VR game data synchronous transmission method based on 5G network

Abstract

The invention provides a VR game data synchronous transmission method based on a 5G network, and the method comprises the steps: obtaining a multi-type data stream generated during the operation of a VR game, extracting scene rendering data and player interaction data from the multi-type data stream, separating a feature value of each data type, and obtaining a data priority sequence; analyzing the real-time requirements of the scene rendering data and the interaction data according to the data priority sequence, and judging a key data packet which needs to be transmitted immediately; the method comprises the following steps: monitoring a current bandwidth fluctuation condition through a network transmission layer, calculating available bandwidth and delay parameters from network state data acquired in real time, and obtaining a resource allocation reference value in a dynamic scene; and for high-priority data packets, a dynamic resource allocation algorithm is adopted to adjust a transmission queue sequence, a resource allocation reference value is corrected through a bandwidth fluctuation parameter, and a transmission time slot allocation scheme of the key data packets is determined.

Description

Technical Field

[0001] This invention relates to the field of VR technology, specifically a method for synchronous transmission of VR game data based on a 5G network. Background Technology

[0002] The synchronous transmission of virtual reality (VR) game data is a crucial frontier in 5G network applications. Its core lies in leveraging the high-speed, low-latency characteristics of networks to achieve real-time performance and stability for immersive gaming experiences. Research in this area is irreplaceable and vital for driving technological innovation in the gaming industry and enhancing user experience. With the widespread adoption of 5G networks, VR games are gradually moving from concept to practical application. However, the reliability of data transmission directly determines the smoothness and immersion of the game, thus becoming an indispensable focus in technological development.

[0003] However, existing solutions still have significant limitations in addressing data loss. Traditional methods rely heavily on general network protocols and lack optimization for the characteristics of VR game data, often leading to low transmission efficiency or experience interruption during network fluctuations. Especially in highly dynamic scenarios, existing mechanisms struggle to effectively differentiate the importance of data types, easily resulting in uneven resource allocation or delays in critical data. These shortcomings limit the full realization of the potential of 5G networks.

[0004] Against this backdrop, the core challenges facing the research have become increasingly apparent, primarily focusing on two technical factors: intelligent handling of data loss and dynamic allocation of network resources. Since VR game data encompasses various types, including scene rendering and player interactions, data loss can lead to screen stuttering or command delays. Current transmission mechanisms lack accurate identification and priority management of data importance. Furthermore, bandwidth fluctuations in 5G networks under extreme conditions further exacerbate the difficulty of synchronizing critical data. These unresolved technical factors directly raise the technical challenge of ensuring a core gaming experience with limited resources.

[0005] Therefore, how to design an intelligent packet loss handling mechanism for different types of data during VR game data transmission on a 5G network, and how to ensure the real-time synchronization of critical data through dynamic resource allocation, has become a key issue that this research urgently needs to address. Solving this problem will directly affect the continuous optimization of the smoothness of VR games and the user's immersive experience in a 5G environment. Summary of the Invention

[0006] This invention provides a method for synchronizing and transmitting VR game data based on a 5G network, mainly including: The system acquires various types of data streams generated during VR game runtime, extracts scene rendering data and player interaction data from them, separates the feature values ​​of each data type through preset classification rules, and obtains a data priority sequence. Based on the data priority sequence, a deep learning model is used to analyze the real-time requirements of scene rendering data and interaction data. If the feature value exceeds the preset threshold, it is marked as high-priority data, and the key data packets that need to be transmitted immediately are identified. By monitoring the current bandwidth fluctuations at the network transport layer, available bandwidth and latency parameters are calculated from the real-time collected network status data to obtain the baseline value for resource allocation in dynamic scenarios. For high-priority data packets, a dynamic resource allocation algorithm is used to adjust the transmission queue order, and the resource allocation benchmark value is corrected by the bandwidth fluctuation parameter to determine the transmission time slot allocation scheme for critical data packets. The system retrieves data loss records that occur during transmission, extracts the loss type and timestamp from the lost data packets, and triggers an intelligent packet loss handling mechanism if the loss type belongs to high-priority data to determine the range of data packets that need to be retransmitted. Based on the range of retransmitted data packets, the transmission tasks are rescheduled using the redundant channel characteristics of the 5G network. By assigning higher transmission weights to the retransmitted data packets through preset priority management rules, an optimized transmission sequence is obtained. The real-time synchronization detection module verifies whether the optimized transmission sequence meets the immersive experience requirements of VR games, and extracts deviation values ​​from the synchronization time difference and image rendering quality to determine the stability of data transmission. If the deviation exceeds the preset tolerance range, the intelligent packet loss processing parameters are adjusted according to bandwidth fluctuations and data type characteristics. By iteratively updating the resource allocation scheme, a new transmission strategy that meets the real-time synchronization requirements is obtained. Obtain network transmission logs after the new transmission strategy is implemented, extract the arrival time and integrity data of key data packets, compare them with preset immersive experience indicators, determine the degree of optimization of the final transmission scheme, and output the results.

[0007] The beneficial effects of this invention are as follows: This invention acquires multiple types of data streams during VR game runtime and extracts key data. It utilizes a deep learning model to analyze real-time data requirements, combines network bandwidth monitoring for dynamic resource allocation, employs an intelligent scheduling algorithm to optimize the transmission order of high-priority data packets, and triggers an intelligent packet loss handling mechanism when data is lost, utilizing redundant 5G network channels for rescheduling transmission. Through real-time synchronization detection to verify transmission effectiveness and iterative optimization, this invention significantly improves the real-time performance and stability of VR game data transmission, reduces the impact of network fluctuations and data loss on the gaming experience, optimizes resource utilization efficiency, and thus significantly improves the user's immersive experience, providing a strong technical guarantee for the smooth operation of VR games in a 5G network environment. Attached Figure Description

[0008] Figure 1 This is a flowchart of a VR game data synchronization transmission method based on a 5G network, as shown in Example 1. Detailed Implementation

[0009] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0010] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0011] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0012] like Figure 1 As shown, this embodiment of a VR game data synchronization transmission method based on a 5G network may specifically include: Step S101: Obtain the multi-type data streams generated during the VR game runtime, extract scene rendering data and player interaction data from them, separate the feature values ​​of each data type through preset classification rules, and obtain the data priority sequence.

