribution map, adjusting the beamforming parameters executed on the transmitter array in real time to obtain the optimized signal power density distribution data, forming a mobile coverage corridor framework, including: Reading the beam direction angle and beam width value of the transmitter antenna according to the blind area distribution map, and using a multi-beam synthesis calculator to generate a beam control instruction sequence; For the described beam control instruction sequence, vector operations are performed on the main lobe direction and sidelobe level of the transmitter through a phased array antenna matrix to obtain a set of beamforming parameters; The antenna gain coefficient of the transmitter is extracted from the set of beamforming parameters, and an optimization operation is performed on the gain coefficient using a deep neural network calculation unit to obtain a power adjustment command; For the power adjustment command, the radiation field of the transmitter is calculated by superposition through an electromagnetic field calculation unit, and the spatial distribution of the radiation field is calculated using a Maxwell's equations solver to generate a mobile coverage corridor framework; It also includes: analyzing the blind area distribution characteristics from the coverage blind area distribution map, determining the beamforming parameters of the transmitter array to obtain an adjusted parameter set, performing beamforming processing, obtaining power density data by updating the signal power. If the blind area distribution in the power density data exceeds a preset threshold, the parameters are readjusted in combination with the distribution map data to obtain an optimized beamforming scheme, calculating the signal power distribution of the transmitter array, and obtaining optimized signal power density distribution data. Specifically, it includes: for the coverage blind area distribution map, obtaining a set of blind area position coordinate points and regional area data through a density clustering calculator to obtain a blind area feature sequence; according to the blind area feature sequence, generating beamforming control parameters of the main lobe direction angle and half-power angle of the beam using an antenna pattern calculator; adjusting the phase factor of the radiation unit of the phased array antenna array through the beamforming control parameters to obtain beam pattern data; performing spatial attenuation calculation on the beam pattern data using an electromagnetic wave propagation loss calculator to obtain an initial power density distribution map; for the initial power density distribution map, using a threshold decision maker to mark the area where the signal intensity is lower than the preset threshold to obtain updated blind area distribution data; if the total blind area area in the updated blind area distribution data exceeds the preset threshold, using a genetic algorithm to iteratively optimize the beamforming control parameters to obtain an optimized beamforming parameter sequence; using an electromagnetic field numerical calculator to verify the optimized beamforming parameter sequence and obtaining optimized signal power density distribution data by solving Maxwell's equations.

7. The method according to claim 1, characterized in that, Based on the real-time obtained mobile coverage corridor framework data, combined with the real-time obtained train position data and speed information, it is determined whether the train is about to enter the next tunnel cut area. If so, the signal power density of the corresponding transmitter is increased to obtain pre-adjusted coverage range expansion parameters, including: Obtaining the real-time position coordinates of the train according to the mobile coverage corridor framework data, and performing filtering processing on the three state variables of the train displacement, speed, and acceleration using a Kalman filter to obtain a train motion trajectory prediction curve; For the train motion trajectory prediction curve, extracting the forward line parameters using a terrain database, and obtaining the projection distance value between the train and the tunnel entrance through a line slope change rate and curvature change rate calculator; According to the projection distance value and the train speed value, generating time series data of the train reaching the tunnel entrance using a motion prediction calculator. If the time series data is less than a preset threshold, a tunnel entrance time prediction value is generated; If the predicted tunnel entrance time is less than the preset threshold, a path loss compensation curve is generated; For the path loss compensation curve, new beam envelope parameters are generated through an antenna pattern calculator; According to the beam envelope parameters, the output power of the transmitter is dynamically adjusted using a power distribution calculator, and a target signal strength value is generated in combination with a signal-to-noise ratio predictor. Coverage radius increment data and beam width adjustment values are generated through a spatial interpolation calculator to obtain pre-adjusted coverage range expansion parameters.

8. The method according to claim 1, wherein According to the pre-adjusted coverage range expansion parameters, the affected transmitter numbers and adjustment amplitudes are extracted from the transmitter array, and the beamforming parameters are calibrated to generate a target mobile coverage corridor signal enhancement scheme, including: According to the coverage range expansion parameters, a spatial position relationship calculator is used to traverse and calculate the transmitter array to obtain the affected transmitter identification numbers and adjustment amplitude values; For the affected transmitter identification numbers, a beam parameter extractor is used to read three basic parameters of the beam direction angle, power gain value, and beam half-power angle of the transmitter to obtain a beam feature data sequence; According to the beam overlap region data between adjacent transmitters in the beam feature data sequence, the beam direction and power gain are iteratively optimized through a recurrent neural network to obtain a beam parameter optimization matrix; For the beam parameter optimization matrix, a time synchronization controller is used to generate the execution timing of the transmitter beam adjustment. The beam direction adjustment rate and power adjustment slope of the transmitter are constrained through a beamforming synchronizer to obtain a synchronous execution instruction set. The synchronous execution instruction set is simulated and verified through a beam adjustment validator, and combined with the signal strength distribution data, a target mobile coverage corridor signal enhancement scheme; It also includes: obtaining the time reference of the dynamic synchronization mechanism, calibrating the beamforming parameters to obtain a calibrated parameter set, extracting the mobile trajectory characteristics from the target mobile data, judging the direction change of the trajectory generation, adjusting the calibrated parameter set, generating the beamforming configuration of the coverage corridor, calculating the signal enhancement gain, and if the signal enhancement gain is lower than the preset threshold, optimizing the parameters of the enhancement scheme to obtain the signal enhancement result of the target mobile coverage corridor.

9. A ground digital television broadcast transmitter signal coverage enhancement system, characterized in that, The system includes: A train position acquisition module for obtaining train position data, real-time collecting the longitude and latitude information of the train in a high-speed mobile scenario, and matching it with a preset operation trajectory database to determine the current position coordinate data of the train; A beamforming parameter extraction module for extracting the transmitter array distribution parameters of the corresponding section from a pre-established signal coverage range database according to the train position coordinate data to obtain the initial beamforming parameters of each transmitter in the corresponding section; A region judgment and parameter adjustment module for calculating the train movement speed and direction according to the transmitter array distribution parameters, judging whether the train is approaching the tunnel cut area, and if so, adjusting the initial beamforming parameters to obtain the signal coverage range change trend; A blind area analysis module for obtaining coverage blind area feedback data according to the signal coverage range change trend, determining the signal power density insufficient section in the tunnel cut area, and generating a coverage blind area distribution map; A dynamic coverage optimization module, which is used to adjust the beamforming parameters of the transmitter array in real time according to the coverage blind area distribution map, obtain the optimized signal power density distribution data, and form a mobile coverage corridor framework; A pre-adjustment module, which is used to judge whether the train is about to enter the next tunnel cut area according to the real-time obtained mobile coverage corridor framework data and the real-time obtained train position data and speed information. If so, it increases the signal power density of the corresponding transmitter to obtain the pre-adjusted coverage range expansion parameter; A signal enhancement scheme generation module, which is used to extract the affected transmitter numbers and adjustment amplitudes from the transmitter array according to the pre-adjusted coverage range expansion parameter, calibrate the beamforming parameters, and generate a target mobile coverage corridor signal enhancement scheme.

10. A digital television, characterized in that, The digital television includes: one or more processors; a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method according to any one of claims 1-8, and perform signal coverage enhancement of the terrestrial digital television broadcast transmitter according to this method.

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