Method for generating and updating a broadcast coverage map and related device

Abstract

The application discloses a broadcast coverage map generation and updating method and related equipment. The method comprises the following steps: obtaining initial broadcast coverage prediction data and broadcast measured data from a terminal; identifying an area existing difference between prediction and measurement and located near a prediction boundary as an uncertain coverage band; performing a limited credibility propagation calibration with a trusted observation data as an anchor point in the uncertain band; setting a multi-condition trigger mechanism for a stable coverage area to maintain coverage stability; and generating or incrementally updating a broadcast coverage map according to the calibration result. The application solves the problems of frequent boundary area drift and excessive propagation of abnormal samples in the prior art through a differentiated calibration strategy, thereby improving the boundary stability and overall accuracy of the broadcast coverage map.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method and related equipment for generating and updating a broadcast coverage map. Background Technology

[0002] Broadcast coverage maps are crucial foundational data for broadcast network planning, automatic frequency selection at terminals, cross-regional handover, emergency broadcast support, and regional service optimization. Broadcast coverage maps are typically generated through two main methods: one is through theoretical predictions based on transmitter locations, transmission power, antenna parameters, propagation models, and geographical information to obtain theoretical coverage results; the other is through measured broadcast signal data obtained via dedicated test vehicles, manual surveys, or terminal reporting to reflect the actual reception in local areas.

[0003] However, actual broadcast reception quality is affected not only by transmission parameters and static geographical environment, but also by building obstruction, local electromagnetic interference, changes in road conditions, differences in terminal hardware, and time variations. Some related technologies propose using measured data to correct and update predicted coverage maps. By weighted fusion of predicted and measured data, a unified fusion process is applied to the entire coverage map to improve its accuracy.

[0004] However, due to the spatial sparseness, uneven sampling paths, large differences in sample timeliness, and unstable terminal observation quality of the measured data, the coverage boundary area is more susceptible to the influence of local abnormal samples because it is located in the signal strength transition zone. This results in frequent drift or jitter of the boundary position. Sparse abnormal samples may be over-propagated to the surrounding stable coverage area, causing the originally accurate predicted coverage status to be unnecessarily corrected, resulting in poor stability of the coverage map. Summary of the Invention

[0005] This application provides a method and related equipment for generating and updating a broadcast coverage map, which improves the stability of the coverage map.

[0006] Firstly, this application provides a method for generating and updating a broadcast coverage map. The method includes: acquiring initial broadcast coverage prediction data for a target broadcast area, and obtaining the predicted coverage state and prediction boundary candidates for each spatial unit according to preset spatial units; acquiring broadcast measurement data uploaded by multiple terminals, preprocessing the broadcast measurement data to obtain the measured observation state of each spatial unit; based on the difference between the predicted coverage state and the measured observation state of each spatial unit, and combined with the prediction boundary candidates, identifying areas located near the predicted coverage boundary and where the predicted coverage state differs from the measured observation state as coverage uncertainty zones, and determining anchor point spatial units within the coverage uncertainty zones. The system identifies the anchor spatial unit and the spatial units to be calibrated associated with it. Based on the observation reliability and spatial propagation constraints of the anchor spatial unit, and following a propagation rule that gradually decreases from the anchor spatial unit to the spatial unit to be calibrated, a constrained confidence propagation calibration is performed on the spatial units to be calibrated to obtain the calibration coverage status and unit confidence of each spatial unit. The calibration intensity of spatial units within the coverage uncertainty zone is greater than that of spatial units within the stable coverage zone, and the original predicted coverage status is maintained when the difference between the predicted coverage status and the actual observation status in the stable coverage zone does not reach the preset trigger condition. A broadcast coverage map is generated or incrementally updated based on the calibration coverage status and unit confidence.

[0007] In the above embodiments, the system distinguishes between coverage uncertainty zones and stable coverage areas, and implements differentiated calibration strategies for each, ensuring that the spatial distribution of calibration processing matches the uncertainty distribution of the actual coverage state. The system identifies areas where predictions and measurements conflict and are located near the prediction boundary as coverage uncertainty zones. Within these zones, anchor point spatial units serve as reliable observation sources, and calibration information is progressively attenuated to the units to be calibrated through a constrained reliability propagation rule, causing the calibration intensity to decrease with propagation distance and terrain constraints. For stable coverage areas outside the coverage uncertainty zones, the system sets trigger condition thresholds, only unfreezing the state and updating the coverage state when the scale, duration, and deviation of abnormal measured samples all meet preset conditions; otherwise, the original prediction data remains unchanged. This prevents local conflict samples in the boundary areas from propagating unconstrainedly to stable areas, thereby reducing the probability of erroneous corrections of stable coverage areas by sparse abnormal samples and improving the stability and overall accuracy of the coverage map at the boundary locations.

[0008] In some embodiments of the first aspect, based on the difference between the predicted coverage state and the measured observation state of each spatial unit, and in conjunction with the predicted boundary candidate, regions located near the predicted coverage boundary and where the predicted coverage state differs from the measured observation state are identified as coverage uncertainty zones. Specifically, this includes: calculating the difference between the field strength corresponding to the predicted coverage state and the average field strength corresponding to the measured observation state within each spatial unit, and the deviation between the acceptability corresponding to the predicted coverage state and the decoding success rate corresponding to the measured observation state; when the difference is greater than a preset field strength error threshold, or the deviation is greater than a preset probability threshold, the corresponding spatial unit is marked as a conflict unit; based on the predicted boundary candidate, a preset distance is extended inward and outward to form an initial boundary zone; the continuous spatial region containing conflict units within the initial boundary zone, and the region adjacent to the conflict unit where the measured undecoding probability continuously exceeds a preset duration, are jointly determined as coverage uncertainty zones; within the coverage uncertainty zone, spatial units where the number of measured samples reaches a preset scale and the measured confidence level is higher than a preset confidence threshold are determined as anchor spatial units.

[0009] In the above embodiments, the system calculates the deviation between the predicted coverage state and the measured observation state in terms of field strength and decoding success rate, and combines this with the spatial location of the predicted boundary candidates to constrain the scope of conflict identification within an initial boundary band formed by extending the predicted boundary inside and outside. This initial boundary band spatially isolates potential coverage state transition areas from stable coverage areas, ensuring that subsequent conflict unit marking and continuous region determination are performed within a limited spatial range, avoiding misclassification of stable areas far from the boundary as uncertain zones. Simultaneously, the system includes areas with a measured undecoding probability continuously exceeding a preset duration in the criteria for determining uncertain coverage zones, incorporating the temporal continuity characteristics of the coverage state into the determination logic, thereby reducing the interference of occasional abnormal samples on boundary identification.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, based on the observation reliability and spatial propagation constraints of the anchor spatial unit, and following a propagation rule of progressive attenuation from the anchor spatial unit to the spatial unit to be calibrated, a constrained confidence propagation calibration is performed on the spatial unit to be calibrated to obtain the calibration coverage status and unit confidence of each spatial unit. This includes: constructing a topology propagation map centered on the anchor spatial unit, the propagation path of which is constrained by road continuity distribution and terrain elevation obstruction; calculating a propagation attenuation factor based on the spatial topology distance between the spatial unit to be calibrated and the anchor spatial unit, the terrain obstruction coefficient on the propagation path, and the time freshness of the measured data within the anchor spatial unit; mapping the measured observation status of the anchor spatial unit to the spatial unit to be calibrated hop by hop outwards, combining the propagation attenuation factor, until a preset maximum propagation hop count is reached or the propagation attenuation factor is lower than a preset attenuation lower limit; weighted fusion of the calibration value reached by the mapping and the predicted coverage status of the spatial unit to be calibrated to update the calibration coverage status of the spatial unit to be calibrated, and determining the unit confidence of the spatial unit to be calibrated based on the propagation attenuation factor and the length of the mapping path.

