Processing method and system for multi-channel data aggregation transmission based on 5G network

Abstract

The invention discloses a processing method for multichannel data aggregation transmission based on a 5G network, and relates to the technical field of data transmission processing. Comprising the following steps: analyzing bandwidth and flow requirements of different channels based on a real-time state of a 5G network, dynamically allocating bandwidths and selecting a proper transmission channel; data streams transmitted by different channels are cached and preprocessed in a distributed mode at edge nodes, redundant data are removed, and a transmission structure is optimized; the preprocessed multi-channel data flow is converged, and the data flow of different channels is distributed to a plurality of links through a load balancing strategy, so that effective data aggregation and distributed transmission are realized; and after data aggregation, the data integrity is detected by using a multi-dimensional verification mechanism, and error data is automatically corrected. According to the multichannel data aggregation and transmission method based on the 5G network, efficient and stable data transmission and error correction are realized by using the edge computing unit and the dynamic scheduling algorithm, and reliable transmission guarantee is provided 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 aggregation transmission. Background Art

[0002] In the context 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 data. With the rapid popularization of mobile terminals and IoT devices, the demand for data transmission continues to increase, especially in scenarios such as industrial automation, smart cities, and unmanned driving that require high-speed transmission and low latency. The multi-channel data convergence transmission method of 5G networks has gradually become a research hotspot. Traditional data transmission methods mostly rely on a single channel transmission mode. Although it has certain feasibility in the transmission of small-scale data volumes, with the diversification of application requirements and the explosive growth of data volume, this single-channel mode cannot meet the increasingly complex and diversified needs.

[0003] However, there are still many problems with the existing multi-channel data aggregation and transmission methods. First, the existing technology generally lacks real-time monitoring and dynamic adjustment mechanisms for different channel bandwidth and traffic requirements, which easily leads to uneven bandwidth utilization and affects the overall transmission efficiency. Secondly, in the processing of the data aggregation front end, the existing methods are relatively weak in dealing with problems such as data redundancy and inconsistent data formats. There is a lack of an efficient edge node distributed caching and preprocessing method, which leads to serious redundant transmission and transmission delay problems during data transmission. 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 transmission.

[0005] Therefore, the problem to be solved by the present invention is how to achieve refined control and efficient allocation 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: 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 edge nodes, 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, so as 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.

[0007] As a preferred solution of the processing method for multi-channel data convergence transmission based on 5G network described in the present invention, wherein: 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, and a set of channel mapping schemes with time priority are generated; the channel mapping scheme is segmented to allocate bandwidth resources according to the 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, and if the channel status deviates from the predicted value, dynamic adjustment is triggered.

[0008] As a preferred solution of the processing method based on 5G network multi-channel data aggregation transmission of the present invention, the channel mapping scheme is: , in, The bandwidth required to transmit the data stream, is the actual bandwidth of the selected channel C, The maximum acceptable delay for transmitting data streams, is the actual delay of the selected channel C, is the maximum jitter value acceptable for the transmitted data stream. is the actual jitter value of the selected channel C.

[0009] As a preferred solution of the processing method based on 5G network multi-channel data aggregation and transmission described in the present invention, wherein: the data traffic of different channels is distributed to multiple links through a 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 aggregation 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; in the aggregation 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 aggregation is completed, a link usage report is generated and fed back to the edge node cache area.

[0010] As a preferred solution of the processing method based on 5G network multi-channel data aggregation 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 stream, 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 Transmission demand value on the link; define link fitness To measure data flow In the link The fitness on: , 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; 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.

[0011] As a preferred solution of the processing method based on 5G network multi-channel data aggregation transmission of the present invention, wherein: the method of 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 includes: data stream 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 The transmission demand on the , 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 the network condition fluctuates, the intelligent scheduling algorithm will adjust the priority according to the real-time status and prioritize high-priority data flows to stable links.

[0012] As a preferred solution of the processing method based on multi-channel data aggregation and transmission of 5G network described in the present invention, wherein: the data traffic of different channels is distributed to multiple links through a load balancing strategy, including: after the data aggregation is completed, based on the label classification and the information of the aggregation priority mark, the verification record of the aggregated data is retrieved, and the data flow is preliminarily compared with the label classification to screen out the data flow segments with abnormalities; for the abnormal data flow preliminarily screened out, a multi-dimensional verification mechanism is adopted to comprehensively analyze the error characteristics in the data flow, 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 of the distributed cache area, and locates the position of the erroneous data in the original data flow 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 flow; the corrected data flow is subjected to a multi-dimensional verification again, and after confirming the data integrity and consistency, it is marked as a high-trust data flow and stored in the temporary data area of ​​the terminal application.

