Information transmission method and device and storage medium

Abstract

The invention relates to an information transmission method and device and a storage medium, and the method comprises the steps: S1, obtaining a predicted value of information transmission efficiency based on real-time network bandwidth data; s2, based on the predicted value of the information transmission efficiency, in combination with a real-time network load, carrying out capacity segmentation adjustment and sending priority configuration of the data packet, and generating data packet transmission configuration; s3, calculating data packet transmission time based on data packet transmission configuration in combination with a real-time network bandwidth, and determining a compression level of the data packet; s4, generating a hierarchical compression data packet based on a compression hierarchical calculation result; and S5, based on the hierarchical compressed data packets, performing error detection and correction in the transmission process, and performing data integrity verification on the transmitted data packets to generate an information transmission verification result. Compared with the prior art, the method has the advantages of reducing data transmission time, improving network utilization rate and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to an information transmission method, device and storage medium. Background Art

[0002] The field of data transmission technology aims to achieve efficient and secure transmission of data between devices through wired and wireless means, including signal modulation, coding, transmission protocols, error detection and correction mechanisms, among which data integrity, security and transmission speed are key factors. In order to match differentiated network environments and application requirements, data transmission technologies include telecommunication networks, fiber optic communications, and satellite communications, supporting data synchronization from simple text messages to complex video streams and large-scale data centers.

[0003] Among them, the information transmission method aims to transmit data between information systems through technologies and technology combinations, improve the efficiency and reliability of data transmission, ensure the security of information during the transmission process, and optimize the transmission process to adapt to a variety of application scenarios and technical requirements, including data compression technology to shorten the transmission time by reducing the amount of data to be transmitted, and data encryption technology to ensure that data is not accessed or tampered with by unauthorized access during the transmission process.

[0004] Traditional information transmission technology lacks flexibility in dealing with rapidly changing network environments and diverse application requirements. When data volumes are large and network conditions are complex, traditional transmission strategies cannot fully adapt to fluctuations in network loads, resulting in uneven bandwidth utilization and data transmission delays. In terms of data compression, a consistent processing strategy is often adopted, ignoring differentiated data types and urgency requirements, resulting in key data delays and non-optimized network resource allocation. The lack of real-time and dynamic processing mechanisms increases the risk of data being damaged or lost during transmission, affecting the overall transmission efficiency and network reliability in data transmission scenarios with high speed and security requirements. Summary of the invention

[0005] The purpose of the present invention is to provide an information transmission method, device and storage medium, which effectively reduce data transmission time and improve network utilization.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] An information transmission method, comprising:

[0008] Step S1: Based on the real-time network bandwidth data, the network traffic data is analyzed and the bandwidth utilization is calculated, and compared with the historical data transmission records, the changes in network performance parameters are analyzed, and the predicted value of information transmission efficiency is obtained;

[0009] Step S2: Based on the predicted value of the information transmission efficiency and in combination with the real-time network load, the capacity division adjustment and sending priority configuration of the data packet are performed to generate a data packet transmission configuration;

[0010] Step S3: Based on the data packet transmission configuration and in combination with the real-time network bandwidth, the data packet transmission time is calculated, and the compression level of the data packet is determined in combination with the time sensitivity and content type of the data packet;

[0011] Step S4: Based on the compression hierarchical calculation results, identify and match the compression algorithms of multiple data packet types, perform data compression processing in combination with the priority, size, content type, and data characteristics of the data packet, and generate a hierarchical compressed data packet;

[0012] Step S5: Based on the hierarchically compressed data packets, error detection and correction are performed during the transmission process, and data integrity verification is performed on the data packets that have completed transmission to generate information transmission verification results.