[0011] In the above embodiments, the topology propagation map constructed by the system limits the propagation path to the spatial range allowed by the continuous distribution of roads and terrain elevation obstruction, ensuring that the propagation of calibration information follows the physical constraints of the actual propagation of electromagnetic waves. The calculation of the propagation attenuation factor integrates the influence of three dimensions: spatial topological distance, terrain obstruction coefficient, and time freshness. This causes the calibration intensity to gradually decrease as the propagation path extends, terrain obstacles increase, and the timeliness of measured data decreases, until the preset maximum propagation hop count is reached or the attenuation factor falls below the lower limit, at which point propagation stops. This progressively attenuating propagation rule ensures that the measured observation state of the anchor point spatial unit only affects the unit to be calibrated within the neighborhood where its credibility can effectively cover. Areas far from the anchor point or where the propagation path is obstructed maintain their original predicted coverage state or receive calibration from other anchor points. This reduces the possibility of abnormal observations from a single anchor point spreading unconstrainedly to the entire area, improving the spatial continuity and physical rationality of the calibration coverage state.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, when the difference between the predicted coverage state and the measured observation state in the stable coverage area does not reach a preset triggering condition, the original predicted coverage state is maintained. Specifically, this includes: for stable coverage areas located outside the coverage uncertainty zone, the abnormal measured sample quantity and abnormal duration of each spatial unit within the area are statistically analyzed in real time; when the abnormal measured sample quantity is greater than a preset first scale threshold, the abnormal duration is greater than a preset second duration threshold, and the average field strength corresponding to the abnormal measured sample deviates from the predicted coverage state by more than a preset tolerance, it is determined that the preset triggering condition has been met, the stable freeze state of the corresponding spatial unit is released, and the predicted coverage state of the corresponding spatial unit is updated to the coverage state determined based on the abnormal measured sample, wherein the preset first scale threshold and the preset second duration threshold are negatively correlated; if the above conditions are not met simultaneously, the abnormal measured sample is determined to be short-term interference or occasional noise, and the spatial units within the stable coverage area maintain the initial broadcast coverage prediction data unchanged.

[0013] In the above embodiments, the system requires that three conditions—the number of abnormal measured samples, the duration of the abnormality, and the degree of field strength deviation—simultaneously meet their respective thresholds before determining that a preset trigger condition has been met and releasing the stable freeze state. Otherwise, the abnormal measured samples are judged as short-term interference or occasional noise, and the original predicted data remains unchanged. This multi-dimensional joint determination requires sufficient evidence to be accumulated to support the update of the coverage status of the stable coverage area, thereby reducing the probability of occasional abnormal samples falsely triggering the stable area.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, broadcast measurement data uploaded by multiple terminals is acquired, and the broadcast measurement data is preprocessed to obtain the measured observation status of each spatial unit. Specifically, this includes: extracting terminal type and antenna status identifiers from the broadcast measurement data; normalizing and compensating the field strength values ​​and signal-to-noise ratio reported by different terminal types according to a preset terminal hardware difference compensation model to eliminate systematic measurement errors caused by heterogeneous terminals; removing outliers after normalization compensation, and spatially aggregating the cleaned data according to map tiles or regular geographic grids to obtain the average field strength, decoding success rate, and frequency optimization degree in each spatial unit; and determining the measured observation status based on the average field strength, decoding success rate, and frequency optimization degree.

[0015] In the above embodiments, the system extracts terminal type and antenna status identifiers, and normalizes and compensates the field strength values ​​and signal-to-noise ratio reported by different terminal types according to a preset terminal hardware difference compensation model. This ensures that the measured data from heterogeneous terminals are converted to a unified measurement benchmark before entering subsequent processing. This normalization compensation reduces systematic measurement errors caused by different terminal hardware characteristics (such as receiver sensitivity, antenna gain, signal processing algorithms, etc.), making the field strength values ​​reported by different terminals at the same spatial location comparable. This conversion process from heterogeneous raw data at the terminal level to a unified observation state at the spatial unit level reduces the impact of terminal hardware differences and occasional measurement noise on the measured observation state, improving the reliability of the measured observation state as a calibration basis.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the measured reliability of the anchor point spatial unit is calculated in the following way: obtaining the total number of samples of measured data in the anchor point spatial unit, the collection time distribution matrix, and the terminal device type diversity index; the larger the total number of samples, the closer the collection time is to the current update time and the more uniform the time distribution, and the higher the terminal device type diversity index, the higher the measured reliability of the calculated output.

[0017] In the above embodiments, when calculating the measured reliability of anchor spatial units, the system comprehensively considers the characteristics of three dimensions: the total number of samples, the acquisition time distribution matrix, and the terminal device type diversity index. This makes the measured reliability no longer dependent on the single indicator of sample quantity, but comprehensively evaluates the sufficiency of the quantity of observation data, the temporal representativeness, and the diversity of devices. This allows the selection of anchor spatial units and the initial intensity of calibration propagation to reflect the actual quality of the observation data, thereby reducing the risk of low-quality observation data spreading to the surrounding area as a calibration source and improving the accuracy of the calibration coverage status.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, generating or incrementally updating the broadcast coverage map based on the calibration coverage status and cell confidence level specifically includes: generating a map layer containing spatial cell identifiers, a set of receivable frequencies, field strength levels, reception quality levels, preferred frequencies, and decoding availability labels based on the calibration coverage status and cell confidence level, thereby obtaining a broadcast coverage map; when receiving continuously uploaded broadcast measurement data, performing local incremental refresh on spatial cells whose status has changed within the coverage uncertainty band and spatial cells within the stable coverage area that have been unfrozen upon reaching a preset trigger condition, and updating the update timestamp and boundary correction results of the corresponding spatial cells, thereby completing the incremental update of the broadcast coverage map.

[0019] In the above embodiments, when the system receives continuously uploaded broadcast measurement data, it only performs local incremental refresh on spatial units in the uncertain coverage zone that have undergone state changes and spatial units in the stable coverage area that have been unfrozen after reaching the preset trigger conditions, instead of performing a global recalculation on the entire coverage map. This makes the computational workload of map updates proportional to the actual range of changes, rather than proportional to the area of ​​the entire target broadcast area. This reduces the computational resource consumption and response time of map updates and improves the update efficiency of broadcast coverage maps in a continuous data stream environment. In a second aspect, embodiments of this application provide a broadcast coverage map generation and updating apparatus, the broadcast coverage map generation and updating apparatus comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the broadcast coverage map generation and updating apparatus to perform the method as described in the first aspect and any possible implementation thereof.

[0020] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a broadcast overlay map generation and updating device, cause the broadcast overlay map generation and updating device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a broadcast overlay map generation and updating device, cause the broadcast overlay map generation and updating device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Understandably, the broadcast coverage map generation and updating device provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application identifies coverage uncertainty zones and implements differentiated calibration strategies for them. It focuses on areas where predictions conflict with actual measurements and are located near the prediction boundary as key calibration targets. This avoids the unconstrained propagation of local conflict samples from the boundary area to the stable area, reduces the frequent drift or jitter of the coverage boundary position, and improves the boundary stability of the coverage map.

