A processing method and system based on 5G network multi-channel data aggregation transmission

Abstract

The present invention discloses a processing method for multi-channel data aggregation and transmission based on 5G network, which relates to the field of data transmission processing technology. It includes analyzing the bandwidth and traffic requirements of different channels based on the real-time status of the 5G network, dynamically allocating bandwidth and selecting suitable transmission channels; performing distributed caching and preprocessing on data streams transmitted through different channels at the edge node, removing redundant data and optimizing the transmission structure; aggregating the preprocessed multi-channel data streams, and distributing the data traffic of different channels to multiple links through a load balancing strategy to achieve effective data aggregation and distributed transmission; after data aggregation, using a multi-dimensional verification mechanism to detect data integrity and automatically correct erroneous data. The present invention is based on a multi-channel data aggregation and transmission method of a 5G network, and utilizes an edge computing unit and a dynamic scheduling algorithm to achieve efficient and stable data transmission and error correction, providing reliable transmission guarantee for data aggregation.

Description

Technical Field

[0001] The present invention relates to the field of data transmission processing technology, and in particular to a processing method and system based on 5G network multi-channel data convergence transmission. Background Art

[0002] Against the backdrop of the rapid development of information network technology today, the high bandwidth, low latency, and wide coverage of 5G networks provide reliable support for the real-time transmission of massive amounts of data. With the rapid popularization of mobile terminals and IoT devices, the demand for data transmission continues to increase. This is especially true in scenarios such as industrial automation, smart cities, and autonomous driving, which require high-speed transmission and low latency. Multi-channel data convergence transmission methods for 5G networks have gradually become a research hotspot. Traditional data transmission methods often rely on a single-channel transmission mode. While this mode is feasible for transmitting small amounts of data, with the diversification of application requirements and the explosive growth of data volumes, this single-channel mode is unable to meet the increasingly complex and diverse needs.

[0003] However, existing multi-channel data aggregation and transmission methods still have many problems. First, existing technologies generally lack real-time monitoring and dynamic adjustment mechanisms for the bandwidth and flow requirements of different channels, which easily leads to uneven bandwidth utilization and affects overall transmission efficiency. Second, in the processing of data aggregation front-end, existing methods are relatively weak in handling problems such as data redundancy and inconsistent data formats. There is a lack of efficient distributed caching and preprocessing methods for edge nodes, resulting in more serious problems of redundant transmission and transmission delay during the data transmission process. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned background technology, the present invention proposes a processing method and system based on 5G network multi-channel data aggregation and transmission.

[0005] Therefore, the problem to be solved by the present invention is how to achieve refined control and efficient distribution of data flow in 5G networks, and improve the real-time, reliability and stability of data transmission.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a processing method for multi-channel data aggregation and transmission based on a 5G network, which includes: based on the real-time status of the 5G network, analyzing the bandwidth and traffic requirements of different channels, dynamically allocating bandwidth and selecting suitable transmission channels; performing distributed caching and preprocessing on data streams transmitted through different channels at the edge node, removing redundant data and optimizing the transmission structure; aggregating the preprocessed multi-channel data streams, and distributing the data traffic of different channels to multiple links through a load balancing strategy to achieve effective data aggregation and distributed transmission; after data aggregation, using a multi-dimensional verification mechanism to detect data integrity and automatically correct erroneous data.

[0008] As a preferred solution of the processing method for multi-channel data convergence and transmission based on 5G network described in the present invention, the analysis of bandwidth and traffic requirements of different channels, dynamic allocation of bandwidth and selection of suitable transmission channels include the following steps: using the edge computing unit in the 5G network to perform short-term trend prediction on the parameters of each channel, generate a predicted channel state matrix, and identify future communication bottlenecks or unstable factors; based on the predicted channel state matrix, matching analysis is performed with the delay, bandwidth and stability requirements of the transmission data flow to generate a set of channel mapping schemes with time priority; the channel mapping scheme is segmented to allocate bandwidth resources according to time priority, so that each data flow can adapt to the best channel or alternative channel in different time periods; based on the segmented bandwidth allocation results, a transmission scheduling table for multi-channel data streams is dynamically generated; during the transmission process, the matching degree between the channel status and the transmission scheduling table is monitored in real time. If the channel status deviates from the predicted value, dynamic adjustment is triggered.