[0013] The step S1 comprises:

[0014] Step S101: Based on the real-time network bandwidth data, obtain the bandwidth utilization rate during the sampling period:

[0015]

[0016] Where: U is the bandwidth utilization rate, p i is the number of packets in the ith second, s i is the average packet size in the ith second, B is the network bandwidth, and n is the duration of the sampling period;

[0017] Step S102: Compare the obtained broadband utilization rate with the broadband utilization rate in the same period in history, and obtain the deviation between the current broadband utilization rate and the historical average level as the utilization rate deviation:

[0018]

[0019] Where: Z is the utilization rate deviation, μ is the arithmetic mean of the broadband utilization rate in the same period of history, and σ is the standard deviation of the broadband utilization rate in the same period of history;

[0020] Step S103: predicting the load trend based on the usage rate deviation, predicting the network delay based on the obtained load trend, and obtaining the predicted value of information transmission efficiency in combination with the current load rate:

[0021]

[0022] Where: E is the information transmission efficiency, α is the adjustment coefficient, D is the network delay, and L is the load rate.

[0023] The step S2 comprises:

[0024] Step S201: The real-time monitoring system continuously tracks network load data and generates a real-time quality score of the network:

[0025]

[0026] Where: Q is the real-time quality score, is the average network delay, P is the data packet loss rate, w1, w2, w3 are weight coefficients;

[0027] Step S202: Monitor the available network bandwidth in real time, and obtain the data packet segmentation result according to the size of the data packet and the service type, wherein the segmentation capacity of each data packet is:

[0028]

[0029] Where: C is the split capacity of each data packet, N is the number of data packets transmitted simultaneously, and S is the original size of a single data packet;

[0030] Step S203: based on the real-time network load and the real-time quality score of the network, the influence of each data packet on the network performance is obtained, the sending priority of each data packet is set and a data packet transmission configuration is generated, wherein the sending priority of each data packet is:

[0031] P i =v1·E i +v2·F i

[0032] Where: P i is the priority score of packet i, E i is the urgency of data packet i, F i is the impact of data packet i on network performance, v1 and v2 are weights.

[0033] The step S3 comprises:

[0034] Step S301: Based on the data packet transmission configuration, the transmission time is estimated according to the size of multiple data packets and the current real-time network bandwidth to obtain the expected transmission time of each data packet:

[0035]

[0036] Where: T i is the estimated transmission time of data packet i, C i is the size of data packet i;

[0037] Step S302: Based on the estimated transmission time of each data packet, combined with the priority and information type of the data packet, a time sensitivity score of each data packet is obtained:

[0038] S i =k1·P i +k2·T max,i

[0039] Where: S i is the time sensitivity score of packet i, T max,i is the maximum tolerated transmission time of data packet i, k1 and k2 are weights;

[0040] Step S303: Based on the time sensitivity score of each data packet, an information entropy algorithm is used to evaluate the data compression requirements and determine the compression level of the data packet in combination with the content type of the data packet and network conditions.

[0041] The mathematical expression of the information entropy is:

[0042]

[0043] Where: H(X) is information entropy, P(x i ) is the data packet content type x i The probability, W weight, max(P(x i )) is the maximum value of the probability of the data packet content type, X is a random variable of the data packet content, representing the set of different possible content types of the data packet, and n1 is the number of data packet content types.

[0044] In the process of determining the compression level of the data packet, when the information entropy of the data is high, a higher level compression method is adopted to reduce the size of the data.

[0045] The step S4 comprises:

[0046] Step S401: Determine a compression algorithm for the data packet according to the compression level of the data packet and the type of the data packet;

[0047] Step S402: Based on the hierarchical compression algorithm list, the priority, size, and content type of the data packet are analyzed to identify the characteristics of various transmission information and obtain the transmission information type analysis result;

[0048] Step S403: Based on the result of the transmission information type analysis, the data packet is compressed, the compression time and efficiency are calculated and recorded, and a hierarchical compressed data packet is generated, wherein the compression efficiency is:

[0049]

[0050] Where: Z i is the compression efficiency of data packet i, V i is the volume before compression, V i ′ is the volume after compression, t iis the compression time of data packet i.

[0051] The step S5 comprises:

[0052] Step S501: Based on hierarchical compression of data packets, real-time network bandwidth monitoring is performed to record rate and load changes in data streams, identify and analyze the impact of abnormal fluctuations on information transmission integrity, and obtain real-time transmission monitoring data;

[0053] Step S502: Based on the real-time transmission monitoring data, analyze the data flow anomalies and identify the error points, evaluate and record the occurrence frequency and type of various error points, and generate an error frequency analysis record;

[0054] Step S503: Based on the error frequency analysis record, integrity check is performed on the data that has been transmitted, and data transmission parameters are adjusted to optimize the continuity and integrity of data transmission, thereby generating an information transmission verification result.