[0024] 2. This application sets multi-dimensional joint triggering conditions for stable coverage areas, requiring that the abnormal measured sample size, abnormal duration and field strength deviation all meet the threshold before the coverage status is updated; otherwise, the original prediction data remains unchanged. This prevents sparse abnormal samples from over-correcting stable coverage areas and reduces the probability that the originally accurate predicted coverage status is unnecessarily corrected.

[0025] 3. This application constructs a topology propagation map constrained by road continuity and terrain obstruction, and performs credibility propagation calibration by adopting a propagation rule with progressive attenuation. This makes the calibration intensity decrease with propagation distance and terrain constraints, avoiding the unconstrained diffusion of abnormal observations from a single anchor point to the entire area, and improving the spatial continuity and physical rationality of the calibration coverage state. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the method for generating and updating a broadcast coverage map in an embodiment of this application; Figure 2 This is a schematic diagram of the physical device structure of a broadcast coverage map generation and updating device in the embodiments of this application. Detailed Implementation

[0027] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more. The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a method for generating and updating a broadcast coverage map in an embodiment of this application.

[0029] S101. Obtain the initial broadcast coverage prediction data of the target broadcast area, and obtain the predicted coverage status and prediction boundary candidates of each spatial unit according to the preset spatial units.

[0030] Among them, the initial broadcast coverage prediction data refers to the theoretical coverage information obtained based on propagation model calculations or historical prediction coverage maps, including parameters such as predicted field strength and predicted receptivity at each spatial location; the preset spatial unit refers to the basic geographic unit used to spatially discretize the target broadcast area, which can be a map tile (such as a 256×256 pixel tile block) or a regular geographic grid (such as a 100m×100m square grid); the predicted coverage status refers to the predicted broadcast signal reception status within each spatial unit, which can be represented as field strength level (such as strong signal, medium signal, weak signal), receptivity label (such as receptive, unreceptive), or decoding availability label (such as decodeable, undecodeable); the predicted boundary candidate refers to the possible location of the coverage boundary identified based on the predicted coverage status, which can be formed by the field strength threshold method (such as extracting contour lines where the field strength equals the reception threshold) or the state transition method (such as the location where the coverage status of adjacent units changes from receptive to unreceptive).

[0031] This step is executed when the system initiates the broadcast coverage map generation process, and is used to create a new coverage map or update it periodically. Specifically, the system obtains initial broadcast coverage prediction data for the target broadcast area (which can be divided by administrative region, transmitter service area, or road corridor area) from the propagation prediction software or historical coverage database. This data includes transmitter location, transmission power, antenna parameters, propagation model parameters, and theoretical field strength distribution calculated based on geographic environment information. Then, according to the selected preset spatial unit type (such as 256×256 pixel tiles under Mercator projection or 100-meter square grid under WGS84 coordinate system), the continuous predicted field strength distribution is discretized into the predicted coverage state of each spatial unit. For multi-frequency broadcast scenarios, each spatial unit can simultaneously record the predicted coverage state and preferred frequency of multiple frequencies. Finally, prediction boundary candidates are extracted using the field strength threshold method or state transition method. These boundary candidates not only come from the current prediction results but can also be combined with historical corrected boundaries and high-risk terrain zones (such as valleys and urban canyons) to form composite boundary candidates, providing a spatial benchmark for subsequent identification of coverage uncertainty zones.

[0032] S102. Obtain broadcast measurement data uploaded by multiple terminals, preprocess the broadcast measurement data, and obtain the measured observation status of each spatial unit.

[0033] Among them, the broadcast measured data refers to the broadcast signal reception measurement data collected and uploaded by multiple terminal devices during actual use, including fields such as reception location coordinates, field strength value, signal-to-noise ratio, decoding success identifier, reception timestamp, terminal type identifier, and antenna status identifier; the measured observation status refers to the comprehensive reception status within each spatial unit obtained after preprocessing and spatial aggregation of the original measured data. It can be determined by one or more indicators among average field strength, decoding success rate, reception continuity, and frequency optimization. For example, when the average field strength is higher than -60dBm and the decoding success rate is higher than 90%, it is determined to be a stable and usable state. When the average field strength is lower than -75dBm or the decoding success rate is lower than 30%, it is determined to be an unusable state.

[0034] This step is continuously executed while the terminal uploads the measured data. The triggering method can be timed (e.g., uploading every 5 minutes), location change (e.g., moving more than 100 meters), or sudden change in reception quality (e.g., a sudden drop in field strength exceeding 10dB). For weak network environments, the data can be cached locally and then re-uploaded. Specifically, after receiving the raw measured data uploaded by the terminal, the system first extracts the terminal type (e.g., vehicle-mounted terminal, handheld terminal, fixed receiver) and antenna status identifiers (e.g., built-in antenna, external antenna, antenna orientation). Based on a preset terminal hardware difference compensation model, the system normalizes and compensates the field strength values ​​and signal-to-noise ratio reported by different terminal types. This compensation model can be based on laboratory calibration results to establish a compensation coefficient table for different terminal types (e.g., if a certain model of handheld terminal has a sensitivity 3dB lower than the reference terminal, then its reported field strength value is compensated by 3dB), or a reference terminal can be selected and a regression compensation model can be constructed based on overlapping observations at the same location (e.g., fitting the field strength difference function of different terminals at the same location using the least squares method) to eliminate systematic measurement errors caused by heterogeneous terminals.

[0035] After normalization compensation, the system employs methods such as rule-based removal based on physical range (e.g., removing samples with field strengths exceeding -30dBm to -100dBm), outlier identification based on box plots (e.g., removing samples exceeding 1.5 times the interquartile range), or drift detection based on historical windows (e.g., removing samples whose deviation from recent averages exceeds a threshold) to remove outliers. The cleaned data is then spatially aggregated according to map tiles or regular geographic grids. The average field strength, variance, decoding success rate, number of samples, temporal coverage, and frequency optimization within each spatial unit are calculated. Based on these aggregation indicators, the actual observation status is determined.

[0036] In some preferred embodiments, the above steps specifically include: Extract the terminal type and antenna status identifier from the broadcast test data; Based on the preset terminal hardware difference compensation model, the field strength value and signal-to-noise ratio reported by different terminal types are normalized and compensated to eliminate the systematic measurement error caused by heterogeneous terminals. Outliers after normalization compensation are removed, and the cleaned data is spatially aggregated according to map tiles or regular geographic grids to obtain the average field strength, decoding success rate and frequency optimization degree in each spatial unit. The measured observation status is determined based on the average field strength, decoding success rate, and frequency optimization.

[0037] The terminal hardware difference compensation model refers to a normalization processing model used to eliminate differences in hardware characteristics between different terminal devices. This model can be implemented through two methods: The first is a lookup table compensation method based on laboratory calibration, which involves pre-measuring the receive sensitivity deviation, antenna gain difference, and signal processing delay of each terminal type relative to a reference terminal under a standard test environment, and establishing a compensation coefficient table (e.g., a field strength compensation of +2.5dB for a certain type of vehicle-mounted terminal, and a signal-to-noise ratio compensation of -1.8dB for a certain type of handheld terminal); the second is a regression compensation method based on measured overlapping observations, which involves selecting a high-precision reference terminal, collecting overlapping measurement samples of different terminals at the same location, and fitting the mapping function between the field strength values ​​of different terminals and the reference value through the least squares method or ridge regression to achieve dynamic compensation. Anomaly removal can be performed by combining three methods: physical range rule removal (field strength exceeding -30dBm to -100dBm), box plot outlier identification (exceeding 1.5 times the interquartile range), or sliding window drift detection (deviation from the recent average value exceeding a threshold).

