Far-field communication parameter optimization method for virtual shooting handheld terminal

Abstract

The invention discloses a far-field communication parameter optimization method of a virtual shooting handheld terminal, which relates to the technical field of virtual shooting, and comprises the following steps: S1, comprehensively scanning a communication environment in a far-field area to obtain spatial data of signal strength, delay distribution and packet loss rate, and aiming at heterogeneity characteristics of different positions, calculating a far-field communication parameter of the virtual shooting handheld terminal; constructing an initial communication environment distribution map, and obtaining a preliminary division result of signal characteristics in the region; s2, according to the initial communication environment distribution diagram, performing space division on a far-field region by adopting a clustering analysis method, grouping positions with poor signal strength and unstable delay, and determining a plurality of sub-region sets with different communication requirements; according to the far-field communication parameter optimization method of the virtual shooting handheld terminal, the reliability and continuity of communication of the handheld terminal in the virtual shooting process are improved, the synchronization effect of a virtual picture and a real shooting picture is guaranteed, and then the overall stability and shooting quality of virtual shooting operation are improved.

Description

Technical Field

[0001] This invention relates to the field of virtual shooting technology, and more specifically to a method for optimizing far-field communication parameters of a handheld virtual shooting terminal. Background Technology

[0002] With the widespread application of virtual shooting technology in film and television production, real-time data interaction between handheld terminals and virtual shooting systems on set has become commonplace. Handheld terminals are typically used for shooting control, image preview, parameter adjustment, and status feedback. Their communication performance directly affects the synchronization between virtual and real scenes, as well as the stability of the shooting process. Especially with the ever-expanding scale of virtual shooting locations, the communication quality of handheld terminals in the far-field region has become a crucial factor restricting shooting efficiency and image consistency.

[0003] In existing virtual shooting systems, the far-field area typically covers a large area with a complex and variable communication environment. Influenced by factors such as site structure, LED display device layout, metal support structures, and the movement of personnel and equipment, wireless signal propagation conditions vary significantly across different spatial locations, manifesting as uneven signal strength distribution, fluctuating communication latency, and unstable packet loss rates. These problems are particularly pronounced when handheld terminals frequently move within the site as needed for shooting, easily leading to image delays, synchronization errors, or even communication interruptions, thus affecting the overall virtual shooting effect. Current technologies for communication management in virtual shooting environments often employ uniform or static communication parameter configurations to reduce system complexity. However, this configuration approach is usually based on average overall or local communication conditions, making it difficult to consider the differences in communication quality requirements between different locations within the far-field area. For example, locations closer to the main shooting area typically require lower latency and higher stability, while locations at the edge of the site or with obstructions are more susceptible to signal attenuation and interference. Uniform communication parameters cannot simultaneously meet these differentiated needs, often resulting in insufficient communication performance in some areas and inefficient utilization of communication resources in other areas. Furthermore, during the cross-regional movement of handheld terminals, the communication environment changes dynamically and suddenly. Existing technologies have limited ability to sense and respond to changes in communication quality, making it difficult to reflect fluctuations in communication status in a timely manner. This causes communication parameter adjustments to often lag behind actual environmental changes, further exacerbating the problem of communication instability in far-field areas. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing far-field communication parameters of a virtual shooting handheld terminal, thereby solving the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing far-field communication parameters of a handheld virtual shooting terminal, applied to the far-field communication environment of a handheld terminal in a virtual shooting location, characterized by comprising: S1, comprehensively scanning the communication environment within the far-field region to obtain spatial data on signal strength, delay distribution, and packet loss rate; constructing an initial communication environment distribution map based on the heterogeneous characteristics of different locations to obtain preliminary division results of signal characteristics within the region; S2, based on the initial communication environment distribution map, using cluster analysis to spatially divide the far-field region, grouping locations with poor signal strength and unstable delay to determine multiple sub-regions with different communication needs. S3. For the divided set of sub-regions, obtain specific data on the requirements for screen synchronization and low latency in each sub-region. By analyzing the characteristics of different communication requirements, determine the corresponding communication parameter configuration scheme for each sub-region. S4. By deploying environmental awareness nodes at the boundaries of each sub-region, collect relevant data on signal penetration and connection interruption in real time. For changes in the communication environment when the handheld terminal moves across regions, obtain dynamic signal quality feedback information. S5. Based on the dynamic signal quality feedback information, if the handheld terminal is detected to have entered a new sub-region, call the pre-established communication parameter configuration scheme, switch parameters according to the characteristics of the new sub-region, and determine the communication stability level after adaptation.

[0006] Preferably, step S1 includes acquiring the radio frequency sampling stream in the far-field region, parsing the radio frequency sampling stream to obtain signal strength values, delay distribution timestamps, and packet loss rate statistics, mapping the signal strength values, delay distribution timestamps, and packet loss rate statistics to a virtual planar grid system to generate a discrete grid cell set; calculating the statistical variance and local entropy of the discrete grid cell set, extracting heterogeneous feature vectors characterizing the instability of the communication environment; determining the signal characteristic category of each grid cell based on the heterogeneous feature vectors, filling the discrete grid cell set with regions according to the signal characteristic categories, constructing an initial communication environment distribution map, and obtaining preliminary division results of signal characteristics within the region.

[0007] Preferably, step S2 includes acquiring discrete grid cell data from the initial communication environment distribution map, constructing a multidimensional feature vector matrix containing signal strength values ​​and delay distribution timestamps; calculating Euclidean distance based on the multidimensional feature vector matrix to aggregate preliminary connected regions, and filtering out abnormal communication blocks with excessive mean signal strength difference and unstable delay variance; acquiring the closed boundary contour of the abnormal communication blocks, and performing secondary segmentation of the closed boundary contour based on grid attribute density to obtain homogeneous spatial cells; matching the communication service level model based on the statistical distribution of the homogeneous spatial cells to determine the corresponding communication resource demand type, and outputting a set of sub-regions with different communication demands based on the communication resource demand type.

[0008] Preferably, step S3 includes obtaining video stream transmission logs of the sub-region set, calculating jitter variance and delay distribution to obtain a service multidimensional vector; inputting the service multidimensional vector into a demand difference analysis model to distinguish between synchronization-priority regions and interaction-priority regions, and outputting a service demand category mapping table; according to the service demand category mapping table, matching a high-order modulation and coding scheme for synchronization-priority regions, and shortening the time slot period of the wireless frame for interaction-priority regions to generate a candidate communication parameter combination sequence; performing numerical simulation calculations on the candidate communication parameter combination sequence to lock the optimal physical layer parameter set, and outputting the communication parameter configuration scheme corresponding to each sub-region.

[0009] Preferably, step S4 includes activating boundary sensing nodes to capture edge field strength data and spectral interference maps, calculating penetration loss values ​​and environmental attenuation factors; combining the terminal's movement trajectory and movement rate vector, mapping the penetration loss values ​​and environmental attenuation factors to trajectory coordinates to locate cross-domain switching points, and constructing a signal coverage hole model; if the density of the signal coverage hole model exceeds the standard, extracting time-varying features to generate a quality fluctuation sequence; establishing a dynamic feedback link based on the quality fluctuation sequence, and outputting dynamic signal quality feedback information for handheld terminals moving across regions.

[0010] Preferably, step S5 includes acquiring time-varying spectral feature data from dynamic signal quality feedback information, comparing it with a fingerprint database to determine a new sub-region identifier; extracting a preset quadrature amplitude modulation order and channel coding redundancy rate based on the new sub-region identifier; resetting the physical layer configuration by parsing the quadrature amplitude modulation order and channel coding redundancy rate, and completing parameter switching for the new sub-region features; collecting bit error distribution data and round-trip delay jitter values ​​after switching, calculating the deviation between the bit error distribution data and the round-trip delay jitter values ​​to obtain a communication adaptability score; if the communication adaptability score meets the standard, calculating the anti-interference margin level after parameter switching, and determining the communication stability level after adaptation.

[0011] Preferably, it also includes S6: Regarding the communication stability level after parameter switching, by continuously monitoring packet loss rate and latency instability index data that are higher than a preset threshold, the real-time transmission status of the handheld terminal during movement is obtained, and the final communication quality optimization result is determined. Specifically, this includes collecting packet loss sequences and latency timestamp data with frequencies higher than a preset threshold after parameter switching, calculating the packet loss interval variance of packet loss sequences with frequencies higher than a preset threshold and the fluctuation amplitude of latency timestamp data, generating a link congestion feature matrix; parsing the link congestion feature matrix to extract abnormal fluctuation segments, and constructing a real-time transmission status vector by combining the physical layer retransmission request count.

[0012] Preferably, step S6 further includes mapping the real-time transmission state vector to a multi-dimensional stability evaluation space, calculating the Euclidean distance between the vector magnitude and the baseline stable state to obtain a stability metric; if the stability metric is within a preset convergence interval, combining the payload throughput data to determine the final communication quality optimization result.

[0013] Preferably, it also includes S7: based on the final communication quality optimization result, if it is found that the signal strength in the sub-region is still lower than the preset threshold, the monitoring frequency of the environmental sensing node is adjusted to obtain fine signal distribution data, and it is determined whether the sub-region division needs to be updated and adjusted. Specifically, this includes obtaining the signal strength value of the sub-region after communication quality optimization; if the signal strength value of the sub-region is lower than the preset coverage threshold, a monitoring frequency doubling instruction is generated; and a fine signal distribution dataset containing spatiotemporal labels is obtained based on the monitoring frequency doubling instruction.

[0014] Preferably, step S7 further includes parsing the refined signal distribution dataset to extract the covering connected components, performing a topological mapping comparison between the covering connected components and the logical boundaries of the current sub-region division; if the comparison result shows that the covering connected components cross the logical boundaries, generating a new boundary division coordinate sequence to complete the update and adjustment of the sub-region division.

[0015] As can be seen from the above technical solution, the present invention has the following beneficial effects:

[0016] This method for optimizing far-field communication parameters of a virtual shooting handheld terminal effectively addresses communication quality issues, given the significant spatial differences, frequent dynamic changes, and high mobility of the far-field communication environment in virtual shooting locations. By comprehensively perceiving and analyzing the far-field communication environment and considering the actual needs of different areas in terms of image synchronization, latency, and stability, communication parameters are specifically configured and dynamically adjusted. This ensures that the handheld terminal maintains relatively stable data transmission even when moving across regions, thereby reducing the adverse effects of packet loss and latency fluctuations. This method avoids the problem of uniform or static parameter configurations being difficult to adapt to complex far-field environments, improving the reliability and continuity of handheld terminal communication during virtual shooting, ensuring synchronization between virtual and real-world footage, and ultimately enhancing the overall stability and shooting quality of virtual shooting operations. Attached Figure Description

[0017] Figure 1 This is a flowchart of the far-field communication parameter optimization method of the present invention; Figure 2 This is a schematic diagram of the sub-region division and boundary node deployment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 and Figure 2 As shown, this invention provides a technical solution: a method for optimizing far-field communication parameters of a handheld virtual shooting terminal, applied to the far-field communication environment of a handheld terminal in a virtual shooting location, including: S1, by comprehensively scanning the communication environment within the far-field area to obtain spatial data on signal strength, delay distribution, and packet loss rate, and constructing an initial communication environment distribution map based on the heterogeneous characteristics of different locations, obtaining preliminary division results of signal characteristics within the area; S2, based on the initial communication environment distribution map, using cluster analysis to spatially divide the far-field area, grouping locations with poor signal strength and unstable delay, and determining multiple sets of sub-regions with different communication requirements; S3, for the divided set of sub-regions, obtaining specific data on image synchronization and low latency requirements within each sub-region, and determining the communication parameter configuration scheme corresponding to each sub-region by analyzing the characteristics of different communication requirements; S4, by... Deploy environmental sensing nodes to collect real-time data on signal penetration and connection interruptions, and obtain dynamic signal quality feedback information for changes in the communication environment when the handheld terminal moves across regions; S5. Based on the dynamic signal quality feedback information, if the handheld terminal is detected to have entered a new sub-region, a pre-established communication parameter configuration scheme is invoked to switch parameters according to the characteristics of the new sub-region, and the communication stability level after adaptation is determined; S6. Based on the communication stability level after parameter switching, by continuously monitoring packet loss rate and latency instability indicators above a preset threshold, the real-time transmission status of the handheld terminal during movement is obtained, and the final communication quality optimization result is determined; S7. Based on the final communication quality optimization result, if it is found that the signal strength in the sub-region is still below the preset threshold, the monitoring frequency of the environmental sensing nodes is adjusted to obtain detailed signal distribution data, and it is determined whether the sub-region division needs to be updated and adjusted.

[0020] In the above implementation, this method is based on the far-field communication characteristics of handheld terminals in a virtual shooting location. It achieves a structured understanding of the complex wireless environment by spatially modeling the communication environment. First, it collects key communication indicators such as signal strength, transmission delay, and packet loss rate from multiple dimensions to reflect the spatial distribution of wireless signals in the far-field area. Then, using clustering analysis, locations with similar communication characteristics are grouped into the same sub-region, effectively distinguishing different communication needs spatially. Based on this, different communication parameter configuration schemes are matched to each sub-region according to the requirements for image synchronization and low-latency transmission during virtual shooting. When the handheld terminal moves between sub-regions, the real-time perception of signal changes by environmental sensing nodes dynamically triggers communication parameter switching, ensuring that the communication link remains adapted to the current spatial environment. By continuously monitoring communication stability indicators and combining feedback results to make necessary corrections to the region division, a closed-loop optimization mechanism is formed.

[0021] S1 includes acquiring the radio frequency sampling stream in the far-field region, parsing the radio frequency sampling stream to obtain signal strength values, delay distribution timestamps, and packet loss rate statistics, mapping the signal strength values, delay distribution timestamps, and packet loss rate statistics to a virtual planar grid system to generate a discrete grid cell set; calculating the statistical variance and local entropy of the discrete grid cell set, extracting heterogeneous feature vectors characterizing the instability of the communication environment; determining the signal characteristic category of each grid cell based on the heterogeneous feature vectors, filling the discrete grid cell set with regions according to the signal characteristic categories, constructing an initial communication environment distribution map, and obtaining preliminary division results of signal characteristics within the region.

[0022] In this embodiment, S1 takes the radio frequency sampling stream from the far-field region as input, first completes time alignment, data parsing, and spatial mapping, then completes grid-level statistical calculation, heterogeneous feature vector extraction, and signal characteristic category determination, ultimately forming an initial communication environment distribution map. The acquisition of the radio frequency sampling stream is jointly completed by the wireless access devices and environmental sensing nodes deployed within the site: each sampling point simultaneously collects the received signal strength value, the local timestamp of the data packet arrival, and the data packet sequence information. The timestamp reference uses a unified site time source, provided by the synchronization timing system of the shooting site. The timing accuracy is determined based on the maximum time deviation allowed for image synchronization. The maximum allowed time deviation is determined by the upper limit of the permissible jitter for image synchronization by the virtual shooting link. Before the site is activated, the system calibrates to obtain the time alignment accuracy requirement corresponding to this upper limit. The timing accuracy is reserved with a safety margin based on this requirement to avoid delay statistical deviations caused by cross-node time drift.

