A method and system for enhancing signal coverage of terrestrial digital television broadcast transmitters

Abstract

This application provides a method and system for enhancing signal coverage of terrestrial digital television broadcast transmitters, comprising: acquiring train location data, collecting latitude and longitude information of trains in real time during high-speed movement scenarios, and matching it with a preset operating trajectory database to determine the current location coordinates of the train; extracting transmitter array distribution parameters for the corresponding section from a pre-established signal coverage range database based on the train location coordinate data, and obtaining the initial beamforming parameters for each transmitter in the corresponding section; acquiring coverage blind zone feedback data based on the signal coverage range change trend, determining sections with insufficient signal power density in tunnel and cutting areas, and generating a coverage blind zone distribution map; and adjusting the beamforming parameters of the transmitter array in real time based on the coverage blind zone distribution map to obtain optimized signal power density distribution data, forming a mobile coverage corridor framework.

Description

Technical Field

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

[0002] With the development of high-speed rail, watching digital television programs smoothly on speeding trains has become a future trend. However, high-speed trains travel at high speeds and often pass through mountainous tunnels or deep ditches with steep slopes on both sides, posing a significant challenge to the stable reception of television signals. The rapid development of high-speed rail has become a core pillar of modern transportation, and the reliability of its communication and signal coverage technologies directly affects the safety and efficiency of train operations, especially in the critical area of ​​digital television signal coverage enhancement systems. As the high-speed rail network expands, ensuring signal continuity and stability in high-speed moving scenarios has become a technical bottleneck that the industry urgently needs to overcome. Digital television signal coverage enhancement is not only an important aspect of improving passenger experience but also an indispensable support means for ensuring the transmission of operational information; its research importance is self-evident. However, existing solutions often reveal significant shortcomings when dealing with the complex environment along high-speed rail lines. Traditional transmitter deployments mostly adopt static coverage modes, which are difficult to adapt to the dynamic needs brought about by high-speed train movement, especially in special terrains such as mountain tunnels or deep cuts, where signal attenuation and coverage blind spots are particularly prominent. Existing adaptive adjustment mechanisms typically lag behind train operation status, lacking forward-looking prediction and real-time optimization capabilities, resulting in significant signal quality fluctuations and difficulty meeting the stringent requirements of high-speed scenarios. The core challenges in this field primarily focus on the dynamic synchronization and adaptive enhancement capabilities of transmitter signal coverage. Specifically, unresolved technical issues such as how to achieve precise matching between the transmitter array and the train's trajectory, how to adjust beamforming parameters in real-time in complex terrain to optimize coverage, and how to intelligently compensate for blind spots based on onboard receiver feedback directly limit the effectiveness of signal enhancement systems in high-speed moving scenarios. Particularly when trains traverse special sections, insufficient predictability of transmitter signal power density and slow response of the control system further exacerbate the difficulty of forming coverage corridors, creating unique technical challenges. Therefore, designing an intelligent control system based on precise train location data, enabling it to trigger signal enhancement modes in specific sections in advance, and forming stable mobile coverage corridors through real-time adjustment of transmitter beamforming parameters, forward predictive enhancement of signal power density, and intelligent compensation for coverage blind spots, becomes a key problem that this research urgently needs to solve. Summary of the Invention

[0003] This invention provides a method for enhancing signal coverage of terrestrial digital television broadcast transmitters, mainly comprising:

[0004] Acquire train location data, collect latitude and longitude information of trains in high-speed moving scenarios in real time, and match it with the preset running trajectory database to determine the current location coordinate data of the train;

[0005] Based on 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.

[0006] The train's speed and direction are calculated based on the transmitter array distribution parameters. It is then determined whether the train is approaching the tunnel cutting area. If so, the initial beamforming parameters are adjusted to obtain the trend of signal coverage change.

[0007] Based on the trend of signal coverage change, obtain feedback data on coverage blind spots, determine the sections with insufficient signal power density in the tunnel cutting area, and generate a coverage blind spot distribution map;

[0008] Based on the coverage blind zone distribution map, the beamforming parameters of the transmitter array are adjusted in real time to obtain optimized signal power density distribution data, thus forming a mobile coverage corridor framework.

[0009] Based on the real-time acquired mobile coverage corridor framework data, combined with the real-time acquired train location data and speed information, it is determined whether the train is about to enter the next tunnel cutting area. If so, the signal power density of the corresponding transmitter is increased to obtain the pre-adjusted coverage range expansion parameters.

[0010] Based on the pre-adjusted coverage extension parameters, the affected transmitter numbers and adjustment ranges are extracted from the transmitter array, the beamforming parameters are calibrated, and a signal enhancement scheme for the target mobile coverage corridor is generated.

[0011] Furthermore, train position data is acquired by collecting real-time latitude and longitude information of trains in high-speed moving scenarios and matching it with a preset operating trajectory database to determine the current position coordinates of the train. This includes: acquiring real-time latitude and longitude coordinates of the high-speed train using a satellite positioning receiver, and converting the world geographic coordinate system into line coordinate data based on the railway mainline mileage using a preset national railway coordinate reference table in a coordinate mapping converter. Reference positioning point data set every 50 meters along the railway mainline is obtained from the track foundation database, and continuous trajectory reference point data is obtained by interpolating the reference positioning points using a cubic spline function. A median filter is applied to the real-time acquired train positioning data using a 200-meter fixed-length sliding window, and a smooth trajectory curve sequence is generated by Bézier curve fitting. If the lateral deviation between the positioning point and the trajectory reference point in the smooth trajectory curve sequence is greater than 10 meters, a particle filter algorithm is used to estimate the state of the deviation point, and a corrected positioning sequence is obtained by training based on historical trajectory data. For the corrected positioning sequence, a similarity score with the reference trajectory is calculated using the dynamic time warping algorithm in the trajectory matching calculator, and the point with the highest score is selected as a candidate matching position. By performing a weighted average calculation on five positioning points before and after the candidate matching position, and combining this with the train's running direction vector, the coordinates of the train's actual position on the railway mainline are obtained through spatial interpolation.

[0012] Furthermore, based on the train's position coordinates, transmitter array distribution parameters for the corresponding section are extracted from a pre-established signal coverage database to obtain the initial beamforming parameters for each transmitter within the corresponding section. This includes: querying the signal coverage database based on the train's actual position coordinates, and extracting the latitude and longitude coordinates and array arrangement parameters of all transmitters within a two-kilometer radius of the train's section from the database using a geographic information spatial query tool to obtain a transmitter distribution parameter table. Based on the transmitter distribution parameter table, the antenna height and tilt angle values ​​for each transmitter are obtained. A regular hexagonal cellular grid algorithm is used to spatially divide the transmitter coverage area, with each grid having a side length of fifty meters, generating a gridded beam control parameter table. The position data of each grid node is extracted from the gridded beam control parameter table. Based on the free-space propagation loss formula, the field strength attenuation value of the electromagnetic wave emitted by each transmitter at each node is calculated, and the superimposed electromagnetic field distribution data is obtained through complex number operations. For the superimposed electromagnetic field distribution data, the least squares method is used to fit and calculate the signal power density value at each grid point, and this is compared with a preset signal strength benchmark value to obtain a power difference matrix. Based on the values ​​in the power difference matrix, a genetic algorithm is used to iteratively optimize the directional angle and gain coefficient of each transmitter antenna. With a population size of 200 and 50 iterations, optimized beam control parameters are obtained. Spatial interpolation is then performed on the optimized beam control parameters, and combined with the transmitter position coordinates to generate a continuous beamforming parameter surface, yielding beamforming parameters and power density distribution data for each transmitter within the segment.