[0038] This step is executed immediately after the terminal uploads data. Triggering methods include timed uploads (every 5 minutes), location change triggers (movement exceeding 100 meters), or sudden changes in reception quality triggers (field strength drops by more than 10dB). For weak network environments, a local caching and retransmission mechanism is supported. Specifically, after receiving the raw measured data, the system first extracts the terminal type identifier (such as device model and hardware version number) and antenna status identifier (built-in antenna, external antenna, antenna orientation angle) from the data packet. Based on a preset compensation model, the field strength value and signal-to-noise ratio are normalized—for example, a field strength value of -68dBm reported by a handheld terminal with a sensitivity 3dB lower is compensated to -65dBm to align with the reference terminal's measurement benchmark.

[0039] After normalization, a three-level anomaly removal process is performed: physically unreasonable values ​​are removed, outliers are identified and statistically analyzed using box plots, and abrupt changes are detected by comparing with historical windows. The cleaned data is spatially aggregated according to map tiles or 100-meter grids. The average field strength, variance, decoding success rate, number of samples, temporal coverage (uniformity of sampling distribution across time periods), and frequency optimization (ranking of reception quality at each frequency point) are calculated for each cell. These indicators are used to determine the measured observation status: when the average field strength is above -60dBm and the decoding success rate is above 90%, it is marked as stable and usable; when the average field strength is below -75dBm or the decoding success rate is below 30%, it is marked as unusable; the rest are intermediate states.

[0040] S103. Based on the difference between the predicted coverage state and the actual observation state of each spatial unit, and combined with the prediction boundary candidate, identify the region located near the predicted coverage boundary and where the predicted coverage state differs from the actual observation state as the coverage uncertainty zone, and determine the anchor spatial unit within the coverage uncertainty zone and the spatial unit to be corrected associated with the anchor spatial unit.

[0041] The difference between predicted coverage and actual observation refers to the degree of deviation between the predicted and actual results within the same spatial cell in terms of indicators such as field strength, acceptability, and decoding success rate. This can be quantified by numerical differences (such as the difference between the predicted field strength and the actual average field strength) or state differences (such as predicted acceptability but actual undecoding). The coverage uncertainty zone refers to the spatial region located near the predicted coverage boundary where there is a significant conflict between the prediction and the actual measurement. The coverage status in this region is unstable or uncertain and requires key calibration. The anchor spatial cell refers to the spatial cell within the coverage uncertainty zone with a sufficient number of actual measurement samples and high measurement reliability. It serves as a reliable observation source to propagate calibration information to surrounding cells. The spatial cell to be calibrated refers to the spatial cell that has a spatial adjacency relationship, road connectivity relationship, or propagation path reachability relationship with the anchor spatial cell and needs to be affected by the calibration of the anchor cell.

[0042] Specifically, the system calculates the difference between the field strength corresponding to the predicted coverage state and the average field strength corresponding to the measured observation state within each spatial cell (e.g., |predicted field strength - measured average field strength|), as well as the deviation between the acceptability corresponding to the predicted coverage state and the decoding success rate corresponding to the measured observation state (e.g., |predicted acceptability probability - measured decoding success rate|). When the difference is greater than a preset field strength error threshold (e.g., 5dB) or the deviation is greater than a preset probability threshold (e.g., 30%), the corresponding spatial cell is marked as a conflicting cell.

[0043] Based on the predicted boundary candidates, an initial boundary zone is formed by extending a preset distance inward and outward (e.g., 200 meters inward and outward each). This initial boundary zone spatially isolates potential coverage transition areas from stable coverage areas. The continuous spatial regions containing conflicting units within the initial boundary zone (connected areas formed by adjacent conflicting units identified through connectivity analysis), as well as regions adjacent to conflicting units whose measured undecoding probability continuously exceeds a preset duration (e.g., undecoding for three consecutive time windows), are collectively defined as coverage uncertainty zones. This incorporates the temporal continuity characteristics of the coverage state into the decision-making logic.

[0044] Finally, within the uncertain coverage zone, spatial units with a measured sample size reaching a preset scale (e.g., sample size ≥ 30) and a measured confidence level higher than a preset confidence level threshold (e.g., confidence level ≥ 0.7) are selected as anchor spatial units, and their surrounding adjacent units, road-connected units, or propagation-reachable units are determined as spatial units to be corrected.

[0045] Anchor point spatial units can be subdivided into forward anchor points (long-term stable and decodable) and reverse anchor points (long-term stable and not decodable). When the number of anchor points is insufficient, prior predictions can be retained or the update frequency can be reduced. By constraining conflict identification within the initial boundary band near the prediction boundary, stable areas far from the boundary are avoided from being misclassified as uncertain zones. At the same time, the interference of occasional anomalous samples on boundary identification is reduced through time continuity determination, allowing subsequent calibration processing to focus on the areas that truly need correction. The bandwidth covering uncertain zones can be adaptively adjusted according to historical conflict density, terrain slope, and building density, further improving the accuracy of identification.

[0046] In some preferred embodiments, the above steps specifically include: Calculate the difference between the field strength corresponding to the predicted coverage state and the average field strength corresponding to the measured observation state within each spatial cell, as well as the deviation between the acceptability corresponding to the predicted coverage state and the decoding success rate corresponding to the measured observation state. When the difference is greater than the preset field strength error threshold, or the deviation is greater than the preset probability threshold, the corresponding spatial unit will be marked as a conflict unit. Based on the predicted boundary candidates, the initial boundary zone is formed by extending a preset distance inward and outward. The continuous spatial region containing conflicting cells within the initial boundary band, and the region adjacent to the conflicting cells whose measured undecoding probability continuously exceeds a preset duration, are jointly defined as the coverage uncertainty band. Within the coverage uncertainty zone, spatial units with a measured sample size reaching a preset scale and a measured confidence level higher than a preset confidence threshold are identified as anchor spatial units.

[0047] The difference calculation includes two categories: numerical difference and state difference. Numerical difference is quantified by field strength deviation (|predicted field strength - measured average field strength|) and probability deviation (|predicted receiveability probability - measured decoding success rate|). State difference refers to the conflict between the prediction and the measurement in terms of classification label (e.g., predicted receiveability but measured undecoding failure). Conflicting units are spatial units whose differences exceed a threshold. The field strength error threshold is generally set to 5dB (considering the inherent error of the propagation model), and the probability threshold is set to 30% (considering sample statistical fluctuations). Predictive boundary candidates can be generated by field strength contour extraction (extracting contour lines where the field strength equals the reception threshold, such as -70dBm) or state transition detection (identifying the transition position of adjacent units from receiveability to unreceivable coverage). This boundary not only comes from the current prediction but can also be combined with historical corrected boundaries and high-risk terrain zones (valleys, urban canyons) to form composite boundary candidates.

[0048] Specifically, the system calculates the difference between the predicted field strength and the measured average field strength, as well as the deviation between the predicted receiveability and the measured decoding success rate, for each unit. When the field strength difference is greater than 5 dB or the deviation is greater than 30%, the unit is marked as a conflict unit—this threshold setting is based on the typical prediction error range of the propagation model and the statistical uncertainty of the measured samples. Then, using the predicted boundary candidate as a spatial reference, an initial boundary band is formed by extending 200 meters inward from the coverage area and outward from the non-coverage area (this distance is set according to the signal attenuation gradient and terrain complexity). This boundary band pre-isolates potential signal strength transition areas from stable areas far from the boundary.