[0023] The parsing process of the RF sampling stream is processed item by item: For each sampling record, the received signal strength value is first read. This value comes from the RF measurement reporting interface of the terminal or node. The measurement period is determined by the sampling frequency, which is determined based on the upper limit of personnel movement speed within the site and the width of the sub-area boundary. This ensures that the terminal generates at least multiple sampling records while crossing the boundary, avoiding dilution of characteristics at the boundary. Subsequently, the sequence information and arrival timestamp of the data packet are read. The sequence information is used to determine packet loss. The packet loss rate statistics are calculated in units of a fixed statistical window. The length of the statistical window depends on the screen synchronization service. The continuity requirement is determined so that the window covers a transmission period of no less than a few consecutive frames, thus ensuring that the impact of packet loss on image synchronization is stably reflected within the window. The delay distribution timestamp is obtained by the difference between the sending-side timestamp and the receiving-side timestamp. The sending-side timestamp comes from the time stamp attached to the same data packet by the sending end, which is aligned with the site time source. The receiving-side timestamp is the local timestamp of the data packet arrival. The difference between the two is corrected for time alignment to form a single transmission delay sample of the data packet. The delay distribution is formed by aggregating all delay samples within the window in chronological order, and is used for subsequent stability analysis. The calculation process of the packet loss rate statistics is as follows: the expected sequence number range is continuously scanned within the statistical window, and the number of missing sequence numbers is counted as the number of lost packets. At the same time, the number of data packets that actually arrive is counted as the number of arrived packets. The packet loss rate statistics are derived from the ratio of the number of lost packets to the expected total number of arrivals. The expected total number of arrivals is determined by the window duration and the sending cycle of the sending end. The sending cycle is determined by the data transmission scheduling strategy of the virtual shooting service. The scheduling strategy is constrained by the frame rate and the encoding fragmentation method, thereby ensuring that the packet loss rate statistics are consistent with the image synchronization load.

[0024] After analysis, the signal strength values, delay distribution timestamps, and packet loss rate statistics are mapped to a virtual planar grid system, generating a set of discrete grid cells. The virtual planar grid system uses the planar coordinates of the shooting site as a reference. The origin and direction of the coordinates are determined by the surveying coordinate system during the site setup phase and aligned with the scene coordinates of the virtual shooting engine to avoid misjudgments of areas caused by inconsistencies between physical and virtual locations. The side length of the grid cells is determined by both spatial resolution requirements and computational load: the spatial resolution requirement is based on the minimum spatial scale of signal changes in the far-field region, which is determined by the site structure features and the size of obstructions, ensuring that the grid cell side length is smaller than the characteristic scale of the main obstructions to reflect signal abrupt changes caused by obstruction; the computational load is determined based on the upper limit of real-time processing latency, which is determined by the response time requirements of parameter switching, thus keeping the number of grid cells within the processing capacity. The mapping process is performed according to the location of the sampled record: the sampling location comes from the positioning results of the handheld terminal or the coverage positioning results of the node. The positioning refresh cycle matches the sampling frequency, and the positioning accuracy requirement is determined based on the side length of the grid cell to ensure that the positioning error does not cross multiple grid cells. Then, the grid cell index is calculated based on the planar coordinates of the sampling location. The signal strength value, delay samples, and packet loss statistics of the sampling record are then assigned to the buffer set of the corresponding grid cell until all grid cells covering the far field region form a discrete grid cell set. To avoid statistical distortion caused by insufficient samples in a single grid cell, a minimum sample number threshold is set for each grid cell. The minimum sample number threshold is determined based on the statistical window length and the sampling frequency, ensuring that each grid cell accumulates at least a sufficient number of samples to characterize the fluctuation trend within a statistical window. When a grid cell does not reach the minimum sample number threshold, the statistics of that grid cell are supplemented by weighted compensation of adjacent grid cells. The weighting is determined based on the spatial distance, which is determined by the planar distance between the grid center points, to reduce classification jitter caused by isolated samples.

[0025] Statistical variance and local entropy are calculated on a discrete set of grid cells, and heterogeneity feature vectors are extracted. The calculation of statistical variance is performed for each grid cell separately for signal strength, delay, and packet loss rate: first, all samples are summarized within the statistical window of that grid cell, and the average value is calculated; then, the deviation between each sample and the average value is measured, the deviation measures are accumulated within the window, and normalized according to the number of samples to obtain the fluctuation intensity characterization of the index within that grid cell; the variance of signal strength reflects the amplitude fluctuations caused by obstruction, reflection, and fading; the variance of delay reflects the delay jitter caused by queuing, retransmission, and link contention; and the variance of packet loss rate reflects the stability changes caused by short-term link interruptions and sudden interference. The calculation of local entropy is completed within the local neighborhood of each grid cell: the range of the local neighborhood is determined by the neighborhood radius, which is determined based on the maximum distance the terminal moves in a short period of time, so that the local neighborhood covers the possible movement range of the terminal within a statistical window, thus allowing the entropy value to reflect the communication uncertainty faced by the terminal within that range; then, the index samples of each grid cell in the local neighborhood are discretized into bins, and the number of bins is determined by both resolution and noise sensitivity, so that the number of bins is sufficient to distinguish between stable and unstable states, while avoiding excessive binning that would amplify random noise; then, the occurrence frequency of each bin is counted and converted into the occurrence ratio, and the uncertainty of the ratio is aggregated into an entropy value according to the calculation rules of information entropy. The higher the entropy value, the more dispersed the index state in the local neighborhood and the more unstable the communication environment. The heterogeneity feature vector is formed by combining the above statistical variance and local entropy value according to a uniform scale. The uniform scale processing is completed by unifying the dimensions of each indicator. The benchmark interval for dimension unification is determined by the normal operation data during the field calibration stage. The benchmark interval is taken from the typical fluctuation range of the stable interval in the normal operation data, and a safety margin is reserved to accommodate short-term fluctuations, so that the feature vector is comparable between different indicators.

[0026] The signal characteristic category of each grid cell is determined based on heterogeneous feature vectors, and an initial communication environment distribution map is constructed. The determination of signal characteristic categories adopts a hierarchical discrimination strategy: firstly, grid cells are divided into strong coverage, transitional coverage, and weak coverage according to signal strength level. The signal strength threshold is described at the point where the signal strength appears. This threshold is jointly determined based on the handheld terminal's receiving sensitivity, target throughput requirements, and modulation / coding lower limit. The receiving sensitivity is given by the terminal's RF specifications, the target throughput requirements are determined by the bit rate and redundancy strategy of the video synchronization service, and the modulation / coding lower limit is determined by the minimum received strength requirement of the link at the target bit error rate. The threshold is based on the above constraints plus a margin for the maximum obstruction loss in the site. The maximum obstruction loss is obtained during the calibration phase from the materials and dimensions of the main obstruction structures in the site. Secondly, grid cells are further subdivided into stable and unstable categories based on delay stability. The latency instability threshold is defined at the point where the latency index appears. This threshold is determined based on the maximum end-to-end latency jitter allowed for frame synchronization. The maximum end-to-end latency jitter is calculated from the upper limit of the frame synchronization error tolerance of the virtual shooting system, and a margin is reserved in conjunction with the discreteness of the wireless scheduling cycle to avoid misjudgment triggered by queuing jitter at the moment of handover. Further subdivision is made according to packet loss stability. The packet loss rate threshold is defined at the point where the packet loss rate index appears. This threshold is determined based on the fault tolerance capability of the frame synchronization service for key frames and differential frames. The fault tolerance capability is determined by the encoding and error correction strategy, and is combined with the constraint setting of the upper limit of retransmission latency. The upper limit of retransmission latency is derived by reverse calculation from the latency instability threshold, thereby ensuring that packet loss control is consistent with the low latency target. After classification, the discrete grid cell set is filled with regions: grid cells of the same signal characteristic category and spatially adjacent are merged into connected components, with connectivity determined by shared boundaries, forming continuous regions; noise reduction is performed on connected regions with areas smaller than the minimum region area threshold, which is determined based on the minimum mobile coverage area of ​​the handheld terminal within a single parameter switching response time, preventing excessively small regions from triggering frequent switching; boundary smoothing is performed on regions with jagged boundaries, with the smoothing scale determined by the grid cell side length and the upper limit of positioning error, to reduce repeated boundary crossings caused by positioning jitter. After region filling is completed, an initial communication environment distribution map is formed, with grid cells as the smallest unit of expression and signal characteristic category as the filling attribute, forming a preliminary division of signal characteristics within the far-field region.

[0027] S2 includes acquiring discrete grid cell data from the initial communication environment distribution map, constructing a multidimensional feature vector matrix containing signal strength values ​​and delay distribution timestamps; calculating Euclidean distance based on the multidimensional feature vector matrix to aggregate preliminary connected regions, and filtering out abnormal communication blocks with excessive mean signal strength difference and unstable variance of delay; acquiring the closed boundary contour of the abnormal communication blocks, and performing secondary segmentation of the closed boundary contour based on grid attribute density to obtain homogeneous spatial cells; matching the communication service level model based on the statistical distribution of homogeneous spatial cells to determine the corresponding communication resource demand type, and outputting a set of sub-regions with different communication demands based on the communication resource demand type.

[0028] In this implementation S2, the initial communication environment distribution map is used as input. Essentially, it is a set of communication features with discrete grid cells as the smallest spatial granularity. Each discrete grid cell is associated with a signal strength value and a delay sample sequence corresponding to the delay distribution timestamp. When executing S2, the initial communication environment distribution map is first traversed by grid, with the traversal order proceeding from smallest to largest grid index to ensure a one-to-one correspondence between subsequent matrix row numbers and spatial locations. For each grid cell, the representative signal strength value within the statistical window is read. This representative value is obtained by first removing outliers from all signal strength samples within the window and then performing central trend aggregation. The outlier removal threshold is explained as follows: the outlier removal threshold is taken from the upper bound of the maximum normal fluctuation range of signal strength in the stable area during the site calibration stage, plus the instantaneous drop margin caused by one obstruction. This obstruction margin is determined by the upper bound of the additional attenuation caused by the material and size of the main obstructing objects in the site, measured during the calibration stage. After reading, the delayed sample sequence corresponding to the delay distribution timestamp is first aligned with a time reference. This alignment uses a unified time source, and the timing accuracy is determined by the maximum allowable time deviation for image synchronization. This maximum time deviation is calculated from the frame alignment error tolerance range of the virtual shooting system, and a clock alignment margin due to transmission scheduling discreteness is added. After alignment, the delayed sample sequence is converted into a delay statistical description, which consists of a delay central trend value and a delay jitter level. The delay central trend value is calculated by sorting the delayed samples within the window by numerical value and selecting a typical position of the main distribution as a representative. The main distribution is determined by continuous dense interval judgment, and the judgment width is determined by the minimum time granularity of the wireless scheduling cycle. The delay jitter level is calculated by normalizing and aggregating the fluctuation amplitude of the delayed samples within the window. The normalization benchmark is determined by the upper bound of the delay fluctuation in the stable region during the calibration phase, and a queuing jitter margin generated during a sudden increase in contention load is added. This margin is derived from the scheduling queuing budget of the virtual shooting service under the maximum number of concurrent terminals. Subsequently, the representative value of signal strength, the central trend value of delay, and the level of delay jitter are concatenated in a fixed order to form the multidimensional feature vector of the grid. The multidimensional feature vectors of all grids are then stacked sequentially to form a multidimensional feature vector matrix. To avoid distance calculation bias caused by different units, scaling is performed on each dimension of the matrix. Scale standardization uses linear scaling to a unified reference interval. The reference interval is described below: it is taken from the upper and lower boundaries of the normal operating data during the calibration phase. The normal operating data comes from continuous sampling statistics during stable virtual shooting operation, and a sampling noise margin is added to cover numerical drift caused by short-term multipath disturbances.

[0029] After completing the multidimensional feature vector matrix, Euclidean distance aggregation is performed to obtain preliminary connected regions. The aggregation process first establishes spatial adjacency constraints, creating candidate edges only for adjacent grids sharing a boundary. The creation of candidate edges is completed by mapping the grid index to planar coordinates and calculating the four-neighbor relationship, thus preventing non-adjacent locations from being merged into the same region due to similar features. For each candidate edge, the Euclidean distance between the multidimensional feature vectors of the two grids is calculated. The calculation process uses a synthetic metric of dimension-wise difference at a unified scale to obtain the overall difference level. The overall difference level is compared with a similarity threshold. The similarity threshold is derived from the upper bound of the maximum overall difference level of adjacent grids within the same stable region during the calibration phase, and is superimposed with the positioning error margin and sampling noise margin. The positioning error margin is determined by the upper bound of the error statistics of the site positioning system under worst-case occlusion conditions, and the sampling noise margin is determined by the upper bound of the measurement accuracy of the RF measurement interface. If the overall difference level does not exceed the similarity threshold, the candidate edge is marked as connected, and the two grids are grouped into the same connected set; if it exceeds the similarity threshold, a boundary relationship is formed between the two grids. The generation of connected sets is achieved by set union operation, which merges candidate edges step by step according to the traversal order, and after the traversal is completed, multiple sets of disjoint connected regions are obtained.

[0030] After obtaining the initial connected regions, abnormal communication blocks are screened to locate areas with significant signal strength differences and insufficient delay stability. First, the mean signal strength difference for each connected region is calculated. The calculation object is the inner and outer adjacent grid pairs at the boundary of the connected region. The calculation process involves taking representative signal strength values ​​of the inner grid and the outer adjacent grid segment by segment along the boundary of the connected region, calculating the absolute value of the difference between the two, forming a difference set, and then averaging the difference set to obtain the mean signal strength difference, which is used to characterize the degree of coverage abrupt change. Following this, a threshold for exceeding the mean signal strength difference is explained: This threshold is derived from the minimum reception quality requirements of the video synchronization service. The minimum reception quality requirements are jointly determined by the terminal receiving sensitivity, target throughput requirements, and coding redundancy strategy, with the maximum additional attenuation due to site obstruction added as a safety margin. The target throughput requirements are jointly determined by frame rate, resolution, bit rate, and retransmission budget, and the maximum additional attenuation due to obstruction is determined by the upper bound of the material and size of the main obstruction structure measured during the calibration phase. Subsequently, the latency instability variance of each connected region is calculated. The calculation object is the latency jitter level of all grids within the connected region. The calculation process is as follows: first, the latency jitter level of each grid in the region is summarized to obtain the region mean; then, the deviation dispersion of the latency jitter level of each grid from the region mean is calculated and normalized to obtain the latency instability variance, which is used to characterize the spatial consistency of latency stability within the region. The latency instability variance exceeding the limit threshold is explained below: this threshold is determined by the maximum allowable latency jitter limit for frame synchronization. The maximum latency jitter limit is calculated from the allowable range of inter-frame alignment error, and a basic jitter margin caused by the dispersion of the wireless scheduling cycle is added. The basic jitter margin is jointly determined by the minimum granularity of the scheduling cycle and the queuing budget under the maximum number of concurrent terminals. The mean difference in signal strength and the variance of delay instability are compared with their corresponding thresholds. Connected regions that exceed both thresholds simultaneously are marked as abnormal communication blocks, while connected regions that exceed only one threshold are marked as transition blocks and enter the subsequent fine discrimination sequence. The fine discrimination triggering condition is determined by the priority configuration of the virtual shooting service. The priority configuration is determined by the image synchronization level of key camera positions in the venue and the upper limit of the number of terminals.