[0013] In 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 edge nodes, 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 after data aggregation using a multi-dimensional verification mechanism, and automatically correct erroneous data.

[0014] 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.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, 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.

[0016] 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 data streams are 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 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

[0017] 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.

[0018] Figure 1 The present invention is a flowchart of a processing method for multi-channel data aggregation and transmission based on 5G network.

[0019] Figure 2 This is a structural diagram of the processing system based on 5G network multi-channel data aggregation and transmission. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and 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.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1 Reference Figure 1~Figure 2 , which is the first embodiment of the present invention, and provides a processing method based on 5G network multi-channel data aggregation transmission, including: 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 suitable transmission channels to provide efficient channel resources for subsequent data aggregation.

[0024] S1.1 Use the edge computing unit in the 5G network to perform short-term trend forecasts on the bandwidth, latency, jitter and other state parameters of each channel, and generate a predicted channel state matrix to identify future communication bottlenecks or unstable factors.

[0025] Specifically, the edge computing unit collects real-time status data of each channel, including parameters such as bandwidth (BW), delay (RTT), and jitter, to establish a historical data set.

[0026] 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.

[0027] 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.

[0028] Specifically, channel mapping scheme for: , in, The bandwidth required to transmit the data stream, is the actual bandwidth of the selected channel C, The maximum acceptable delay for transmitting data streams, is the actual delay of the selected channel C, is the maximum jitter value acceptable for the transmitted data stream. is the actual jitter value of the selected channel C.

[0029] S1.3 The channel mapping scheme allocates 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 the reliability of transmission.

[0030] S1.4 Based on the segmented bandwidth allocation results, a transmission schedule for multi-channel data streams is dynamically generated, where the schedule includes the channel usage order, bandwidth allocation, and alternative channel switching rules for each data stream, and cache management is performed at the edge node to cope with rapid changes in network status.

[0031] 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.

[0032] Specifically, the matching degree calculation process is: , Where n is the number of channels, is the predicted channel state value, It is the actual channel status value.

[0033] when When , the data flow is switched to the alternative channel in the mapping scheme, where The threshold to set.

[0034] 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.

[0035] S2.1 In the edge node, a distributed cache is created for the data streams received from each channel.

[0036] The cache area classifies labels according to the source and priority of the data flow to ensure storage isolation and scheduling control of different data flows.

[0037] S2.2 In the cache area, data fingerprint technology is used to perform redundancy detection on the label-classified data stream, generate a unique fingerprint identifier, and remove duplicate data based on the fingerprint comparison result. That is, if the fingerprints of two data blocks are the same, they are regarded as redundant data, and the duplicate data is removed to improve storage and transmission efficiency.

[0038] 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.

[0039] S2.3 The data stream that has undergone redundancy detection is standardized based on preset structured rules.

[0040] 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.

[0041] S2.4 Perform content compression on the standardized data stream, perform lossy compression on the non-critical information of the data stream, and perform lossless compression on the critical data portion to ensure the transmission efficiency and content integrity of the compressed data.

[0042] S2.5 stores the data stream that has been standardized and compressed back to the edge cache, where the data tag priority in the cache is synchronized with the multi-channel aggregation priority of S3.1.

[0043] 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.

[0044] 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.

[0045] The data flows in the cache are tagged and classified by source and priority, and a mapping rule is defined to match the priority tag with the aggregation priority requirement of S3.1 to ensure that high-priority data can be preferentially scheduled to links with sufficient resources and stable status in step S3.1.

[0046] 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.

[0047] If the network fluctuates, the edge node updates the priority tag of the data flow in the cache according to 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.

[0048] S3: Aggregate the pre-processed multi-channel data streams and distribute the data traffic of different channels to multiple links through load balancing strategies to achieve effective data aggregation and distributed transmission, and improve transmission efficiency and stability.

[0049] S3.1 extracts the standardized and compressed multi-channel data streams from the edge node cache, generates a transmission task queue based on the label classification and priority marking information, and places the high-priority data streams at the front of the queue so that they can be processed first and allocated to the appropriate link.