[0049] Within the initial boundary zone, connectivity analysis identifies continuous spatial regions containing conflicting units (contiguous areas formed by adjacent conflicting units). Regions adjacent to conflicting units whose measured undecoding probability continuously exceeds a preset duration (e.g., undecoding for three consecutive time windows) are also included, collectively forming the coverage uncertainty zone—temporal continuity determination reduces interference from sporadic anomalous samples. Anchor points are selected within the coverage uncertainty zone: a sample size ≥ 30 (ensuring statistical representativeness) and a measured confidence level ≥ 0.7 (calculated comprehensively based on sample size, temporal distribution, and equipment diversity). Their surrounding adjacent units, road-connected units, or propagation-reachable units are identified as objects to be corrected.

[0050] In some specific instances, the measured reliability of the anchor point spatial cell is calculated in the following way: Obtain the total number of samples, the collection time distribution matrix, and the terminal device type diversity index of the measured data within the anchor point spatial unit; The larger the total number of samples, the closer the collection time is to the current update time and the more uniform the time distribution, and the higher the diversity index of terminal device types, the higher the reliability of the calculated output.

[0051] Among them, the total number of samples refers to the number of measured data records collected within the anchor point spatial unit; the collection time distribution matrix refers to the distribution of measured data in the time dimension, which can be statistically distributed in a two-dimensional "day-hour" format (e.g., dividing a week of 168 hours into 168 time buckets and recording the number of samples in each bucket), used to assess the temporal representativeness of the observation data; the terminal device type diversity index refers to the richness of the types of terminal devices participating in the observation within the anchor point unit, which can be calculated according to the entropy value of the proportion of different terminal types (e.g., Shannon entropy, Herfindahl inverse index) or simply classified and counted (e.g., the number of terminal types ≥ 5 indicates high diversity).

[0052] The reliability calculation is performed when determining the anchor point spatial cell. It is used to evaluate the quality of the anchor point observation data and serves as the initial strength basis for propagation calibration. Specifically, the system obtains the total number of measured data samples within the anchor point spatial cell (e.g., 120 samples), the acquisition time distribution matrix (e.g., counting the number of samples in each time bucket and calculating the time distribution uniformity, which can be measured by the inverse of variance or entropy), the terminal device type diversity index (e.g., calculating the entropy of vehicle-mounted terminals, handheld terminals, fixed receivers, etc., participating in the observation), and auxiliary reliability factors such as positioning accuracy (the proportion of samples with GPS positioning error less than 10 meters), motion state consistency (the proportion of samples that are stationary or in uniform linear motion), and antenna orientation consistency (the degree of concentration of antenna orientation distribution). Then, based on the pre- The reliability calculation model is designed to comprehensively output the measured reliability value (e.g., reliability = w1 × normalized total number of samples + w2 × time distribution uniformity + w3 × terminal type diversity index + w4 × positioning accuracy factor, where w1, w2, w3, and w4 are weighting coefficients). The larger the total number of samples (e.g., ≥100), the closer the collection time is to the current update time (e.g., the latest sample collection time is no more than 24 hours from the current time), the more uniform the time distribution (e.g., the smaller the variance of the number of samples in each time bucket), and the higher the terminal device type diversity index (e.g., entropy value ≥1.5), the higher the calculated measured reliability (e.g., it can reach 0.9 or above).

[0053] S104. Based on the observation reliability and spatial propagation constraints of the anchor spatial unit, and following the propagation rule of gradual attenuation from the anchor spatial unit to the spatial unit to be calibrated, a restricted confidence propagation calibration is performed on the spatial unit to be calibrated to obtain the calibration coverage state and unit confidence of each spatial unit. Among them, the calibration intensity of the spatial unit in the coverage uncertainty zone is greater than that of the spatial unit in the stable coverage zone, and the original predicted coverage state is maintained when the difference between the predicted coverage state and the measured observation state in the stable coverage zone does not reach the preset triggering condition.

[0054] Among them, observation reliability refers to the quality level of measured data within the anchor point spatial unit, which is jointly determined by factors such as the total number of samples, the distribution of collection time, the diversity of terminal equipment types, positioning accuracy, and consistency of motion state; spatial propagation constraints refer to the spatial conditions that limit the propagation path of calibration information, including the continuous distribution of roads (calibration information propagates preferentially along roads), terrain elevation barriers (mountains, buildings, and other obstacles limit propagation), and administrative boundary restrictions (propagation is restricted across administrative regions); the stepwise attenuation propagation rule refers to the rule that the calibration intensity decreases stepwise with propagation distance, terrain obstacles, and time duration when calibration information propagates outward from the anchor point spatial unit, which can be quantified by the propagation attenuation factor; calibration Coverage status refers to the updated coverage status of each spatial cell after calibration with limited confidence propagation, including field strength level, preferred frequency, decoding availability, etc. Cell confidence refers to the degree of confidence of the calibrated coverage status, which can be represented by a continuous value of 0-1 or three levels of high, medium and low, reflecting the level of certainty of the cell's coverage status. Calibration intensity refers to the degree of influence of calibration information on the target cell, which can be reflected by fusion weight (the weight of measured observations in fusion), propagation influence range (the maximum distance or number of hops that the anchor point can influence), and update priority (the update response speed of different regions). Stable coverage area refers to the area located outside the coverage uncertainty zone and whose prediction is consistent with the actual measurement for a long time and whose coverage status is stable.

[0055] Specifically, the system constructs a topological propagation graph centered on anchor point spatial units. The nodes of this graph are spatial units, and the edges are propagation paths between units. The propagation paths are limited by the continuous distribution of roads (such as establishing propagation edges only along road grids) and terrain elevation barriers (such as the attenuation coefficient increasing when the path passes through mountains or densely built areas). Different barrier coefficients can be used to classify the propagation paths when they pass through tunnels, bridges, valleys, and urban canyons.

[0056] Based on the spatial topological distance between the spatial unit to be corrected and the anchor spatial unit (the shortest path hop count in the propagation graph), the terrain blocking coefficient on the propagation path (cumulative blocking factor), and the time freshness of the measured data within the anchor spatial unit (the time difference between the acquisition time and the current update time), a propagation attenuation factor is calculated. This factor decreases with increasing distance, increased blocking, and decreased timeliness. The measured observation status of the anchor spatial unit (such as measured average field strength and decoding success rate) is combined with the propagation attenuation factor and mapped outward hop by hop to the spatial unit to be corrected. For each hop propagated outward, the attenuation factor is multiplied by the attenuation coefficient (e.g., 10% attenuation per hop) until the preset maximum number of propagation hops (e.g., 5 hops) is reached or the propagation attenuation factor falls below the preset attenuation lower limit (e.g., attenuation to below 0.1), at which point propagation stops.

[0057] The calibration coverage state of the spatial cell to be calibrated is updated by weighted fusion of the calibration value reached by the mapping and the predicted coverage state of the spatial cell to be calibrated (e.g., calibration coverage state = propagation attenuation factor × measured observation value + (1 - propagation attenuation factor) × predicted coverage state), and the cell confidence is determined based on the propagation attenuation factor and the mapping path length (confidence increases with the increase of attenuation factor and the shortening of path).

[0058] For spatial cells within uncertain coverage zones, due to significant conflicts between predictions and measurements, the calibration intensity is set relatively high (e.g., the fusion weight of measured observations can reach 0.7-0.9), resulting in a large propagation impact range.