[0031] For anomalous communication blocks, a closed boundary contour is first extracted, and then secondary segmentation is performed based on the grid attribute density to obtain homogeneous spatial units. The closed boundary contour extraction is achieved through boundary tracing: boundary grids are searched within the anomalous communication block. The criteria for determining a boundary grid are that the grid belongs to the anomalous communication block and at least one adjacent grid sharing a boundary does not belong to the anomalous communication block. After selecting the initial boundary grid, the process advances segment by segment along the boundary according to a fixed direction rule. At each step, the geometric position and orientation of the current boundary segment are recorded. The advancement rule is constrained by "keeping the anomalous block on the same side," until the process returns to the initial boundary segment to form a closed sequence, thus obtaining the closed boundary contour. To reduce the impact of jagged edges caused by grid discretization on segmentation stability, the contour is smoothed. The smoothing scale is explained below: the smoothing scale is jointly determined by the grid cell side length and the upper bound of the positioning error. The upper bound of the positioning error originates from the statistical upper bound of the site positioning system under worst-case occlusion conditions, and is superimposed with the position jump margin caused by short-term multipath disturbances. Subsequently, a grid attribute density is constructed within the closed boundary contour. The attribute density is based on the degree of feature homogeneity clustering. The calculation process involves defining a local neighborhood for each internal grid, with the neighborhood radius following this parameter. The neighborhood radius is determined by the maximum movement distance of the terminal within a statistical window. The maximum movement distance is obtained by multiplying the terminal's maximum movement speed by the statistical window duration. The statistical window duration is determined by the continuity requirements of the video synchronization service, ensuring the window covers the transmission cycle of several consecutive frames. For each neighboring grid within the neighborhood, the feature difference level between it and the central grid is calculated, and the number of neighbors whose difference level does not exceed the density threshold is counted as the density value. The density threshold follows this parameter. The density threshold is taken from the upper bound of the maximum feature difference between adjacent grids within the same stable sub-region during the calibration phase, and is superimposed with sampling noise margin and positioning error margin to ensure that normal fluctuations within the stable region are not misjudged as low density. Based on the density value distribution, dense core areas and sparse transition areas are identified. The threshold for determining dense core areas follows this threshold. The density threshold for the core area is taken from the lower bound of the density values ​​within the stable sub-region, and a boundary uncertainty margin is added. The boundary uncertainty margin is jointly determined by the contour smoothing scale and the upper bound of the positioning error. Region growing is performed using the dense core area as a seed. During the region growing process, candidate grids sharing the same boundary are selected from the edge of the current seed set at each step. The feature difference level between the candidate grid and the seed set is calculated. If the difference level does not exceed an expansion threshold, it is incorporated. The expansion threshold follows this threshold. The expansion threshold follows the same determination logic as the density threshold and adds a multipath fluctuation margin at the boundary. The multipath fluctuation margin is determined by the distribution of reflectors within the site and the upper bound of the maximum instantaneous fluctuation during the calibration phase. After region growing is completed, multiple homogeneous spatial units with consistent internal characteristics are formed.To suppress frequent cross-regional migrations caused by excessively small segmentation units, a minimum homogeneous unit area threshold is set. This threshold is explained as follows: it is determined by the minimum effective dwell time for parameter switching, which is jointly determined by the parameter switching effective time and the terminal's maximum moving speed. The parameter switching effective time is determined by the maximum latency budget required for the communication protocol stack to complete configuration distribution, link renegotiation, and effective confirmation. Homogeneous units with an area smaller than this threshold are merged into the adjacent homogeneous unit with the longest contact length with their boundary. This contact length is calculated from the number of shared boundary segments, thus maintaining spatial continuity.

[0032] After obtaining homogeneous spatial units, communication service level model matching is performed to determine the types of communication resource requirements and output a set of sub-regions with different communication requirements. First, statistical distribution characteristics are calculated for each homogeneous spatial unit. These characteristics include at least signal strength level, delay center trend level, delay jitter level, packet loss rate level, and packet loss fluctuation amplitude. The signal strength level is calculated by summing the representative signal strength values ​​of the grids within the unit and performing a weighted average. The weighting is taken as the number of valid samples within the statistical window for that grid. The number of valid samples is obtained by counting samples after outlier removal, thus reducing the amplified impact of sparsely sampled grids on the results. The delay center trend and delay jitter levels are obtained using the same weighted aggregation. The packet loss rate level is calculated by weighting the statistical values ​​of packet loss rates of the grids within the unit and normalizing the dispersion of each grid's packet loss rate statistical value relative to the unit mean to obtain the packet loss fluctuation amplitude. The communication service level model is pre-established during the system deployment phase. The level division is based on the requirements of virtual shooting for image synchronization and low latency, divided into at least three levels. Each level is configured with a latency upper limit threshold, a jitter upper limit threshold, and a packet loss upper limit threshold. The latency upper limit threshold is explained as follows: It is determined by the maximum end-to-end latency allowed for frame synchronization. This maximum end-to-end latency is jointly determined by the frame alignment error tolerance range, the encoding / decoding buffer budget, and the rendering pipeline buffer budget, minus the parameter switching effective time budget to reserve latency space during switching. The jitter upper limit threshold is explained as follows: It is calculated from the inter-frame consistency requirements and includes a basic jitter margin due to the discreteness of the wireless scheduling cycle. The packet loss upper limit threshold is explained as follows: It is determined by the tolerable net packet loss level under coding redundancy and retransmission strategies. The net packet loss level is constrained by the retransmission latency budget, which is determined by the remaining latency after deducting the encoding / decoding and rendering budgets from the latency upper limit threshold. The statistical distribution characteristics of homogeneous spatial units are compared item by item with the model thresholds. Units meeting all thresholds of the stricter tier are classified as high-level communication resource demand types, units meeting all thresholds of the intermediate tier are classified as medium-level communication resource demand types, and the rest are classified as basic-level communication resource demand types. After type determination, spatially adjacent homogeneous spatial units with consistent resource requirements are merged. The merging process uses shared boundaries as a criterion and performs connectivity merging to form the final sub-regions. The sub-region boundaries are then regularized to reduce repeated crossings caused by positioning jitter. The regularization scale, following this parameter description, is determined by the upper bound of the positioning error and the grid edge length, ensuring that boundary variations do not exceed the spatial projection corresponding to the upper bound of the positioning error. Finally, a set of sub-regions with different communication requirements is output according to resource requirement type.

[0033] S3 includes obtaining video stream transmission logs for a set of sub-regions, calculating jitter variance and delay distribution to obtain a service multidimensional vector; inputting the service multidimensional vector into a demand difference analysis model to distinguish between synchronization-priority regions and interaction-priority regions, and outputting a service demand category mapping table; based on the service demand category mapping table, matching a high-order modulation and coding scheme for synchronization-priority regions, and shortening the time slot period of the radio frame for interaction-priority regions to generate a candidate communication parameter combination sequence; performing numerical simulation calculations on the candidate communication parameter combination sequence to lock the optimal physical layer parameter set, and outputting the communication parameter configuration scheme corresponding to each sub-region.

[0034] In this implementation, a collection relationship is first established between sub-regions and video stream transmission logs. This relationship uses the sub-region number as an index and the terminal identifier and video stream identifier as association keys. Transmission records of the same terminal within the same statistical window are bucketed according to the actual sub-region where the terminal is located. The statistical window length is determined based on the continuity requirements of the virtual shooting scene synchronization service, ensuring the window covers at least several consecutive frames of generation, encoding, transmission, decoding, and presentation cycles. It also meets the parameter switching response latency budget, which is determined by the maximum time budget required for the protocol stack to complete parameter distribution, link renegotiation, and effectiveness confirmation, thus avoiding delays in demand identification. During bucketing, terminals moving across sub-regions are collected according to the sub-region number where the spatial location corresponding to the arrival time of each log record falls, ensuring the statistical results reflect the actual link status of the terminal within that sub-region.

[0035] After aggregation, the video stream transmission logs of the sub-region set are obtained, and jitter variance and delay distribution are calculated to obtain the service multi-dimensional vector. The logs are derived from the joint records of the video sending module, the wireless transceiver module, and the receiving and presentation module. The log fields include at least the frame sequence number, frame generation timestamp, frame transmission timestamp, frame arrival timestamp, frame decoding completion timestamp, frame display timestamp, retransmission flag, and frame loss flag. The timestamp reference adopts a unified site time synchronization source. The time synchronization accuracy is described here. The time synchronization accuracy is determined by the maximum time deviation allowed for image synchronization. The maximum time deviation is calculated from the frame alignment error tolerance range and the alignment margin caused by the minimum time granularity of wireless scheduling is added. For each sub-region, log records are traversed in ascending order of frame number within the statistics window. First, the end-to-end service latency is calculated for each frame. The end-to-end service latency is calculated by subtracting the frame generation timestamp from the frame display timestamp, reflecting the end-to-end latency from content generation to final presentation. When a frame display timestamp is missing, the frame decoding completion timestamp is used as a substitute, and this substitution is adopted as the unified configuration standard for the site to maintain statistical consistency. Then, a latency sample sequence is constructed, consisting of all end-to-end service latencies within the statistics window ordered by frame number. Missing samples are marked. The criteria for a missing sample are the presence of a frame generation timestamp but the absence of an arrival timestamp or decoding completion timestamp. Missing samples are marked as dropped frames and removed from the latency sample sequence to avoid misinterpreting missing frames as excessive latency. Outlier suppression is performed on the delayed sample sequence. The outlier suppression threshold is explained here. The outlier suppression threshold is taken from the upper bound of the maximum normal fluctuation of end-to-end delay in the stable region of the field calibration stage under the condition of the maximum number of concurrent terminals, and is superimposed with the queuing delay margin introduced by a retransmission trigger. The queuing delay margin is derived from the retransmission timing parameters of the wireless protocol stack and the maximum queue depth budget, so as to make the statistics more stable and not be skewed by occasional anomalies.The calculation of latency distribution is explicitly described here as a process of sorting, segmenting, counting, and extracting features from the latency sample sequence: First, the latency samples are sorted by numerical value, and a distribution table is established based on the sorted samples; then, the width of the distribution segments is set according to the minimum time granularity of wireless scheduling. The segment width is determined by the minimum time unit supported by the scheduler, ensuring that the segmentation of the distribution table is consistent with the discreteness of the scheduling; subsequently, the number of samples in each segment is counted to form a distribution count, and the latency center trend and latency tail proportion features are extracted from the distribution count. The latency center trend is obtained by selecting the representative value corresponding to the segment with the most dense samples. The representative value is taken as the average level of the samples in that segment to reflect the main distribution; the latency tail proportion feature is obtained by counting the proportion of the number of samples exceeding the preset latency threshold to the total number of samples. The preset latency threshold is determined by the maximum end-to-end latency allowed by the screen synchronization. The maximum end-to-end latency is jointly determined by the frame alignment error tolerance range, the encoding / decoding buffer budget, and the rendering pipeline buffer budget, and deducts the parameter switching effective time budget to reserve latency space during the switching period. The parameter switching effective time budget is determined by the maximum time budget of protocol stack issuance and effective confirmation.

[0036] The calculation of jitter variance is explicitly stated here as the calculation of the dispersion of the delay difference sequence between adjacent frames: First, the delay sample sequence is paired adjacently according to the frame number. For each pair of adjacent frames, the difference in end-to-end service delay is taken to form a jitter sample sequence. Then, the average level of the jitter sample sequence is calculated to characterize the average jitter offset within the window. Subsequently, the deviation of each jitter sample from the average level is calculated, and the deviations are aggregated across the entire window and normalized according to the number of samples to obtain the jitter variance, which is used to characterize the jitter intensity. To avoid interference from structural jumps caused by frame rate switching or encoding mode switching on jitter statistics, frame rate markers and encoding mode markers are read from the log. If a mode switch occurs within the statistical window, the jitter variance is calculated in segments with the switching point as the boundary, and weighted and aggregated according to the number of effective frames in each segment. The number of effective frames here refers to the number of delay samples retained after outlier suppression. In addition to jitter variance and latency distribution characteristics, the service multidimensional vector also incorporates two observations: frame drop rate and retransmission trigger rate. The frame drop rate is calculated by the number of frame drop markers within the statistical window and the total number of frames within the window. The retransmission trigger rate is calculated by the number of retransmission marker frames within the statistical window and the total number of frames within the window. These two metrics are used to distinguish between differences caused by insufficient link quality and contention load. The latency center trend, latency tail proportion characteristics, jitter variance, frame drop rate, and retransmission trigger rate are combined in a fixed order to form the service multidimensional vector for this sub-region. Scale uniformity is then applied to each dimension. The benchmark range for scale uniformity is described here: it is taken from the statistical upper and lower bounds of the virtual shooting service during stable operation in the calibration phase, and a load fluctuation margin under the condition of the maximum number of terminals is added, so that the vector differences between different sub-regions can be consistently explained by the same analysis model.

[0037] After inputting the multi-dimensional business vector into the demand difference analysis model, the model determines the demand category for each sub-region and outputs a business demand category mapping table. The demand difference analysis model is established using a rule-based constraint-based discriminant structure combined with offline calibration parameters: In the offline calibration phase, log samples are collected under both strong constraints of screen synchronization and strong constraints of interactive response. Based on the system's business scheduling configuration, category labels are assigned to the samples, thus obtaining the typical indicator ranges for the two types of businesses. In the online discrimination phase, the multi-dimensional business vector of the current sub-region is compared item by item with the two typical indicator ranges, prioritizing the category with the higher degree of constraint. The criteria for determining a synchronization-priority region are specified here as follows: if the proportion of delay tails exceeds the synchronization sensitivity threshold and the jitter variance exceeds the jitter sensitivity threshold, while the frame drop rate does not exceed the synchronization frame drop upper limit threshold, it indicates that the main contradiction in this region is timing consistency and presentation stability. The synchronization sensitivity threshold is defined here as the upper bound of the delay tail proportion feature under the strong constraints of image synchronization, while still meeting the allowable range of frame alignment error, plus the statistical fluctuation margin caused by sampling noise and timing alignment error. The jitter sensitivity threshold is defined here as the upper bound of the jitter variance under the same mode, while still meeting the allowable range of frame alignment error, plus the basic jitter margin caused by the discreteness of wireless scheduling. The basic jitter margin is jointly determined by the queuing budget under the minimum scheduling time granularity and the maximum number of concurrent terminals. The synchronization frame drop upper limit threshold is defined here as being determined by the video coding redundancy strategy for the tolerable net frame drop level. The net frame drop level is constrained by the maximum end-to-end latency budget, which is obtained by subtracting the fixed budget for encoding / decoding and rendering from the aforementioned preset latency threshold. The criteria for determining interaction-priority regions are specified here as follows: if the central trend of latency exceeds the interaction latency threshold and the retransmission trigger rate exceeds the retransmission sensitivity threshold, while the jitter variance does not exceed the jitter sensitivity threshold, it indicates that the main issues in this region are response latency and link recovery efficiency. The interaction latency threshold is defined here as being determined by the upper limit of the allowable lag in human-machine operation of the interaction control module, minus the control-side processing budget and the rendering-side application budget. The control-side processing budget is determined by the maximum time budget for control message parsing and status updates, while the rendering-side application budget is determined by the maximum power generation and synthesis time budget of the interaction echo link. The retransmission sensitivity threshold is defined here as being taken from the upper bound of the retransmission trigger rate when the interaction latency threshold is still met under the strong constraint mode of interaction response, plus the contention load margin under the maximum number of concurrent terminals. After the determination is completed, a business requirement category mapping table is output using the sub-region number as an index. The mapping table includes the sub-region number, business requirement category, a summary of key indicators used for determination, and a determination timestamp, thus providing a direct basis for subsequent parameter matching.