[0013] The process involves acquiring the data stream generated during VR game runtime, separating it into multiple data types using a pre-defined filter to obtain an initial data set. Scene rendering data and player interaction data are extracted from this initial data set, and feature values ​​for each data type are determined using pre-defined classification rules, resulting in a feature value set. The feature value set is then sorted, and a priority evaluation algorithm is used to calculate data priorities, resulting in a priority list. If duplicate values ​​exist in the priority list, a secondary sort is performed based on the feature values' timestamps, resulting in an adjusted priority sequence. This adjusted priority sequence is then grouped using a clustering analysis algorithm to obtain grouped data clusters. Based on these grouped data clusters, time series analysis is used to determine the data stream's changing trends, yielding trend feature values. Finally, these trend feature values ​​are matched against pre-defined conditions to determine the final data priority sequence.

[0014] For example, acquiring the data stream generated during the runtime of a VR game is the first step in building a data analysis system.

[0015] For example, in a VR shooting game, the data stream may contain massive amounts of real-time information such as the player's position coordinates, viewpoint changes, shooting actions, and the state of objects in the scene. Separating multiple types of data through preset filters can be understood as splitting this mixed information according to type.

[0016] For example, filters can be configured with rules to categorize coordinates and viewpoints as scene rendering data, and shooting and movement actions as player interaction data, thus obtaining an initial dataset. The advantage of this separation is that subsequent analysis can focus more on the characteristics of specific data types, improving processing efficiency. After extracting scene rendering data and player interaction data from the initial dataset, preset classification rules must be used to determine feature values.

[0017] In one possible implementation, the feature values ​​of scene rendering data could be the screen refresh rate per second or scene complexity; for example, a level might have a complexity of 80 due to the large number of enemies. Meanwhile, the feature values ​​of player interaction data might be the operation frequency, such as the player shooting 20 times per minute. This results in a set of feature values ​​containing various quantifiable metrics. When sorting the feature value set, a priority evaluation algorithm can be used to calculate data priority.

[0018] For example, scenes with low screen refresh rates may be given higher priority because they affect the user experience, thus creating a priority list.

[0019] It should be noted that if there are duplicate values ​​in the priority list, such as two sets of data having a priority of 0.8, they will be sorted a second time based on the timestamp.

[0020] Specifically, more recent timestamps may reflect a player's current state and therefore have higher priority, resulting in a more accurate adjusted priority sequence. Next, a clustering analysis algorithm is used to group the adjusted sequences.

[0021] In one embodiment, the data is assumed to be divided into three clusters: high-frequency interaction, low-frequency interaction, and high-load rendering. These grouped data clusters clearly reflect the behavioral patterns of different scenarios or players. This grouping facilitates more targeted subsequent analysis. Based on the grouped data clusters, time series analysis is used to determine the trend of data flow changes.

[0022] For example, if the operation frequency of a high-frequency interaction cluster increases from 20 times per minute to 30 times per minute, the trend characteristic value may be displayed as "upward trend".

[0023] Preferably, the data priority sequence is determined by matching trend feature values ​​with preset conditions, such as defining "upward trend and more than 25 times" as high priority.

[0024] For example, a player's frequent actions and scene rendering needs during a boss battle might be prioritized. The advantage of this approach is that it can dynamically optimize resource allocation, such as prioritizing the rendering of critical scenes or responding to player actions, thereby enhancing the VR experience.

[0025] In one embodiment, consider player A's operation frequency in a maze scene is 15 times / minute with a refresh rate of 60 frames, while player B's operation frequency in a battle scene is 30 times / minute with a refresh rate of 50 frames. After priority evaluation, player B has a higher priority due to its more frequent interactions and higher rendering requirements. Timestamps show that player B's data is more recent, further confirming its priority.

[0026] Understandably, this multi-faceted analysis ensures the rationality of the priority sequence, while enhancing the system's adaptability by predicting player behavior through trend analysis.

[0027] For example, preloading high-priority scene resources can significantly reduce lag and enhance immersion.

[0028] Step S102: Based on the data priority sequence, a deep learning model is used to analyze the real-time requirements of scene rendering data and interaction data. If the feature value exceeds the preset threshold, it is marked as high-priority data, and the key data packets that need to be transmitted immediately are identified.

[0029] Deep learning models are used to analyze scene rendering and interaction data to obtain a set of feature values ​​for real-time requirements. If the values ​​in the feature set exceed a preset threshold, high-priority data is determined through labeling. Based on the labeling results, key data packets corresponding to the high-priority data are obtained. A transmission scheduling tool is used to sort the key data packets to obtain the priority order of transmission requirements. Time series analysis is used to group the sorted key data packets to obtain grouped data clusters. Based on the grouped data clusters, the dynamic adjustment sequence of transmission requirements is determined to finalize the transmission order.

[0030] For example, a set of feature values ​​for real-time requirements can be obtained by analyzing scene rendering data and interaction data using a deep learning model.

[0031] For example, in VR games, scene rendering data may include the rate of change of light and shadow in each frame, while interaction data may cover the player's gesture response speed.

[0032] In one embodiment, the deep learning model can be based on a convolutional neural network to extract the frequency of changes in the edges of objects in the scene as feature values. Numerically, it is assumed that when the changes exceed 50 times per second, it indicates that the complexity of the scene is high. The feature values ​​of the interactive data may be based on the player's operation latency time. For example, if the latency is less than 20 milliseconds, it indicates a strong demand for real-time performance.

[0033] Specifically, this analysis can quickly identify which data has the greatest impact on user experience, thus providing a basis for subsequent processing. If the values ​​in the feature set exceed a preset threshold, high-priority data is determined through a labeling process.

[0034] In one possible implementation, the threshold for scene rendering data is set to 60 changes per second, and the threshold for interaction data is set to a delay of 15 milliseconds.

[0035] For example, when a frame changes 70 times, or when the player's input latency is only 10 milliseconds, these data are marked as high priority. The advantage of this marking process is that it efficiently distinguishes which data needs to be processed first, ensuring smooth visuals and precise controls. Based on the marking results, the key data packets corresponding to the high-priority data are obtained.

[0036] Understandably, key data packets may contain data units that include complex scene rendering instructions or real-time player feedback.

[0037] For example, a sudden explosion effect in a scene contains a large amount of particle rendering information in its data packet; a player's rapid turning action during interaction carries precise coordinate updates in its data packet. The key to this step is to transform abstract feature values ​​into concrete, operable units, providing a clear objective for transmission. A transmission scheduling tool is then used to sort the critical data packets, obtaining the priority order of transmission needs.

[0038] Preferably, the scheduling tool can be dynamically adjusted based on bandwidth utilization and data packet size.