[0059] For spatial cells within a stable coverage area, the system sets a stable freeze state and counts the number of abnormal measured samples and the duration of abnormality in real time. When the number of abnormal measured samples is greater than a preset first scale threshold (e.g., ≥20 samples), the duration of abnormality is greater than a preset second duration threshold (e.g., ≥72 hours), and the average field strength corresponding to the abnormal measured sample deviates from the predicted coverage state by more than a preset tolerance (e.g., ≥8dB), all three conditions must be met simultaneously to determine that the preset trigger condition has been reached. The stable freeze state of the corresponding spatial cell is then lifted and the coverage state is updated based on the abnormal measured samples. The preset first scale threshold and the preset second duration threshold are negatively correlated (e.g., a longer duration is required when the sample size is small, and a shorter duration is acceptable when the sample size is large; this can be set by looking up a table or function relationship).

[0060] If the above conditions are not met simultaneously, abnormal measured samples will be judged as short-term interference or occasional noise, and the spatial cells in the stable coverage area will maintain the initial broadcast coverage prediction data unchanged. When multiple anchor points are in effect simultaneously, the conflict resolution rule of prioritizing the maximum confidence level or weighted average can be adopted. After calibration, the boundary smoothing rule can be executed to avoid jagged abrupt changes at the boundary. When a local update fails, a rollback strategy can be triggered to restore the previous version state.

[0061] In some preferred embodiments, the above steps include: A topology propagation map is constructed with anchor point spatial units as the center. The propagation path of the topology propagation map is limited by the continuous distribution of roads and the obstruction of terrain elevation. The propagation attenuation factor is calculated based on the spatial topological distance between the spatial unit to be corrected and the anchor spatial unit, the terrain blocking coefficient on the propagation path, and the time freshness of the measured data within the anchor spatial unit. The measured observation status of the anchor point spatial unit is combined with the propagation attenuation factor and mapped outward hop by hop to the spatial unit to be corrected until the preset maximum propagation hop count is reached or the propagation attenuation factor is lower than the preset attenuation lower limit. The calibration coverage state of the spatial cell to be calibrated is updated by weighted fusion of the calibration value arrived by the mapping and the predicted coverage state of the spatial cell to be calibrated, and the cell confidence of the spatial cell to be calibrated is determined based on the propagation attenuation factor and the mapping path length. The topology propagation graph is a directed weighted graph with anchor spatial units as nodes and propagation paths between units as edges. The edge weights are determined by spatial topological distance, terrain blocking coefficient, and road continuity. Propagation paths are preferentially established along the road grid (propagation edges are only established between road units). When the path crosses mountains, densely built-up areas, tunnels, bridges, valleys, or urban canyons, different blocking coefficient levels are used (e.g., mountain blocking coefficient 1.5, urban canyon coefficient 1.2, tunnel coefficient 2.0). Spatial topological distance refers to the shortest path hop count (non-Euclidean distance) in the propagation graph, reflecting the number of relay units the signal needs to pass through for propagation. Time freshness refers to the time difference between the acquisition time of the anchor point's measured data and the current update time; the more recent the acquisition time, the higher the freshness. The propagation attenuation factor is a quantitative indicator of the decrease in calibration strength with propagation distance. The calculation formula can be expressed as: Attenuation factor = Basic reliability × (1 - Attenuation coefficient)^ Hop count × (1 - Blocking coefficient) × Freshness weight. This factor decreases as the number of hops increases, the blocking increases, and the timeliness decreases.

[0062] Specifically, the system constructs a topology propagation graph centered on each anchor point. Only propagation edges between continuously reachable units are retained in the graph. Paths traversing terrain obstacles are assigned additional blocking coefficients—for example, a blocking coefficient of 1.3 is set when a path crosses a valley, and 1.2 when it crosses an urban canyon. This ensures that the propagation path conforms to the physical constraints of actual electromagnetic wave propagation. For each unit to be corrected, the shortest topological distance (hop count) between it and the anchor point, the cumulative blocking coefficient of the path (the product of the blocking coefficients of each segment), and the time freshness of the anchor point data (e.g., freshness is 1.0 within 24 hours, decaying to 0.5 after 72 hours) are calculated together to determine the propagation attenuation factor.

[0063] The system maps the measured observation status of the anchor point (e.g., measured average field strength -62dBm, decoding success rate 95%) outwards hop by hop, in conjunction with the attenuation factor: the attenuation factor for the first hop is assumed to be 0.85, then the mapped field strength is -62 × 0.85 + predicted value × 0.15; the attenuation factor for the second hop is reduced to 0.72, and so on, until the maximum propagation hop count is reached (e.g., 5 hops) or the attenuation factor falls below the lower limit (e.g., 0.1), at which point the system stops. The calibration coverage status of the unit to be calibrated is updated through weighted fusion: calibration value = attenuation factor × measured observation value + (1 - attenuation factor) × predicted value. The unit confidence is negatively correlated with the attenuation factor and path length (the shorter the path and the smaller the attenuation, the higher the confidence).

[0064] In other specific instances, maintaining the original predicted coverage state when the difference between the predicted coverage state and the actual observed state in a stable coverage area does not reach a preset trigger condition includes: For stable coverage areas located outside the coverage uncertainty zone, the abnormal measured sample size and abnormal duration of each spatial unit within the area are statistically analyzed in real time. When the number of abnormal measured samples is greater than the preset first scale threshold, the duration of the abnormality is greater than the preset second duration threshold, and the average field strength corresponding to the abnormal measured sample deviates from the predicted coverage state by more than the preset tolerance, the preset triggering condition is determined to be met, the stable freeze state of the corresponding spatial unit is released, and the predicted coverage state of the corresponding spatial unit is updated to the coverage state determined based on the abnormal measured sample. The preset first scale threshold and the preset second duration threshold are negatively correlated. If the above conditions are not met simultaneously, the abnormal measured samples will be judged as short-term interference or occasional noise, and the spatial cells in the stable coverage area will maintain the initial broadcast coverage prediction data unchanged.

[0065] Abnormal measured samples refer to measurement samples that significantly deviate from the predicted coverage state within a stable coverage area. Examples include a predicted field strength of -55 dBm but a measured value of -70 dBm, or a predicted receiveable field strength but a continuously undecodeable field strength. The preset first scale threshold and the preset second duration threshold are negatively correlated and can be set via a lookup table or function. For example, when the number of abnormal samples reaches 50, the duration is required to be ≥48 hours; when the number of samples is only 20, the duration is required to be ≥96 hours. This reflects the principle that "the observation period can be shortened when the sample size is large, and the verification period needs to be extended when the sample size is small." The preset tolerance is the maximum acceptable range for field strength deviation, generally set to 8 dB. Exceeding this threshold indicates that the coverage environment may have undergone substantial changes (such as obstruction by new buildings or adjustments to transmission power).

[0066] Specifically, the system maintains an anomaly sample counter and an anomaly duration counter for each unit within the stable coverage area, and counts in real time the number of samples whose field strength deviation exceeds the tolerance (e.g., deviation ≥ 8dB) or whose decoding fails, as well as the number of time windows in which they persist. When the number of anomaly samples in a unit reaches the first scale threshold (e.g., ≥ 20), the anomaly duration reaches the second duration threshold (e.g., ≥ 72 hours), and the average field strength deviation of the anomaly sample from the predicted value exceeds the tolerance (e.g., deviation ≥ 8dB), all three conditions are met simultaneously to determine that the preset trigger condition has been met. This multi-dimensional joint determination ensures that only persistent, large-scale, and significant anomalies can trigger updates, reducing the probability of false triggering by short-term interference or occasional noise.