[0050] S3.2 Calculate the transmission requirements of each data stream based on its characteristics and generate a multi-link transmission strategy, where 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: 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 .

[0051] Furthermore, the optimal link allocation scheme for each data stream is calculated based on the current state of the link (such as bandwidth, delay) and data flow requirements. When generating the link allocation scheme, the current state of the link and the data flow requirements need to be considered to achieve the optimal utilization of link resources. The allocation scheme contains the specific link number, the required bandwidth allocation and delay requirements to ensure the optimal utilization of link resources and the balanced distribution of load. Specifically, the link fitness is defined as To measure data flow In the link The fitness on: , 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.

[0052] 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.

[0053] S3.3 applies the multi-link transmission strategy to the data flow aggregation process, dynamically allocates each data flow to different links through the intelligent scheduling algorithm, and adjusts the data flow allocation priority based on the link status to adapt to the volatility of the network status.

[0054] Specifically, data flow Priority score It can be 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.

[0055] Assume the adjustment factor is , used to intelligently adjust allocation when the link is overloaded: , in, To adjust the weight of delay and jitter is the adjustment factor.

[0056] When network conditions fluctuate, such as when certain link delays increase or bandwidth becomes narrower, the intelligent scheduling algorithm adjusts priorities based on real-time status and prioritizes high-priority data streams to stable links to ensure the transmission stability of critical data.

[0057] S3.4 During the convergence process, the load status of each link is monitored in real time. If an overloaded link occurs, the load status monitored in real time is fed back to the channel mapping solution so that the channel allocation solution for the next time period can better avoid the overload situation and intelligently redistribute the data flow of the overloaded link to ensure transmission efficiency and stability.

[0058] 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 overloading.

[0059] S3.5 After the aggregation is completed, a link usage report is generated and fed back to the edge node cache area to optimize the link selection and load balancing strategy for subsequent transmission and provide empirical parameter support for subsequent data aggregation.

[0060] 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.

[0061] S4.1 After data aggregation is completed, based on the label classification and aggregation priority tag information, retrieve the verification record of the aggregated data, and perform a preliminary integrity comparison on the data flow according to the label classification to screen out the data flow segments with abnormalities.

[0062] 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.

[0063] 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, and replaces the abnormal data to complete the error correction operation; while replacing the data, the link status information is updated and the error correction completion status of the current data stream is recorded to ensure that there are no obvious errors when the data stream enters the next step of verification.

[0064] S4.4 Perform a multi-dimensional check on the corrected data stream again. After confirming the 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.

[0065] Finally, a data transmission quality report is generated to record information such as the error type and error correction success rate during the verification and error correction process, and is fed back to the edge nodes to further optimize the multi-channel transmission and aggregation processing process and improve the accuracy and reliability of subsequent transmissions.

[0066] Furthermore, this embodiment also provides a processing system based on 5G network multi-channel data aggregation and transmission, including 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 edge nodes, 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 after data aggregation using a multi-dimensional verification mechanism, and automatically correct erroneous data.

[0067] This embodiment also provides a computer device, which is suitable for the case of a processing method based on 5G network multi-channel data aggregation transmission, 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 5G network multi-channel data aggregation transmission as proposed in the above embodiment.

[0068] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0069] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a processing method for realizing multi-channel data aggregation and transmission based on a 5G network as proposed in the above embodiment is implemented.

[0070] 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 fitness, and high fitness 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 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.

[0071] Example 2 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 transmission. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0072] The experimental scenario selected a high-density network environment to simulate the data flow transmission requirements in different application scenarios. The prepared hardware equipment includes 5G base stations, multi-channel network routers, and multiple edge computing units. The experiment uses a time series prediction algorithm (LSTM) to analyze the bandwidth, delay, and jitter of the network channel in real time, providing a dynamic bandwidth allocation strategy for the data aggregation process.

[0073] 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 game data, temperature and humidity sensors, etc. Each data stream was assigned to a specific transmission channel to transmit the data to the edge node through the 5G network.

[0074] In the early stages of the experiment, the channel status data (bandwidth, latency, jitter, etc.) collected by the 5G network base station was transmitted to the edge computing unit. The edge unit used the LSTM algorithm to generate a channel status prediction matrix for the next 5 minutes to identify possible bottlenecks.

[0075] According to the predicted channel status matrix, the transmission channel of each data stream is dynamically allocated to meet the bandwidth, delay and jitter requirements of different data streams. High-priority data streams are preferentially allocated to channels with sufficient bandwidth and high stability, while medium and low-priority data streams choose channels with moderate bandwidth.