[0039] For example, data packets representing explosion effects might be large but infrequent, while data packets representing turning actions might be small but frequent. The tool prioritizes the transmission of smaller data packets to reduce latency. This sorting method optimizes network resource allocation and improves transmission efficiency. Time series analysis is then used to group the sorted key data packets into data clusters.

[0040] Specifically, time series analysis can observe the trend of data packet transmission frequency changes within 5 seconds.

[0041] For example, data packets representing an explosion effect might be concentrated within a single second, while data packets representing a turning motion would be evenly distributed. After grouping, the data clusters reflect the temporal characteristics of different data types, facilitating subsequent dynamic adjustments. Based on the grouped data clusters, the dynamic adjustment sequence for transmission requirements is determined, and the final transmission order is finalized.

[0042] In one embodiment, if a cluster of explosion effect data is detected followed by a large number of turning action data clusters, the system will temporarily increase the transmission priority of interactive data to ensure that the player's operation is not affected by lag.

[0043] It should be noted that this dynamic adjustment can be flexibly changed according to the real-time needs of the game, avoiding resource waste while ensuring the continuity of user experience.

[0044] For example, in tense combat scenarios, interactive data may consistently take higher priority than rendered data, while the opposite is true in calm exploration scenarios. This approach significantly improves the adaptability and efficiency of data processing.

[0045] Step S103: Monitor the current bandwidth fluctuation through the network transport layer, calculate the available bandwidth and latency parameters from the real-time collected network status data, and obtain the resource allocation benchmark value under dynamic scenarios.

[0046] By continuously monitoring bandwidth fluctuation data at the network transport layer, real-time network status information is obtained, yielding the current transmission monitoring results. Real-time network status data is extracted from the transmission monitoring results and processed using data computing tools to obtain a combined value of available bandwidth and latency parameters. For the latency parameter in the combined value, the changing trend under dynamic scenarios is analyzed to obtain the grouping criteria for scenario adaptation. Available bandwidth is classified according to the grouping criteria to determine the resource allocation scheme in the dynamic scenario. If the allocation ratio in the resource allocation scheme exceeds a preset threshold, the transmission sequence corresponding to the bandwidth fluctuation is adjusted using scheduling tools to obtain an optimized allocation sequence. The optimized allocation sequence is verified through time series analysis to determine the stability of transmission monitoring and to determine the final resource allocation baseline value. Using pre-established mapping rules, the final resource allocation baseline value is matched with network transmission requirements to obtain the adjusted transmission configuration.

[0047] For example, by continuously monitoring bandwidth fluctuation data at the network transport layer, dynamic changes in the network environment can be captured in real time.

[0048] For example, in a real-time rendering scenario, if network bandwidth drops from 100Mbps to 60Mbps during peak hours, the monitoring tool can immediately record this fluctuation and generate corresponding status information. The core of this approach is to ensure the real-time nature of the network status through continuous data collection, providing a reliable basis for subsequent analysis.

[0049] In one possible implementation, the monitoring system can collect bandwidth data once per second and combine it with indicators such as packet loss rate to form a multi-dimensional state description. Extracting the real-time network state data from the transmission monitoring results and processing it using data computing tools is a crucial step.

[0050] Specifically, a weighted average method can be used to combine bandwidth and latency data into a single value.

[0051] For example, assuming the available bandwidth is 50 Mbps and the latency is 20 milliseconds, the calculation tool might assign a weight of 70% to bandwidth and 30% to latency to arrive at a composite parameter. The advantage of this method is that it can quickly quantify network performance, providing a reference for dynamic adjustments. When analyzing the trend of the latency parameter in the composite value under dynamic scenarios, the concept of a time window can be introduced.

[0052] Preferably, a 5-second time window is set to observe the change in latency from 15 milliseconds to 25 milliseconds. This trend analysis can help determine whether the network is suitable for data transmission with high real-time requirements.

[0053] For example, in a collaborative virtual scenario, increased latency can cause lag in interactions, thus requiring adjustments to the grouping criteria. When classifying available bandwidth based on grouping criteria, bandwidth can be categorized into three levels: high, medium, and low.

[0054] In one embodiment, assuming a total bandwidth of 80Mbps, high-priority data requires 50Mbps, medium-priority data requires 20Mbps, and the remainder is allocated to low-priority data. This classification allows the resource allocation scheme to more rationally match scenario requirements, preventing low-value data from consuming excessive resources. If the allocation ratio in the resource allocation scheme exceeds a preset threshold, for example, if high-priority data accounts for more than 70%, the scheduling tool will intervene to adjust the transmission sequence.

[0055] Understandably, this adjustment may delay the transmission of some less important data to ensure the priority of critical data packets.

[0056] For example, after adjustment, the transmission time of high-priority data was reduced from 50 milliseconds to 30 milliseconds, improving real-time performance. When verifying the optimized allocation sequence through time series analysis, transmission stability under bandwidth fluctuations can be observed.

[0057] For example, in a 10-minute test, if the latency jitter is controlled within 5 milliseconds, it indicates that the sequence optimization is effective. This verification method can ensure the reliability of resource allocation and avoid performance degradation caused by network jitter. When matching the final resource allocation baseline with transmission requirements using pre-established mapping rules, a mapping table can be built based on historical data.

[0058] In one possible implementation, if the baseline value is 60Mbps, the matched transmission configuration might prioritize transmitting interactive data and then prioritize transmitting rendering data. The advantage of this matching is its ability to dynamically adapt to different scenario requirements, thereby improving transmission efficiency.

[0059] It should be noted that the business logic of this method originates from the optimization of scenarios requiring real-time performance.

[0060] For example, in virtual reality applications, low latency of interactive data directly impacts user experience, while bandwidth requirements for rendering data determine image quality. Through the above analysis and adjustments, a balance can be achieved between the two with limited resources, significantly improving the overall performance of the system.

[0061] Step S104: For high-priority data packets, a dynamic resource allocation algorithm is used to adjust the transmission queue order, and the resource allocation benchmark value is corrected by the bandwidth fluctuation parameter to determine the transmission time slot allocation scheme for key data packets.