[0067] Once the triggering conditions are met, the system releases the stable freeze state of the cell and updates its predicted coverage state to a new coverage state based on the statistical analysis of abnormal measured samples (e.g., updating the predicted field strength of -55dBm to the measured average field strength of -68dBm, and updating the receivable tag to unreceivable). It then enters an observation period to monitor whether the anomaly persists. If none of the three conditions are met simultaneously, the abnormal samples are classified as short-term interference (e.g., temporary construction or vehicle obstruction) or occasional noise. The stable area cells maintain their original predicted data, and the abnormal samples are not included in the coverage state calculation. This freeze-trigger mechanism enables the coverage state of stable areas to resist interference, avoids over-correction of accurate prediction results by sparse abnormal samples, and balances response speed and update reliability through a negative correlation between scale and duration.

[0068] S105. Generate or incrementally update the broadcast coverage map based on the calibration coverage status and unit confidence.

[0069] The broadcast coverage map refers to a geographic information layer containing coverage status information of each spatial unit. The output format can be a map tile layer (such as PNG format raster tiles), a regular grid database (such as a relational database table or NoSQL document), or a vector area layer (such as GeoJSON or Shapefile format). The output fields include spatial unit identifier (such as tile number or grid coordinates), set of receivable frequencies (such as [FM93.5, FM101.2]), field strength level (such as strong, medium, weak), reception quality level (such as excellent, good, poor), preferred frequency (such as FM93.5), decoding availability label (such as available, unavailable), unit confidence (such as 0.85), update timestamp (such as 2026-04-25 10:30:00), and status source label (such as prediction, measured, fusion calibration, frozen and maintained). Incremental update refers to only partially refreshing the spatial units whose status has changed when receiving continuously uploaded broadcast measured data. The update objects include spatial units whose status has changed within the coverage uncertainty zone and spatial units in the stable coverage area that have been unfrozen after reaching the preset trigger conditions.

[0070] Specifically, based on the calibration coverage status and cell confidence of each spatial cell, the system generates a map layer containing the aforementioned fields and assigns a map version number (e.g., v1.0.20260425) and an update timestamp. It provides a RESTful API interface (e.g., GET / coverage / tiles / {z} / {x} / {y}) or an offline cached layer download interface for terminal calls, maintaining a version correspondence between online and offline cached layers. Upon receiving continuously uploaded broadcast measurement data, the system re-executes steps S102 to S104, recalculating the local coverage uncertainty zone (boundary candidates may have moved), identifying spatial cells whose status has changed (e.g., those originally receivable but becoming unreceivable after calibration, or those originally frozen but unfrozen after triggering conditions are met), and only performs local incremental updates on these cells, updating the corresponding spatial cell's calibration coverage status, cell confidence, update timestamp, and boundary correction results (e.g., the boundary shrinks inward by 50 meters or expands outward by 100 meters), completing the incremental update of the broadcast coverage map.

[0071] If a partial refresh fails (e.g., due to data anomalies or insufficient computing resources), the system can retain the previous map version and record the failed cells, waiting to retry in the next update cycle to ensure the availability of the map service. This makes the computational load of map updates proportional to the actual area that has changed, rather than proportional to the area of ​​the entire target broadcast region, thereby reducing the computational resource consumption and response time of map updates. It also provides interpretability of coverage status for the terminal, allowing the terminal to decide whether to adopt the coverage status or initiate a remeasurement based on cell confidence.

[0072] In some preferred embodiments, the above steps specifically include: Based on the calibration coverage status and cell confidence, a map layer is generated that includes spatial cell identifiers, receivable frequency set, field strength level, reception quality level, preferred frequency, and decoding availability label, resulting in a broadcast coverage map; When receiving continuously uploaded broadcast measurement data, the system performs local incremental updates on spatial units in uncertain coverage zones that have undergone state changes and spatial units in stable coverage areas that have been unfrozen after meeting preset trigger conditions. It also updates the update timestamps and boundary correction results of the corresponding spatial units, thus completing the incremental update of the broadcast coverage map.

[0073] In addition to those listed above, the output fields may also include map version number (e.g., v1.0.20260425), update timestamp (e.g., 2026-04-25 10:30:00), status source label (indicating that the status of the cell comes from prediction, measurement, fusion calibration or freeze-and-hold), and boundary correction results (recording the displacement of the boundary relative to the previous version, such as shrinking inward by 50 meters or expanding outward by 100 meters).

[0074] Specifically, based on the calibration coverage status and unit confidence of each unit, the system generates a complete map layer containing spatial unit identifiers (tile numbers or grid coordinates), a set of receivable frequencies (e.g., [FM93.5, FM101.2]), field strength level (strong / medium / weak), reception quality level (excellent / good / poor), preferred frequency (e.g., FM93.5), decoding availability label (available / unavailable), and unit confidence (e.g., 0.85). The system also assigns version numbers and update timestamps to the map layer and provides the terminal with RESTful API interfaces (e.g., GET / coverage / tiles / {z} / {x} / {y}) or offline cache download interfaces.

[0075] Upon receiving continuously uploaded broadcast measurement data, the system re-executes steps S102 to S104, recalculating the local coverage uncertainty zone (boundary candidates may have moved), identifying cells with changed states—comparing the old and new calibration coverage states, marking cells with changed states and stable area cells that have been unfrozen due to trigger conditions, and performing local incremental updates only on these cells: updating their calibration coverage state, cell confidence, update timestamp, and boundary correction results, while leaving the remaining cells unchanged. If data anomalies or insufficient computing resources cause update failure during the local update process, the system can retain the previous version of the map and record the failed cell identifier, waiting to retry in the next update cycle to ensure the continuous availability of the map service. This incremental update mechanism makes the computational load proportional to the actual area of ​​change, rather than proportional to the entire target broadcast area. For example, if only 5% of the cells in a 10 square kilometer area have changed states, the incremental update only processes 0.5 square kilometers of cells, saving 95% of computing resources compared to global recalculation, significantly reducing map update response time and resource consumption, enabling the system to efficiently handle dynamic update needs in a continuous data stream environment. The following describes the broadcast coverage map generation and updating device in the embodiments of this invention from the perspective of hardware processing. Please refer to [link to relevant documentation]. Figure 2 This is a schematic diagram of a physical device structure for generating and updating a broadcast coverage map in an embodiment of this application.

[0076] It should be noted that, Figure 2 The structure of the broadcast coverage map generation and updating device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0077] like Figure 2As shown, the broadcast coverage map generation and updating device includes a Central Processing Unit (CPU) 201, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 202 or a program loaded from storage section 208 into Random Access Memory (RAM) 203, such as performing the methods described in the above embodiments. The RAM 203 also stores various programs and data required for system operation. The CPU 201, ROM 202, and RAM 203 are interconnected via bus 204. An input / output (I / O) interface 205 is also connected to bus 204.

[0078] The following components are connected to I / O interface 205: input section 206 including audio input devices, push-button switches, etc.; output section 207 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 208 including a hard disk, etc.; and communication section 209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 209 performs communication processing via a network such as the Internet. Drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0079] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs the various functions defined in the present invention.

[0080] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0081] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0082] Specifically, the broadcast coverage map generation and updating device in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the broadcast coverage map generation and updating method provided in the above embodiment.