[0013] Furthermore, the train's speed and direction are calculated based on the transmitter array distribution parameters to determine if the train is approaching the tunnel cutting area. If so, the initial beamforming parameters are adjusted to obtain the signal coverage change trend. This includes: sampling the train's position points using a fixed ten-second time window based on the transmitter array distribution parameters and real-time train coordinate data; calculating the train's motion direction angle and instantaneous speed values ​​through cubic polynomial fitting; extracting track elevation data within a five-kilometer radius ahead of the train from the terrain feature database; segmenting and fitting the track longitudinal profile elevation curve using a terrain feature classifier; and determining the tunnel cutting distribution data based on slope abrupt change points. For the tunnel cutting distribution data, a deep neural network analyzer is used to extract features from the tunnel cross-sectional area and cutting depth data to generate a terrain impact prediction parameter sequence. Based on the terrain impact prediction parameter sequence, a recurrent neural network calculation unit adaptively adjusts the antenna beam direction angle and transmit power in the beamforming parameters to obtain a beam adjustment command sequence. The adjusted beamforming parameters were verified using a space electromagnetic field simulation calculator, and electromagnetic field distribution data were obtained by solving Maxwell's equations. Based on the electromagnetic field distribution data and the train's trajectory, a dynamic planning method for the transmitter power density distribution was used with an electromagnetic wave propagation path calculator to obtain data on the trend of signal coverage variation.

[0014] Furthermore, based on the signal coverage change trend, feedback data on coverage blind spots is obtained to identify sections with insufficient signal power density within the tunnel cutting area, generating a coverage blind spot distribution map. This includes: collecting signal data packets once per second via an onboard receiver based on the real-time train trajectory and signal coverage change curve; extracting received signal strength values ​​and signal quality indicators from the data packets to obtain a signal state sequence; using a sliding median filter to eliminate noise in the signal strength values ​​for the signal state sequence; and calculating the signal strength change curve through least squares fitting; setting a signal strength threshold based on the signal strength change curve; and using a threshold decision device to mark the time points and corresponding geographical locations where the signal strength is below the threshold, generating signal weakening point location data; extracting the interruption occurrence time and recovery time from the communication interruption event log recorded by the onboard receiver; and combining this with train positioning data to generate a signal interruption interval boundary point sequence; and using a support vector machine to extract features from the signal weakening point location data and the signal interruption interval boundary point sequence. The training samples include three feature dimensions: signal strength, interruption duration, and geographical location, resulting in a signal coverage blind spot feature vector. Based on the feature vector of the signal coverage blind zone, a grid density clustering algorithm is used to spatially cluster the blind zone location points. The distribution data of the core area of ​​the blind zone is obtained by calculating the density of location points within the grid cells. A Kriging spatial interpolation algorithm is used to fit the three-dimensional boundary of the core area of ​​the blind zone. Combined with terrain elevation data, a boundary surface of the coverage blind zone is generated, resulting in a three-dimensional map of the coverage blind zone distribution.

[0015] Furthermore, based on the coverage blind zone distribution map, beamforming parameters for the transmitter array are adjusted in real time to obtain optimized signal power density distribution data, forming a mobile coverage corridor framework. This includes: based on the coverage blind zone distribution map, a beam controller reads the beam direction angle, beamwidth, and beam gain parameters of each transmitter antenna, and generates a beam control command sequence using a multi-beam synthesizer. For the beam control command sequence, vector calculations are performed on the main lobe direction, side lobe level, and null direction of each transmitter using the phased array antenna matrix to obtain a beamforming parameter set. The antenna gain coefficient of each transmitter is extracted from the beamforming parameter set, and the antenna pattern directivity is set according to the blind zone location density. A deep neural network computing unit dynamically optimizes the transmitter power allocation to obtain a power adjustment command. For the power adjustment command, the transmitter radiation field is superimposed using an electromagnetic field calculation unit, and the spatial electromagnetic field distribution is calculated using a Maxwell's equations solver to obtain power density data. Based on power density data, a regular hexagonal grid divider is used to divide the coverage area into a honeycomb pattern. The grid side length varies with the signal strength gradient to generate an adaptive grid partition map. Cubic spline interpolation is then performed on the signal strength data in the adaptive grid partition map, and combined with terrain undulation data to generate the spatial contour surface of the mobile coverage corridor.

[0016] Furthermore, the blind zone distribution characteristics are analyzed from the coverage blind zone distribution map to determine the beamforming parameters of the transmitter array, obtaining an adjusted parameter set. Beamforming processing is then performed, and power density data is obtained by updating the signal power. If the blind zone distribution in the power density data exceeds a preset threshold, the parameters are readjusted based on the distribution map data to obtain an optimized beamforming scheme. The signal power distribution of the transmitter array is calculated, and the optimized signal power density distribution data is obtained, including: extracting the set of blind zone location coordinates and area data from the coverage blind zone distribution map; performing spatial clustering of the blind zone location points using a density clustering calculator; and generating a blind zone feature sequence based on the cluster center spacing and area. For the blind zone feature sequence, an antenna pattern calculator is used to generate beamforming control parameters, including four basic parameters: beam main lobe direction angle, beam half-power angle, main lobe gain value, and null direction angle. Based on the beamforming control parameters, the phase factor of each radiating element is adjusted using a phased array antenna array, and the beam pattern data is obtained by allocating the feed current amplitude of each element using an amplitude weighting processor. An electromagnetic wave propagation loss calculator is used to calculate spatial attenuation of the beam pattern data. An initial power density distribution map is obtained by superimposing the electromagnetic field distributions generated by each transmitter. For this initial power density distribution map, a threshold decision device is used to mark regions with signal strength below a preset threshold, generating updated blind zone distribution data. If the total blind zone area in the updated blind zone distribution data exceeds the preset threshold, a genetic algorithm is used to iteratively optimize the beamforming control parameters, with the population size dynamically adjusted according to the blind zone area. Based on the optimized beamforming control parameters, a beamforming calculator is used to reconstruct the radiation pattern of the transmitter array, generating a new beamforming parameter sequence. An electromagnetic field numerical calculator is used to verify the optimized beamforming parameter sequence, and the optimized signal power density distribution data is obtained by solving Maxwell's equations.

[0017] Furthermore, based on the real-time acquired mobile coverage corridor framework 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 cutting area. If so, the signal power density of the corresponding transmitter is increased to obtain the pre-adjusted coverage range expansion parameters. This includes: using a Kalman filter to filter the three state variables of train displacement, speed, and acceleration based on the mobile coverage corridor framework data and the train's real-time position coordinates, and generating a train trajectory prediction curve using a time series predictor. For the trajectory prediction curve, the terrain database is used to extract the parameters of the line ahead, and the projected distance between the train and the tunnel entrance is calculated by combining the rate of change of line gradient and the rate of change of curvature. Based on the line projection distance and the train speed value, the time interval between the train's arrival at 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 tunnel wall reflection loss and multipath effect, generating 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 using an antenna pattern calculator. Based on the beam envelope parameters, the transmitter output power is dynamically adjusted using a power allocation calculator, and the target signal strength value is generated in conjunction with a signal-to-noise ratio predictor. A sector boundary generator is used to reconstruct the beam coverage area, and a spatial interpolation calculator is used to generate coverage radius increment data and beamwidth adjustment values ​​to obtain coverage range extension parameters.

[0018] Furthermore, based on the pre-adjusted coverage extension parameters, the affected transmitter numbers and adjustment amplitudes are extracted from the transmitter array. Beamforming parameters are calibrated to generate a signal enhancement scheme for the target mobile coverage corridor. This includes: traversing the transmitter array using a spatial location calculator based on the coverage extension parameters, and calculating the affected transmitter identification number and adjustment amplitude value using the signal beam cross area and boundary distance. For the affected transmitter identification number, a beam parameter extractor reads three basic parameters of each transmitter: beam direction angle, power gain value, and beam half-power angle, generating a beam feature data sequence. Beam overlap area data between adjacent transmitters is extracted from the beam feature data sequence, and the beam direction and power gain are iteratively optimized using a recurrent neural network to obtain a beam parameter optimization matrix. Based on the beam parameter optimization matrix, a time synchronization controller generates the execution timing sequence for transmitter beam adjustment, and a clock pulse distributor determines the command execution order for each transmitter. For the command execution order, a beamforming synchronizer constrains the beam direction adjustment rate and power adjustment slope of each transmitter, generating a synchronized execution command set. The synchronous execution instruction set was simulated and verified using a beam adjustment verifier, and a signal enhancement coverage map was generated using an electromagnetic field superposition calculator. A boundary detector was used to extract the edges of the signal enhancement coverage map, and a mobile coverage corridor enhancement scheme was generated by combining this with signal strength distribution data.