[0055] An information transmission device includes a memory, a processor, and a program stored in the memory, wherein the processor implements the above method when executing the program.

[0056] A storage medium stores a program, which implements the above method when executed.

[0057] Compared with the prior art, the present invention has the following beneficial effects: by analyzing real-time network bandwidth data and comparing it with historical data, the real-time evaluation capability of bandwidth utilization is improved; by combining the adjustment of data packet capacity and sending priority to match network load changes, the management and scheduling of data streams are optimized, and the risk of network congestion is reduced; a matching compression strategy is selected according to the data packet type and content, which effectively reduces data transmission time and improves network utilization; by matching compression algorithms and performing data integrity verification, the security and integrity of data transmission are improved, the risk of data corruption is reduced, the efficiency and reliability of data transmission are improved, delays and errors in the transmission process are reduced, and the overall network service quality and user experience are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of the working process of an embodiment of the present invention;

[0059] Figure 2 This is a detailed flow chart of step S1 of an embodiment of the present invention;

[0060] Figure 3 A detailed flow chart of step S2 of an embodiment of the present invention;

[0061] Figure 4 This is a detailed flow chart of step S3 of an embodiment of the present invention;

[0062] Figure 5 A detailed flow chart of step S4 of an embodiment of the present invention;

[0063] Figure 6 A detailed flow chart of step S5 of an embodiment of the present invention;

[0064] Figure 7 4 is a system flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0066] See also Figure 1 The present invention provides a technical solution, an information transmission method, comprising the following steps:

[0067] Step S1: Based on the real-time network bandwidth data, the network traffic data is analyzed and the bandwidth utilization rate is calculated. By comparing with the historical data transmission records, the changes in network performance parameters are analyzed to obtain the transmission efficiency prediction information;

[0068] Step S2: Based on the transmission efficiency prediction information and in combination with the real-time network load, the data packet capacity and network bandwidth are analyzed to perform capacity segmentation adjustment of the data packet and configuration of the sending priority, thereby generating a data packet transmission configuration;

[0069] Step S3: Based on the data packet transmission configuration and in combination with the real-time network bandwidth, the data packet transmission time is calculated, the time sensitivity and content type of the data packet are analyzed, the compression level of the data packet is calculated, and the compression classification calculation result is obtained;

[0070] Step S4: Based on the compression classification calculation results, identify and match the compression algorithms of multiple data packet types, combine the priority, size, content type, and data characteristics of the data packet, perform data compression processing, and generate a hierarchical compressed data packet;

[0071] Step S5: Based on the hierarchical compression of the data packets, error detection and correction are performed during the transmission process, and data integrity verification is performed on the data packets that have completed the transmission to generate information transmission verification results.

[0072] Transmission efficiency prediction information includes bandwidth fluctuation index, expected data delay and load peak index. Data packet transmission configuration includes adjusted data packet size, data priority sequence and expected transmission time. Compression hierarchical calculation results include data compression ratio, data compression time and data compression performance evaluation. Hierarchical compression data packets specifically include compression algorithm type, data packet compression volume and data packet compression efficiency. Information transmission verification results include error detection rate, transmission error correction times and data integrity status.

[0073] See also Figure 2 ,Based on the real-time network bandwidth data, the network traffic data is analyzed and the bandwidth utilization rate is calculated. By comparing with the historical data transmission records, the changes in network performance parameters are analyzed, and the specific steps for obtaining the transmission efficiency prediction information are as follows:

[0074] S101: Based on the real-time network bandwidth data, analyze the current network traffic data and calculate the bandwidth usage rate, and obtain the bandwidth usage rate analysis result in the following process:

[0075] In sub-step S101, based on the real-time network bandwidth data, the number and size of data packets per second are counted, the data flow per second is calculated, the flow is divided by the maximum bandwidth of the network interface, and the bandwidth utilization rate is calculated, including the overall bandwidth utilization rate, and the differentiated services, including the proportion of bandwidth occupied by video streaming and email transmission, are analyzed. The formula is: Among them, U is the bandwidth utilization, p i is the number of packets in the ith second, s i is the average data packet size in the i-th second, B is the network bandwidth, and the bandwidth utilization analysis results are obtained.