[0009] As a preferred solution of the processing method based on 5G network multi-channel data aggregation and transmission according to the present invention, the channel mapping scheme is:

[0010] ,

[0011] in, The bandwidth required to transmit the data stream, is the actual bandwidth of the selected channel C, is the maximum acceptable delay for transmitting data streams, is the actual delay of the selected channel C, The maximum jitter value acceptable for the transmission data stream is is the actual jitter value of the selected channel C.

[0012] As a preferred solution of the processing method for multi-channel data convergence and transmission based on 5G network described in the present invention, wherein: the data traffic of different channels is distributed to multiple links through the load balancing strategy, including the following steps: extracting the multi-channel data stream that has completed standardization and compression processing from the edge node cache area, and generating a transmission task queue according to the priority tag based on label classification and priority tag information; calculating the transmission demand according to the characteristics of each data stream, and generating a multi-link transmission strategy; applying the multi-link transmission strategy to the data stream convergence process, dynamically allocating each data stream to different links through an intelligent scheduling algorithm, and adjusting the data stream allocation priority based on the link status; during the convergence process, monitoring the load status of each link in real time. If an overloaded link occurs, it is fed back to the channel mapping scheme, and the data stream of the overloaded link is redistributed; after the convergence is completed, generating a link usage report and feeding it back to the edge node cache area.

[0013] As a preferred solution of the processing method based on 5G network multi-channel data convergence transmission of the present invention, wherein: the calculation of transmission requirements and the generation of multi-link transmission strategy include the following: analyzing the characteristics of each data flow, including bandwidth requirements , delay tolerance , data volume and transmission frequency , generate the transmission demand matrix :

[0014] ,

[0015] in, 、 and is a weight parameter used to adjust the impact of bandwidth, delay, and frequency on transmission requirements; For data flow In the link Transmission demand value on the link; define the link fitness To measure data flow In the link The fitness on:

[0016] ,

[0017] in, is the current load of the link, is the current jitter of the link, is the current delay of the link, For Link The round trip delay, For Link The round trip delay, For Link Current jitter;

[0018] like Indicates that the link is suitable for data flow , and higher Value indicates link More suitable for transmitting data streams , should be allocated first.

[0019] As a preferred solution of the processing method based on 5G network multi-channel data convergence transmission of the present invention, wherein: the method of dynamically allocating each data stream to different links through the intelligent scheduling algorithm and adjusting the data stream allocation priority based on the link status includes: data stream Priority score Calculated by the following formula:

[0020] ,

[0021] in, 、 and are the sensitivity weights of data streams to bandwidth, delay and jitter, respectively. For data flow In the link The transmission demand on the transmission demand; let the adjustment coefficient be , used to intelligently adjust allocation when the link is overloaded:

[0022] ,

[0023] in, To adjust the weight of delay and jitter effects, is the adjustment factor; when the network condition fluctuates, the intelligent scheduling algorithm will adjust the priority according to the real-time status and allocate high-priority data streams to stable links.

[0024] As a preferred solution of the processing method for multi-channel data aggregation and transmission based on 5G network described in the present invention, the method comprises: distributing data traffic of different channels to multiple links through a load balancing strategy, including: after data aggregation is completed, retrieving the verification record of the aggregated data based on the label classification and the aggregation priority tag information, and performing a preliminary integrity comparison on the data stream according to the label classification to screen out the data stream segments with abnormalities; for the abnormal data stream preliminarily screened out, a multi-dimensional verification mechanism is adopted to comprehensively analyze the error characteristics in the data stream and generate a verification report of the error type and location; the generated verification report uses the link transmission status information and the backup data in the distributed cache area to locate the position of the erroneous data in the original data stream according to the timestamp and label classification information, and automatically extracts the corresponding backup data from the cache area to replace the abnormal data, thereby completing the error correction operation; while replacing the data, the link status information is updated to record the error correction completion status of the current data stream; the corrected data stream is subjected to another multi-dimensional verification, and after confirming the data integrity and consistency, it is marked as a high-trust data stream and stored in the temporary data area of the terminal application.