[0019] Furthermore, the time reference of the dynamic synchronization mechanism is obtained, beamforming parameters are calibrated to obtain a calibrated parameter set, movement trajectory features are extracted from the target movement data, the direction change of trajectory generation is judged, the calibrated parameter set is adjusted, beamforming configuration of the coverage corridor is generated, and signal enhancement gain is calculated. If the signal enhancement gain is lower than a preset threshold, the parameters of the enhancement scheme are optimized to obtain the signal enhancement result of the target movement coverage corridor. This includes: using a timing synchronization controller to pulse synchronize the beam parameters of each transmitter according to the reference clock signal of the dynamic synchronization mechanism, and compensating for the beam direction angle deviation value using a phase difference calculator to obtain a calibrated parameter set. The train position coordinate sequence is extracted from the target movement data, the trajectory curve equation is generated using a least squares fitter, and the inflection point position of the trajectory curve is marked using a curvature calculator to obtain a motion feature sequence. For the motion feature sequence, a neural network predictor is used to perform forward estimation of the train movement direction, and the direction change rate parameter is obtained by sliding calculation through a time window. Based on the direction change rate parameter, a beam pattern generator is used to adjust the beam pointing of each transmitter, and beamforming configuration data is generated using a phased array antenna parameter calculator. For the beamforming configuration data, an electromagnetic field strength calculator is used to spatially superimpose the transmitter's radiation field to obtain the signal enhancement gain value. If the signal enhancement gain value is lower than a preset threshold, a genetic algorithm is used to jointly optimize the beam direction angle and power gain, generating an optimized parameter set through iterative calculation. Based on the optimized parameter set, an electromagnetic field simulation calculator is used to reconstruct the beam coverage of each transmitter, obtaining the signal enhancement result data.

[0020] This invention provides a terrestrial digital television broadcast transmitter signal coverage enhancement system, mainly comprising:

[0021] The train position acquisition module is used to acquire train position data, collect the latitude and longitude information of the train in high-speed moving scenarios in real time, and match it with the preset running trajectory database to determine the current position coordinate data of the train;

[0022] The beamforming parameter extraction module is used to extract the transmitter array distribution parameters of the corresponding section from the pre-established signal coverage database based on the train position coordinate data, and obtain the initial beamforming parameters of each transmitter in the corresponding section.

[0023] The area judgment and parameter adjustment module is used to calculate the train's moving speed and direction based on the transmitter array distribution parameters, determine whether the train is approaching the tunnel cutting area, and if so, adjust the initial beamforming parameters to obtain the signal coverage range change trend.

[0024] The blind spot analysis module is used to obtain coverage blind spot feedback data based on the signal coverage range change trend, determine the sections with insufficient signal power density in the tunnel cutting area, and generate a coverage blind spot distribution map.

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

[0026] The pre-adjustment module is used to determine whether a train is about to enter the next tunnel cutting area based on the real-time acquired mobile coverage corridor framework data, combined with the real-time acquired train position data and speed information. If so, the signal power density of the corresponding transmitter is increased to obtain the pre-adjusted coverage range extension parameters.

[0027] The signal enhancement scheme generation module is used to extract the affected transmitter numbers and adjustment amplitudes from the transmitter array based on the pre-adjusted coverage extension parameters, calibrate the beamforming parameters, and generate a signal enhancement scheme for the target mobile coverage corridor.

[0028] A 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 implement the method described above and enhance the signal coverage of a terrestrial digital television broadcast transmitter according to the method.

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

[0030] This invention discloses a method and system for enhancing signal coverage of terrestrial digital television broadcast transmitters. The method dynamically adjusts the beamforming parameters of the transmitter array by real-time acquisition of train position data and matching it with a preset trajectory, combined with a pre-established signal coverage database. For special terrains such as tunnels and road cuts, this invention can predict the trend of signal coverage changes and generate distribution maps based on feedback data from coverage blind spots, optimizing the signal power density distribution in real time. Furthermore, this invention can adjust the signal coverage parameters of the area the train is about to enter in advance based on train position and speed information, employing a dynamic synchronization mechanism for secondary calibration, thereby forming a targeted signal enhancement scheme for mobile coverage corridors. This method effectively solves the communication coverage problem in high-speed mobile scenarios, especially under complex terrain conditions, significantly improving signal quality and continuity, and providing strong support for the stable operation of high-speed train communication systems. Attached Figure Description

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

[0032] Figure 2 This is a structural diagram of a terrestrial digital television broadcast transmitter signal coverage enhancement system according to the present invention. Detailed Implementation

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

[0034] like Figure 1 This embodiment of a method for enhancing the signal coverage of a terrestrial digital television broadcast transmitter may specifically include:

[0035] S101. In high-speed moving scenarios, real-time acquisition of train position data and determination of its precise coordinates are performed. The current latitude and longitude information of the train is obtained through a satellite positioning device and matched with a preset railway trajectory database to generate the position coordinate data of the train on the main railway line.

[0036] The latitude and longitude coordinates of the train during high-speed operation are obtained in real time using satellite positioning devices to ensure the timeliness of location data. Based on a pre-set national railway coordinate conversion table, the world geographic coordinate system is converted into line coordinates based on the railway mainline mileage, which facilitates matching and calculation with the transmitter coverage parameters.

[0037] S1011. Extract the benchmark positioning point data set every fifty meters on the railway mainline from the track foundation database, use cubic spline interpolation to generate a continuous trajectory for the benchmark points, obtain a smooth trajectory benchmark point sequence, and optimize the smoothness of the trajectory curve by Bézier curve fitting.

[0038] The interval between reference points on the railway mainline is set at fifty meters, and each reference point includes longitude, latitude, and mileage information. These points are fitted using cubic spline interpolation to generate a continuous and smooth trajectory sequence, ensuring trajectory accuracy. Bézier curves further optimize the smoothness of the curves, making subsequent matching calculations more reliable. Taking the Beijing-Shanghai High-Speed ​​Railway as an example, a total of 26,360 reference points are set within its 1,318-kilometer length. After interpolation, a trajectory point sequence with smaller intervals can be generated, meeting the requirements for high-precision positioning.

[0039] S1012. The real-time acquired train positioning data is smoothed and the deviation is corrected. If the lateral deviation between the positioning point and the reference point in the trajectory curve sequence exceeds ten meters, the particle filter algorithm is used to perform state estimation based on historical trajectory data to generate a corrected positioning sequence. The similarity score with the reference trajectory is calculated by the dynamic time warping algorithm to determine the actual position coordinates of the train.

[0040] A fixed-length sliding window of 200 meters is used to perform median filtering on the real-time positioning data to remove the influence of outliers. Taking a train traveling at 350 km / h as an example, sampling is performed once per second, with approximately 40 sampling points within the window to ensure data smoothness. If the deviation between the positioning point and the reference point exceeds ten meters, a particle filtering algorithm combined with historical data is used for correction. Next, a dynamic time warping algorithm calculates the similarity between the measured trajectory and the reference trajectory, selects the point with the highest score as a candidate position, and performs a weighted average of the five preceding and following positioning points. This is further calibrated by combining the running direction vector to obtain the precise coordinates of the train on the railway mainline.