[0076] S102: Based on the bandwidth usage analysis result, by comparing and analyzing with historical data transmission records, analyzing and identifying the change trend of network bandwidth usage, and obtaining the load trend calculation result, the specific process is as follows;

[0077] In sub-step S102, based on the bandwidth usage analysis results, the current bandwidth usage is compared with the historical data of the same period in the past, by calculating the average and standard deviation of the bandwidth usage at the same time point in the past seven days, and using the Z-score method to determine whether there is a significant change in the current bandwidth usage, as the usage deviation, the formula is: Among them, Z is the Z score, that is, the usage deviation, which indicates the deviation between the current bandwidth usage and the historical average bandwidth usage, U is the current bandwidth usage, μ is the historical average bandwidth usage, and σ is the standard deviation of the historical bandwidth usage. The changing trend of the network bandwidth usage is analyzed and identified to obtain the load trend calculation result.

[0078] S103: Based on the load trend calculation result, the network delay and load data are evaluated, and the information transmission efficiency of the network is analyzed and predicted. The specific process of generating transmission efficiency prediction information is as follows;

[0079] In sub-step S103, based on the load trend calculation results, the network delay data and current load data are evaluated, and a prediction model is constructed using historical delay data. The upcoming network delay is predicted by the weighted moving average method. Combined with the current network load trend, an evaluation model is constructed to predict the information transmission efficiency of the network. The formula is: Among them, E is the predicted information transmission efficiency, L is the current load rate of the network, D is the predicted network delay, and α is the adjustment coefficient, which reflects the impact of increased delay on information transmission efficiency and generates transmission efficiency prediction information.

[0080] See also Figure 3 Based on the transmission efficiency prediction information and the real-time network load, the capacity of the data packet is segmented and adjusted and the sending priority is configured by analyzing the data packet capacity and network bandwidth. The specific steps for generating the data packet transmission configuration are as follows:

[0081] S201: Based on the transmission efficiency prediction information, the real-time network load data is monitored and the network quality is evaluated to obtain the network status evaluation result. Specifically, the process is as follows;

[0082] In sub-step S201, based on the transmission efficiency prediction information, a real-time monitoring system is used to continuously track network load data, including data inflow and outflow rates, peak load conditions, and service quality indicators, and the real-time quality score of the network is calculated, taking into account delay, packet loss rate, and bandwidth utilization. The formula is: Where Q is the network quality score, is the average delay time, is the measured value, P is the packet loss rate, U is the bandwidth utilization rate, w1 is the weight coefficient of the average delay time, w2 is the weight coefficient of the packet loss rate, w3 is the weight coefficient of the bandwidth utilization rate, which reflects the importance of network quality assessment. The network status assessment result is obtained, among which the network quality score and the status assessment result are not exactly the same, but the "network quality score Q" is a quantitative expression of the "network status assessment result".

[0083] S202: Based on the network status evaluation result, the data packet capacity and the available network bandwidth are analyzed, and the capacity segmentation strategy is matched for various data packets to obtain the data segmentation calculation result. Specifically, the process is as follows;

[0084] In sub-step S202, based on the network status evaluation results, the network available bandwidth is monitored in real time, and capacity is divided according to the size of the data packet and the service type, including real-time video and file download, to ensure the optimal configuration of data packet transmission. The formula is: Among them, C is the segmentation capacity of each data packet, B is the available bandwidth, N is the number of data packets transmitted simultaneously, and S is the original size of a single data packet. It ensures that the transmission of the packet will not exceed the network capacity and cause congestion, and obtains the data segmentation calculation result.