[0062] High-priority data packets are retrieved from the transmission queue, and their order is adjusted using a dynamic resource allocation algorithm to obtain an adjusted transmission sequence. Based on the adjusted transmission sequence, bandwidth fluctuation parameters are obtained, and the trend within the fluctuation range is determined. For this trend, the resource allocation baseline value is updated through a correction process to obtain an optimized baseline value. If the optimized baseline value exceeds a preset threshold, time slot allocation is adjusted using a scheduling tool to determine the transmission time slots for critical data packets. Time series analysis is used to verify the adjusted time slot allocation, assess the stability of the transmission sequence, and obtain the verification results. Based on the verification results, the transmission requirements of critical data are matched using pre-established mapping rules to obtain an adjusted transmission configuration. Using the adjusted transmission configuration, a priority sorting tool is used to process high-priority data packets to determine the final transmission scheme.

[0063] For example, retrieving high-priority data packets through a transmission queue is a crucial step in network transmission.

[0064] For example, in a corporate intranet, video conferencing packets are typically marked as high priority. These packets can be identified by priority tags, such as based on the DSCP field in the IP header, to distinguish them from regular packets.

[0065] Preferably, this method relies on predefined priority rules to ensure that important business data is transmitted first. When using a dynamic resource allocation algorithm to adjust the order...

[0066] Understandably, the algorithm will rearrange data packets based on real-time network load.

[0067] In one possible implementation, assuming the network bandwidth is 100Mbps and the current high-priority data packets occupy 30Mbps, the algorithm will dynamically adjust the transmission order based on the waiting time and priority of the data packets in the queue.

[0068] For example, if the latency of a high-priority data packet exceeds 50ms, its transmission order will be prioritized. This adjustment can reduce the latency of critical data. When obtaining bandwidth fluctuation parameters based on the adjusted transmission sequence...

[0069] Specifically, the fluctuation range can be determined by statistically analyzing the changes in transmission rate per unit time.

[0070] For example, within 5 seconds, the bandwidth fluctuates from 80Mbps to 95Mbps, a fluctuation range of 15Mbps. The resource allocation baseline value can be updated through a correction process to address this trend.

[0071] In one embodiment, if the initial baseline value is 70 Mbps, after detecting fluctuations, it can be adjusted to 75 Mbps based on the trend to adapt to dynamic changes. If the optimized baseline value exceeds a preset threshold, such as 80 Mbps, the time slot allocation is adjusted through a scheduling tool.

[0072] It should be noted that time slot allocation can be based on the TDMA mechanism, which arranges critical data packets in more stable time slots.

[0073] For example, video conferencing data packets can be transmitted within the first 200ms of each second to ensure stability. This method effectively improves transmission efficiency. Time series analysis is used to verify the adjusted time slot allocation.

[0074] For example, the arrival time distribution of data packets can be observed over a continuous 10 seconds. If 90% of the data packets arrive within the expected time slot, the sequence is considered stable. This verification method can intuitively reflect the reliability of the adjustment. The verification results are then used to match the transmission requirements of critical data.

[0075] In one embodiment, if the verification shows that stability meets the requirements, the bandwidth requirement for the video conference is adjusted from 30Mbps to 35Mbps using mapping rules to meet peak demand. This matching process better adapts to actual business needs. High-priority data packets are then processed using a priority sorting tool with the adjusted transmission configuration.

[0076] Specifically, the weighted fair queue algorithm can be used.

[0077] For example, assigning 60% weight to high-priority packets ensures their priority transmission. This approach can protect critical data during bandwidth contention. This is crucial when determining the final transmission scheme.

[0078] For example, the above adjustments can be combined to form a complete solution: high-priority data packets occupy a fixed 35Mbps bandwidth, with time slots allocated in the first 200ms. This solution can significantly improve transmission stability and efficiency, especially ensuring the smooth operation of critical services during network congestion.

[0079] Step S105: Obtain data loss records that occurred during transmission, extract the loss type and timestamp from the lost data packets, and if the loss type belongs to high-priority data, trigger the intelligent packet loss processing mechanism to determine the range of data packets that need to be retransmitted.

[0080] Data loss records are acquired during transmission, and analysis tools are used to extract the loss type and timestamp, resulting in a categorized set of lost data. The loss type is determined from this categorized set; if the loss type belongs to high-priority data, a trigger condition is established using a preset threshold, resulting in a subset of lost data to be processed. For this subset, an intelligent packet loss handling mechanism is activated, and the retransmission range is determined through timestamp analysis, resulting in an initial retransmission data range. Based on this initial range, a range judgment tool is used to process the set of lost data packets, determining an optimized set of retransmission data packets. Using this optimized set, bandwidth parameters from the transmission process are obtained to determine if the bandwidth supports retransmission, resulting in a bandwidth matching result. If the bandwidth supports retransmission, a priority sorting tool is used to adjust the transmission queue, determining the transmission time slot for the final retransmission data packets. Based on the transmission time slot for the final retransmission data packets, time series analysis is used to verify transmission stability and determine the adjusted transmission configuration.

[0081] For example, data loss records are obtained through the transmission process, and analysis tools are used to extract the loss type and timestamp to obtain a categorized set of lost data.

[0082] For example, in network transmission, assuming 100 data losses occur in a day, the analysis tool can categorize the lost data packets into high-priority and low-priority categories based on their priority markers and transmission timestamps, accurate to the millisecond level, such as 13:45:23.567. This clearly distinguishes which losses affect critical business operations. From the categorized set of lost data, the loss type is determined. If the loss type belongs to high-priority data, a preset threshold is used to determine the triggering condition, resulting in the subset of lost data that needs to be processed.

[0083] Specifically, assuming the threshold for high-priority data loss is set at 5 times per hour, if 6 instances of high-priority data loss are detected within one hour, the processing mechanism is triggered. This method can quickly identify issues that require priority resolution.

[0084] For example, if a subset contains 10 lost data packets with timestamps concentrated within a certain 5-minute period, it indicates a possible sudden network congestion. For the subset of lost data that needs processing, an intelligent packet loss handling mechanism is activated, and the retransmission range is determined through timestamp analysis to obtain a preliminary retransmission data range.

[0085] In one possible implementation, analysis reveals that the timestamps of lost data packets are distributed between 13:50:00 and 13:55:00. Therefore, the initial range is defined as all high-priority data within this 5-minute period. This mechanism effectively focuses the retransmission range and avoids wasting resources. Based on the initial retransmission data range, a range determination tool is used to process the set of lost data packets to determine the optimized set of retransmission data packets.

[0086] Preferably, the tool removes data packets that have already been confirmed by the receiver. Assuming an initial range of 20 data packets, only 15 are determined to require retransmission, resulting in a more streamlined set. This filtering improves retransmission efficiency. By using the optimized set of retransmitted data packets, the bandwidth parameters during transmission are obtained, and it is determined whether the bandwidth supports retransmission, thus obtaining a bandwidth matching result.