[0041] Whether on straight sections or curved areas, such as the complex line from Changzhou North Station to Nanjing South Station, trajectory deviation issues can be effectively addressed through direction vectors and weighted calculations, laying the foundation for signal coverage optimization. Understandably, the sliding window length and interpolation interval can be adjusted according to the actual scenario to balance accuracy and computational resource usage.

[0042] S1013. Based on the corrected positioning sequence and 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 to provide dynamic basis for transmitter signal coverage adjustment.

[0043] The train's direction of travel is calculated from the coordinate difference between adjacent positioning points. Combined with mileage and superelevation parameters from the infrastructure database, the reliability of the positioning results can be further verified. Taking the section between Xuzhou East Station and Bengbu South Station as an example, the trajectory data of this 203-kilometer section can achieve a positioning accuracy of 5 meters after the above processing, providing real-time support for dynamic adjustments to enhance the signal.

[0044] By collecting and processing the aforementioned location data, precise input data can be provided for beam optimization of the transmitter array, ensuring a high degree of synchronization between signal coverage and train movement, ultimately improving the stability of digital television signals in high-speed rail scenarios. Specific algorithm implementation details can be set by technical personnel according to actual needs and will not be elaborated upon here.

[0045] S102. Based on the real-time position coordinate data of the train, extract the transmitter array distribution parameters of the corresponding section from the preset signal coverage database and generate the initial beamforming configuration. Obtain the position, height and tilt information of each transmitter, and then calculate the signal power density reference value and the optimized beam parameter distribution.

[0046] By utilizing train location coordinate data and accessing the signal coverage database through a geographic information query module, the distribution parameters of the transmitter array within the current train section can be quickly extracted. These parameters include key information such as the transmitter's latitude and longitude coordinates, antenna installation height, and antenna downtilt angle, providing a foundation for subsequent beam optimization. Taking the Wuhan-Changsha section of the Beijing-Guangzhou High-Speed ​​Railway as an example, the transmitter spacing in this section is approximately 2000 meters, the antenna height ranges from 18 to 25 meters, and the antenna tilt angle is typically set between 4 and 8 degrees to ensure compatibility with the terrain along the high-speed railway.

[0047] S1021. Obtain the transmitter distribution parameter table from the signal coverage database and perform gridding. Based on the regular hexagonal cellular 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] Based on the extracted transmitter distribution parameter table, a regular hexagonal cellular grid algorithm was used to spatially divide the coverage area. Each hexagonal cell had a side length of 50 meters, and the spacing between adjacent grid points was fixed, avoiding the deformation problem of traditional rectangular grids in curved areas. Taking the Wuhan-Changsha section as an example, approximately 750 grid nodes were formed within a 1000-meter radius on both sides of the railway, with the node density gradually decreasing with increasing distance from the transmitter. This gridding method can uniformly cover complex terrain, providing accurate spatial references for subsequent signal calculations. A beam control parameter table recorded the coordinate data of each grid node, ensuring a high degree of traceability in the calculation process.

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

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

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

[0052] The beam parameters optimized by the genetic algorithm are further processed, and a continuous beamforming parameter surface is generated using cubic spline interpolation. Taking the section from Nanchang West to Hangzhou East as an example, this section has significant terrain undulations. The antenna azimuth angle optimization range is set to ±30 degrees, and the gain coefficient adjustment range is 6 to 9 dB. After interpolation, the beam parameter surface can smoothly transition the coverage areas between different transmitters, avoiding signal abrupt changes. In the curved sections, increasing the azimuth angle adjustment range and dynamically changing the gain coefficient effectively compensates for attenuation caused by terrain, ensuring the stability of signal coverage. Optimization results show that fine-tuning the antenna downtilt angle can significantly reduce coverage dead zones, while dynamic adjustment of the gain coefficient enhances the signal penetration capability in complex environments.

[0053] Through the above steps, beamforming parameters and power density distribution data of the transmitter array can be dynamically generated based on the train's location. This method fully leverages the advantages of gridding and algorithm optimization to ensure that signal coverage is highly matched to the actual needs of high-speed rail lines. Understandably, the grid side length and algorithm parameters can be adjusted according to specific scenarios to adapt to coverage requirements under different terrain conditions.

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

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

[0056] By combining transmitter array distribution parameters with train coordinate information, the system analyzes train motion in real time and predicts whether its route will enter special terrain areas. Through comprehensive calculations of speed, direction, and terrain features, it ensures that beam parameters can adapt to signal coverage requirements in advance. Taking the Chengdu-Chongqing high-speed railway as an example, when the train reaches a speed of 350 km / h, its motion trend can be quickly identified, providing a basis for transmitter adjustments.

[0057] S1031. Train position is sampled and motion parameters are calculated using a fixed time window. The instantaneous speed and direction angle of the train are generated by fitting the position data with a cubic polynomial. At the same time, the elevation data of the line ahead is extracted from the terrain feature database to provide basic support for subsequent terrain judgment.

[0058] A 10-second time window was set, and the train position was sampled once per second, collecting a total of 10 data points. A cubic polynomial was used to fit these points to calculate the train's instantaneous speed and heading angle. For example, on the Chengdu-Chongqing line, a change in heading angle from 95 degrees to 110 degrees indicates that the train has entered a left-turn section. Simultaneously, elevation data for the area within 5 kilometers ahead of the train, including information such as gradient and tunnel locations, was obtained from a terrain feature database. The elevation data was stored at 20-meter intervals, reflecting subtle changes in the longitudinal profile of the line and providing accurate input for terrain classification.

[0059] S1032. The elevation data is analyzed by a terrain feature classifier and the tunnel cutting features are extracted. A deep neural network is used to extract features of terrain parameters to generate a prediction sequence. Then, a recurrent neural network is used to adaptively adjust the beam parameters to ensure that the signal coverage adapts to the special terrain requirements.

[0060] Elevation data is segmented and fitted, and a terrain feature classifier identifies abrupt slope changes to generate tunnel and cutting distribution data. Taking the Humen-Shenzhen North section of the Guangzhou-Shenzhen-Hong Kong High-Speed ​​Railway as an example, this section includes three tunnels and two cuttings, among which the Shiziyang Tunnel is 4500 meters long with a cross-sectional area of ​​approximately 90 square meters. A deep neural network employs an 8-layer convolutional structure, inputting parameters such as tunnel cross-sectional area, burial depth, and slope to extract terrain-related features. Taking the Oujiang Tunnel of the Wenzhou-Fuzhou Railway as an example, with a maximum burial depth of 180 meters and a slope changing from 2.5% to 3.2%, the network predicts a signal attenuation of 12 decibels at the tunnel entrance. Subsequently, the recurrent neural network adjusts beam parameters based on the predicted sequence. Before the Mawei Tunnel of the Shanghai-Kunming High-Speed ​​Railway, the transmitter beam direction is deflected by 15 degrees in advance, increasing power by 3 decibels to compensate for attenuation caused by waveguide effects. This adjustment considers antenna turning speed limitations, not exceeding 3 degrees per second, to ensure mechanical feasibility.

[0061] S1033. Verify the adjusted beam parameters and generate coverage trend data using spatial electromagnetic field simulation. 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.

[0062] A space electromagnetic field simulation calculator was used to verify the adjusted beam parameters, and Maxwell's equations were solved to simulate the propagation characteristics of electromagnetic waves. Taking the Qinling tunnel group of the Baoji-Lanzhou High-Speed ​​Railway as an example, the simulation showed that electromagnetic waves propagated in waveguide mode within the tunnels, and wall reflections caused periodic fluctuations in signal strength. In the Ganxi Tunnel (3200 meters long), the overlap area of ​​adjacent transmitter beams expanded from 150 meters to 220 meters. Subsequently, a dynamic planning method was used, combining an electromagnetic wave propagation path calculator with train trajectory calculations, to optimize the power density distribution and ensure seamless coverage. The simulation process considered the effects of multipath propagation and terrain obstruction, improving the accuracy of predictions.