[0085] S203: Based on the data segmentation calculation result, combined with the real-time network load and network quality, the sending priority of the segmented data packet is adjusted, and the process of generating the data packet transmission configuration is specifically as follows;

[0086] In sub-step S203, based on the data segmentation calculation results, combined with real-time network load and network quality data, the sending priority of multiple data packets is adjusted, including calculating the priority score of each data packet based on its urgency and impact on the overall network performance, the formula is P i =v1·E i +v2·F i , where P i is the priority score of packet i, E i Indicates the urgency of data packet i, F i represents the impact of data packet i on network performance, v1 and v2 are adjustment coefficients, which are used to balance the weight of urgency and performance impact and generate data packet transmission configuration. i Will be affected by C and Q.

[0087] See also Figure 4 , based on the data packet transmission configuration, combined with the real-time network bandwidth, the data packet transmission time is calculated, the time sensitivity and content type of the data packet are analyzed, and the compression level of the data packet is calculated. The specific steps for obtaining the compression classification calculation result are as follows:

[0088] S301: Based on the data packet transmission configuration, the transmission time of multiple data packets is calculated by combining the current real-time network bandwidth to obtain the data transmission time prediction information. Specifically, the process is as follows;

[0089] In sub-step S301, based on the data packet transmission configuration, the transmission time is estimated according to the size of multiple data packets and the current real-time network bandwidth. For each data packet, the estimated transmission completion time is calculated considering the size after segmentation and the actual available network bandwidth. The formula is: Among them, T i is the transmission time of data packet i, C i is the size of data packet i, B is the current real-time network bandwidth, and the transmission time of multiple data packets is predicted to obtain data transmission time prediction information.

[0090] S302: Based on the data transmission time prediction information, combined with the priority and information type of the data packet, the time sensitivity of multiple data packets is analyzed to obtain the time sensitivity evaluation result. Specifically, the process is as follows;

[0091] In sub-step S302, based on the data transmission time prediction information, the time sensitivity of multiple data packets is evaluated. According to the priority and information type of the data packets, including real-time communication and batch data, the time limit and business requirements of the data packets are considered, and a scoring model is applied to determine the time sensitivity of each packet. The formula is S i =k1·P i +k2·T max,i , where S i is the time sensitivity score of packet i, P i is the priority of data packet i, T max,i is the maximum tolerated transmission time of data packet i, k1 and k2 are weight coefficients, which are used to balance the impact of priority and time limit to obtain the time sensitivity evaluation result.

[0092] S303: Based on the time sensitivity evaluation result, an information entropy algorithm is used to evaluate the data compression requirement in combination with the content type of the data packet and the network condition, and the compression level of multiple data packets is calculated to obtain the compression classification calculation result. Specifically, the process is as follows;

[0093] In sub-step S303, based on the time sensitivity assessment results, combined with the content type of the data packet and the current network conditions, including text, pictures, and videos, a data compression demand assessment is performed, and based on the compressibility of the data packet content and the network bandwidth conditions, the optimal compression level of various types of data packets is calculated to obtain the compression grading calculation results.

[0094] Information entropy algorithm, the formula is:

[0095]

[0096] Calculate the compression level of the data packet, where H(X) is the information entropy, which represents the measure of uncertainty of the information, and P(x i ) is the data packet content type x i The probability of the packet is , W is the weight coefficient, which is used to adjust the calculation accuracy of the information entropy and to consider the impact of the uncertainty of the packet content type on the information entropy. max(P(x i )) is the maximum value of the probability of the data packet content type, which is used to normalize the correction factor, X is the random variable of the data packet content, that is, the set of different possible content types of the data packet, n1 is the number of data packet content types, that is, the number of types of data packet content, i is the index of the data packet content type, from 1 to n1, x i is the i-th data packet content type, and P is the probability.

[0097] The specific execution process of the formula is as follows:

[0098] The number of occurrences of various data packet content types is counted, and the probabilities of various data packet content types are calculated. Based on the probability values, the logarithmic function in the information entropy formula is used to calculate the contribution of various data packet content types to the information entropy. A weight coefficient is introduced to adjust the calculation accuracy of the information entropy. At the same time, based on the probabilities of various data packet content types, a correction factor is calculated. The impact of the uncertainty of the data packet content type on the information entropy is considered, and the results of each part are added together to obtain the information entropy value. When the information entropy of the data is high, a higher level of compression method is adopted to reduce the size of the data and evaluate the compression level required for the data packet.