[0087] For example, if the current bandwidth is 50Mbps, and retransmission requires 10Mbps, a successful match will occur if the bandwidth is sufficient. This judgment ensures that retransmission will not exacerbate the network load. Based on the bandwidth matching result, if the bandwidth supports retransmission, the transmission queue is adjusted using a priority sorting tool to determine the transmission time slot for the final retransmitted data packet.

[0088] In one embodiment, assuming there are 50 data packets to be transmitted in the queue, the tool prioritizes 15 retransmitted data packets at the front of the queue, allocating time slots for transmission within the first 3 seconds. This adjustment prioritizes the timeliness of critical data. Based on the transmission time slots of the final retransmitted data packets, time series analysis is used to verify transmission stability and determine the adjusted transmission configuration.

[0089] Understandably, analyzing the transmitted data over a 10-minute period and observing whether the packet loss rate decreases after time slot adjustment—for example, from 5% to 2%—proves that the configuration is stable. This verification provides a basis for subsequent optimization.

[0090] It should be noted that each of the above steps revolves around ensuring the transmission of high-priority data, forming a complete chain from loss detection to retransmission configuration.

[0091] For example, intelligent packet loss handling mechanisms use timestamps to pinpoint problems, range assessment tools optimize resource allocation, and bandwidth matching prevents network overload. This multi-faceted collaborative approach not only enables rapid recovery of critical data but also maintains overall transmission stability, supporting business continuity.

[0092] Step S106: Based on the range of retransmitted data packets, the transmission task is rescheduled using the redundant channel characteristics of the 5G network. The retransmitted data packets are assigned higher transmission weights through preset priority management rules to obtain an optimized transmission sequence.

[0093] By analyzing the characteristics of redundant channels, the current set of available channels is obtained. Based on the matching of the available channel set with the data range, the set of scheduling tasks corresponding to the retransmitted data is determined. For the scheduling task set, the retransmitted data is sorted according to priority rules to obtain the transmission weight allocation result. The transmission sequence is adjusted based on the transmission weight allocation result to obtain the optimized transmission sequence structure. Based on the optimized transmission sequence structure, the channel load balancing status is determined, and load adjustment parameters are determined. The scheduling task set is updated using the load adjustment parameters to obtain the final transmission task sequence. The stability of the final transmission task sequence is verified through time series analysis, and the transmission configuration parameters are determined.

[0094] For example, when analyzing the status of a 5G network through the characteristics of redundant channels, the feature of multi-channel parallel transmission in a 5G network can be used to identify currently available redundant channels.

[0095] For example, within the coverage area of ​​a base station, suppose there are three independent channels, namely channels A, B, and C, with bandwidths of 100Mbps, 80Mbps, and 120Mbps respectively. When analyzing network status, the set of available channels can be filtered out by detecting the latency and packet loss rate of each channel.

[0096] For example, if channel A has a delay of 10ms and no packet loss, channel B has a delay of 15ms and a packet loss rate of 1%, and channel C has a packet loss rate of 5% due to interference, then the set of available channels is {A, B}. When determining the set of scheduling tasks corresponding to the retransmitted data by matching the set of available channels with the data range, it can be understood as mapping the amount of data to be retransmitted to the channel capacity.

[0097] Specifically, assuming the total amount of data to be retransmitted is 500MB, channels A and B handle 60% and 40% of the task respectively, the task set can be divided into task 1 (300MB, channel A) and task 2 (200MB, channel B).

[0098] In one possible implementation, task partitioning also considers the fragmentation characteristics of data packets to ensure clear data boundaries after fragmentation. This applies when prioritizing the set of scheduled tasks.

[0099] Preferably, weights can be assigned based on the data type.

[0100] For example, high-priority data such as video streams have a weight of 0.8, while low-priority data such as log files have a weight of 0.3. For Task 1 and Task 2 above, if Task 1 contains 80% high-priority data and Task 2 contains 50%, the transmission weight allocation result might be that Task 1 has a higher priority and is ranked earlier. This weight allocation can optimize the transmission timeliness of critical data. When adjusting the transmission sequence based on the transmission weight allocation result, Task 1 can be placed at the beginning of the sequence, followed by Task 2, forming an optimized transmission sequence structure.

[0101] For example, after sequence adjustment, channel A transmits 300MB first, followed by channel B transmitting 200MB. This structure reduces the waiting time for high-priority data.

[0102] It should be noted that during the adjustment process, the sequence continuity must be ensured to avoid additional overhead caused by frequent channel switching. When determining the channel load balance status, the adjustment parameters can be determined by comparing the real-time load of each channel.

[0103] In one embodiment, if channel A is at 90% load while channel B is at only 40%, the load adjustment parameter can be set to migrate a portion of the data from task 1 (e.g., 50MB) to channel B. Such a parameter avoids single-channel overload and improves overall transmission efficiency. After updating the scheduled task set using the load adjustment parameter, the final transmission task sequence may become task 1 (250MB, channel A) and task 2 (250MB, channel B).

[0104] Specifically, this balanced allocation can make full use of the parallel capabilities of redundant channels.

[0105] Understandably, the updated task sequence is better suited to dynamic network environments. The stability of the final transmission task sequence was verified through time series analysis.

[0106] For example, transmission data can be collected continuously for 10 minutes to observe throughput fluctuations. If the throughput of Task 1 and Task 2 remains stable at 25MB / s and 20MB / s respectively, without significant jitter, the transmission configuration parameters can be set to the current bandwidth allocation ratio. This verification method ensures that the configuration meets long-term operational needs.

[0107] Preferably, a stable sequence can also reduce the retransmission frequency and reduce the waste of network resources.

[0108] Step S107: Verify whether the optimized transmission sequence meets the immersive experience requirements of VR games through the real-time synchronization detection module. Extract deviation values ​​from the synchronization time difference and image rendering quality to determine the stability of data transmission.

[0109] Synchronization time data of the transmission sequence is acquired in real time, and the time difference is extracted. Based on the time difference, a detection module calculates the deviation value, obtaining the deviation analysis result. The stability of data transmission is assessed based on the deviation analysis result, determining the stability state. The adjustment requirements for the optimized sequence are obtained from the stability state, yielding sequence adjustment parameters. The transmission sequence is updated using these parameters, resulting in the adjusted sequence structure. The adjusted sequence structure is analyzed using rendering to assess the rendering quality data. The final stability configuration is determined by comparing the rendering quality data with the deviation value.