[0063] The above steps achieve dynamic optimization of beam parameters through real-time analysis of train movement and terrain. Whether on flat tracks or in complex tunnel areas, such as the multi-tunnel terrain of the Guizhou section, the system can adjust the transmitter configuration in advance to ensure stable signal coverage. Understandably, 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 predictive capabilities. For example, when a train approaches a curve or tunnel, the system can use changes in azimuth angle to plan beam adjustment paths in advance. This proactive design significantly reduces the risk of signal interruption and lays a solid foundation for the construction of coverage corridors; the specific adjustment range can be set by technicians based on terrain complexity.

[0065] S104. Based on the trend of signal coverage change, obtain the coverage blind spot data fed back by the vehicle receiver and analyze the signal weakening and interruption situation to generate a coverage blind spot distribution map.

[0066] By utilizing train trajectory and signal coverage trends, the system receives real-time signal data uploaded by onboard equipment to identify sections with insufficient signal power in areas such as tunnels and road cuts. Taking the Zhengzhou South to Wuhan section of the Beijing-Guangzhou High-Speed ​​Railway as an example, the onboard receiver collects signal status data every second, including indicators such as signal strength, bit error rate, and signal-to-noise ratio. The system uses this information to dynamically map the distribution of blind spots, improving the targeting of coverage adjustments.

[0067] S1041. Extract the signal state sequence from the data packet of the vehicle receiver 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. Mark the weakening point position below the preset threshold to lay the foundation for blind spot positioning.

[0068] The system receives data packets uploaded every second by the onboard equipment and extracts the received signal strength and quality indicators to form a time series. Taking the Hefei-Nanjing section as an example, the signal strength fluctuates between -40 and -95 dB, while the signal-to-noise ratio remains between 15 and 35 dB. A 21-point sliding median filter is used to process the series to remove noise interference. The filtered curve shows periodic characteristics related to the distance between base stations. Next, the signal strength change trend is calculated by fitting using the least squares method. Inflection points often correspond to abrupt changes in terrain. A threshold of -85 dB is set, and locations below this value are marked as signal weakening points. A weakening point sequence is generated by combining train positioning data, providing high-precision input for analysis.

[0069] S1042. Analyze communication interruption events and extract boundary point sequences. Combine signal weakening points with grid density clustering algorithm to calculate the core area of ​​the blind zone. Then, generate a three-dimensional blind zone boundary surface through Kriging interpolation to fully reflect the distribution characteristics of the insufficiently covered area.

[0070] The occurrence and recovery times of communication interruptions were extracted from the vehicle-mounted receiver logs, and a sequence of boundary points for the interruption intervals was generated by combining this data with location data. Taking the Hexi Corridor section of the Lanzhou-Xinjiang High-Speed ​​Railway as an example, there were approximately 12 interruption events per day, lasting from 0.5 to 8 seconds, mostly concentrated in the deep cutting areas of the Gobi Desert. Subsequently, a grid density clustering algorithm was used to analyze the spatial distribution, combining weakened point data. The grid cell side length was set to 100 meters, and the density threshold was 5 points. Taking the Bengbu-Xuzhou section of the Beijing-Shanghai High-Speed ​​Railway as an example, clustering identified 8 blind spots, with the largest blind spot located in the cutting area near Suzhou East. Furthermore, the Kriging interpolation algorithm was used to perform 3D fitting on the core area of ​​the blind spots, generating a boundary surface with a resolution of 10 meters. After integrating terrain elevation data, the pattern of blind spots changing with the terrain was visually displayed.

[0071] By smoothing signal states and correlating the spatiotemporal characteristics of interruption events, the features of coverage blind spots are accurately captured. Field measurements on the Guizhou section of the Shanghai-Kunming High-Speed ​​Railway show that tunnels and cuttings account for 85% of the weakening points, with individual weakening points reaching distances of up to 800 meters. This analytical method effectively reveals the causal relationship between signal attenuation and terrain, providing a reliable basis for transmitter adjustments.

[0072] Support Vector Machines (SVMs) further enhance the accuracy of blind zone feature extraction. Taking the Changsha-Guangzhou section of the Wuhan-Guangzhou High-Speed ​​Railway as an example, the model was trained using 6,000 historical samples, including signal strength, interruption duration, and location information, and calculated using a Gaussian kernel function. When the signal strength is below -90 dB and the interruption lasts for more than 2 seconds, the blind zone detection probability reaches 92%. This machine learning approach improves the robustness of blind zone identification, making it particularly suitable for dynamic scenarios in complex terrain.

[0073] Whether on flat roads or in the multi-tunnel environment of Guizhou, the system can accurately locate areas with insufficient signal. The grid side length and threshold can be adjusted according to the characteristics of the line to ensure the adaptability of the algorithm. 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 and optimize the signal power density distribution according to the coverage blind zone distribution map. Form a stable mobile coverage corridor framework through multi-beam synthesis, phased array adjustment and electromagnetic field calculation.

[0075] By analyzing blind spot distribution maps to identify areas with insufficient signal coverage, the system dynamically adjusts 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 blind spot locations, ensuring that beam coverage is highly matched with train operation requirements, thus significantly improving signal stability.

[0076] S1051. Extract beam parameters from the coverage blind zone distribution map and generate a control command sequence. Use a multi-beam synthesizer to process the antenna directional 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 set.

[0077] The transmitter antenna beam direction angle and width values ​​are read from the blind zone distribution map. Taking the Changsha South to Guangzhou South section of the Wuhan-Guangzhou High-Speed ​​Railway as an example, the antenna array contains 32 radiating elements, with an adjustable range of ±60 degrees horizontally and ±30 degrees vertically, and a beamwidth varying between 15 and 45 degrees. A multi-beam synthesizer generates a control command sequence based on these parameters to ensure the beam is pointed towards the core area of ​​the blind zone. Next, the phased array antenna matrix adjusts the main lobe direction and side lobe levels through vector operations, achieving a maximum main lobe gain of 15 dB, maintaining a side lobe-to-main lobe level ratio of over 20 dB, and aligning the null direction with the interference source. This adjustment method controls the beamwidth to 300 to 500 meters in the beam overlap area, ensuring a 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 a Maxwell's equations solver to calculate the spatial electromagnetic field and obtain refined power density data.

[0079] Gain coefficients are extracted from beamforming parameter sets and optimized using a deep neural network computing 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. Inputting blind zone coordinates, area, and terrain slope features, the power allocation is iteratively adjusted through backpropagation. The power variation range of a single transmitter is ±6 dB, and the power difference between adjacent transmitters does not exceed 10 dB. The optimized power command is input to the electromagnetic field computing unit, superimposed with the radiation fields of each transmitter. Maxwell's equations are solved using the finite-difference time-domain method, with a grid size of 1 / 10 of the wavelength, satisfying the Coulomb condition. On the Urumqi-Hami section of the Lanzhou-Xinjiang High-Speed ​​Railway, calculations show a path loss index of approximately 2.8, a clear signal strength attenuation with distance, and power density data reflecting the blind zone filling effect.

[0080] The coverage corridor framework was improved through adaptive mesh generation and interpolation techniques. A regular hexagonal mesh generator was used to divide the coverage area into a honeycomb pattern, with the side length dynamically adjusted according to the signal strength gradient. Along the Chengdu-Chongqing High-Speed ​​Railway, the side length was set to 50 meters per 100 meters when the gradient was greater than 3 dB, and 200 meters per 100 meters when it was less than 1 dB. Subsequently, cubic spline interpolation combined with terrain data was used 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, presenting a dumbbell-shaped vertical cross-section to accurately characterize the signal distribution characteristics.