[0025] On the second aspect, an embodiment of the present invention provides a processing system for multi-channel data aggregation and transmission based on a 5G network, which includes: a bandwidth analysis module, which is used to analyze the bandwidth and traffic requirements of different channels based on the real-time status of the 5G network, dynamically allocate bandwidth and select a suitable transmission channel; a data caching and preprocessing module, which is used to perform distributed caching and preprocessing on data streams transmitted through different channels at the edge node, remove redundant data and optimize the transmission structure; a data aggregation and load balancing module, which is used to aggregate the preprocessed multi-channel data streams, and distribute the data traffic of different channels to multiple links through a load balancing strategy; a data integrity detection and correction module, which is used to detect data integrity using a multi-dimensional verification mechanism after data aggregation, and automatically correct erroneous data.

[0026] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the processing method based on 5G network multi-channel data aggregation and transmission as described in the first aspect of the present invention are implemented.

[0027] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program instructions are executed by a processor, the steps of the processing method based on 5G network multi-channel data aggregation and transmission as described in the first aspect of the present invention are implemented.

[0028] The beneficial effects of the present invention are as follows: the present invention reduces bandwidth consumption and transmission delay through caching and preprocessing steps, realizes efficient management and compression of data streams, enhances the resource utilization of the system in large-scale data processing, and ensures that the data stream is more efficient during transmission; through the load balancing distribution strategy, the matching degree between the data stream and the link is evaluated by link adaptability, and high-adaptability links are allocated preferentially, thereby improving the utilization efficiency of network resources and avoiding link congestion. The present invention is based on a multi-channel data aggregation and transmission method of a 5G network, and utilizes an edge computing unit and a dynamic scheduling algorithm to achieve efficient and stable data transmission and error correction, providing a reliable transmission guarantee for data aggregation. Through the method of the present invention, network transmission not only improves bandwidth utilization and response speed, but also effectively reduces the risk of data loss during transmission, making it more advantageous in high-load, high-speed 5G application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 The present invention is a flowchart of a processing method for multi-channel data aggregation and transmission based on 5G network.

[0031] Figure 2 This is a structural diagram of the processing system based on multi-channel data aggregation and transmission of 5G networks. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0035] Example 1

[0036] Reference Figure 1~Figure 2 , which is the first embodiment of the present invention, provides a processing method based on 5G network multi-channel data aggregation transmission, including:

[0037] S1: Based on the real-time status of the 5G network, analyze the bandwidth and traffic requirements of different channels, dynamically allocate bandwidth and select appropriate transmission channels to provide efficient channel resources for subsequent data aggregation.

[0038] S1.1 Utilize the edge computing unit in the 5G network to perform short-term trend forecasts on the bandwidth, latency, and jitter of each channel, and generate a predicted channel state matrix to identify future communication bottlenecks or instability factors.

[0039] Specifically, the edge computing unit collects real-time status data of each channel, including parameters such as bandwidth (BW), latency (RTT), and jitter, to establish a historical data set.

[0040] Use time series prediction algorithms (such as ARIMA, LSTM, etc.) to make short-term predictions on these state parameters, obtain the state of each channel in the future time period, and generate a predicted channel state matrix ,in, , where t is the time period.

[0041] S1.2 Based on the predicted channel state matrix, a matching analysis is performed with the delay, bandwidth, and stability requirements of the transmission data flow to generate a set of channel mapping solutions with time priority. , which includes information about the best matching channel and alternative channels.

[0042] Specifically, channel mapping scheme for:

[0043] ,

[0044] in, The bandwidth required to transmit the data stream, is the actual bandwidth of the selected channel C, is the maximum acceptable delay for transmitting data streams, is the actual delay of the selected channel C, The maximum jitter value acceptable for the transmission data stream is is the actual jitter value of the selected channel C.

[0045] S1.3 uses the channel mapping scheme to allocate bandwidth resources in segments according to time priority, so that each data stream can adapt to the best channel or alternative channel in different time periods, thereby improving transmission reliability.