[0038] When generating candidate communication parameter combination sequences based on the business requirement category mapping table, first match the higher-order modulation and coding scheme for the synchronization priority area, then shorten the time slot period of the radio frame for the interaction priority area, and form candidate sequences respectively. The selection process for high-order modulation and coding schemes in synchronization-priority areas is described in the following steps: First, unsupported modulation and coding schemes are filtered out from the set of modulation and coding schemes supported by the wireless standard. The terminal's support capability is determined by the capability negotiation results between the terminal and the access side during the access establishment phase. Then, the reception quality constraints are evaluated for each of the remaining schemes. The reception quality constraints are composed of the error target and the coverage margin. The error target is defined here as the upper limit of the visual distortion tolerance of the synchronized video stream. The upper limit of the visual distortion tolerance is derived from the key frame protection strategy and the differential frame error propagation characteristics and fixed by the site service configuration. The coverage margin is defined here as the difference between the representative value of the signal strength of the sub-area and the terminal's reception sensitivity, minus the additional attenuation due to the maximum obstruction and the instantaneous fading margin. The additional attenuation due to the maximum obstruction is determined by the upper limit of the material and size of the main obstruction in the site measured during the calibration phase. The instantaneous fading margin is determined by the upper limit of the worst instantaneous drop in the stable area and is superimposed with the cross-grid sampling deviation margin caused by the positioning error. After the above constraint screening, the modulation and coding positions that meet the constraints are arranged from high to low efficiency to form a modulation and coding candidate set for the synchronization priority region. Then, each modulation and coding candidate is combined with the error correction redundancy parameter and the retransmission strategy parameter to obtain a candidate communication parameter combination sequence. The error correction redundancy parameter is defined here by the error target and the end-to-end delay budget. The end-to-end delay budget is determined by the remaining delay after deducting the fixed budget for encoding / decoding and rendering from the preset delay threshold. The retransmission strategy parameter is defined here by the use of a shorter retransmission trigger interval and a limited retransmission upper limit. The retransmission trigger interval is determined by the minimum time granularity of wireless scheduling and the service frame interval. The retransmission upper limit is determined by the remaining number of times after deducting the retransmission round-trip budget from the end-to-end delay budget. The retransmission round-trip budget is determined by the time slot period and the scheduling waiting budget.

[0039] When shortening the time slot period of a radio frame in an interaction-priority area, the current radio frame structure configuration is first read to obtain the existing time slot period, and the configurable time slot period range is obtained. This configurable time slot period range is jointly limited by the radio standard specification and the scheduler's implementation capabilities, and is also constrained by the terminal's processing capacity limit, which is determined by the minimum processing interval declared by the terminal during capability negotiation. Subsequently, multiple candidate time slot period values ​​are generated within the configurable range. The candidate step granularity is determined by the smallest configuration unit supported by the scheduler. For each candidate time slot period value, a scheduling priority parameter and an uplink / downlink resource ratio parameter are matched simultaneously. The scheduling priority parameter is determined by the priority level of the interactive control data, which is set by the temporal dependency of the control commands relative to the video data in the system service configuration. The uplink / downlink resource ratio parameter is jointly determined by the uplink control command bandwidth requirement and the downlink echo bandwidth requirement. The uplink control command bandwidth requirement is derived from the control message size, transmission frequency, and acknowledgment mechanism, while the downlink echo bandwidth requirement is derived from the echo message size and echo frequency. This forms a sequence of candidate communication parameter combinations for the interaction-priority area. To control the size of the candidate sequences and keep the computational costs manageable, pruning is performed on the candidate sequences. The pruning conditions are described here as eliminating combinations that do not meet the minimum throughput requirement and combinations that do not meet the maximum response latency budget. The minimum throughput requirement is determined by the video bitrate and error correction redundancy overhead, and the maximum response latency budget is determined by the remaining latency after deducting the control-side processing budget and the rendering-side application budget from the interaction latency threshold.

[0040] When performing numerical simulations on candidate communication parameter combinations to determine the optimal physical layer parameter set, an input dataset is first constructed for each sub-region. This input dataset consists of signal strength statistics, delay statistics, packet loss statistics for that sub-region, and retransmission trigger statistics generated in the video stream transmission log, all aligned within the same statistical window. The simulation process evaluates each candidate combination individually, with evaluation metrics including at least the expected end-to-end delay level, delay jitter intensity, net packet loss level, and effective throughput level. The calculation process for the expected end-to-end latency is explained here by link composition: First, the physical layer transmission time is obtained based on the modulation and coding efficiency of the candidate combinations and the amount of allocated resources. The physical layer transmission time is determined by the amount of data that can be carried per unit resource and the amount of video load data. Next, the media access waiting time is obtained based on the candidate time slot period and scheduling priority. The media access waiting time is determined by the time slot period length and queuing order. Then, the queuing time is obtained based on the upper limit of the number of concurrent terminals and the queue depth budget. The queue depth budget is determined by the scheduler buffer capacity and the maximum burst data volume budget. The maximum burst data volume budget is determined by the coding fragmentation strategy and the upper limit of the keyframe size. Then, the retransmission time is obtained based on the retransmission trigger interval and the retransmission upper limit. The retransmission time is obtained by aggregating the expected number of triggers and the scheduling cycle budget occupied by each retransmission. The expected number of triggers is derived from the historical retransmission trigger rate and candidate error correction redundancy capability of the sub-region. Finally, the encoding / decoding buffer time and the rendering pipeline buffer time are added. The buffer time is given by the fixed system configuration and is configured in accordance with the service frame interval, thus obtaining the expected end-to-end latency. The calculation process for latency jitter intensity is explained here as a discrete event aggregation: within the statistical window, discrete fluctuations in scheduling allocation, queuing changes, and retransmission triggers are simulated at frame intervals to form an end-to-end latency prediction sequence for each frame. Then, the discreteness of the latency difference sequence between adjacent frames is calculated and normalized to obtain the jitter intensity. The normalization benchmark is determined by the minimum time granularity of wireless scheduling. The calculation process for net packet loss level is explained here as a derivation of recovery capability: first, the original packet loss level is obtained based on the historical packet loss statistics of the sub-region. Then, the recoverable bit error rate and loss ratio are derived based on the candidate error correction redundancy parameters. The error correction recovery ratio is jointly limited by the redundancy intensity and the bit error rate target. Next, the loss ratio that can be recovered through retransmission is derived based on the candidate retransmission strategy. The retransmission recovery ratio is jointly limited by the retransmission trigger interval, the retransmission upper limit, and the end-to-end latency budget. The net packet loss level is obtained by subtracting the above two types of recovery ratios from the original packet loss level. The calculation process for effective throughput is explained here by deducting overhead: First, the theoretical carrying capacity is obtained based on the allocation of candidate resources and modulation and coding efficiency. Then, error correction redundancy overhead and retransmission overhead are deducted. Error correction redundancy overhead is determined by redundancy parameters, and retransmission overhead is determined by the expected number of triggers and the resources occupied by each retransmission, thus obtaining the effective throughput level.

[0041] After completing the calculations for all candidate combinations, the optimal physical layer parameter set is locked, and the communication parameter configuration scheme corresponding to each sub-region is output. The optimal determination criterion here adopts a hierarchical priority strategy and provides the parameter sources: For synchronization-priority regions, the combination with the smallest jitter intensity is selected from the candidate set that satisfies the jitter upper limit threshold and the net packet loss upper limit threshold. The jitter upper limit threshold is explained here as being calculated from the allowable range of inter-frame alignment error and superimposed with the basic margin of wireless scheduling discreteness. The net packet loss upper limit threshold is explained here as being determined by the tolerable net packet loss level under coding redundancy and retransmission budget, which is constrained by a preset delay threshold; when the jitter intensity is the same or close, the combination with a lower expected end-to-end delay level and a higher effective throughput level is selected. For interaction-priority areas, the combination with the lowest expected end-to-end latency is first selected from the candidate set that meets both the maximum response latency threshold and the net packet loss upper limit threshold. The maximum response latency threshold is explained here as being obtained by subtracting the control-side processing budget and the rendering-side application budget from the upper limit of the allowable lag for human-machine operation. When the expected end-to-end latency levels are the same or close, the combination with the lower predicted retransmission trigger rate and lower net packet loss is selected to reduce the uncertainty of interactive feedback. The final output communication parameter configuration scheme is indexed by the sub-area number and includes modulation and coding scheme configuration, radio frame slot period configuration, scheduling priority configuration, uplink and downlink resource ratio configuration, error correction redundancy configuration, and retransmission strategy configuration. It also records the applicable service requirement category and the determination timestamp for this scheme.

[0042] S4 includes activating boundary sensing nodes to capture edge field strength data and spectral interference maps, calculating penetration loss values ​​and environmental attenuation factors; combining the terminal's movement trajectory and movement rate vector, mapping the penetration loss values ​​and environmental attenuation factors to trajectory coordinates to locate cross-domain handover points, and constructing a signal coverage hole model; if the density of the signal coverage hole model exceeds the standard, extracting time-varying features to generate a quality fluctuation sequence; establishing a dynamic feedback link based on the quality fluctuation sequence, and outputting dynamic signal quality feedback information for handheld terminals moving across regions.

[0043] In this embodiment, the activation range and corresponding boundary number of the boundary sensing nodes are first determined. The activation range is deployed along the boundary contour of the sub-region. The deployment spacing is determined by the minimum spatial scale of signal change at the boundary. The minimum spatial scale is determined by the characteristic size of the main obstruction of the site, the distribution density of reflectors, and the grid side length, so that the field strength changes observed by adjacent nodes are different and there are no continuous blind spots. The node coordinates are aligned with the site coordinate system. The alignment accuracy is constrained by the upper bound of the positioning error and the boundary regularization scale. The upper bound of the positioning error is taken from the statistical upper bound of the positioning system error under the worst occlusion condition, and the position jump margin caused by short-term multipath disturbance is superimposed. The boundary regularization scale is taken from the spatial scale used when regularizing the sub-region boundary, thereby ensuring the spatial consistency of subsequent trajectory mapping.

[0044] When the boundary sensing node is activated to capture edge field strength data and spectral interference maps, it enters continuous sampling mode and performs dual-channel observations on the preset operating frequency band. Edge field strength data is acquired in time-series format, while the spectral interference map is acquired as a two-dimensional record of frequency and time. The sampling of edge field strength data is described here as periodically measuring the received signal strength at the monitoring frequency and adding a timestamp. The monitoring frequency is determined by the maximum moving speed of the terminal and the boundary band width, ensuring that the terminal forms at least multiple field strength samples while traversing the boundary band. The boundary band width is jointly determined by the upper bound of the positioning error and the boundary regularization scale. The acquisition of the spectral interference map is described here as measuring the interference power point by point on the operating frequency band according to the scanning step and adding a timestamp. The scanning step is determined by the minimum resource granularity of the wireless standard, ensuring consistency between interference assessment and resource allocation granularity. The coverage duration of each scan is constrained by the statistical window length, which is described here as being consistent with the time scale used for handover determination, ensuring that the interference map and field strength sequence are aligned within the same time range. To ensure that the field strength and interference data are aligned in time, the node timestamp reference adopts a unified site time source. The time accuracy is determined by the maximum time deviation allowed for image synchronization. The maximum time deviation is calculated from the frame alignment error tolerance range and is superimposed with the alignment margin caused by the minimum time granularity of wireless scheduling.

[0045] The calculation of penetration loss is presented in its entirety in plain text. First, reference points are determined on both sides of the boundary for comparison. The selection rule is to establish inner and outer reference points centered on the boundary contour, offset along the normal direction by one grid center distance on the inner and outer sides of the boundary, respectively. The grid center distance is determined by the grid edge length, ensuring consistency between the spatial granularity of the reference points and the distribution map. Second, the field strength sample sequences of the inner and outer reference points are summarized within the same statistical window, sorted by timestamp to ensure alignment. Then, outlier removal is performed on the sample sequences on both sides. The outlier removal threshold is taken from the upper bound of the maximum normal fluctuation of the field strength in the stable region during the calibration phase, and superimposed with an instantaneous drop margin caused by one occlusion. This occlusion margin is determined by the additional attenuation upper bound caused by the material and size of the main occluder. After the removal process, representative field strength values ​​are calculated for both sample sequences. This calculation involves determining the central tendency of the removed samples, using the typical location of the main distribution as a representative. The main distribution identification width is determined by the time granularity corresponding to the monitoring frequency and the minimum time granularity of wireless scheduling. Finally, the difference between the inner and outer representative field strength values ​​is calculated and used as the penetration loss value to characterize the additional attenuation introduced by structural penetration or occlusion at the boundary. To avoid bias in the difference caused by inconsistent sampling times on both sides, time alignment is performed on the sample sequences before calculating the difference. Time alignment here refers to nearest-neighbor pairing within the same timestamp neighborhood. The width of the timestamp neighborhood is determined by the timing accuracy and the sampling interval corresponding to the monitoring frequency, ensuring that the pairing error does not exceed the time alignment margin.