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

[0082] The density clustering calculator was used to analyze the blind zone distribution map and extract location coordinates and area data. Taking the Bengbu-Nanjing South section of the Beijing-Shanghai High-Speed ​​Railway as an example, with a clustering radius of 300 meters and a minimum number of points of 5, 23 blind zones were identified, with the largest blind zone area reaching 0.8 square kilometers. Based on these characteristics, the antenna pattern calculator generated the main lobe direction and half-power angle parameters, increasing the half-power angle to 35 degrees in the tunnel area of ​​the Guizhou section to cope with multipath effects. After adjusting the phase factor and feed amplitude of the phased array antenna, the electromagnetic wave propagation loss calculator generated the initial power density distribution map. If the threshold decision device detected a deep blind zone area with a signal below -95 dB exceeding 10%, a genetic algorithm was used to optimize the parameters, dynamically adjusting the population size to 400, with a crossover probability of 0.8 and a mutation probability of 0.05. After optimization, the deep blind zone area of ​​the Jiangjin-Chongqing section of the Chengdu-Chongqing High-Speed ​​Railway decreased from 3.2% to 1.8%, verifying the effectiveness of the method.

[0083] Whether in flat Gobi desert areas or densely populated urban areas, such as the frigid section of the Harbin-Dalian high-speed railway, the system can compensate for environmental impacts by adjusting power and direction. Specific optimization strategies can be flexibly set according to the characteristics of the line.

[0084] S106. Based on the real-time acquired mobile coverage corridor framework data and train location information, determine whether the area is approaching the next tunnel cutting area and adjust the transmitter signal power density in advance to generate coverage range extension parameters.

[0085] By combining mobile coverage corridor framework data and real-time train coordinates, predictive models are used to assess train operation trends. Taking the Zhengzhou-Wuhan section of the Beijing-Guangzhou High-Speed ​​Railway as an example, the system optimizes transmitter parameters in advance through high-precision trajectory prediction and terrain matching, avoiding signal interruptions in tunnel areas and improving passenger experience.

[0086] S1061. A Kalman filter is used to process train motion data and predict trajectory curves. The parameters of the line ahead are extracted from the terrain database to calculate the projected distance to the tunnel entrance. The arrival prediction value is generated through time series analysis.

[0087] Train displacement, velocity, and acceleration data are acquired with a sampling period of 0.1 seconds. State estimation is performed using a Kalman filter, with the observation noise variance set to 0.1 and the process noise variance to 0.01. After filtering through 20 sampling points, a smooth trajectory curve is generated, with the prediction error controlled within 0.8 meters. Subsequently, the gradient and curvature information of the preceding line are extracted from the terrain database. Taking the Guizhou section of the Shanghai-Kunming High-Speed ​​Railway as an example, the gradient change rate fluctuates between ±30‰, and the curvature radius ranges from 2000 to 6000 meters. The system calculates the projected distance between the train and the tunnel entrance, with an error of less than 1.5 seconds before the Zunyi tunnel group. Based on the distance and velocity, the motion prediction calculator generates an arrival time series; if it is less than a preset threshold, subsequent adjustments are triggered. This method ensures the real-time performance and accuracy of the prediction, providing an adjustment window for beam optimization.

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

[0089] Based on the predicted time, a tunnel attenuation characteristic calculator was used to analyze signal propagation. Taking the Majitang Tunnel of the Wuhan-Guangzhou High-Speed ​​Railway as an example, the attenuation in the middle of the 3200-meter-long tunnel reaches 15 dB, with reflection loss of approximately 0.8 dB each time. Multipath effects cause periodic fluctuations in intensity. After the calculator modeled and generated a loss compensation curve, the beam range optimizer widened the half-power angle from 25 degrees to 40 degrees, reducing the main lobe gain by 2.5 dB but expanding the coverage area by 35%. Based on this, the antenna pattern calculator generated new beam envelope parameters. In the Hexi Corridor section of the Lanzhou-Xinjiang High-Speed ​​Railway, the signal fluctuation at 1500 meters after optimization was reduced to 3 dB. This adjustment effectively addresses the tunnel environment and improves signal penetration.

[0090] Coverage expansion parameters were improved through power allocation and sector reconstruction. Taking the Bengbu-Nanjing section of the Beijing-Shanghai High-Speed ​​Railway as an example, at a distance of 1000 meters from the tunnel entrance, the transmitter power was linearly increased at a slope of 0.01 dB per meter, resulting in a 12 dB increase in intensity at the entrance, compensating for 80% of the loss. The power allocation calculator, combined with the signal-to-noise ratio predictor, generated the target signal strength value, while the sector boundary generator reconstructed the coverage area through adaptive grid division. On the Jiangjin-Chongqing West section of the Chengdu-Chongqing High-Speed ​​Railway, the grid side length changed with the signal gradient, decreasing to 50 meters in areas of high intensity and increasing to 200 meters in flat areas. The coverage radius expanded from 200 meters to 350 meters, and the beamwidth adjustment value fluctuated within ±15 degrees, increasing the coverage area by approximately 45%.

[0091] The synergistic optimization of the above steps is particularly evident in the Shenzhen North to Hong Kong West Kowloon section of the Guangzhou-Shenzhen-Hong Kong Express Rail Link. This section comprises 93% tunnels. By jointly adjusting the prediction threshold, power slope, and beamwidth, signal uniformity within the tunnels was significantly improved, and the intensity standard deviation was reduced to 4.2 dB. This proactive adjustment ensures signal continuity under complex terrain, and specific parameters can be flexibly configured according to the characteristics of the line.

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

[0093] By traversing the transmitter array using coverage extension parameters, the system identifies the transmitters requiring adjustment and their amplitudes. Taking the Beijing-Guangzhou High-Speed ​​Railway from city A to city B as an example, the system quickly locates the affected transmitters and optimizes their parameters by accurately calculating the beam crossover area, thereby improving signal enhancement.

[0094] S1071. Calculate the affected transmitters and their adjustment range based on the coverage extension parameters and extract beam features. Optimize beam direction and power gain using a recurrent neural network. Generate execution command sequence through time synchronization to ensure that the adjustment process is coordinated and consistent.

[0095] A spatial location relationship calculator was used to analyze the transmitter array, calculate the beam cross-area and boundary distance, and identify the affected transmitters and adjustment ranges. Taking the Wuhan-Changsha section as an example, the cross-area accounts for 25% to 35% of the single beam coverage, and the boundary distance is 300 to 500 meters, typically involving 2 to 3 adjacent transmitters. A beam parameter extractor reads the azimuth angle, power gain, and half-power angle of these transmitters to generate a feature sequence. In the Guizhou section of the Shanghai-Kunming High-Speed ​​Railway, the adjustable azimuth angle is ±60 degrees, the gain is between 12 and 18 dB, and the half-power angle is between 15 and 45 degrees. The sequence shows a negative correlation between direction and gain. A recurrent neural network with an 8-layer fully connected structure optimized the parameters, with 64 hidden layer neurons, controlling the azimuth angle difference to within 30 degrees and the gain difference to no more than 6 dB. A time synchronization controller generated the adjustment timing sequence, with 5-millisecond intervals and no less than 15 milliseconds in the Guangzhou-Shenzhen-Hong Kong High-Speed ​​Railway section, and the total duration controlled within 100 milliseconds. The beamforming synchronizer limits the direction adjustment rate to 3 degrees per second and the power slope to no more than 0.5 dB per millisecond, generating a synchronization command set to ensure smooth adjustment.

[0096] S1072. The beam pointing is adjusted and the configuration is optimized through calibration and trajectory prediction. Kalman filtering and neural networks are used to predict changes in train direction. Combined with genetic algorithms to iteratively optimize parameters, an enhanced coverage scheme is generated to improve signal strength and boundary clarity.