[0110] For example, obtaining the synchronization time data of the transmission sequence in real time and extracting the time difference is a fundamental step in optimizing the transmission sequence.

[0111] For example, in a 5G network environment, assuming data packets are transmitted through multiple channels, synchronization time data might show that the arrival time of channel A is 10ms, and that of channel B is 12ms, with a time difference of 2ms. This time difference can be extracted using the timestamp function of network devices, reflecting the transmission delay differences between channels.

[0112] Specifically, the time synchronization module of the base station can record the sending and receiving time of each data packet, and then calculate the difference to provide a basis for subsequent analysis. Based on the time difference, a detection module calculates the deviation value, and the deviation analysis results are crucial for judging transmission quality.

[0113] In one possible implementation, the detection module can be set to a normal range of 0-3ms for the time difference. If the difference is within 2ms, the deviation may be low, indicating relatively stable transmission; if the difference for a certain channel increases to 5ms, the deviation increases, indicating a potential problem.

[0114] Preferably, the detection module can also combine historical data to analyze the trend of difference fluctuations, providing more comprehensive deviation results. The stability of data transmission is assessed based on the deviation analysis results; determining the stability status requires consideration of the actual scenario.

[0115] For example, if the deviation value remains at a low level and the fluctuation does not exceed 1ms, it can be judged as a high stability state; conversely, if the deviation value frequently exceeds the range, the stability state changes to one that needs optimization.

[0116] Understandably, this judgment relies on preset threshold rules, such as triggering an adjustment signal after three consecutive deviations exceeding the limit, which helps to quickly respond to network changes. Obtaining the adjustment requirements of the optimization sequence from the stability state and obtaining the sequence adjustment parameters is the core step of dynamic optimization.

[0117] In one embodiment, if low stability is detected in a certain channel, parameter adjustments may include reducing the data allocation ratio for that channel, such as from 50% to 30%, and increasing the load on other channels. This parameter design effectively distributes the load and improves overall transmission efficiency. Updating the transmission sequence by adjusting the sequence parameters and obtaining the adjusted sequence structure are specific steps in performing optimization.

[0118] Specifically, assuming the original sequence has data evenly distributed across three channels, the adjusted sequence might have a structure where channel A accounts for 20%, channel B for 40%, and channel C for 40%. This adjustment takes effect in real-time based on parameters, ensuring the sequence matches the network status. Analyzing the adjusted sequence structure using image rendering data provides a direct verification of the transmission effect.

[0119] For example, in video streaming scenarios, if adjusting the sequence reduces the frame drop rate from 5% to 2%, it indicates an improvement in rendering quality.

[0120] It should be noted that rendering analysis can be achieved through frame rate statistics tools on the terminal device, intuitively reflecting changes in user experience. Determining the final stability configuration based on a comparison of rendering quality data and deviation values ​​is the final step in the solution process.

[0121] In one embodiment, if the rendering quality improves and the deviation value stabilizes at a low level, the final configuration may fix the current sequence structure and record the parameters for future reference.

[0122] Preferably, this comparison can also reveal hidden problems, such as low deviation values ​​but no improvement in rendering quality, indicating a need to check the data encoding process. This multi-dimensional verification ensures the reliability of the configuration and significantly improves the stability and efficiency of transmission tasks.

[0123] In step S108, if the deviation value exceeds the preset tolerance range, the intelligent packet loss processing parameters are adjusted according to bandwidth fluctuations and data type characteristics. By iteratively updating the resource allocation scheme, a new transmission strategy that meets the real-time synchronization requirements is obtained.

[0124] If the deviation exceeds the tolerance range, fluctuation characteristic data is obtained through bandwidth fluctuation analysis to obtain the fluctuation characteristic distribution. Packet loss parameters are matched with the fluctuation characteristic distribution and data type characteristics to determine the adjusted packet loss parameter configuration. The resource allocation scheme is updated using the adjusted packet loss parameters to obtain an optimized resource allocation structure. A new transmission strategy is generated using the optimized resource allocation structure, and the real-time synchronization status is assessed. If the real-time synchronization status does not meet the synchronization requirements, the transmission strategy is iteratively updated and adjusted to obtain strategy parameters that meet the requirements. The final transmission strategy configuration is determined based on a comparative analysis of the strategy parameters and bandwidth fluctuations. Data type characteristics are processed using the final transmission strategy configuration to obtain stable synchronization transmission results.

[0125] In one possible implementation, when the deviation value exceeds the tolerance range, bandwidth fluctuation analysis can be performed by starting with the actual data flow during the transmission process to obtain fluctuation characteristic data.

[0126] For example, by monitoring bandwidth changes over a period of time, recording fluctuations from a peak bandwidth of 100Mbps to a minimum of 60Mbps, a fluctuation characteristic distribution can be obtained. This distribution may show the characteristic of frequent bandwidth switching in a short period of time, directly affecting the stability of synchronous transmission in VR games.

[0127] It should be noted that the extraction of fluctuation feature data can help identify critical periods of bandwidth instability, providing a basis for subsequent optimization.

[0128] Specifically, when matching packet loss parameters based on fluctuation characteristic distribution and data type characteristics, analysis can be performed on common video and audio streams in VR games. Video streams are highly sensitive to packet loss, while audio streams emphasize continuity.

[0129] For example, under conditions of significant bandwidth fluctuations, the packet loss rate of a video stream might increase from 1% to 5%, causing stuttering. In this case, by matching data type characteristics, the packet loss parameters can be adjusted to prioritize ensuring the lowest possible packet loss rate for the video stream, such as keeping it below 2%. This adjusted packet loss parameter configuration effectively balances the needs of different data types.

[0130] Preferably, when updating the resource allocation scheme using the adjusted packet loss parameters, more bandwidth resources can be allocated to the video stream.

[0131] For example, with a total bandwidth of 80Mbps, 60Mbps can be allocated for video streaming, and the remaining 20Mbps for audio streaming and control signals, thus forming an optimized resource allocation structure. This structure can reduce the risk of packet loss for critical data streams and improve transmission efficiency.

[0132] In one embodiment, after generating a new transmission strategy using an optimized resource allocation structure, the real-time synchronization status can be determined by monitoring the synchronization time difference.

[0133] For example, the synchronization time difference was 50 milliseconds before the adjustment and was reduced to 20 milliseconds after the adjustment, which is close to the ideal requirements for an immersive VR game experience.