[0099] See also Figure 5 Based on the compression classification calculation results, the compression algorithms of various data packet types are identified and matched, and the data compression processing is performed based on the priority, size, content type, and data characteristics of the data packet. The specific steps for generating a hierarchical compressed data packet are as follows:

[0100] S401: Based on the compression classification calculation results, the characteristics of various types of data packets are analyzed, and compression strategies and compression algorithms are matched for data packets of various compression levels to obtain a hierarchical compression algorithm list. Specifically, the process is as follows;

[0101] In sub-step S401, based on the compression classification calculation results, the differentiated types of data packets are analyzed, including text, image, and video, and the compression strategy suitable for the characteristics of the data packets is determined. For multiple compression levels, the compression algorithm is matched, including using a fast but low compression algorithm for high-priority real-time video data, and using a high compression algorithm for non-real-time large file data. The formula is A i =f(K i ,T i ), where A i Indicates the selected compression algorithm number, K i is the compression level of data packet i, T i is the type of data packet i, f is a mapping function, which determines the compression algorithm according to the compression level and data type, and obtains a hierarchical compression algorithm list.

[0102] S402: Based on the hierarchical compression algorithm list, analyzing the priority, size, and content type of the data packet, identifying the characteristics of various transmission information, and obtaining the transmission information type analysis result. The specific process is as follows;

[0103] In sub-step S402, based on the hierarchical compression algorithm list, an iterative analysis of the data packets is performed, considering the priority, size and content type of the data packets, and evaluating the transmission characteristics of the differentiated data packets, including using a fast transmission path for small data packets with high priority, and optimizing the transmission protocol for large data packets according to the content type. The formula is F i =g(P i ,Ci ,T i ), where F i is the transmission characteristic identifier of data packet i, P i , C i , T i are the priority, size and type of data packet i respectively, and g is a function used to analyze its transmission requirements according to these characteristics of the data packet and obtain the analysis result of the transmission information type.

[0104] S403: Based on the result of the transmission information type analysis, the data packet is compressed, and the compression time and efficiency are calculated and recorded. The specific process of generating a hierarchical compressed data packet is as follows;

[0105] In sub-step S403, based on the results of the transmission information type analysis, the data packets are actually compressed, the compression time and efficiency of each data packet after compression are calculated, and storage and transmission are optimized. The formula is: Among them, Z i is the compression efficiency of data packet i, V i is the original data packet volume, V′ i is the volume after compression, t i To measure the time required for compression, indicate the efficiency of compression, evaluate the performance of compression algorithms, and generate graded compressed data packets.

[0106] See also Figure 6 , based on hierarchical compression of data packets, error detection and correction are performed during the transmission process, and data integrity verification is performed on the data packets that have completed transmission. The specific steps for generating information transmission verification results are:

[0107] S501: Based on hierarchical compression of data packets, real-time network bandwidth monitoring is performed to record rate and load changes in data streams, identify and analyze the impact of abnormal fluctuations on information transmission integrity, and obtain real-time transmission monitoring data. Specifically, the process is as follows;

[0108] In sub-step S501, based on hierarchical compression of data packets, real-time network bandwidth monitoring is implemented to record the data flow rate and load changes in the network. Through real-time data analysis, abnormal fluctuations are identified and the impact of fluctuations on information transmission integrity is evaluated. The formula is: Among them, R i It represents the bandwidth change rate within the time interval Δt, ΔB is the bandwidth change within the target time period, identifies abnormal fluctuations in the data stream and evaluates the impact on transmission integrity to obtain real-time transmission monitoring data.