[0046] S1.4 Dynamically generates a transmission schedule for multi-channel data streams based on the segmented bandwidth allocation results. The schedule includes the channel usage order, bandwidth allocation, and alternative channel switching rules for each data stream, and performs cache management at the edge node to cope with rapid changes in network status.

[0047] S1.5 During the transmission process, the matching degree between the channel status and the scheduling table is monitored in real time. If the channel status deviates from the predicted value, dynamic adjustment is triggered and the data stream is switched to the alternative channel in the mapping scheme to maintain the continuity of transmission and the efficiency of the convergence process.

[0048] Specifically, the matching degree calculation process is:

[0049] ,

[0050] Where n is the number of channels, is the predicted channel state value, The actual channel status value.

[0051] when When , the data flow is switched to the alternative channel in the mapping scheme, where The threshold to set.

[0052] S2: Distributed caching and preprocessing of data streams transmitted through different channels are performed at edge nodes to remove redundant data and optimize the transmission structure, ensuring that data is standardized and compressed before aggregation.

[0053] S2.1 In the edge node, a distributed cache is created for the data streams received from each channel.

[0054] The cache area classifies data flows by label according to their source and priority, ensuring storage isolation and scheduling control of different data flows.

[0055] S2.2 In the cache, data fingerprinting technology is used to perform redundancy detection on the tagged data stream, generate a unique fingerprint identifier, and remove duplicate data based on the fingerprint comparison results. That is, if the fingerprints of two data blocks are the same, they are considered redundant data, and the duplicate data is removed to improve storage and transmission efficiency.

[0056] Furthermore, if, during data compression and caching, it is found that some data blocks have a high duplication rate, the compression or redundancy strategy can be further optimized based on the feedback from S4.

[0057] S2.3 Standardize the data stream that has undergone redundancy detection based on preset structured rules.

[0058] Among them, the standardization process converts data streams of different formats and resolutions into a unified structural format to facilitate subsequent transmission and processing compatibility.

[0059] S2.4 Perform content compression on the standardized data stream, perform lossy compression on non-critical information in the data stream, and perform lossless compression on critical data to ensure the transmission efficiency and content integrity of the compressed data.

[0060] S2.5 stores the normalized and compressed data stream back into the edge cache, where the data tag priority in the cache is synchronized with the multi-channel aggregation priority of S3.1.

[0061] Preferably, the priority scheduling of S3 can directly refer to the label and priority data of S2 and form a mapping rule so that high-priority data is preferentially scheduled to the best link, thereby improving the efficiency and accuracy of the overall transmission.

[0062] Specifically, a priority classification scheme is established based on the importance of the data flow, transmission requirements (bandwidth, latency, etc.) and transmission frequency, which are divided into three categories: high, medium and low.

[0063] The data flows in the cache are tagged and classified by source and priority. A mapping rule is defined to match the priority tags with the aggregation priority requirements of S3.1 to ensure that high-priority data can be scheduled preferentially to links with sufficient resources and stable status in the S3.1 step.

[0064] At regular intervals, the edge node re-evaluates the status of multiple channels in S3.1 (including bandwidth, latency, etc.) based on network conditions and synchronizes the priority tags of data flows in the cache area to make them consistent with the latest multi-channel aggregation priority.

[0065] If network fluctuations occur, edge nodes update the priority tags of data flows in the cache based on the channel state matrix. The aggregation requirements of high-priority data (such as transmission bandwidth and latency) are quickly updated through the label mapping mechanism to ensure that high-priority data is still transmitted first during fluctuations.

[0066] S3: Aggregates pre-processed multi-channel data streams and distributes data traffic from different channels to multiple links through load balancing strategies, achieving effective data aggregation and distributed transmission, improving transmission efficiency and stability.

[0067] S3.1 extracts standardized and compressed multi-channel data streams from the edge node cache. Based on label classification and priority marking information, it generates a transmission task queue according to the priority marking, placing high-priority data streams at the front of the queue for priority processing and allocation to the appropriate link.