[0083] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the broadcast coverage map generation and updating device described in the above embodiments; or it may exist independently and not assembled into the broadcast coverage map generation and updating device. The storage medium carries one or more computer programs that, when executed by a processor of the broadcast coverage map generation and updating device, cause the broadcast coverage map generation and updating device to implement the broadcast coverage map generation and updating method provided in the above embodiments.

[0084] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0085] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for generating and updating a broadcast coverage map, characterized in that, The method includes: Acquire the initial broadcast coverage prediction data of the target broadcast area, and obtain the predicted coverage status and prediction boundary candidates of each spatial unit according to the preset spatial units; Acquire broadcast measurement data uploaded by multiple terminals, preprocess the broadcast measurement data, and obtain the measured observation status of each spatial unit; Based on the difference between the predicted coverage state and the measured observation state of each spatial unit, and in conjunction with the predicted boundary candidate, the region located near the predicted coverage boundary and where the predicted coverage state differs from the measured observation state is identified as the coverage uncertainty zone, and the anchor spatial unit within the coverage uncertainty zone and the spatial unit to be corrected associated with the anchor spatial unit are determined. Based on the observation reliability and spatial propagation constraints of the anchor spatial unit, and following the propagation rule of progressive attenuation from the anchor spatial unit to the spatial unit to be calibrated, a restricted confidence propagation calibration is performed on the spatial unit to be calibrated to obtain the calibration coverage state and unit confidence of each spatial unit. The calibration intensity of the spatial unit in the coverage uncertainty band is greater than that of the spatial unit in the stable coverage area, and the original predicted coverage state is maintained when the difference between the predicted coverage state and the measured observation state in the stable coverage area does not reach the preset trigger condition. The broadcast coverage map is generated or incrementally updated based on the calibration coverage status and the unit confidence level.

2. The method according to claim 1, characterized in that, The step of identifying areas near the predicted coverage boundary and where the predicted coverage state differs from the measured observation state, based on the difference between the predicted and measured coverage states of each spatial unit and in conjunction with the predicted boundary candidates, as coverage uncertainty zones, specifically includes: Calculate the difference between the field strength corresponding to the predicted coverage state and the average field strength corresponding to the measured observation state in each spatial cell, as well as the deviation between the acceptability corresponding to the predicted coverage state and the decoding success rate corresponding to the measured observation state. When the difference is greater than a preset field strength error threshold, or the deviation is greater than a preset probability threshold, the corresponding spatial unit is marked as a conflict unit. Based on the predicted boundary candidates, an initial boundary band is formed by extending a preset distance inward and outward. The continuous spatial region containing the conflicting unit within the initial boundary band, and the region adjacent to the conflicting unit whose measured undecoding probability continuously exceeds a preset time, are jointly determined as the coverage uncertainty band. Within the coverage uncertainty zone, spatial units with a measured sample size reaching a preset scale and a measured confidence level higher than a preset confidence threshold are identified as anchor spatial units.

3. The method according to claim 1, characterized in that, Based on the observation reliability and spatial propagation constraints of the anchor spatial unit, and following a propagation rule that gradually decreases from the anchor spatial unit to the spatial unit to be calibrated, a restricted confidence propagation calibration is performed on the spatial unit to be calibrated, to obtain the calibration coverage status and unit confidence of each spatial unit, including: A topology propagation graph is constructed with the anchor point spatial unit as the center. The propagation path of the topology propagation graph is constrained by the continuous distribution of roads and the obstruction of terrain elevation. The propagation attenuation factor is calculated based on the spatial topological distance between the spatial unit to be corrected and the anchor spatial unit, the terrain blocking coefficient on the propagation path, and the time freshness of the measured data within the anchor spatial unit. The measured observation state of the anchor point spatial unit is combined with the propagation attenuation factor and mapped outward hop by hop to the spatial unit to be corrected until the preset maximum propagation hop count is reached or the propagation attenuation factor is lower than the preset attenuation lower limit. The calibration coverage state of the spatial cell to be calibrated is updated by weighted fusion of the calibration value reached by the mapping and the predicted coverage state of the spatial cell to be calibrated, and the cell confidence of the spatial cell to be calibrated is determined based on the propagation attenuation factor and the mapping path length.

4. The method according to claim 1, characterized in that, When the difference between the predicted coverage state and the measured observation state in the stable coverage area does not reach a preset trigger condition, the original predicted coverage state is maintained, specifically including: For the stable coverage area located outside the coverage uncertainty zone, the abnormal measured sample size and abnormal duration of each spatial unit within it are statistically analyzed in real time. When the abnormal measured sample size is greater than a preset first scale threshold, the abnormal duration is greater than a preset second duration threshold, and the average field strength corresponding to the abnormal measured sample deviates from the predicted coverage state by more than a preset tolerance, it is determined that the preset triggering condition has been met, the stable freeze state of the corresponding spatial unit is released, and the predicted coverage state of the corresponding spatial unit is updated to the coverage state determined based on the abnormal measured sample. The preset first scale threshold and the preset second duration threshold are negatively correlated. If the above conditions are not met simultaneously, the abnormal measured sample will be determined as short-term interference or occasional noise, and the spatial cells in the stable coverage area will maintain the initial broadcast coverage prediction data unchanged.

5. The method according to claim 1, characterized in that, Acquire broadcast measurement data uploaded from multiple terminals, preprocess the broadcast measurement data to obtain the measured observation status of each spatial unit, specifically including: Extract the terminal type and antenna status identifier from the broadcast measurement data; Based on the preset terminal hardware difference compensation model, the field strength value and signal-to-noise ratio reported by different terminal types are normalized and compensated to eliminate the systematic measurement error caused by heterogeneous terminals. Outliers after normalization compensation are removed, and the cleaned data is spatially aggregated according to map tiles or regular geographic grids to obtain the average field strength, decoding success rate and frequency optimization degree in each spatial unit. The measured observation state is determined based on the average field strength, the decoding success rate, and the frequency optimization.

6. The method according to any one of claims 1 to 5, characterized in that, The measured reliability of the anchor point spatial unit is calculated in the following way: Obtain the total number of samples, the collection time distribution matrix, and the terminal device type diversity index of the measured data within the anchor point spatial unit; The larger the total number of samples, the closer the collection time is to the current update time and the more uniform the time distribution, and the higher the diversity index of terminal device types, the higher the reliability of the calculated output of the measured data.

7. The method according to claim 6, characterized in that, Generate or incrementally update the broadcast coverage map based on the calibration coverage status and the cell confidence level, specifically including: Based on the calibration coverage status and the cell confidence level, a map layer is generated that includes spatial cell identifiers, a set of receivable frequencies, field strength levels, reception quality levels, preferred frequencies, and decoding availability labels, thus obtaining the broadcast coverage map; Upon receiving continuously uploaded broadcast measurement data, the spatial units within the uncertain coverage zone that have undergone state changes and the spatial units within the stable coverage area that have been unfrozen upon reaching the preset trigger condition are locally incrementally refreshed, and the update timestamps and boundary correction results of the corresponding spatial units are updated, thus completing the incremental update of the broadcast coverage map.

8. A device for generating and updating a broadcast coverage map, characterized in that, The broadcast coverage map generation and updating device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the broadcast coverage map generation and updating device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on a broadcast overlay map generation and update device, the broadcast overlay map generation and update device performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on a broadcast coverage map generation and updating device, the broadcast coverage map generation and updating device performs the method as described in any one of claims 1-7.

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