[0109] S502: Based on the real-time transmission monitoring data, analyze the data flow anomaly and identify the error points, evaluate and record the occurrence frequency and type of various error points, and generate the error frequency analysis record. The specific process is as follows;

[0110] In sub-step S502, based on real-time transmission monitoring data, analyze the abnormal phenomena in the data stream, identify the error points, count the frequency and type of multiple types of errors, and determine the key fault source. The formula is E i =h(F i ,T e ), where E i represents the score of error point i, F i is the occurrence frequency of error point i, T e is the error type, h is a scoring function used to estimate the severity of the error based on the error frequency and type, and generate an error frequency analysis record.

[0111] S503: Based on the error frequency analysis record, the integrity check is performed on the data that has been transmitted, and the data transmission parameters are adjusted to optimize the continuity and integrity of the data transmission. The specific process of generating the information transmission verification result is as follows;

[0112] In sub-step S503, based on the error frequency analysis record, the integrity check is performed on the data that has been transmitted, the check result is analyzed, and the data transmission parameters are adjusted to optimize the continuity and integrity of the data. The formula is: Among them, V i is the integrity ratio of data packet i, S i is the original size of data packet i, S err,i For the size of the detected erroneous data, the integrity of each data packet is calculated, the transmission parameters are optimized, the efficiency and accuracy of data transmission are improved, and the information transmission verification results are generated.

[0113] See also Figure 7 , an information transmission system is used to execute the information transmission method, an information transmission system comprising:

[0114] The network status assessment module monitors network traffic in real time and calculates usage rate based on real-time network bandwidth data, and obtains network performance analysis results by comparing with historical data transmission records;

[0115] The data packet configuration module analyzes the data packet capacity and bandwidth based on the network performance analysis results and the current network load, adjusts the data packet capacity segmentation and sending priority, and generates segmentation adjustment configuration results;

[0116] The compression demand assessment module calculates the transmission time of the data packet based on the segmentation adjustment configuration result, assesses the time sensitivity and content type of the data packet, analyzes the compression level of the data packet, and obtains the compression demand assessment result;

[0117] The information compression processing module generates the transmission data compression result by analyzing the priority, size, and content type of various data packets and matching the compression algorithm based on the compression demand assessment results;

[0118] The data transmission monitoring module monitors the information transmission process in real time based on the transmission data compression results, identifies and records transmission errors by monitoring abnormal fluctuations in network load and data flow, and generates transmission error detection information;

[0119] The integrity check module is based on the transmission error detection information. It performs integrity check on the data packets that have completed transmission, adjusts the data transmission parameters to optimize the integrity and continuity of the data, and generates information transmission verification results.

[0120] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

Claims

1. An information transmission method, characterized in that: include: Step S1: Based on the real-time network bandwidth data, the network traffic data is analyzed and the bandwidth utilization is calculated, and compared with the historical data transmission records, the changes in network performance parameters are analyzed, and the predicted value of information transmission efficiency is obtained; Step S2: Based on the predicted value of the information transmission efficiency and in combination with the real-time network load, the capacity division adjustment and sending priority configuration of the data packet are performed to generate a data packet transmission configuration; Step S3: Based on the data packet transmission configuration and in combination with the real-time network bandwidth, the data packet transmission time is calculated, and the compression level of the data packet is determined in combination with the time sensitivity and content type of the data packet; Step S4: Based on the compression hierarchical calculation results, identify and match the compression algorithms of multiple data packet types, perform data compression processing in combination with the priority, size, content type, and data characteristics of the data packet, and generate a hierarchical compressed data packet; Step S5: Based on the hierarchically compressed data packets, error detection and correction are performed during the transmission process, and data integrity verification is performed on the data packets that have completed transmission to generate information transmission verification results.

2. The information transmission method according to claim 1, characterized in that: The step S1 comprises: Step S101: Based on the real-time network bandwidth data, obtain the bandwidth utilization rate during the sampling period: Where: U is the bandwidth utilization rate, p i is the number of packets in the ith second, s i is the average packet size in the ith second, B is the network bandwidth, and n is the duration of the sampling period; Step S102: Compare the obtained broadband utilization rate with the broadband utilization rate in the same period in history, and obtain the deviation between the current broadband utilization rate and the historical average level as the utilization rate deviation: Where: Z is the utilization rate deviation, μ is the arithmetic mean of the broadband utilization rate in the same period of history, and σ is the standard deviation of the broadband utilization rate in the same period of history; Step S103: predicting the load trend based on the usage rate deviation, predicting the network delay based on the obtained load trend, and obtaining the predicted value of information transmission efficiency in combination with the current load rate: Where: E is the information transmission efficiency, α is the adjustment coefficient, D is the network delay, and L is the load rate.