[0134] Understandably, if the synchronization time difference still does not reach the target of less than 15 milliseconds, it is necessary to iteratively update the transmission strategy, such as further compressing the size of non-critical data packets, to finally obtain strategy parameters that meet the requirements.

[0135] For example, when comparing strategy parameters with bandwidth fluctuations, a scenario where bandwidth suddenly drops from 70Mbps to 50Mbps can be observed. The adjusted strategy may compensate for video stream demands by dynamically reducing the audio stream bitrate, ensuring the stability of synchronous transmission. After the final transmission strategy configuration is complete, when processing data type characteristics, screen tearing or audio interruptions can be significantly reduced.

[0136] For example, through the final transmission strategy configuration, the transmission latency of the video stream was reduced from 40 milliseconds to 15 milliseconds, and the audio stream latency remained within 10 milliseconds. This stable synchronous transmission result ensures the real-time consistency between player movements and the visuals in VR games, enhancing immersion.

[0137] In one embodiment, this optimization can also reduce experience fluctuations during network congestion and extend stable operation time.

[0138] Step S109: Obtain the network transmission log after the new transmission strategy is implemented, extract the arrival time and integrity data of key data packets, and determine the degree of optimization of the final transmission scheme by comparing it with the preset immersive experience indicators and output the results.

[0139] After the new transmission strategy is implemented, network logs are obtained. Arrival time and integrity data of key data packets are extracted from the logs to obtain a preliminary feature set. The arrival times from this preliminary feature set are compared with preset metrics using a time comparison method to determine the time deviation distribution. Based on integrity data and data judgment logic, packets with integrity values ​​below the preset metric are marked as abnormal, resulting in an anomaly distribution. The anomaly distribution and time deviation distribution are combined with immersive experience requirements using a decision tree algorithm to determine the optimization level of the current transmission strategy and obtain an optimization evaluation value. If the optimization level does not reach the preset threshold after obtaining the optimization evaluation value, an updated transmission strategy configuration is generated by adjusting packet priorities, resulting in new strategy parameters. Data packets in the network logs are reprocessed using the new strategy parameters, and the time comparison and data judgment process is iteratively updated to determine the final transmission configuration. A stable immersive experience transmission scheme is generated based on the final transmission configuration, resulting in transmission results that meet the preset metrics.

[0140] For example, after obtaining the network logs following the execution of the new transmission policy, parsing the logs to extract the arrival time and integrity data of key data packets is a core step. For instance...

[0141] In one possible implementation, log analysis tools can be used to filter out the sending and receiving timestamps of each data packet, and combined with the integrity flag field, a preliminary dataset containing time differences and packet loss information can be obtained.

[0142] For example, if a data packet is sent at 10:00:00 and received at 10:00:02, and its integrity is marked as "complete," then the time difference is recorded as 2 seconds. This method intuitively reflects the transmission delay through timestamp comparison, providing a basis for subsequent analysis. Determining the time deviation distribution by matching the arrival times in the initial feature set with preset indicators using a time comparison method is a key analytical approach.

[0143] Specifically, the preset target might be a delay of no more than 1.5 seconds.

[0144] In one embodiment, if the dataset shows that 70% of data packets have a latency of less than 1 second, 20% have a latency of 1 to 2 seconds, and 10% have a latency of more than 2 seconds, then the time deviation distribution indicates that most data packets meet the requirements, but there is still room for optimization. This distribution analysis helps to identify latency bottlenecks. Abnormal data packets are then flagged based on integrity data and data judgment logic.

[0145] Understandably, data packets with an integrity level below a preset threshold (such as 95%) will be flagged.

[0146] For example, if a data packet loses part of its content due to network jitter, and its integrity is only 80%, it is marked as abnormal.

[0147] Preferably, the anomaly distribution result can be obtained by statistically analyzing the proportion of abnormal data packets, for example, an anomaly rate of 15%. This marking method can clearly locate weak links in the transmission. Using the anomaly distribution result and the time deviation distribution, and integrating immersive experience requirements through a decision tree algorithm to determine the degree of optimization, is a crucial step in the technical implementation.

[0148] In one possible implementation, the decision tree can be configured with a rule that the optimization level is "high" if the anomaly rate is below 10% and the latency is below 1.5 seconds.

[0149] For example, with a current anomaly rate of 15% and an average latency of 1.8 seconds, the optimized evaluation value might be "Medium," indicating that further adjustments are needed. This method improves the comprehensiveness of the evaluation through multi-dimensional fusion. After obtaining the optimized evaluation value, if it does not reach the threshold, a new policy configuration is generated by adjusting the packet priority.

[0150] For example, when network bandwidth is limited, video data packets are given the highest priority, followed by text data packets.

[0151] Specifically, in the original strategy, all data packets had the same priority; after the adjustment, the video data packet latency was reduced from 2 seconds to 1.2 seconds. This adjustment effectively ensures the transmission efficiency of critical data. Reprocessing the network logs with the new strategy parameters and iterating in a loop is key to ensuring this effect.

[0152] Understandably, each iteration compares changes in time deviation and integrity. For example, in the second round, the anomaly rate drops to 8%, and the latency stabilizes at 1.3 seconds, ultimately determining the transmission configuration. This iterative approach gradually approaches the optimal solution. A stable immersive experience transmission scheme is then generated based on the final transmission configuration.

[0153] For example, video smoothness has been improved to the point of no noticeable stuttering, and data integrity has reached 98%.

[0154] It should be noted that this solution ensures both low latency and high integrity through priority adjustment and iterative optimization, bringing a better user experience.

[0155] In one embodiment, the solution can also be extended to support dynamic bandwidth allocation, further improving stability.

[0156] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

[0157] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and additions without departing from the principle of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention.

Claims

1. A method for synchronous transmission of VR game data based on a 5G network, characterized in that, include: The system acquires various types of data streams generated during VR game runtime, extracts scene rendering data and player interaction data from them, separates the feature values ​​of each data type, and obtains a data priority sequence. Based on the data priority sequence, the real-time requirements of scene rendering data and interaction data are analyzed to determine the key data packets that need to be transmitted immediately. By monitoring the current bandwidth fluctuations at the network transport layer, available bandwidth and latency parameters are calculated from the real-time collected network status data to obtain the baseline value for resource allocation in dynamic scenarios. For high-priority data packets, a dynamic resource allocation algorithm is used to adjust the transmission queue order, and the resource allocation benchmark value is corrected by the bandwidth fluctuation parameter to determine the transmission time slot allocation scheme for critical data packets.