[0076] 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.

[0077] The data stream is standardized and compressed and then 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 can be scheduled first 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, and 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 the stability of transmission. Some experimental data are as follows:

[0078] Table 1 Some experimental data tables 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 flow 40 6 0.8 30 18 2.5 Fault alarm data flow 80 12 1.5 60 22 3.5

[0079] As can be seen from the table data, 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, and quickly switches to the backup link when the channel fluctuates, ensuring the continuous transmission of high-priority data. Compared with the static allocation method in the existing technology, this allocation strategy has higher flexibility and can effectively cope with the high-density data transmission requirements in the 5G network environment.

[0080] Moreover, by removing duplicate data through redundancy detection and fingerprint identification, the duplication ratio of temperature and humidity sensor data streams in the experiment was reduced by 15%, and the data transmission efficiency was increased by 20% after compression processing. The prior art did not adopt a redundancy detection mechanism, resulting in an increase in invalid transmission of duplicate data, while the present invention greatly improved the transmission efficiency by deduplication and compression of data achieved through edge computing.

[0081] 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 are verified, showing its significant improvement in data transmission efficiency and transmission reliability.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 based on 5G network multi-channel data aggregation transmission, 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 the pre-processed multi-channel data streams, and distribute the data traffic of different channels to multiple links through load balancing strategies to achieve effective data aggregation and distributed transmission; After the data is aggregated, a multi-dimensional verification mechanism is used to detect data integrity and automatically correct erroneous data.

2. The processing method based on 5G network multi-channel data aggregation transmission as claimed in claim 1, characterized in that: The analyzing the bandwidth and flow requirements of different channels, dynamically allocating bandwidth and selecting a suitable transmission channel comprises the following steps: Using edge computing units in 5G networks, short-term trend forecasts are made for each channel parameter, a predicted channel state matrix is ​​generated, and future communication bottlenecks or unstable factors are identified; Based on the predicted channel state matrix, a matching analysis is performed with the delay, bandwidth and stability requirements of the transmission data stream to generate a set of channel mapping schemes with time priority; The channel mapping scheme allocates 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; 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.

3. The processing method based on 5G network multi-channel data aggregation transmission as claimed in claim 2, 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, The maximum acceptable delay for transmitting data streams, is the actual delay of the selected channel C, is the maximum jitter value acceptable for the transmitted data stream. is the actual jitter value of the selected channel C.

4. The processing method based on 5G network multi-channel data aggregation transmission as claimed in claim 3, 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 the transmission requirements based on the characteristics of each data stream and generate a multi-link transmission strategy; Apply multi-link transmission strategy to the data flow aggregation process, dynamically allocate each data flow to different links through intelligent scheduling algorithm, and adjust the data flow allocation priority based on link status; During the convergence process, the load status of each link is monitored in real time. If an overloaded link appears, feedback is given to the channel mapping scheme, 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.

5. The processing method based on 5G network multi-channel data aggregation transmission as claimed in claim 4, characterized in that: The calculation of transmission requirements and generation of 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.

6. The processing method based on 5G network multi-channel data aggregation transmission as claimed in claim 5, characterized in that: The method of dynamically allocating each data stream to different links by using an intelligent scheduling algorithm and adjusting the data stream allocation priority based on the link status 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 factor 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 the priority based on the real-time status and allocates high-priority data streams to stable links.

7. The processing method based on 5G network multi-channel data aggregation transmission as claimed in claim 6, 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 mark information, the verification record of the aggregated data is retrieved, and a preliminary integrity comparison is performed on the data stream according to the label classification to screen out the data stream segments with abnormalities; 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 the link transmission status information and the backup data in the distributed cache area to classify the information according to the timestamp and label, locates the position of the 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 a multi-dimensional check again to confirm data integrity and consistency, then marked as a high-trust data stream and stored in the temporary data area of ​​the terminal application.

8. A processing system based on 5G network multi-channel data aggregation transmission, based on the processing method based on 5G network multi-channel data aggregation transmission according to any one of claims 1 to 7, 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; Data caching and preprocessing module, used to perform distributed caching and preprocessing of data streams transmitted through different channels at edge nodes, remove redundant data and optimize 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.

9. 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 7 are implemented.

10. 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 as described in any one of claims 1 to 7 are implemented.

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