[0097] Using a 10MHz rubidium atomic clock as a reference, beam parameter deviations are compensated using a phase difference calculator. The sampling rate is 1MHz, synchronization accuracy is better than 50 nanoseconds, and azimuth calibration reaches 0.1 degrees, generating a calibration parameter set. Coordinate sequences are extracted from train position data, sampled at 10Hz, and trajectory curves are generated using least-squares fitting of cubic polynomials. Taking the Zunyi-Guiyang North section as an example, with a curvature radius ranging from 2000 to 6000 meters, 12 inflection points are marked, and the maximum rate of change is 0.15 per kilometer. An 8-layer neural network predictor, inputting position, velocity, and acceleration, with a 2-second time window, predicts 3 seconds in advance at a speed of 350 km / h with an error less than 0.5 degrees. The beam pattern generator adjusts the pointing according to the rate of change. The phased array antenna is controlled by 32 elements, with horizontal adjustment of ±60 degrees and vertical adjustment of ±30 degrees, and a gain range of 12 dB. If the gain is below the 15 dB threshold, the genetic algorithm iterates for 50 generations with a population size of 200, a crossover probability of 0.8, and a mutation probability of 0.05, achieving an optimization accuracy of 0.2 degrees and 0.1 dB, and finally generating an enhancement scheme.

[0098] The effectiveness of the solution was verified through electromagnetic field simulation. On the Jiangjin-Chongqing West section of the Chengdu-Chongqing High-Speed ​​Railway, a three-dimensional finite difference method was used for calculation, with a grid size of 1 / 10 of the wavelength. Considering terrain undulations, the signal enhancement coverage map showed an intensity increase of 12 dB. The boundary detector used adaptive thresholding to extract edges. On the Hexi Corridor section of the Lanzhou-Xinjiang High-Speed ​​Railway, the ambiguity decreased from 150 meters to 50 meters, resulting in a clearer corridor outline and ensuring uniform signal distribution and sharp boundary edges.

[0099] The above steps demonstrate excellent performance in complex environments. Taking the Harbin-Dalian High-Speed ​​Railway in frigid regions as an example, the simulation considers the impact of snow accumulation, increasing attenuation by 4 to 7 decibels. By reconstructing the coverage area, the loss is compensated, maintaining boundary clarity and improving intensity. Specific timing and optimization parameters can be adjusted according to the characteristics of the line, allowing the enhancement scheme to flexibly adapt to different scenario requirements.

[0100] like Figure 2 This invention provides a terrestrial digital television broadcast transmitter signal coverage enhancement system, mainly comprising:

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

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

[0103] The area judgment and parameter adjustment module is used to calculate the train's moving speed and direction based on the transmitter array distribution parameters, determine whether the train is approaching the tunnel cutting area, and if so, adjust the initial beamforming parameters to obtain the signal coverage range change trend.

[0104] The blind spot analysis module is used to obtain coverage blind spot feedback data based on the signal coverage range change trend, determine the sections with insufficient signal power density in the tunnel cutting area, and generate a coverage blind spot distribution map.

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

[0106] The pre-adjustment module is used to determine whether a train is about to enter the next tunnel cutting area based on the real-time acquired mobile coverage corridor framework data, combined with the real-time acquired train position data and speed information. If so, the signal power density of the corresponding transmitter is increased to obtain the pre-adjusted coverage range extension parameters.

[0107] The signal enhancement scheme generation module is used to extract the affected transmitter numbers and adjustment amplitudes from the transmitter array based on the pre-adjusted coverage extension parameters, calibrate the beamforming parameters, and generate a signal enhancement scheme for the target mobile coverage corridor.

[0108] A 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 implement the method described above and enhance the signal coverage of a terrestrial digital television broadcast transmitter according to the method.

[0109] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for enhancing signal coverage of a terrestrial digital television broadcast transmitter, characterized in that, The method includes: acquiring train position data, collecting latitude and longitude information of the train in a high-speed moving scenario in real time, and matching it with a preset running trajectory database to determine the current position coordinate data of the train; extracting transmitter array distribution parameters of the corresponding section from a pre-established signal coverage range database based on the train position coordinate data to obtain the initial beamforming parameters of each transmitter in the corresponding section; calculating the train's moving speed and direction based on the transmitter array distribution parameters, determining whether the train is approaching the tunnel cutting area, and if so, adjusting the initial beamforming parameters to obtain the signal coverage range change trend; and acquiring coverage blind zone feedback data based on the signal coverage range change trend to determine the signal power within the tunnel cutting area. For areas with insufficient signal density, a coverage blind spot distribution map is generated. Based on the coverage blind spot distribution map, the beamforming parameters of the transmitter array are adjusted in real time to obtain optimized signal power density distribution data, forming a mobile coverage corridor framework. Based on the real-time acquired mobile coverage corridor framework data, combined with real-time acquired train position data and speed information, it is determined whether the train is about to enter the next tunnel cutting area. If so, the signal power density of the corresponding transmitter is increased to obtain the pre-adjusted coverage range expansion parameters. Based on the pre-adjusted coverage range expansion parameters, the affected transmitter numbers and adjustment ranges are extracted from the transmitter array, the beamforming parameters are calibrated, and a target mobile coverage corridor signal enhancement scheme is generated.

2. The method according to claim 1, characterized in that, The process of acquiring train position data, which involves real-time collection of latitude and longitude information of trains in high-speed moving scenarios and matching it with a preset operating trajectory database to determine the current position coordinates of the train, includes: acquiring real-time latitude and longitude coordinate data of the high-speed train; using a preset railway coordinate lookup table in a coordinate mapping converter to convert the latitude and longitude coordinate data into line coordinate data based on the railway mainline mileage; acquiring reference positioning point data set at fixed intervals along the railway mainline from the track foundation database; using a cubic spline function to interpolate the reference positioning point data to obtain continuous trajectory reference point data; performing median filtering on the line coordinate data through a fixed-length sliding window and fitting a Bezier curve to obtain a smooth trajectory curve sequence; if the lateral deviation between the positioning point in the smooth trajectory curve sequence and the continuous trajectory reference point is greater than a preset threshold, then using a particle filter algorithm to estimate the state of the positioning point, and training based on historical trajectory data to obtain the current position coordinates of the train.

3. The method according to claim 1, characterized in that, The process of extracting transmitter array distribution parameters for the corresponding section from a pre-established signal coverage database based on train position coordinate data to obtain initial beamforming parameters for each transmitter within the corresponding section includes: receiving train position coordinate information; obtaining a transmitter distribution parameter table from the signal coverage database based on the position coordinate information, the transmitter distribution parameter table containing the latitude and longitude coordinates and array arrangement parameters of the transmitters; spatially dividing the transmitter coverage area using a regular hexagonal cellular grid algorithm based on the transmitter distribution parameter table to obtain a gridded beam control parameter table, the gridded beam control parameter table containing grid node position data; calculating the superimposed electromagnetic field distribution data using the free space propagation loss formula for the grid node position data, and fitting the grid point signal power density value using the least squares method; and iteratively optimizing the transmitter antenna direction angle and gain coefficient using a genetic algorithm based on the difference matrix between the grid point signal power density value and the preset signal strength reference value to obtain the beamforming parameter surface.

4. The method according to claim 1, characterized in that, The process of calculating the train's speed and direction based on the transmitter array distribution parameters, determining whether the train is approaching the tunnel cutting area, and adjusting the initial beamforming parameters to obtain the signal coverage change trend includes: sampling the train's position points using a fixed time window, calculating the train's direction angle and instantaneous speed value through cubic polynomial fitting; extracting track elevation data from a terrain feature database based on the train's direction angle, and performing segmented fitting of the elevation data using a terrain feature classifier to obtain tunnel cutting distribution data; extracting features from the tunnel cutting distribution data using a deep neural network analyzer to generate a terrain impact prediction parameter sequence; adjusting the beamforming parameters based on the terrain impact prediction parameter sequence, and solving Maxwell's equations using a space electromagnetic field simulation calculator to obtain electromagnetic field distribution data; and dynamically planning the transmitter power density distribution based on the electromagnetic field distribution data and the train's trajectory to obtain signal coverage change trend data.