2. The VR game data synchronization transmission method based on a 5G network according to claim 1, characterized in that, The process of obtaining the data priority sequence specifically includes: The data stream generated during the VR game runtime is acquired, and multiple types of data are separated through preset filters to obtain an initial data set; Scene rendering data and player interaction data are extracted from the initial dataset, and feature values ​​of each data type are determined using preset classification rules to obtain a feature value set. The feature set is sorted, and the data priority is calculated using a priority evaluation algorithm to obtain a priority list. If there are duplicate values ​​in the priority list, the priority list is sorted again based on the timestamp of the feature value to obtain the adjusted priority sequence. The adjusted priority sequence is grouped using a clustering analysis algorithm to obtain the grouped data clusters. Based on the grouped data clusters, time series analysis is used to determine the changing trend of the data stream and obtain trend characteristic values; The final data priority sequence is determined by matching trend feature values ​​with preset conditions.

3. The VR game data synchronization transmission method based on a 5G network according to claim 1, characterized in that, The process of identifying the critical data packets that need to be transmitted immediately specifically includes: By analyzing scene rendering data and interaction data using deep learning models, a set of feature values ​​for real-time requirements is obtained. If the values ​​in the feature set exceed a preset threshold, high-priority data are determined through a labeling process. Based on the results of the tagging process, obtain the key data packets corresponding to the high-priority data; A transmission scheduling tool is used to sort critical data packets to obtain the priority order of transmission requests; Time series analysis is used to group the sorted key data packets to obtain the grouped data clusters. Based on the grouped data clusters, determine the dynamic adjustment sequence of transmission requirements and the final transmission order.

4. The VR game data synchronization transmission method based on a 5G network according to claim 1, characterized in that, The process of obtaining the resource allocation baseline value under dynamic scenarios specifically includes: By continuously monitoring bandwidth fluctuation data at the network transport layer, real-time network status information is obtained, and the current transmission monitoring results are obtained. Real-time network status data is extracted from transmission monitoring results and processed using data computing tools to obtain a combined value of available bandwidth and latency parameters. For the delay parameter in the combined value, analyze the changing trend under dynamic scenarios to obtain the grouping basis for scenario adaptation; Based on the grouping criteria, available bandwidth is classified and processed to determine the resource allocation scheme in dynamic scenarios; If the allocation ratio in the resource allocation scheme exceeds the preset threshold, the transmission sequence corresponding to the bandwidth fluctuation is adjusted by the scheduling tool to obtain the optimized allocation sequence. The optimized allocation sequence is verified by time series analysis to determine the stability of transmission monitoring and to determine the final resource allocation baseline value. By using pre-established mapping rules, the final resource allocation baseline value is matched with network transmission requirements to obtain the adjusted transmission configuration.

5. The VR game data synchronization transmission method based on a 5G network according to claim 1, characterized in that, The process of determining the transmission time slot allocation scheme for key data packets specifically includes: High-priority data packets are obtained from the transmission queue, and their order is adjusted using a dynamic resource allocation algorithm to obtain the adjusted transmission sequence. Based on the adjusted transmission sequence, obtain the bandwidth fluctuation parameters and determine the trend of change within the fluctuation range; In response to the changing trend, the resource allocation baseline value is updated through a correction process to obtain an optimized baseline value; If the optimized baseline value exceeds the preset threshold, the time slot allocation is adjusted through the scheduling tool to determine the transmission time slot of the critical data packets; Time series analysis was used to verify the adjusted time slot allocation, determine the stability of the transmission sequence, and obtain the verification results. Based on the verification results, the transmission requirements of key data are matched by pre-established mapping rules to obtain the adjusted transmission configuration; By adjusting the transmission configuration, a priority sorting tool is used to process high-priority data packets to determine the final transmission scheme.

6. The VR game data synchronization transmission method based on a 5G network according to claim 1, characterized in that, Also includes: The system retrieves data loss records that occur during transmission, extracts the loss type and timestamp from the lost data packets, and triggers an intelligent packet loss handling mechanism if the loss type belongs to high-priority data to determine the range of data packets that need to be retransmitted. Based on the range of retransmitted data packets, the transmission tasks are rescheduled using the redundant channel characteristics of the 5G network. By assigning higher transmission weights to the retransmitted data packets through preset priority management rules, an optimized transmission sequence is obtained.

7. A VR game data synchronization transmission method based on a 5G network according to claim 6, characterized in that, The process of determining the range of data packets that need to be retransmitted specifically includes: Data loss records are obtained during the transmission process, and analysis tools are used to extract the loss type and timestamp to obtain a categorized set of lost data. The loss type is determined from the classified loss data set. If the loss type belongs to high-priority data, the triggering condition is determined by the preset threshold to obtain the subset of loss data to be processed. For the subset of lost data that needs to be processed, the intelligent packet loss handling mechanism is activated, and the retransmission range is determined by timestamp analysis to obtain the preliminary retransmission data range. Based on the initial retransmission data range, a range judgment tool is used to process the set of lost data packets and determine the optimized set of retransmission data packets. By optimizing the set of retransmitted data packets, the bandwidth parameters during the transmission process are obtained, and it is determined whether the bandwidth supports retransmission to obtain the bandwidth matching result. Based on the bandwidth matching results, if the bandwidth supports retransmission, the transmission queue is adjusted using a priority sorting tool to determine the transmission time slot for the final retransmitted data packet. Based on the transmission time slot of the final retransmitted data packet, time series analysis is used to verify transmission stability and determine the adjusted transmission configuration.

8. A VR game data synchronization transmission method based on a 5G network according to claim 6, characterized in that, The process of obtaining the optimized transmission sequence specifically includes: By analyzing the characteristics of redundant channels, the status of the 5G network can be obtained and the set of currently available channels can be acquired. Based on the matching of the available channel set with the data range, determine the set of scheduling tasks corresponding to the retransmitted data; For the set of scheduled tasks, the retransmitted data is sorted according to priority rules to obtain the transmission weight allocation result; The transmission sequence is adjusted based on the transmission weight allocation results to obtain the optimized transmission sequence structure. Based on the optimized transmission sequence structure, determine the channel load balancing status and determine the load adjustment parameters; The load adjustment parameters are used to update the set of scheduled tasks to obtain the final transmission task sequence; The stability of the final transmission task sequence was verified through time series analysis, and the transmission configuration parameters were determined.

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