[0046] The calculation process of the environmental attenuation factor is presented here in plain text. The environmental attenuation factor is used to comprehensively characterize the impact of interference occupancy and fading fluctuations on link quality. First, the interference occupancy intensity is calculated based on the spectral interference map. The calculation of the interference occupancy intensity is explained here as follows: for each scan frequency point, the number of times the interference power exceeds the interference threshold and the duration of the occurrence are counted within a statistical window, and the entire frequency band is aggregated to form an occupancy metric. The interference threshold is explained here as being jointly determined by the upper bound of the receiver noise floor and the minimum detectable interference margin. The upper bound of the noise floor is given by the RF specification, and the minimum detectable interference margin is determined by the minimum sensitivity requirement of the wireless standard to the decrease in signal-to-noise ratio. Subsequently, the fading intensity is calculated based on the edge field strength data. The calculation involves obtaining a representative field strength value from the field strength sample sequence, then statistically analyzing the downlink deviation of each sample relative to the representative value and aggregating them within a statistical window. The aggregation method prioritizes samples with larger deviations to highlight the impact of deep fading on the link. To ensure comparability of fading intensity between different nodes, normalization is performed on the fading intensity. The normalization benchmark is taken from the upper bound of the typical downlink deviation in the stable region during the calibration phase under the maximum number of concurrent terminals, and a sampling noise margin is added. Finally, the interference occupancy intensity and fading intensity are synthesized on a unified scale to obtain the environmental attenuation factor. The synthesis weight is determined by the service demand category, which is derived from a service demand category mapping table. For synchronization-priority regions sensitive to jitter, the weight of the fading item is increased; for interaction-priority regions sensitive to retransmission and response, the weight of the interference item is increased, ensuring that the environmental attenuation factor aligns with service risk.

[0047] When locating cross-domain handover points by combining the terminal's movement trajectory and movement rate vector, and mapping the penetration loss value and environmental attenuation factor to the trajectory coordinates, the system first acquires the trajectory point sequence within the statistical window. This sequence consists of location coordinates and timestamps. The positioning refresh cycle is described here as matching the monitoring frequency, ensuring consistency between the trajectory point time granularity and the node observation time granularity. The calculation of the movement rate vector involves pairing adjacent trajectory points according to their timestamps, calculating the displacement, and dividing by the time interval to obtain the rate magnitude. Simultaneously, the rate direction is determined by the displacement direction, forming a directional rate description. When the time interval is less than the minimum time interval threshold, the pair of trajectory points is discarded to avoid overestimating the speed due to positioning jitter. The minimum time interval threshold is described here as being determined by the positioning system's minimum stable refresh interval, with a timing alignment margin added. The location of cross-domain handover points is described here as identifying locations in the trajectory point sequence where the sub-region number changes. The sub-region number is obtained by inputting the trajectory point coordinates into the sub-region boundary contour for spatial attribution determination. After determining the cross-domain handover point, a boundary neighborhood is established around it. The radius of this boundary neighborhood is determined by the upper bound of the positioning error and the boundary normalization scale, ensuring that the neighborhood covers the uncertain area of ​​the cross-domain determination. Subsequently, the penetration loss value and environmental attenuation factor calculated by the boundary sensing nodes are mapped to the cross-domain handover point. The mapping method involves spatially weighted aggregation of node observations within the boundary neighborhood that occur within the same time period as the cross-domain handover point. The spatial weights decrease and are normalized according to the planar distance from the cross-domain handover point to the node, maximizing the contribution of near-end nodes. When node observations are missing within the same time period, the missing observation handling rule is to use observations from adjacent time periods and apply attenuation weights based on the time difference. The allowable range of the time difference is determined by the sampling interval corresponding to the monitoring frequency. The mapping results formed by the cross-domain handover point are arranged in chronological order to obtain a mapping sequence. The mapping sequence is then resampled according to the movement rate. The resampling step size is determined by the monitoring frequency, ensuring that the time granularity of the mapping sequence is consistent with the subsequent quality fluctuation sequence.

[0048] When constructing the signal coverage hole model, the model uses a set of discrete sampling points in the trajectory coordinate system as its carrier. These discrete sampling points consist of cross-domain handover points and trajectory points within their boundary neighborhoods. For each discrete sampling point, the comprehensive attenuation intensity is calculated. Here, the comprehensive attenuation intensity is described as a weighted sum of the penetration loss mapping value and the environmental attenuation factor mapping value at that point on a unified scale, with the weights following the aforementioned service requirement category weights for consistency. Subsequently, hole detection is performed on the discrete sampling points. The detection rule here is that if the comprehensive attenuation intensity exceeds the coverage hole detection threshold, the sampling point is marked as a hole point. The coverage hole detection threshold is determined here by the terminal receiving sensitivity, target throughput requirements, and minimum acceptable retransmission burden: the terminal receiving sensitivity is given by the terminal RF specifications; the target throughput requirements are determined by the video stream bitrate and error correction redundancy overhead, the video stream bitrate is given by the frame rate, resolution, and encoding configuration, and the error correction redundancy overhead is determined by the error correction redundancy configuration; the minimum acceptable retransmission burden is derived by working backward from the maximum end-to-end latency budget, which is obtained by subtracting the fixed budget for encoding / decoding and rendering from the maximum end-to-end latency allowed for image synchronization; the threshold is based on the above constraints and includes the maximum occlusion attenuation and instantaneous fading margin, so that the hole detection targets coverage gaps that are perceptible to the service. After marking the void points, they are clustered to form void clusters. The clustering rule is that void points that simultaneously satisfy both spatial and temporal adjacency are grouped into the same void cluster. The spatial adjacency distance threshold is determined by the average spacing of the trajectory points. The average spacing is calculated by the positioning refresh cycle and the maximum moving speed of the terminal, plus a positioning error margin. The temporal adjacency interval threshold is determined by the reciprocal of the monitoring frequency, plus a timing alignment margin. The spatial distribution, duration, and frequency of occurrence of the void clusters form a signal coverage void model, which is used to describe the coverage gap structure generated near the boundary as the device moves.

[0049] If the signal coverage hole model density exceeds the limit, time-varying features are extracted to generate a quality fluctuation sequence. The hole model density is calculated as the ratio of the number of hole points to the total number of sampling points within a statistical window. Simultaneously, the average duration of hole clusters is calculated to form a comprehensive density index. The average duration of a hole cluster is obtained by statistically averaging the start and end time differences of each hole cluster, with the start and end times determined by the timestamps of the earliest and latest hole points within that cluster. The density exceeding threshold is determined by the business requirement category and derived from calibration phase data: In the strong constraint mode of screen synchronization, the upper bound of the comprehensive hole density index that still meets the jitter and latency thresholds is selected as the density exceeding threshold for synchronization-priority areas, and a statistical fluctuation margin caused by positioning errors and sampling noise is added; in the strong constraint mode of interactive response, the upper bound of the comprehensive hole density index that still meets the maximum response latency threshold is selected as the density exceeding threshold for interactive-priority areas, and a contention load margin is added. The contention load margin is determined by the scheduling queuing budget under the maximum number of concurrent terminals. After determining that the density exceeds the standard, the process of extracting time-varying features is specified item by item as follows: the time step is determined by the monitoring frequency; the time-varying feature of penetration loss is obtained by the difference in the penetration loss mapping value between adjacent time steps; the time-varying feature of environmental decay is obtained by the difference in the environmental decay factor mapping value between adjacent time steps; the time-varying feature of void state is formed by marking the existence of void points in each time step; the evolution feature of void clusters is obtained by statistically analyzing the existence length and the number of new and disappearing void clusters in consecutive time steps. The above time-varying features are combined in a fixed order to form a quality fluctuation sequence, which is used to characterize the quality fluctuation trend and mutation risk in cross-domain processes.

[0050] When establishing a dynamic feedback link based on the quality fluctuation sequence and outputting dynamic signal quality feedback information, the source, sink, and transmission priorities of the feedback link are first determined. The source is the boundary sensing node and trajectory processing module, and the sink is the parameter switching determination module. The transmission priority is determined by the service requirement category; feedback priority is higher in synchronization-priority areas than in interaction-priority areas to ensure timely switching decisions. The quality fluctuation sequence is then encapsulated, including at least the boundary number, cross-domain switching point coordinates, timestamp sequence, penetration loss time-varying characteristic sequence, environmental attenuation time-varying characteristic sequence, cavity density index, density exceedance marker, and cavity cluster duration index. To ensure the feedback information is consistent with the switching decision timing, timing alignment and arrival delay compensation are performed on the encapsulated data. Timing alignment is based on a unified timing source, and arrival delay compensation involves measuring the end-to-end transmission delay of the feedback link and performing reverse correction on the feedback timestamp. The end-to-end transmission delay is obtained through round-trip measurements of link probe messages, with the probe period determined by the monitoring frequency. After alignment, the dynamic signal quality feedback information is output.

[0051] S5 includes acquiring time-varying spectral feature data from dynamic signal quality feedback information, comparing it with a fingerprint database to determine a new sub-region identifier; extracting the preset quadrature amplitude modulation order and channel coding redundancy rate based on the new sub-region identifier; resetting the physical layer configuration by parsing the quadrature amplitude modulation order and channel coding redundancy rate, and completing parameter switching for the new sub-region features; collecting bit error distribution data and round-trip delay jitter values ​​after switching, calculating the deviation between the bit error distribution data and round-trip delay jitter values ​​to obtain a communication adaptability score; if the communication adaptability score meets the standard, calculating the anti-interference margin level after parameter switching, and determining the communication stability level after adaptation.

[0052] In this embodiment, the statistical window covered by this determination is first determined. The length of the statistical window is constrained by the parameter switching effective time budget and the terminal's maximum moving speed, so that the terminal stays in the new sub-region for at least one statistical window after crossing the domain to form stable statistics. The parameter switching effective time budget is determined by the maximum time budget required for the protocol stack to complete configuration distribution, link renegotiation and effective confirmation. The terminal's maximum moving speed is taken from the speed limit in the site safety regulations or shooting path restrictions, and the speed estimation margin introduced by the positioning error is added.

[0053] When acquiring time-varying spectral feature data from dynamic signal quality feedback information and comparing it with the fingerprint database to determine new sub-region identifiers, the first step is to extract the spectrum scanning result sequence from the dynamic signal quality feedback information by timestamp. This spectrum scanning result sequence consists of a frequency point power sequence, an interference occupancy duration sequence, and a spectrum morphology change sequence. The frequency point set is consistent with the current service-occupied frequency band to ensure comparability. Subsequently, noise floor calibration and scaling are performed on the frequency point power sequence. Noise floor calibration uses the statistical upper bound of the noise floor measured under the lowest site load condition as a benchmark, superimposed with the receiver measurement accuracy error margin. The receiver measurement accuracy error margin is given by the equipment's RF specifications. Scaling scaling uses a unified benchmark range for linear scaling. The benchmark range is taken from the upper and lower boundaries obtained from the statistical analysis of the full site spectrum power distribution during the fingerprint database establishment phase, superimposed with the interference fluctuation margin. The interference fluctuation margin is determined by the upper bound of the occupancy duration fluctuation under the maximum number of concurrent terminals. To ensure time alignment between sequences, the spectral scan result sequences are time-aligned. Time alignment here refers to aligning the current sequence with the fingerprint database reference sequence based on a unified time source and according to the nearest timestamp principle. The maximum allowable time difference threshold for alignment is determined by the scan cycle and time accuracy, ensuring that the alignment error does not exceed the allowable range of morphological changes within one scan cycle. The scan cycle is determined by the scan duration of the boundary sensing node, and the time accuracy is determined by the maximum allowable time deviation for image synchronization, plus the alignment margin caused by the minimum time granularity of wireless scheduling.

[0054] The fingerprint database here refers to a set of sub-region spectral fingerprints pre-established during the site deployment phase. The establishment process involves selecting representative points in each sub-region, performing multiple spectral scans, and forming a reference sequence. The number of representative points is determined by the sub-region area and the degree of interference heterogeneity. The degree of interference heterogeneity is derived from the distribution of grid features and statistical analysis of abnormal communication blocks, ensuring that the representative points can cover the main differences in interference patterns within the sub-region. After each reference sequence undergoes the same noise floor calibration and scale consistency processing, reference features are extracted and bound to the sub-region number and the collection time period label. The comparison process unfolds here according to the calculation steps: First, fingerprint features for matching are extracted from the current time-varying spectral feature data. The fingerprint features include at least the frequency power distribution morphology features, interference peak location features, and occupancy duration features. The frequency power distribution morphology features are obtained by calculating the central trend of the power sequence of each frequency point within a statistical window and forming a morphology vector according to the frequency point order. The central trend is calculated by removing outliers and taking the representative value of the main distribution. The outlier removal threshold is taken from the upper bound of the maximum normal fluctuation of repeated scans in the same sub-region during the fingerprint database establishment phase and superimposed with the measurement noise margin. The interference peak location features are obtained by searching for the frequency point with the highest power in each scan and counting its occurrence frequency within the statistical window, and the peak location with the highest occurrence frequency is taken as representative. The occupancy duration features are obtained by counting and summing the duration of continuous occupancy of frequency points whose power exceeds the interference threshold within the statistical window. The interference threshold is determined by adding the minimum detectable interference margin to the upper bound of the receiver noise floor. The upper bound of the noise floor is given by the radio frequency index, and the minimum detectable interference margin is determined by the minimum sensitivity requirement of the wireless standard to the decrease in signal-to-noise ratio. Subsequently, a comprehensive difference value is calculated for the current fingerprint feature and each reference fingerprint feature in the fingerprint database. This comprehensive difference value calculation involves separately calculating morphological differences, peak position differences, and duration differences. These three types of differences are then aggregated using a unified scale. The benchmark range for this unified scale is taken from the upper bound of the maximum normal difference within the same sub-region during the fingerprint database establishment phase, plus the interference fluctuation margin under the maximum number of concurrent terminals. The comprehensive difference values ​​obtained from all reference fingerprints are sorted, and the reference fingerprint with the smallest comprehensive difference value is selected, with its bound sub-region number used as a candidate new sub-region identifier. To avoid misjudgments caused by transient interference, a similarity threshold is set to verify candidates. This similarity threshold is implemented as a comprehensive difference value threshold, which is taken from the upper bound of the maximum comprehensive difference value between reference fingerprints in the same sub-region at different time periods during the calibration phase, and is superimposed with the fluctuation margin caused by sudden interference near the boundary. The fluctuation margin is determined by the upper bound of the maximum fluctuation of the occupancy duration feature within the boundary band. When the comprehensive difference value of the candidate reference fingerprint does not exceed this threshold, a new sub-region is identified. When it exceeds this threshold, the scanning cycle is extended by one step and the fingerprint features are re-extracted for re-judgment. The extension amount is determined by the scanning duration, so that the re-judgment is based on a more complete interference pattern.