5. The method according to claim 1, characterized in that, The process of obtaining coverage blind zone feedback data based on the signal coverage range change trend, determining sections with insufficient signal power density within the tunnel cutting area, and generating a coverage blind zone distribution map includes: acquiring data packets collected by the vehicle-mounted receiver; extracting received signal strength values ​​and signal quality indicators from the data packets to obtain a signal state sequence; using a sliding median filter to eliminate noise in the signal state sequence to obtain a signal strength change curve; marking the time points and corresponding geographical locations where the signal strength is lower than the preset signal strength threshold based on the signal strength change curve and a preset signal strength threshold to obtain signal weakening point location data; extracting the interruption occurrence time and recovery time from the communication interruption event log recorded by the vehicle-mounted receiver, and generating a signal interruption interval boundary point sequence by combining it with train positioning data; and using a grid density clustering algorithm to spatially cluster the signal weakening point location data and the signal interruption interval boundary point sequence, and generating a coverage blind zone distribution map by calculating the location point density within the grid cell.

6. The method according to claim 1, characterized in that, The process of adjusting beamforming parameters for the transmitter array in real time based on the coverage blind zone distribution map to obtain optimized signal power density distribution data and form a mobile coverage corridor framework includes: reading the beam direction angle and beamwidth values ​​of the transmitter antenna based on the blind zone distribution map; generating a beam control command sequence using a multi-beam synthesizer; performing vector operations on the main lobe direction and side lobe level of the transmitter using a phased array antenna matrix to obtain a beamforming parameter set for the beam control command sequence; extracting the antenna gain coefficient of the transmitter from the beamforming parameter set; and calculating the gain coefficient of a single antenna using a deep neural network. The algorithm optimizes the gain coefficient to obtain a power adjustment command. Based on this command, the electromagnetic field calculation unit performs superposition calculations on the transmitter radiation field, and uses a Maxwell's equations solver to calculate the spatial distribution of the radiation field, generating a mobile coverage corridor framework. The algorithm also includes: analyzing blind zone distribution characteristics from a coverage blind zone distribution map, determining the transmitter array's beamforming parameters, obtaining an adjusted parameter set, performing beamforming processing, updating the signal power to obtain power density data, and if the blind zone distribution in the power density data exceeds a preset threshold, readjusting the parameters based on the distribution map data. The optimized beamforming scheme is obtained, the signal power distribution of the transmitter array is calculated, and the optimized signal power density distribution data is acquired. Specifically, this includes: for the coverage blind zone distribution map, obtaining the set of blind zone location coordinates and area data using a density clustering calculator to obtain a blind zone feature sequence; based on the blind zone feature sequence, generating beamforming control parameters for the main lobe direction angle and half-power angle using an antenna pattern calculator; adjusting the phase factor of the phased array antenna array radiating elements using the beamforming control parameters to obtain beam pattern data; and using an electromagnetic wave propagation loss calculator to... The beam pattern data is used to perform spatial attenuation calculations to obtain an initial power density distribution map. For this initial power density distribution map, a threshold decision device is used to mark regions where the signal strength is below a preset threshold, obtaining updated blind zone distribution data. If the total blind zone area in the updated blind zone distribution data exceeds the preset threshold, a genetic algorithm is used to iteratively optimize the beamforming control parameters to obtain an optimized beamforming parameter sequence. An electromagnetic field numerical calculator is used to verify the optimized beamforming parameter sequence, and the optimized signal power density distribution data is obtained by solving Maxwell's equations.

7. The method according to claim 1, characterized in that, The process involves using real-time acquired mobile coverage corridor framework data, combined with real-time acquired train position data and speed information, to determine whether the train is about to enter the next tunnel cutting area. If so, the signal power density of the corresponding transmitter is increased to obtain pre-adjusted coverage range expansion parameters. This includes: obtaining the real-time position coordinates of the train based on the mobile coverage corridor framework data; using a Kalman filter to filter the three state variables of train displacement, speed, and acceleration to obtain a predicted train trajectory curve; and using a terrain database to extract the preceding line parameters from the predicted train trajectory curve, and obtaining the projected distance between the train and the tunnel entrance using a line gradient change rate and curvature change rate calculator. Based on the projected distance and train speed values, a motion prediction calculator is used to generate time series data of the train's arrival at the tunnel entrance. If the time series data is less than a preset threshold, a predicted tunnel entrance time 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, a new beam envelope parameter is generated using an antenna pattern calculator. Based on the beam envelope parameter, a power allocation calculator is used to dynamically adjust the transmitter output power. Combined with a signal-to-noise ratio predictor, a target signal strength value is generated. A spatial interpolation calculator is used to generate coverage radius increment data and beamwidth adjustment values ​​to obtain the pre-adjusted coverage range extension parameters.

8. The method according to claim 1, characterized in that, The process of extracting the affected transmitter numbers and adjustment magnitudes from the transmitter array based on the pre-adjusted coverage expansion parameters, calibrating beamforming parameters, and generating a target mobile coverage corridor signal enhancement scheme includes: using a spatial location calculator to traverse the transmitter array based on the coverage expansion parameters to obtain the affected transmitter identification numbers and adjustment magnitude values; for the affected transmitter identification numbers, using a beam parameter extractor to read the transmitter's three basic parameters—beam direction angle, power gain value, and beam half-power angle—to obtain a beam feature data sequence; based on the beam overlap area data between adjacent transmitters in the beam feature data sequence, using a recurrent neural network to iteratively optimize the beam direction and power gain to obtain a beam parameter optimization matrix; and for the beam parameters... The algorithm optimizes the matrix, uses a time synchronization controller to generate the execution timing of transmitter beam adjustment, constrains the transmitter's beam direction adjustment rate and power adjustment slope through a beamforming synchronizer to obtain a synchronous execution command set, and uses a beam adjustment verifier to simulate and verify the synchronous execution command set. It also incorporates a signal enhancement scheme for the target mobile coverage corridor based on signal strength distribution data. The algorithm further includes: obtaining the time reference for the dynamic synchronization mechanism, calibrating beamforming parameters to obtain a calibrated parameter set, extracting movement trajectory features from the target movement data, determining changes in the direction of trajectory generation, adjusting the calibrated parameter set, generating the beamforming configuration for the coverage corridor, calculating the signal enhancement gain, and optimizing the enhancement scheme parameters if the signal enhancement gain is lower than a preset threshold to obtain the signal enhancement result for the target mobile coverage corridor.

9. A terrestrial digital television broadcast transmitter signal coverage enhancement system, characterized in that, The system includes: a train position acquisition module, used to acquire train position data, collect latitude and longitude information of the train in real time during high-speed movement scenarios, and match it with a preset running trajectory database to determine the current position coordinates of the train; a beamforming parameter extraction module, used to extract transmitter array distribution parameters of the corresponding section from a pre-established signal coverage database based on the train position coordinate data, and obtain the initial beamforming parameters of each transmitter in the corresponding section; a region judgment and parameter adjustment module, used to calculate the train's speed and direction based on the transmitter array distribution parameters, determine whether the train is approaching the tunnel cutting area, and if so, adjust the initial beamforming parameters to obtain the signal coverage change trend; and a blind spot analysis module, used to obtain coverage blind spot feedback data based on the signal coverage change trend to determine the tunnel cutting area. For sections with insufficient signal power density within the area, a coverage blind spot distribution map is generated. The dynamic coverage optimization module adjusts the beamforming parameters of the transmitter array in real time based on the coverage blind spot distribution map to obtain optimized signal power density distribution data, forming a mobile coverage corridor framework. The pre-adjustment module determines whether a train is about to enter the next tunnel cutting area based on the real-time acquired mobile coverage corridor framework data, combined with real-time acquired 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 parameters. The signal enhancement scheme generation module extracts the affected transmitter numbers and adjustment ranges from the transmitter array based on the pre-adjusted coverage range expansion parameters, calibrates the beamforming parameters, and generates a target mobile coverage corridor signal enhancement scheme.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the terrestrial digital television broadcast transmitter signal coverage enhancement method according to any one of claims 1-8.

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