[0068] S3.2 Calculate the transmission requirements of each data stream based on its characteristics and generate a multi-link transmission strategy. The strategy includes a link allocation plan and corresponding transmission bandwidth requirements to optimize link resource allocation and traffic load. The specific operations are as follows:

[0069] Analyze the characteristics of each data flow, including bandwidth requirements , delay tolerance , data volume and transmission frequency , generate the transmission demand matrix :

[0070] ,

[0071] in, 、 and is a weight parameter used to adjust the impact of bandwidth, delay, and frequency on transmission requirements; For data flow In the link The transmission demand value on .

[0072] Furthermore, based on the current state of the link (such as bandwidth, delay) and data flow requirements, the optimal link allocation plan for each data flow is calculated. When generating the link allocation plan, the current state of the link and the data flow requirements need to be considered to achieve optimal utilization of link resources. The allocation plan includes the specific link number, the required bandwidth allocation and delay requirements to ensure optimal utilization of link resources and balanced load distribution. Specifically, the link fitness is defined as To measure data flow In the link The fitness on:

[0073] ,

[0074] in, is the current load of the link, is the current jitter of the link, is the current delay of the link, For Link The round trip delay, For Link The round trip delay, For Link Current jitter.

[0075] like Indicates that the link is suitable for data flow , and higher Value indicates link More suitable for transmitting data streams , should be allocated first.

[0076] S3.3 applies a multi-link transmission strategy to the data flow convergence process, dynamically allocates each data flow to different links through an intelligent scheduling algorithm, and adjusts the data flow allocation priority based on the link status to adapt to the volatility of the network status.

[0077] Specifically, data flow Priority score It can be calculated by the following formula:

[0078] ,

[0079] in, 、 and are the sensitivity weights of data streams to bandwidth, delay and jitter, respectively. For data flow In the link transmission requirements.

[0080] Assume the adjustment coefficient is , used to intelligently adjust allocation when the link is overloaded:

[0081] ,

[0082] in, To adjust the weight of delay and jitter is the adjustment factor.

[0083] When network conditions fluctuate, such as when certain links experience increased latency or narrowed bandwidth, the intelligent scheduling algorithm adjusts priorities based on real-time conditions, prioritizing high-priority data streams to stable links to ensure stable transmission of critical data.

[0084] S3.4 During the convergence process, the load status of each link is monitored in real time. If an overloaded link occurs, the real-time monitored load status is fed back to the channel mapping solution, so that the channel allocation solution for the next time period can better avoid overload conditions and intelligently redistribute the data flow of the overloaded link to ensure transmission efficiency and stability.

[0085] Among them, intelligent redistribution includes allocating data flows on overloaded links to backup links or links with lower loads to balance the utilization of link resources and avoid data loss or delay caused by overload.

[0086] After S3.5 aggregation is completed, a link usage report is generated and fed back to the edge node cache to optimize the link selection and load balancing strategy for subsequent transmission, providing empirical parameter support for subsequent data aggregation.

[0087] S4: After data aggregation, a multi-dimensional verification mechanism is used to detect data integrity and automatically correct erroneous data to ensure that the final aggregated data is accurate and provide highly reliable transmission results for terminal applications.

[0088] S4.1 After data aggregation is completed, based on the label classification and aggregation priority mark information, retrieve the verification records of the aggregated data, and perform a preliminary integrity comparison on the data flow by label classification to screen out data flow segments with anomalies.

[0089] S4.2 For the abnormal data streams initially screened out, a multi-dimensional verification mechanism is used, including timestamp comparison, data packet structure verification, and content consistency verification, to comprehensively analyze the error characteristics in the data stream and generate a verification report on the error type and location.

[0090] S4.3 The generated verification report uses the link transmission status information recorded in the previous steps and the backup data in the distributed cache area, classifies the information according to timestamps and tags, accurately locates the position of the erroneous data in the original data stream, automatically extracts the corresponding backup data from the cache area, replaces the abnormal data, and thus completes the error correction operation; while replacing the data, it updates the link status information and records the error correction completion status of the current data stream to ensure that there are no obvious errors when the data stream enters the next step of verification.

[0091] S4.4 Perform another multi-dimensional check on the corrected data stream to confirm data integrity and consistency, mark it as a high-trust data stream, and store it in the temporary data area of the terminal application, ready for subsequent application calls.