[0055] When extracting the preset quadrature amplitude modulation order and channel coding redundancy rate based on the new sub-region identifier, the sub-region parameter index table is accessed first. The index table uses the sub-region number as the key and points to the output communication parameter configuration scheme record. The generation of the index table is explained here as being obtained by expanding the communication parameter configuration scheme at the physical layer dimension. The expanded content includes at least the modulation position number, modulation order, channel coding scheme number, channel coding redundancy rate, retransmission strategy constraints, and time slot configuration constraints. The quadrature amplitude modulation order is described here as one of the physical layer parameters locked in the candidate combination calculation. Its determination is jointly limited by the reception quality constraint and the throughput requirement constraint: The reception quality constraint is jointly determined by the terminal reception sensitivity, the representative value of the signal strength in the sub-region, and the maximum occlusion additional attenuation margin. The terminal reception sensitivity is given by the terminal RF specification. The representative value of the signal strength is obtained by taking the formed grid statistics and weighting and summing them by sub-region. The maximum occlusion additional attenuation margin is determined by the upper bound of the main occlusion structure material and size measured during the calibration stage and superimposed with the instantaneous fading margin. The instantaneous fading margin is determined by the worst instantaneous drop upper bound of the stable region. The throughput requirement constraint is determined by the video bitrate and the error correction redundancy overhead. The video bitrate is given by the frame rate, resolution, and encoding configuration. The error correction redundancy overhead is determined by the redundancy rate in the configuration scheme of the sub-region. The channel coding redundancy rate is defined here as being jointly limited by the error target and the end-to-end delay budget. The error target is determined by the upper limit of the visual distortion tolerance and is fixed by the service configuration. The end-to-end delay budget is obtained by subtracting the encoding / decoding buffer budget and the rendering pipeline buffer budget from the maximum allowed end-to-end delay for image synchronization, and also subtracting the parameter switching effective time budget to reserve delay space during the switching period. After the search is completed, the quadrature amplitude modulation order and the channel coding redundancy rate are used as target parameters, and the system enters the physical layer configuration reset stage.

[0056] When resolving the quadrature amplitude modulation order and channel coding redundancy rate and resetting the physical layer configuration to complete parameter switching, a validity check is first performed on the target parameters. This validity check includes terminal capability verification and access-side capability verification: Terminal capability verification confirms that the target modulation order and target coding redundancy configuration fall within the terminal's supported range by comparing the capability negotiation results from the access establishment phase; access-side capability verification confirms that the target configuration is effective under the current carrier and current time slot structure by comparing the scheduler's capability table. After successful verification, the access side generates a configuration delivery message with an effective timestamp. The effective timestamp is generated by a unified timing source and meets the effective lead time requirement. The effective lead time is jointly determined by the upper bound of the maximum transmission delay of the delivery link and the queue congestion margin. The upper bound of the maximum transmission delay of the delivery link is obtained through round-trip measurements of link probe messages, and the queue congestion margin is determined by the upper bound of the scheduling queuing budget under the condition of the maximum number of concurrent terminals. After receiving the message, the terminal loads local physical layer parameters and adjusts the transmission buffer before the effective timestamp arrives. This adjustment involves dividing incomplete data blocks by frame sequence number and effective timestamp. Data blocks before the effective timestamp are transmitted using the old configuration, while data blocks after the effective timestamp are re-encoded and queued according to the new configuration. This avoids decoding failures caused by inconsistent encoding at the handover boundary within the same frame. When the effective timestamp arrives, both the terminal and the access side simultaneously enable the new modulation order and new coding redundancy configuration to complete the parameter switching for the new sub-region characteristics. To reduce the risk of configuration inconsistencies during handover, a handover protection period is set. This period is set to be no less than one radio frame period, determined by the current time slot period and the minimum scheduling time granularity. Within the handover protection period, the transmission priority of control signaling is increased, and its retransmission limit is raised to ensure configuration consistency.

[0057] After the handover is completed, when collecting error distribution data and round-trip delay jitter values ​​and calculating the communication adaptability score, the collection window is first defined as the aforementioned statistical window, and error distribution data and round-trip delay samples are collected within this window. The collection of error distribution data here refers to counting and statistically analyzing the physical layer decoding results by transport block or coding block. The statistical fields must include at least the number of decoding failures, the number of residual error events after error correction, and the number of retransmission triggers, forming a distribution sequence in chronological order. To ensure comparability of the distributions, the statistical granularity and coding block division rules are taken from the current channel coding configuration and are consistent with the records on the access side. The collection of round-trip delay jitter values ​​is described here as follows: within the collection window, link probe messages are sent according to the probe cycle, and the sending timestamp and return timestamp of each message are recorded. The determination of the probe cycle is described here as being constrained by the terminal's processing capacity and service load. The probe cycle is taken as the larger value between the monitoring frequency and the terminal's minimum stable probe interval. The terminal's minimum stable probe interval is determined by the upper limit of the terminal's measurement capacity, and the service load constraint is determined by the video frame interval and the minimum scheduling time granularity, so that the probe messages do not crowd out critical service resources. Then, a round-trip delay sample is calculated for each message. The round-trip delay sample is obtained by subtracting the sending timestamp from the return timestamp. Then, the round-trip delay samples are paired up in pairs according to the timestamp order, and the difference between adjacent samples is calculated to form a jitter sample sequence. Then, the jitter sample sequence is averaged and its dispersion is calculated. The dispersion calculation is described here as follows: the deviation of each jitter sample from the average value is aggregated and normalized according to the number of samples to obtain the round-trip delay jitter value.

[0058] The calculation of the communication suitability score is carried out here according to the deviation calculation and aggregation steps. First, the target baseline for bit error rate and the target baseline for jitter are determined. The target baseline for bit error rate is determined by the upper bound of the tolerable decoding failure corresponding to the channel coding redundancy rate of the sub-region. The upper bound of the tolerable decoding failure is given by the matching result of the bit error rate target and the redundancy strength and is constrained by the upper limit of the visible distortion tolerance. The target baseline for jitter is determined by the service requirement category. The synchronization priority type area adopts a more stringent jitter upper limit baseline. The jitter upper limit baseline is obtained by converting the frame alignment error tolerance range and superimposing the wireless scheduling discreteness basic margin. The interaction priority type area adopts a response-related jitter baseline. The response-related jitter baseline is determined by the fluctuation space obtained by deducting the control side processing budget and the rendering side application budget from the maximum response delay threshold. Next, the bit error rate (BER) is calculated. Here, BER is defined as the difference between the number of decoding failures within the acquisition window and the target baseline for BER, and is scaled to be consistent. The scale consistency benchmark is taken from the maximum normal upper bound of the difference between the number of decoding failures and the baseline when using the same physical layer configuration in the same sub-region during the calibration phase, plus a measurement noise margin. Then, jitter is calculated. Jitter is defined as the difference between the round-trip delay jitter value and the target jitter baseline, and is scaled to be consistent. The scale consistency benchmark is taken from the maximum normal upper bound of the difference between the jitter value and the baseline under the same service mode during the calibration phase, plus a probe message transmission jitter margin. The probe message transmission jitter margin is determined by the upper bound of the scheduling queuing budget and the time slot period. Finally, the BER and jitter are aggregated according to the service requirement category weights to obtain a communication suitability score. The weights are determined by the output service requirement category mapping table. For synchronization-priority regions, the jitter weight is increased; for interaction-priority regions, the weights of BER and retransmission trigger-related items are increased, aligning the score with service risk.

[0059] The communication adaptability score threshold is explained here as being derived by reverse engineering based on meeting the service threshold conditions during the calibration phase: For synchronization-priority areas, the threshold is taken from the lower bound of the score when the frame alignment error tolerance range, jitter upper limit baseline, and net packet loss upper limit threshold are still met, plus a statistical fluctuation margin, which is jointly determined by the lower bound of the sample size within the acquisition window and the upper bound of the measurement noise; For interaction-priority areas, the threshold is taken from the lower bound of the score when the maximum response delay threshold and control command success rate threshold are still met, plus a contention load margin, which is determined by the upper bound of the scheduling queuing budget under the maximum number of concurrent terminals. If the communication adaptability score meets the standard, the anti-interference margin level after parameter switching is calculated, and the communication stability level after adaptation is judged accordingly.

[0060] The calculation of the anti-interference margin level is carried out here according to the steps of calculating the actual interference intensity, calculating the required threshold, and classifying the margin. First, the actual interference intensity index is calculated. The actual interference intensity index is obtained from the spectrum scanning results in the acquisition window. The central trend of the interference power sample in the frequency band occupied by the service is used as the representative value of the interference. The duration of the interference exceeding the interference threshold is statistically analyzed as the interference occupancy supplement. The interference threshold is determined by adding the minimum detectable interference margin to the upper limit of the receiver noise floor. The upper limit of the noise floor is given by the radio frequency index. The minimum detectable interference margin is determined by the minimum sensitivity requirement of the wireless standard to the decrease in signal-to-noise ratio. The central trend calculation also adopts the method of taking the representative value of the main distribution after outlier removal. The outlier removal threshold is taken from the upper limit of the maximum normal interference fluctuation during the fingerprint database establishment stage and superimposed with the measured noise margin. Next, the anti-interference requirement threshold is calculated. This threshold is determined by the minimum received quality requirement corresponding to the current orthogonal amplitude modulation order and channel coding redundancy rate. The minimum received quality requirement is derived from the link budget table during system deployment and combined with terminal receiver sensitivity verification. Simultaneously, the maximum obstruction-added attenuation margin and instantaneous fading margin are superimposed to reflect far-field environmental fluctuations. The maximum obstruction-added attenuation margin is determined by the upper bound of the main obstruction structure calibration in the site, and the instantaneous fading margin is determined by the upper bound of the worst-case instantaneous drop in the stable region. Then, the anti-interference margin is calculated. This margin is defined as the remaining space of the link relative to the anti-interference requirement threshold after combining the representative value of the received signal strength within the acquisition window with the actual interference strength index. The representative value of the received signal strength is obtained by removing outliers from the field strength samples of the service-occupied frequency band by the terminal or boundary sensing node and calculating the central trend. The anti-interference margin is mapped to an anti-interference margin level. The level threshold is explained here as being obtained from the calibration phase statistics: Under the same physical layer configuration in the same sub-region, the lower bound of the anti-interference margin that still meets the target baseline for bit error rate and jitter is selected as the high-level threshold, and the lower bound of the anti-interference margin that still meets the basic usable threshold is selected as the medium-level threshold. Anything below the medium-level threshold is judged as a low-level threshold. The basic usable threshold is jointly determined by the minimum throughput requirement and the maximum response latency budget. The minimum throughput requirement is determined by the video bitrate and redundancy overhead, and the maximum response latency budget is obtained by subtracting the processing and rendering budget from the interaction latency threshold. Finally, the communication compatibility score and the anti-interference margin level are jointly used to determine the communication stability level. The joint determination rule is as follows: when the score meets the standard and the anti-interference margin level is high, it is determined to be a high stability level; when the score meets the standard and the anti-interference margin level is medium, it is determined to be a medium stability level; when the score meets the standard but the anti-interference margin level is low, it is determined to be a usable but risky stability level. The new sub-region identifier, the actual effective modulation order and coding redundancy rate, the communication compatibility score, the anti-interference margin level, and the communication stability level are used as outputs.

[0061] S6 includes collecting packet loss sequences and delay timestamp data with frequencies exceeding a preset threshold after parameter switching; calculating the variance of packet loss intervals for packet loss sequences with frequencies exceeding the preset threshold and the fluctuation amplitude of delay timestamp data to generate a link congestion feature matrix; parsing the link congestion feature matrix to extract abnormal fluctuation segments; constructing a real-time transmission state vector by combining it with physical layer retransmission request counts; mapping the real-time transmission state vector to a multi-dimensional stability evaluation space; calculating the Euclidean distance between the vector magnitude and the baseline stable state to obtain a stability metric; and determining the final communication quality optimization result by combining the net throughput data if the stability metric is within a preset convergence interval.

[0062] In this embodiment, the monitoring object is the transmission process of the same handheld terminal link after parameter switching. The monitoring range covers the time period during which the terminal is moving within the current sub-region. The purpose is to characterize congestion and stability changes using two types of link phenomena: packet loss and latency, and to provide communication quality optimization results when stability reaches convergence conditions. After the step is started, the monitoring statistics window and sliding step size are first set. The statistics window length is determined based on the video frame rhythm and retransmission observation period, ensuring that one statistics window covers the transmission period of no less than a few consecutive frames and at least one retransmission timing period. The retransmission observation period is determined by the retransmission timing parameters of the wireless protocol stack. The sliding step size is set to the smallest time granularity corresponding to the monitoring frequency, so that there is overlap between adjacent windows, thereby capturing the start and end changes of sudden congestion. The monitoring frequency is determined based on the terminal's maximum moving speed and the sub-region boundary regularization scale, so that the terminal can still form multi-window sampling during short-distance movement, avoiding lag in stability judgment. All sampling timestamps use the same site time source. The time accuracy is determined by the maximum time deviation allowed by the image synchronization, and the alignment margin caused by the minimum time granularity of wireless scheduling is added to ensure that packet loss events and delay samples are on the same time axis.

[0063] When collecting packet loss sequences and delay timestamp data with frequencies exceeding a preset threshold after parameter switching, the packet loss event stream is first obtained from the link statistics module. Here, a packet loss event is defined as an event record generated by the receiving end after detecting missing packets according to sequence number continuity. The event record includes at least the event occurrence timestamp, the range of missing sequence numbers, and the corresponding window number. The sequence number continuity detection process involves incrementing the sequence number of each arriving packet. If a skipped sequence number is detected, the missing sequence number within the skipped range is counted as a packet loss, and the arrival time at which the first missing sequence number is detected is used as the packet loss event timestamp. If subsequent retransmissions fill in the missing sequence number, the event also records a retransmission recovery flag for subsequent alignment with the physical layer retransmission count. The delay timestamp data is synchronously acquired from the delay measurement module. Here, the delay timestamp data is explained as follows: for each valid arriving packet, the sending timestamp and arrival timestamp are read, and a single packet delay sample is calculated. The sending timestamp comes from the unified timing mark attached to the packet by the sender, and the arrival timestamp comes from the unified timing time recorded by the receiver. To avoid deviations introduced by timing drift, a time alignment calibration is performed on the sender and receiver within a statistical window. The calibration magnitude is estimated by the clock offset measured from the round trip of the link probe message. The calibrated timestamps are then used to calculate the delay samples. After acquiring the event stream and sample stream, the time axis is segmented by a sliding window. For each window, the number of packet loss events within that window is counted and divided by the window duration to obtain the packet loss frequency. Windows with a packet loss frequency exceeding a preset threshold frequency are marked as windows of interest. The packet loss events within the windows of interest are sorted by timestamp to form a packet loss sequence. Simultaneously, delay samples within the same time range are extracted as delay timestamp data.

[0064] The preset threshold frequency is described here as a window threshold used to filter for "excessively frequent packet loss." Its determination process is constrained by both business tolerance and the calibration upper bound. First, the upper limit of business packet loss tolerance is determined. This upper limit is jointly limited by the video coding redundancy strategy and the retransmission budget. The coding redundancy strategy is provided by the video coding configuration, and the retransmission budget is determined by the remaining latency after deducting the fixed budget for encoding / decoding and rendering from the maximum end-to-end latency budget. The maximum end-to-end latency budget is determined by the maximum end-to-end latency allowed for image synchronization. Then, during the site calibration phase, packet loss event streams under stable operating conditions are collected. The maximum normal upper bound of the frequency of packet loss events per unit time under stable conditions is calculated. This upper bound is then converted to the frequency upper limit obtained from the upper limit of business packet loss tolerance, and the more stringent one is taken as the preset threshold frequency. A statistical fluctuation margin caused by sampling noise and positioning jitter is then added to ensure that normal fluctuations do not frequently trigger the attention window.