3. The information transmission method according to claim 2, characterized in that: The step S2 comprises: Step S201: The real-time monitoring system continuously tracks network load data and generates a real-time quality score of the network: Where: Q is the real-time quality score, is the average network delay, P is the data packet loss rate, w1, w2, w3 are weight coefficients; Step S202: Monitor the available network bandwidth in real time, and obtain the data packet segmentation result according to the size of the data packet and the service type, wherein the segmentation capacity of each data packet is: Where: C is the split capacity of each data packet, N is the number of data packets transmitted simultaneously, and S is the original size of a single data packet; Step S203: based on the real-time network load and the real-time quality score of the network, the influence of each data packet on the network performance is obtained, the sending priority of each data packet is set and a data packet transmission configuration is generated, wherein the sending priority of each data packet is: P i =v1·E i +v2·F i Where: P i is the priority score of packet i, E i is the urgency of data packet i, F i is the impact of data packet i on network performance, v1 and v2 are weights.

4. The information transmission method according to claim 3, characterized in that: The step S3 comprises: Step S301: Based on the data packet transmission configuration, the transmission time is estimated according to the size of multiple data packets and the current real-time network bandwidth to obtain the expected transmission time of each data packet: Where: T i is the estimated transmission time of data packet i, C i is the size of data packet i; Step S302: Based on the estimated transmission time of each data packet, combined with the priority and information type of the data packet, a time sensitivity score of each data packet is obtained: S i =k1·P i +k2·T max,i Where: S i is the time sensitivity score of packet i, T max,i is the maximum tolerated transmission time of data packet i, k1 and k2 are weights; Step S303: Based on the time sensitivity score of each data packet, an information entropy algorithm is used to evaluate the data compression requirements and determine the compression level of the data packet in combination with the content type of the data packet and network conditions.

5. The information transmission method according to claim 4, characterized in that: The mathematical expression of the information entropy is: Where: H(X) is information entropy, P(x i ) is the data packet content type x i The probability, W weight, max(P(x i )) is the maximum value of the probability of the data packet content type, X is a random variable of the data packet content, representing the set of different possible content types of the data packet, and n1 is the number of data packet content types.

6. The information transmission method according to claim 4, characterized in that: In the process of determining the compression level of the data packet, when the information entropy of the data is high, a higher level compression method is adopted to reduce the size of the data.

7. The information transmission method according to claim 4, characterized in that: The step S4 comprises: Step S401: Determine a compression algorithm for the data packet according to the compression level of the data packet and the type of the data packet; Step S402: Based on the hierarchical compression algorithm list, the priority, size, and content type of the data packet are analyzed to identify the characteristics of various transmission information and obtain the transmission information type analysis result; Step S403: Based on the result of the transmission information type analysis, the data packet is compressed, the compression time and efficiency are calculated and recorded, and a hierarchical compressed data packet is generated, wherein the compression efficiency is: Where: Z i is the compression efficiency of data packet i, V i is the volume before compression, V i ′ is the volume after compression, t i is the compression time of data packet i.

8. An information transmission method according to claim 7, characterized in that: The step S5 comprises: Step S501: Based on hierarchical compression of data packets, real-time network bandwidth monitoring is performed to record rate and load changes in data streams, identify and analyze the impact of abnormal fluctuations on information transmission integrity, and obtain real-time transmission monitoring data; Step S502: Based on the real-time transmission monitoring data, analyze the data flow anomalies and identify the error points, evaluate and record the occurrence frequency and type of various error points, and generate an error frequency analysis record; Step S503: Based on the error frequency analysis record, integrity check is performed on the data that has been transmitted, and data transmission parameters are adjusted to optimize the continuity and integrity of data transmission, thereby generating an information transmission verification result.

9. An information transmission device, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 8 is implemented.

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