[0092] Finally, a data transmission quality report is generated, recording information such as the error type and error correction success rate during the verification and error correction process, and fed back to the edge node to further optimize the multi-channel transmission and aggregation processing process and improve the accuracy and reliability of subsequent transmission.

[0093] Furthermore, this embodiment also provides a processing system for multi-channel data aggregation and transmission based on 5G networks, including a bandwidth analysis module for analyzing the bandwidth and traffic requirements of different channels based on the real-time status of the 5G network, dynamically allocating bandwidth and selecting suitable transmission channels; a data caching and preprocessing module for distributed caching and preprocessing of data streams transmitted through different channels at edge nodes, removing redundant data and optimizing the transmission structure; a data aggregation and load balancing module for aggregating the preprocessed multi-channel data streams, and distributing the data traffic of different channels to multiple links through a load balancing strategy; a data integrity detection and correction module for detecting data integrity using a multi-dimensional verification mechanism after data aggregation, and automatically correcting erroneous data.

[0094] This embodiment also provides a computer device, which is suitable for the processing method based on multi-channel data aggregation and transmission of 5G network, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the processing method based on multi-channel data aggregation and transmission of 5G network proposed in the above embodiment.

[0095] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0096] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the processing method for realizing multi-channel data aggregation and transmission based on 5G network as proposed in the above embodiment.

[0097] In summary, the present invention reduces bandwidth consumption and transmission delay through caching and preprocessing steps, realizes efficient management and compression of data streams, enhances the resource utilization of the system in large-scale data processing, and ensures that the data stream is more efficient during transmission; through the load balancing distribution strategy, the matching degree between the data stream and the link is evaluated by link adaptability, and high-adaptability links are allocated preferentially, thereby improving the utilization efficiency of network resources and avoiding link congestion. The present invention is based on a multi-channel data aggregation and transmission method of a 5G network, and utilizes an edge computing unit and a dynamic scheduling algorithm to achieve efficient and stable data transmission and error correction, providing a reliable transmission guarantee for data aggregation. Through the method of the present invention, network transmission not only improves bandwidth utilization and response speed, but also effectively reduces the risk of data loss during transmission, making it more advantageous in high-load, high-speed 5G application scenarios.

[0098] Example 2

[0099] Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides a processing method based on 5G network multi-channel data aggregation and transmission. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0100] The experimental scenario selected a high-density network environment to simulate the data flow transmission requirements of different application scenarios. The prepared hardware equipment included 5G base stations, multi-channel network routers, and multiple edge computing units. The experiment used a time series prediction algorithm (LSTM) to analyze network channel bandwidth, latency, and jitter in real time, providing a dynamic bandwidth allocation strategy for the data aggregation process.

[0101] The test data streams were divided into different priorities (high, medium, and low), simulating the needs of different application scenarios such as video surveillance, real-time gaming data, and temperature and humidity sensors. Each data stream was assigned to a specific transmission channel, which was then transmitted to the edge node via the 5G network.

[0102] In the initial stages of the experiment, channel status data (such as bandwidth, latency, and jitter) collected by 5G network base stations was transmitted to the edge computing unit. Using the LSTM algorithm, the edge unit generated a five-minute channel status prediction matrix to identify potential bottlenecks.

[0103] Based on the predicted channel status matrix, the system dynamically allocates transmission channels for each data stream to meet the bandwidth, latency, and jitter requirements of different data streams. High-priority data streams are preferentially assigned to channels with sufficient bandwidth and high stability, while medium and low-priority data streams are assigned to channels with moderate bandwidth.

[0104] The experiment further uses redundancy detection technology to optimize the data flow of the edge node cache area. For repeated data blocks, the redundant data is removed by comparing the unique fingerprint identifier.