[0065] When calculating the packet loss interval variance and the fluctuation amplitude of delay timestamp data for packet loss sequences with frequencies exceeding a preset threshold, and generating a link congestion feature matrix, the packet loss interval sample sequence is first calculated within each attention window. The packet loss interval is defined here as the time difference between the timestamps of adjacent packet loss events. The calculation process involves pairing packet loss sequences chronologically and subtracting the former's timestamp from the latter's timestamp to obtain the interval sample. If the number of packet loss events within the attention window is less than 2, no interval sample is generated for that window, and it is marked as a low-sample window. The low-sample window serves only as supplementary evidence in subsequent abnormal segment extraction. The calculation of the packet loss interval variance is explained here as follows: first, the average interval of the interval sample sequence is calculated; then, the deviation of each interval sample from the average interval is calculated; and the deviation is aggregated within the window and normalized according to the sample size to reflect whether packet loss exhibits clustered bursts. Subsequently, the latency fluctuation amplitude is calculated within the same attention window. Here, latency fluctuation amplitude is defined as the intensity of fluctuations in latency samples within the window. The calculation process involves first performing outlier suppression on the latency samples. The outlier suppression threshold is defined here as the upper bound of the maximum normal latency fluctuation under stable conditions during the calibration phase, and then adding a queuing delay margin introduced by a retransmission trigger. The queuing delay margin is derived from the retransmission timing and maximum queue depth budget. Next, the central trend of the suppressed latency samples is calculated as the representative value of the latency. The central trend is taken as the representative value of the main distribution, and the main distribution identification width is determined by the minimum time granularity of wireless scheduling. Finally, the maximum uplink deviation and maximum downlink deviation of the latency samples relative to the representative value of the latency are calculated separately, and these two are aggregated into the fluctuation amplitude to characterize the latency fluctuations caused by queuing and contention. In addition to the packet loss interval variance and latency fluctuation amplitude, the packet loss frequency and average latency within the attention window are calculated simultaneously. The average latency is obtained by averaging the suppressed latency samples and is used to reflect the overall latency level within the window. The features of each focus window are arranged in a fixed order to form a feature record, and then stacked in window time order to form a link congestion feature matrix. The matrix row index is the window number, and the column features are packet loss interval variance, latency fluctuation amplitude, packet loss frequency, and average latency. To avoid different units of measurement causing one column to dominate the analysis results, scaling is performed on each column of features. The scaling benchmark range is described here as being taken from the statistical upper and lower bounds of each feature under stable conditions during the calibration phase, and the load fluctuation margin under the maximum number of concurrent terminals is superimposed to ensure that the contributions of each column are on the same order of magnitude.

[0066] When extracting abnormal fluctuation segments from the link congestion feature matrix and constructing the real-time transmission state vector by combining it with physical layer retransmission request counts, the feature matrix is ​​first scanned in time series according to window number. During the scanning process, the feature changes of adjacent windows are compared to identify two types of anomalies: sudden and persistent. Extracting abnormal fluctuation segments here refers to merging the set of windows that meet the sudden increase condition or the persistent high-level condition. The sudden increase condition refers to the increment of the delay fluctuation amplitude of a certain window relative to the previous window exceeding the sudden increase threshold. The sudden increase threshold here is defined as the maximum normal upper bound of the increment of the delay fluctuation amplitude of adjacent windows under stable conditions during the calibration phase, plus the basic jump margin caused by scheduling discreteness. The basic jump margin is jointly determined by the minimum time granularity of wireless scheduling and the time slot period. The sustained high threshold condition refers to a situation where the packet loss frequency and latency fluctuation amplitude of several consecutive windows simultaneously exceed the corresponding high threshold. The high threshold is determined by both the service threshold and the calibration upper bound. The packet loss high threshold is the stricter of the service packet loss tolerance upper limit and the calibration normal upper bound, plus a statistical fluctuation margin. The latency high threshold is the stricter of the allowed latency fluctuation threshold for screen synchronization or interactive response and the calibration normal upper bound, plus a retransmission queuing margin. The number of consecutive windows is determined by the minimum acknowledgment time for congestion formation and dissipation. The minimum acknowledgment time is determined by both the retransmission timing period and the queue dissipation time budget. The queue dissipation time budget is determined by the upper bound of the queuing budget under the maximum number of concurrent terminals. Consecutive windows meeting the conditions are merged into the same abnormal fluctuation segment, and the segment endpoint is determined by a recovery judgment. The recovery judgment is defined as both the packet loss frequency and latency fluctuation amplitude falling back to within the high threshold and continuously maintaining at least the preset recovery window number. The recovery window number is determined by both the retransmission timing period and the sampling window overlap ratio, ensuring stable recovery.

[0067] After identifying the abnormal fluctuation segment, the physical layer retransmission request count is read and aligned to the window granularity. The physical layer retransmission request count is defined here as the cumulative number of retransmission requests triggered by the physical layer for the same transport block. The count originates from retransmission control records on the terminal or access side, and the aggregation method is to sum the number of retransmission requests occurring within each window to obtain the window count. The packet loss interval variance, delay fluctuation amplitude, packet loss frequency, average delay, and retransmission request count for each window within the abnormal fluctuation segment are combined in a fixed order to form a real-time transmission state vector. Scale unification is then performed on each dimension in the same manner as described above. The scale unification benchmark uses the statistical upper and lower bounds of the corresponding indicators under stable conditions during the calibration phase, plus a load fluctuation margin, to ensure the state vector is on a unified evaluation scale.

[0068] When mapping the real-time transmission state vector to a multi-dimensional stability assessment space and calculating the Euclidean distance between the vector magnitude and the baseline stable state to obtain the stability metric, a coordinate system for the stability assessment space is first established. This coordinate system is defined here as being composed of the dimensions of the real-time transmission state vector, and a uniform scale is used as the coordinate scale to ensure consistent meaning for the vector distance. The baseline stable state is defined here as the reference vector for the stable operating state. This reference vector is calculated from the set of stable windows selected during the calibration phase or the most recent stable operation. The selection criteria for the stable window set are: packet loss frequency below a preset threshold frequency, delay fluctuation amplitude below the delay fluctuation threshold, and retransmission request count below the upper bound of the normal retransmission state. The delay fluctuation threshold is converted from the frame alignment error tolerance range and superimposed with the basic margin of wireless scheduling discreteness. The upper bound of the normal retransmission state is taken from the maximum normal upper bound of the retransmission request count under stable conditions and superimposed with the contention load margin. The reference vector is obtained by calculating the central trend of the real-time transmission state vector within the stable window set dimension by dimension. The central trend is taken as the representative value of the main distribution to reduce the impact of occasional spikes. The calculation of the vector magnitude here is explained as follows: a comprehensive intensity value is obtained by uniformly scaling the values ​​of each dimension of the real-time transmission state vector, used to characterize the overall level of current congestion intensity. The calculation of the Euclidean distance here is explained as follows: a comprehensive difference value is obtained by uniformly scaling the differences between the real-time transmission state vector and the reference vector in each dimension, used to characterize the degree of deviation of the current state from the stable reference. The stability metric here is explained as follows: Euclidean distance is used as the primary metric, and vector magnitude is used as an auxiliary metric to identify the difference between overall load increases and abnormal offsets. When the Euclidean distance is increasing while the vector magnitude does not change significantly, it is judged as a local abnormal offset; when both the Euclidean distance and the vector magnitude increase simultaneously, it is judged as overall congestion enhancement.

[0069] If the stability metric value falls within the preset convergence interval, the final communication quality optimization result is determined by combining the payload throughput data. The preset convergence interval is defined here as the range within which the stability metric value is allowed to fluctuate around the reference vector. Its determination process involves selecting a set of stable windows that meet the service threshold during the calibration phase, calculating the Euclidean distance distribution for each window within this set, and taking the maximum normal upper bound of the Euclidean distance as the upper boundary of the convergence interval. Simultaneously, the lower boundary of the convergence interval is set as the minimum measurable fluctuation range close to the reference vector. This minimum measurable fluctuation range is determined jointly by the upper bound of the measurement noise and the scale-consistent resolution. A statistical fluctuation margin and a short-term offset margin caused by positioning jitter are superimposed on the upper boundary. The statistical fluctuation margin is determined jointly by the lower bound of the number of samples within each window and the upper bound of the measurement noise. The positioning jitter offset margin is determined jointly by the upper bound of the positioning error and the window sliding step size. The payload throughput data here refers to the effective service throughput after deducting error correction redundancy overhead and retransmission overhead. The calculation process involves counting the number of video payload bytes successfully delivered to the upper layer within a statistical window and dividing by the window duration to obtain the throughput. Successful delivery is determined by the frame data that has been decoded and entered the rendering queue, avoiding the inclusion of unrendered data in the effective throughput. The minimum throughput requirement here is determined by the video bitrate and the bandwidth budget for critical control messages. The video bitrate is given by the frame rate, resolution, and encoding configuration, while the bandwidth budget for critical control messages is derived from the size of the interactive control messages, the sending frequency, and the acknowledgment mechanism. The final determination of the communication quality optimization result is as follows: assuming the stability metric is within the convergence range, compare whether the payload throughput is not lower than the minimum throughput requirement, and simultaneously check whether the length of abnormal fluctuation segments does not exceed the segment length threshold. The segment length threshold is determined by the upper limit of the allowed continuous stuttering duration for screen synchronization, and deducts the time budget that the rendering buffer can absorb. The rendering buffer's absorbable time budget is given by the rendering pipeline buffer configuration. When the payload throughput meets the minimum throughput requirement and the length of abnormal fluctuation segments does not exceed the segment length threshold, the communication quality optimization is deemed to have achieved its goal, and the optimization result is output, including the current sub-region number, stability metric, payload throughput, and convergence determination flag. When either condition is not met, the communication quality optimization is deemed to have failed to achieve its goal, and the result data including the location of the abnormal fluctuation segment, the real-time transmission state vector, the stability metric, and the payload throughput is output.

[0070] S7 includes obtaining the signal strength value of the sub-region after communication quality optimization; if the signal strength value of the sub-region is lower than the preset coverage threshold, generating a monitoring frequency doubling instruction; obtaining a refined signal distribution dataset containing spatiotemporal labels based on the monitoring frequency doubling instruction; parsing the refined signal distribution dataset to extract the coverage connected components; performing a topological mapping comparison between the coverage connected components and the logical boundary of the current sub-region division; if the comparison result shows that the coverage connected components cross the logical boundary, generating a new boundary division coordinate sequence to complete the update and adjustment of the sub-region division.

[0071] In this embodiment, the processing object is the current sub-region after communication quality optimization. The processing goal is to identify sub-regions with insufficient coverage and trigger refined mapping. When the coverage connectivity structure is inconsistent with the existing logical boundary, a new boundary division coordinate sequence is generated, thereby completing the update and adjustment of the sub-region division. After the step is started, the evaluation time range is first determined. The evaluation time range is described here as being taken from the continuous stable period after the communication quality optimization result is determined to be stable. The length of the continuous stable period is determined by the number of consecutive windows required for the stability metric value to be in the convergence interval, so that the signal strength judgment is based on the stable operating state rather than instantaneous fluctuations. At the same time, the site time synchronization source is uniformly used to mark the timestamp. The time synchronization accuracy is determined by the maximum time deviation allowed by the image synchronization, and the alignment margin caused by the minimum time granularity of wireless scheduling is superimposed to ensure that the subsequent spatiotemporal tag data is aligned on the same time axis.

[0072] When obtaining the signal strength values ​​of a sub-region after communication quality optimization, the representative signal strength values ​​and the number of valid samples for all grid cells within that sub-region are first read from the grid statistics module. The representative signal strength value, as explained here, originates from the grid-level statistical results after parsing the RF sampling stream. The calculation process involves first removing outliers from the signal strength samples within the statistical window, and then calculating the central tendency of the remaining samples to obtain the representative value. The outlier removal threshold, as explained here, is taken from the upper bound of the maximum normal fluctuation of signal strength in the stable area during the site calibration phase, and is superimposed with the instantaneous drop margin caused by a single obstruction. The obstruction margin is determined by the upper bound of the additional attenuation caused by the material and size of the main obstructing elements in the site. The number of valid samples, as explained here, is the sample count retained after outlier removal, used to reflect the statistical reliability of the grid. Subsequently, the representative values ​​of the grids within the sub-region are weighted and aggregated to obtain the sub-region signal strength value. The weighting rule is explained here: the grid with more effective samples has a higher weight, and the weights are normalized to ensure that sparsely sampled grids do not disproportionately affect the results. To avoid the results being dominated by local points due to excessively large weights of individual grids, an upper limit is set on the weights. The upper limit is explained here as being determined by the upper bound of the normal distribution of the number of effective samples within the sub-region. This upper bound is taken from the statistical upper bound of the number of samples in similar sub-regions during the calibration phase, plus the measurement task scheduling fluctuation margin. After aggregation is completed, the obtained sub-region signal strength value is compared with the preset coverage threshold. If the sub-region signal strength value is lower than the preset coverage threshold, a monitoring frequency doubling command is generated.

[0073] The preset coverage threshold, as explained here, is the threshold for determining insufficient coverage. Its determination is constrained by the terminal's receiving capability, service throughput requirements, and the current physical layer configuration lower limit. First, the terminal's receiving sensitivity is determined. This sensitivity is derived from the terminal's RF specifications and confirmed through capability negotiation during the access establishment phase. Second, the target throughput requirement is determined. This is determined by the video bitrate, error correction redundancy overhead, and retransmission overhead budget. The video bitrate is given by the frame rate, resolution, and encoding configuration. The error correction redundancy overhead is determined by the channel coding redundancy configuration used in the current sub-area. The retransmission overhead budget is derived by working backward from the maximum end-to-end latency budget, which is obtained by subtracting the encoding / decoding buffer budget and rendering pipeline buffer budget from the maximum end-to-end latency allowed for image synchronization. Finally, the modulation / coding lower limit is determined. This is determined by the minimum receive quality requirement corresponding to the current modulation level and coding redundancy configuration under the error target constraint. The error target is determined by the upper limit of acceptable image visual distortion and is fixed by the service configuration. The above constraints are combined to obtain the coverage threshold benchmark. Based on this, the maximum occlusion additional attenuation margin and the instantaneous fading margin are superimposed. The maximum occlusion additional attenuation margin is determined by the upper bound of the additional attenuation measured during the calibration phase of the main occlusion structure materials and dimensions. The instantaneous fading margin is determined by the upper bound of the worst-case instantaneous drop in the stable area, thus making the threshold correspond to the coverage risk boundary perceptible to the service. The monitoring frequency doubling instruction here specifies at least the target sub-region number, doubling ratio, and effective period. The doubling ratio is 2, and the effective period covers the continuous period required for refined mapping and is aligned with the evaluation time range.

[0074] When acquiring a refined signal distribution dataset containing spatiotemporal labels based on the monitoring frequency doubling command, the monitoring frequency of sampling nodes and boundary sensing nodes within the sub-region is first increased to twice the original monitoring frequency, and the terminal-side measurement reporting frequency is simultaneously increased, thereby increasing the number of samples within the same time period. The original monitoring frequency, as explained here, is determined by the terminal's maximum moving speed and the grid side length, ensuring that the terminal generates at least multiple sampling records while traversing a grid side length. The doubled monitoring frequency makes the sampling points denser within the same distance, improving the spatial resolution of the coverage connected component extraction. The refined signal distribution dataset, as explained here, consists of multiple sample records. Each sample record contains at least a signal strength measurement value, spatial coordinates of the sampling point, and a sampling timestamp. The spatial coordinates are from the positioning system and aligned with the site coordinate system, and the timestamp comes from a unified time synchronization source. To ensure the quality of spatiotemporal labels, the positioning coordinates are smoothed to suppress positioning jumps. The smoothing time constant is determined by the positioning refresh cycle and the doubled monitoring frequency, ensuring that the smoothing does not cross the boundary band width, thus smoothing out changes in the actual boundary. Quality screening is performed on the sample records. The screening rules are described here as follows: records with missing coordinates, missing timestamps, or signal strength measurements exceeding the device's range are removed. The remaining records are then sorted by timestamp to form a traceable dataset. To facilitate subsequent spatial analysis, the sample records are aggregated according to a refined grid index, which is determined by the refined grid cell into which the sampling coordinates fall.

[0075] When analyzing the refined signal distribution dataset to extract the covered connected components, a refined grid system is first established. The refined grid side length is described here as a smaller division scale than the original grid side length. This division scale is constrained by both the doubled sampling density and the upper limit of the computational load, ensuring that each refined grid meets at least the minimum sample number requirement during the survey period. The minimum sample number is defined here as the lower bound of the sample number obtained by multiplying the doubled monitoring frequency by the survey period length, and a sampling loss margin is added to cover sample gaps caused by short-term connection interruptions. For each refined grid, the signal strength samples collected in that grid are summarized, and a representative value of the refined grid signal strength is calculated. The representative value calculation process is described here as first performing outlier removal, then calculating the central trend. The outlier removal threshold follows the same logic as the aforementioned outlier removal threshold and is adapted according to the upper bound of the measurement noise, which is given by the device's RF specifications. The central trend is taken from the representative value of the main distribution to reduce the bias of the representative value caused by occasional deep fading. The main distribution identification width is determined by the minimum time granularity of wireless scheduling and the sampling interval corresponding to the monitoring frequency. Subsequently, coverage determination is performed on the refined grid to form a coverage binary map. The coverage determination threshold here is explained as using the same logic as the preset coverage threshold, but an additional spatial aliasing margin corresponding to the upper bound of the positioning error is superimposed during the refined mapping stage to prevent positioning jitter near the boundary from causing frequent flipping of coverage markers. Grids with a signal strength representative value of the refined grid that is not lower than the coverage determination threshold are marked as covered grids, and grids that are lower than the coverage determination threshold are marked as non-covered grids. Coverage connected component extraction here is explained as performing connected clustering on the covered grid. The connectivity criterion adopts the adjacency relationship of shared boundaries. The clustering process traverses according to the refined grid index, merging adjacent covered grids into the same connected set to form a covered connected component. To suppress the formation of pseudo-connected components by noise points, a minimum area threshold is set for the covered connected component. The minimum area threshold here is explained as being jointly determined by the minimum resolvable area corresponding to the upper bound of the positioning error and the minimum mobile coverage area of ​​the terminal within the parameter switching effective time budget. The parameter switching effective time budget is determined by the maximum time budget of protocol stack issuance and effective confirmation. Connected components with an area smaller than this threshold are considered noise and are removed from the effective covered connected component set.

[0076] When performing topological mapping comparison between the covered connected components and the logical boundaries of the current sub-region, the logical boundary contour sequence of the current sub-region is first obtained. The contour sequence is constructed sequentially by the boundary point coordinates, forming a non-self-intersecting closed contour. For each covered connected component, its outer boundary contour is extracted. The outer boundary contour extraction process is described here as performing boundary tracing on the outer perimeter of the covered grid within the connected component. Boundary tracing uses the boundary between the covered and non-covered grids as the boundary segment and continuously tracks according to a fixed direction rule until a closed contour sequence is formed. To reduce the impact of grid jaggedness on topological judgment, smoothing is performed on the outer boundary contour. The smoothing scale is described here as being jointly determined by the refined grid side length and the upper bound of the positioning error, ensuring that the smoothing does not cross the true boundary change of a refined grid side length. After acquiring the two types of contours, topology mapping comparison is performed. This topology mapping comparison includes two parts: cross-intersection detection and cross-domain coverage detection. Cross-intersection detection is achieved by segment-by-segment intersection determination of the line segment sequences of the outer boundary contour and the logical boundary contour. During the determination, coordinates are unified to the same quantization precision, which is determined by the edge length of the refined grid. Cross-domain coverage detection involves statistically assigning the coverage grids within the covered connected domain to adjacent sub-regions according to their spatial affiliation. Spatial affiliation is determined by the point determination of the logical boundary contour, and the effective coverage area ratio of the covered connected domain within adjacent sub-regions is calculated. The effective coverage area is obtained by multiplying the number of coverage grids by the area of ​​the refined grid. The crossing determination threshold is defined as a crossing when the effective coverage area ratio of the covered connected domain in a non-sub-region exceeds the crossing threshold. The crossing threshold is jointly determined by the boundary band area corresponding to the boundary regularization scale and the minimum effective area of ​​the covered connected domain, ensuring that minor penetration within the boundary band does not trigger boundary adjustments. The boundary regularization scale is taken from the spatial scale of the sub-region boundary regularization and is on the same order of magnitude as the upper bound of the positioning error. The minimum effective area of ​​the covered connected domain is taken from the aforementioned minimum area threshold.

[0077] If the comparison results indicate that the covered connected domain crosses the logical boundary, a new boundary division coordinate sequence is generated, and the sub-region division is updated and adjusted. The generation of the new boundary division coordinate sequence is described here as being completed in three stages: anchor point positioning, boundary segment replacement, and consistency constraints. In the anchor point positioning stage, the set of intersection points between the outer boundary contour of the covered connected domain and the logical boundary contour is first determined. These intersection points are obtained through segment-by-segment intersection determination, and their index positions in the two contour sequences are recorded. When no intersection points exist but the cross-domain coverage detection determines a crossover, the anchor points are selected from several points on the outer boundary contour of the covered connected domain that are closest to the logical boundary. The closest distance is calculated using the distance from the point to the line segment and selected in order of distance. The number of selected points is determined by the refinement mesh side length and the boundary band width. In the boundary segment replacement stage, the logical boundary is divided into several boundary segments using anchor points. The coverage consistency on both sides of each boundary segment is calculated. Coverage consistency is defined here as the comparison between the connectivity of the coverage grid and the number of coverage holes within a certain buffer zone on both sides of the boundary segment. The width of the buffer zone is determined by the upper bound of the positioning error and the edge length of the refined grid. The number of coverage holes is obtained by statistically analyzing the number and area of ​​connected clusters of non-covered grids within the buffer zone. The boundary segment retention method that makes the coverage connectivity stronger and the coverage holes fewer in the same sub-region is selected, and the boundary segment on the other side is replaced with the corresponding segment of the boundary contour outside the coverage connected domain, so that the updated boundary is more in line with the actual coverage structure. The consistency constraint stage performs deduplication, order correction, and morphological constraints on the generated boundary coordinate sequence. The deduplication rule here is to delete duplicate points whose distance between adjacent points is less than the edge length of the refined mesh. The order correction rule is to reorder the coordinate sequence according to the boundary orientation to form a non-self-intersecting closed contour. The morphological constraints here are to apply minimum turning angle and minimum edge segment length constraints. The minimum turning angle and minimum edge segment length are jointly determined by the upper bound of the positioning error and the maximum moving speed of the terminal, ensuring that the terminal does not repeatedly cross the boundary in a short period of time under normal movement and positioning jitter conditions. After generating the new boundary division coordinate sequence, the sub-region division results are updated. The updated content here includes the sub-region boundary contour, sub-region number, and mesh affiliation, and the fingerprint database index used for cross-domain identification and the sub-region parameter index table used for parameter switching are updated simultaneously.

[0078] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing far-field communication parameters of a handheld virtual shooting terminal, applied to the far-field communication environment of a handheld terminal in a virtual shooting location, characterized in that, include: S1. By comprehensively scanning the communication environment in the far-field region, spatial data on signal strength, delay distribution, and packet loss rate are obtained. Based on the heterogeneous characteristics of different locations, an initial communication environment distribution map is constructed, and preliminary division results of signal characteristics within the region are obtained. S2. Based on the initial communication environment distribution map, cluster analysis is used to spatially divide the far-field region, grouping locations with poor signal strength and unstable delay to determine multiple sub-region sets with different communication needs. S3. For the set of divided sub-regions, obtain the specific data on the requirements for screen synchronization and low latency in each sub-region, and determine the communication parameter configuration scheme for each sub-region by analyzing the characteristics of different communication requirements. S4. By deploying environmental sensing nodes at the boundaries of each sub-region, relevant data on signal penetration and connection interruption are collected in real time, and dynamic signal quality feedback information is obtained in response to changes in the communication environment when the handheld terminal moves across regions. S5. Based on dynamic signal quality feedback information, if the handheld terminal is detected to have entered a new sub-region, the pre-established communication parameter configuration scheme is invoked to switch parameters according to the characteristics of the new sub-region and determine the communication stability level after adaptation.

2. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 1, characterized in that: S1 includes: The radio frequency sampling stream in the far field region is acquired, and the signal strength value, delay distribution timestamp and packet loss rate statistics are obtained by parsing the radio frequency sampling stream. The signal strength value, delay distribution timestamp and packet loss rate statistics are then mapped to a set of discrete grid cells in a virtual planar grid system. Calculate the statistical variance and local entropy of the discrete grid cell set, and extract the heterogeneity feature vector that characterizes the instability of the communication environment; Based on the heterogeneity feature vector, the signal characteristic category of each grid cell is determined. The discrete grid cell set is then filled with regions according to the signal characteristic category to construct an initial communication environment distribution map and obtain the preliminary classification results of signal characteristics within the region.

3. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 1, characterized in that: S2 includes: Obtain discrete grid cell data from the initial communication environment distribution map, and construct a multidimensional feature vector matrix containing signal strength values ​​and delay distribution timestamps; The Euclidean distance is calculated based on the multidimensional feature vector matrix to aggregate preliminary connected regions, and abnormal communication blocks with excessive mean signal strength difference and unstable variance of delay are screened out. Obtain the closed boundary contour of the abnormal communication block, and perform secondary segmentation of the interior of the closed boundary contour based on the grid attribute density to obtain homogeneous spatial units; Based on the statistical distribution of homogeneous spatial units, a communication service level model is matched to determine the corresponding communication resource demand type, and a set of sub-regions with different communication demands is output according to the communication resource demand type.

4. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 1, characterized in that: S3 includes: Obtain video stream transmission logs for the sub-region set, calculate jitter variance and delay distribution to obtain a multi-dimensional service vector; Input the multi-dimensional vector of business into the demand difference analysis model, distinguish between synchronous priority areas and interaction priority areas, and output a business demand category mapping table; Based on the business requirement category mapping table, a higher-order modulation and coding scheme is matched for synchronization-priority areas, and the time slot period of the radio frame is shortened for interaction-priority areas to generate a candidate communication parameter combination sequence. Numerical simulations are performed on candidate communication parameter combinations to determine the optimal physical layer parameter set and output the communication parameter configuration scheme for each sub-region.

5. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 1, characterized in that: S4 includes: Activate boundary sensing nodes to capture edge field strength data and spectral interference maps, and calculate penetration loss values ​​and environmental attenuation factors; By combining the terminal's movement trajectory and movement rate vector, the penetration loss value and environmental attenuation factor are mapped to the trajectory coordinates to locate the cross-domain handover point, and a signal coverage hole model is constructed. If the signal coverage hole model density exceeds the standard, extract time-varying features to generate a quality fluctuation sequence; A dynamic feedback link is established based on the quality fluctuation sequence to output dynamic signal quality feedback information for handheld terminals when they move across regions.

6. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 1, characterized in that: S5 includes: Obtain time-varying spectral feature data from dynamic signal quality feedback information and compare it with the fingerprint database to determine new sub-region identifiers; The preset orthogonal amplitude modulation order and channel coding redundancy rate are extracted based on the new sub-region identifier; The physical layer configuration is reset by analyzing the orthogonal amplitude modulation order and channel coding redundancy rate, and parameter switching is completed for the new sub-region characteristics. Collect bit error distribution data and round-trip delay jitter value after handover, and calculate the deviation between bit error distribution data and round-trip delay jitter value to obtain communication adaptability score; If the communication compatibility score meets the standard, the anti-interference margin level after parameter switching is calculated to determine the communication stability level after adaptation.

7. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 1, characterized in that, It also includes S6, which, regarding the communication stability level after parameter switching, continuously monitors packet loss rate and latency instability data exceeding preset thresholds to obtain the real-time transmission status of the handheld terminal during movement, and determines the final communication quality optimization result, specifically including: Collect packet loss sequences and delay timestamp data with frequencies higher than a preset threshold after parameter switching, calculate the packet loss interval variance of packet loss sequences with frequencies higher than the preset threshold and the fluctuation amplitude of delay timestamp data, and generate a link congestion feature matrix. The abnormal fluctuation segments are extracted by parsing the link congestion feature matrix, and a real-time transmission state vector is constructed by combining the physical layer retransmission request count.

8. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 7, characterized in that: S6 further includes: The real-time transmitted state vector is mapped to a multi-dimensional stability evaluation space, and the stability metric is obtained by calculating the Euclidean distance between the vector magnitude and the baseline stable state. If the stability metric is within the preset convergence range, the final communication quality optimization result is determined by combining the payload throughput data.

9. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 7, characterized in that, This also includes S7. Based on the final communication quality optimization results, if it is found that the signal strength in a sub-region is still lower than the preset threshold, the monitoring frequency of the environmental sensing nodes is adjusted to obtain detailed signal distribution data, and it is determined whether the sub-region division needs to be updated and adjusted. Specifically, this includes: Obtain the signal strength value of the sub-region after communication quality optimization. If the signal strength value of the sub-region is lower than the preset coverage threshold, generate a monitoring frequency doubling instruction. A refined signal distribution dataset containing spatiotemporal labels is obtained based on the monitoring frequency doubling command.

10. The method for optimizing far-field communication parameters of a virtual shooting handheld terminal according to claim 9, characterized in that: The S7 also includes: The covered connected components are extracted by analyzing the refined signal distribution dataset, and the covered connected components are topologically mapped and compared with the logical boundaries of the current sub-region. If the comparison results show that the covered connected domain crosses the logical boundary, a new boundary division coordinate sequence is generated to complete the update and adjustment of the sub-region division.

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