[0105] The data stream is standardized and compressed before being stored in the edge cache to ensure the uniformity and compatibility of the data structure during transmission. The priority of each data stream is synchronized in the edge cache to ensure that high-priority data is prioritized in the next round of transmission. During the experiment, the pre-processed data stream is aggregated to the terminal server through a multi-link transmission strategy. The load balancing strategy balances the link traffic in a multi-channel environment. During transmission, the link load status is monitored in real time. If overload occurs, the data stream automatically switches to the backup link to maintain transmission stability. Some experimental data are as follows:

[0106] Table 1 Part of experimental data

[0107] Parameter name Channel 1 Bandwidth (Mbps) Channel 1 Delay (ms) Channel 1 Jitter (ms) Channel 2 Bandwidth (Mbps) Channel 2 Delay (ms) Channel 2 Jitter (ms) Live video streaming 100 15 2 50 25 5 Game data stream 60 10 1 40 20 4 Temperature sensor data stream 20 5 0.5 15 10 2 Humidity sensor data stream 25 8 1 20 12 3 Environmental monitoring data stream 40 6 0.8 30 18 2.5 Fault alarm data stream 80 12 1.5 60 22 3.5

[0108] As the table shows, bandwidth allocation based on the predicted channel state matrix significantly improves the transmission reliability of high-priority data streams, such as real-time video streams and fault alarm data streams. Channel 1 provides stable bandwidth and low latency for high-priority data streams, while also rapidly switching to a backup link in the event of channel fluctuations, ensuring the continued transmission of high-priority data. This allocation strategy offers greater flexibility than the static allocation method used in existing technologies and is highly effective in addressing the high-density data transmission demands of 5G networks.

[0109] Furthermore, by removing duplicate data through redundancy detection and fingerprinting, the duplication ratio of the temperature and humidity sensor data streams in the experiment was reduced by 15%, and data transmission efficiency increased by 20% after compression. Existing technologies do not employ redundancy detection mechanisms, resulting in increased ineffective transmission of duplicate data. However, this invention significantly improves transmission efficiency by implementing data deduplication and compression through edge computing.

[0110] In summary, through measures such as dynamic allocation, multi-link load balancing and redundant data removal, the application advantages of the present invention in a high-density 5G network environment have been verified, showing its significant improvement in data transmission efficiency and transmission reliability.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A processing method for multi-channel data aggregation and transmission based on a 5G network, characterized by: include: Based on the real-time status of the 5G network, analyze the bandwidth and traffic requirements of different channels, dynamically allocate bandwidth, and select the appropriate transmission channel; Distributed caching and preprocessing of data streams transmitted through different channels are performed at edge nodes to remove redundant data and optimize the transmission structure. Aggregate pre-processed multi-channel data streams and distribute data traffic from different channels to multiple links through load balancing strategies to achieve effective data aggregation and distributed transmission; After data aggregation, a multi-dimensional verification mechanism is used to detect data integrity and automatically correct erroneous data; Analyzing the bandwidth and traffic requirements of different channels, dynamically allocating bandwidth, and selecting a suitable transmission channel includes the following steps: Utilize edge computing units in 5G networks to perform short-term trend forecasts on channel parameters, generate a predicted channel state matrix, and identify future communication bottlenecks or instability factors. Based on the predicted channel state matrix, a matching analysis is performed with the delay, bandwidth and stability requirements of the transmission data flow to generate a set of channel mapping solutions with time priority; The channel mapping scheme allocates bandwidth resources in segments according to time priority, so that each data flow can adapt to the best channel or alternative channels in different time periods; Based on the segmented bandwidth allocation results, a transmission schedule for multi-channel data streams is dynamically generated; During the transmission process, the matching degree between the channel status and the transmission schedule is monitored in real time. If the channel status deviates from the predicted value, dynamic adjustment is triggered.

2. The processing method for 5G network multi-channel data aggregation and transmission according to claim 1, characterized in that: The channel mapping scheme is: ， in, The bandwidth required to transmit the data stream, is the actual bandwidth of the selected channel C, is the maximum acceptable delay for transmitting data streams, is the actual delay of the selected channel C, The maximum jitter value acceptable for the transmission data stream is is the actual jitter value of the selected channel C.

3. The processing method for 5G network multi-channel data aggregation and transmission according to claim 2, characterized in that: The method of distributing data traffic of different channels to multiple links by using a load balancing strategy includes the following steps: Extract the standardized and compressed multi-channel data stream from the edge node cache, and generate a transmission task queue based on the priority tag based on the tag classification and priority tag information; Calculate transmission requirements based on the characteristics of each data stream and generate a multi-link transmission strategy; Applying multi-link transmission strategies to the data flow aggregation process, dynamically assigning each data flow to different links through an intelligent scheduling algorithm, and adjusting the data flow allocation priority based on link conditions; During the convergence process, the load status of each link is monitored in real time. If an overloaded link occurs, the data flow of the overloaded link is fed back to the channel mapping solution and the data flow of the overloaded link is redistributed. After the aggregation is completed, a link usage report is generated and fed back to the edge node cache.

4. The processing method for 5G network multi-channel data aggregation and transmission according to claim 3, characterized in that: Calculating transmission requirements and generating a multi-link transmission strategy includes the following: Analyze the characteristics of each data flow, including bandwidth requirements , delay tolerance , data volume and transmission frequency , generate the transmission demand matrix : ， in, 、 and is a weight parameter used to adjust the impact of bandwidth, delay, and frequency on transmission requirements; For data flow In the link The transmission demand value on ; Defining link fitness To measure data flow In the link The fitness on: ， in, is the current load of the link, For Link Current jitter, is the current delay of the link, For Link The round trip delay, For Link The round trip delay, For Link Current jitter; like Indicates that the link is suitable for data flow , and higher Value indicates link More suitable for transmitting data streams , should be allocated first.

5. The processing method for 5G network multi-channel data aggregation and transmission according to claim 4, characterized in that: The method of dynamically allocating data streams to different links through an intelligent scheduling algorithm and adjusting data stream allocation priorities based on link conditions includes: Data Flow Priority score Calculated by the following formula: ， in, 、 and are the sensitivity weights of data streams to bandwidth, delay and jitter, respectively. For data flow In the link Transmission requirements on Assume the adjustment coefficient is , used to intelligently adjust allocation when the link is overloaded: ， in, To adjust the weight of delay and jitter effects, is the adjustment factor; When network conditions fluctuate, the intelligent scheduling algorithm adjusts priorities based on real-time status and prioritizes high-priority data flows to stable links.

6. The processing method for 5G network multi-channel data aggregation and transmission according to claim 5, characterized in that: The method of distributing data traffic of different channels to multiple links by using a load balancing strategy includes: After data aggregation is completed, based on the label classification and aggregation priority information, the verification records of the aggregated data are retrieved, and a preliminary integrity comparison is performed on the data stream according to the label classification to screen out data stream segments with anomalies; For the abnormal data streams initially screened, a multi-dimensional verification mechanism is used to comprehensively analyze the error characteristics in the data stream and generate a verification report of the error type and location; The generated verification report uses link transmission status information and backup data in the distributed cache area, classifies information according to timestamps and tags, locates the position of erroneous data in the original data stream, and automatically extracts the corresponding backup data from the cache area to replace the abnormal data, thereby completing the error correction operation; While replacing the data, the link status information is updated to record the error correction completion status of the current data stream; The corrected data stream is subjected to another multi-dimensional check to confirm data integrity and consistency, and then marked as a high-trust data stream and stored in the temporary data area of the terminal application.

7. A processing system based on 5G network multi-channel data aggregation and transmission, based on the processing method based on 5G network multi-channel data aggregation and transmission according to any one of claims 1 to 6, characterized in that: Also includes, The bandwidth analysis module is used to analyze the bandwidth and traffic requirements of different channels based on the real-time status of the 5G network, dynamically allocate bandwidth, and select the appropriate transmission channel; The data caching and preprocessing module is used to perform distributed caching and preprocessing of data streams transmitted through different channels at edge nodes, remove redundant data, and optimize the transmission structure; The data aggregation and load balancing module is used to aggregate the pre-processed multi-channel data streams and distribute the data traffic of different channels to multiple links through the load balancing strategy; The data integrity detection and correction module is used to detect data integrity and automatically correct erroneous data using a multi-dimensional verification mechanism after data aggregation.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the processing method based on 5G network multi-channel data aggregation and transmission as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the processing method based on 5G network multi-channel data aggregation and transmission according to any one of claims 1 to 6 are implemented.

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