Communication system, communication setting method, and program

Abstract

The invention relates to the technical field of computer communication, and discloses a communication system, a communication setting method and a program, and the system comprises a network congestion state evaluation module which is used for evaluating a network congestion state; the transmission rate regulation and control module is used for regulating and controlling the data packet transmission rate based on the network congestion state evaluation result so as to reduce delay fluctuation; the data packet processing module is used for performing segment processing on the data packet according to the regulated transmission rate and generating a transmission sequence; the channel error probability evaluation module is used for evaluating the error probability of the current channel; the redundant coding control module is used for regulating and controlling a redundant coding proportion based on a channel error probability evaluation result so as to enhance data integrity; and the data sending module sends the data packet added with the redundant codes to target equipment to complete a communication process, a computer communication method is optimized through an intelligent means, the transmission rate is dynamically adjusted to suppress delay during congestion, the redundant codes are adjusted according to the channel error probability to ensure completeness, and the communication efficiency and reliability are improved.

Description

Technical Field

[0001] The present application relates to the field of computer communication technology, and in particular to a communication system, a communication setting method and a program. Background Art

[0002] Computer communication methods generally refer to the technical specifications and processes for exchanging data over a network. The core of these methods lies in how to effectively transmit and receive data packets to achieve information exchange between devices.

[0003] However, this method faces some challenges in practical applications: for example, when the network is congested, how to dynamically control the transmission rate of data packets becomes a key issue, because an inappropriate transmission rate will aggravate delay fluctuations and affect communication efficiency; at the same time, since there may be a probability of erroneous transmission in the channel, setting the redundant coding ratio has also become a difficult problem that needs to be solved urgently. If the ratio is inappropriate, it may lead to damage to data integrity and cannot ensure the accuracy of the received data.

[0004] These problems all require optimized solutions through intelligent control methods to improve the stability and reliability of communications. Summary of the Invention

[0005] In order to solve the problems raised by the above background technology, the present application provides a communication system, a communication setting method and a program.

[0006] The present application provides a communication system, a communication setting method, and a program, which adopt the following technical solutions: A communication system, comprising: A network congestion status evaluation module, used to evaluate the network congestion status; The transmission rate control module adjusts the data packet transmission rate based on the network congestion status assessment results to reduce delay fluctuations; The data packet processing module segments the data packets according to the regulated transmission rate and generates a transmission sequence; A channel error probability assessment module is used to assess the error probability of the current channel; Redundant coding control module, which adjusts the redundant coding ratio based on the channel error probability assessment result to enhance data integrity; The data sending module sends the data packet with redundant coding to the target device to complete the communication process.

[0007] In a specific embodiment, the transmission rate control module controls the data packet transmission rate based on the network congestion status evaluation result to reduce the delay fluctuation operation, specifically including: determining the network congestion status value C (0≤C≤1); based on the formula Calculate the target transmission rate, where Indicates the maximum allowed transmission rate, C indicates the network congestion state; if Lower than the preset minimum transmission rate , then set equal Otherwise, follow Performs throttling; applies the regulated transmission rate to the fragmented packets.

[0008] In a specific embodiment, the redundant coding control module adjusts the redundant coding ratio based on the channel error probability to enhance data integrity, specifically including obtaining the error probability P (0≤P≤1) of the current channel: Calculate the redundant coding ratio, where A and B are constants set by the system, and P is the channel error probability; if K exceeds the preset maximum ratio limit , then the limit K is equal to ; The calculated K is used to add redundant information to the data packet for protection.

[0009] In a specific embodiment, the transmission rate control module controls the data packet transmission rate based on the network congestion status evaluation result, and further includes: monitoring the current congestion level change through a sliding time window. ,in is the congestion status value at the current moment, The congestion state value at the previous moment defines the congestion relief condition And set is a smaller threshold; If the above conditions are met, then according to the final target rate ,in is the weight coefficient, is the current transmission rate weight, is the historical transmission rate weight, and 1. Adjust the final transmission rate, is the current transmission rate, is the historical reference rate.

[0010] In one embodiment, when C is greater than or equal to the dynamic congestion threshold When the transmission rate control module uses the linear relationship Instead of the original quadratic function expression, As an exponential decay coefficient adjustment factor, it further reduces jitter under sudden high latency. e is the base of a natural constant, which is used as the base of the exponential function. The maximum allowed transmission rate in a bursty high congestion state.

[0011] In a specific embodiment, the network congestion status evaluation module introduces a weighted moving average strategy to evaluate the real-time congestion level. , ω is the time smoothing factor, C(n) is the original congestion value measured at the current moment, and C(n-1) is the congestion weighted value at the previous moment; The transmission rate control module updates In the calculation part, replace the original basic formula with , Used to balance the relationship between convergence speed and accuracy, for The weighted congestion value at the time, Indicates the maximum theoretical transmission rate allowed by the system.

[0012] In a specific embodiment, the process of optimizing the redundant coding ratio includes: calculating the cumulative error rate by continuously counting the historical error events of the latest M transmissions , is the error statistics window size, where is the error probability of the i-th transmission, combined with the latest instantaneous error rate To recalibrate the model parameters , ensuring adaptive learning capabilities, is the error sensitivity factor, Cumulative error rate is the instantaneous error rate, is the historical parameter value.

[0013] In a specific embodiment, the transmission rate control module proposes a dual-level scheduling mode to control key priority traffic, and executes basic rate matching rules in the normal operation phase; when sudden severe congestion detection occurs, a fast response algorithm is used. , where μ is the aggregation coefficient, ν is the load distribution factor, and ζ is the compensation constant, A guaranteed rate set for basic services.

[0014] A communication setting method, comprising: Step 1: Based on the network congestion status assessment results, the packet transmission rate is adjusted to reduce delay fluctuations; Step 2: Segment the data packets according to the adjusted transmission rate and generate a transmission sequence; Step 3: Adjust the redundant coding ratio based on the channel error probability assessment result to enhance data integrity; Step 4: Send the data packet with redundant coding to the target device to complete the communication process.

[0015] A program includes computer-readable codes, and when the computer-readable codes are run on a computer device, the computer device is caused to execute all the steps of a computer communication system and method.

[0016] In summary, this application includes at least one of the following beneficial technical effects: The disclosed embodiments provide an optimization method for computer communication through intelligent control means. When network congestion occurs, the degree of network congestion can be accurately assessed based on real-time monitoring of core indicators such as packet loss rate, round-trip delay, and bandwidth utilization, and the data packet transmission rate can be dynamically adjusted: when the network is unobstructed, the transmission rate can be fully increased to utilize bandwidth resources; when congestion intensifies, the rate can be promptly reduced to avoid queue backlogs, effectively suppressing the problem of increased delay fluctuations caused by improper transmission rate, and significantly improving communication efficiency.

[0017] At the same time, the system dynamically adjusts the redundant coding ratio by analyzing the channel error probability: reducing redundancy to improve transmission efficiency when the channel quality is good, and increasing redundancy to enhance error correction capabilities when interference increases. For example, by adaptively optimizing coding parameters based on statistics of historical error events and real-time error rates, it can not only significantly reduce the data transmission bit error rate, but also avoid excessive consumption of bandwidth due to redundancy, significantly improve the stability and reliability of communications, and ensure the efficiency and accuracy of information exchange between devices. Especially in highly dynamic and high-interference scenarios such as satellite communications and industrial Internet of Things, it can effectively guarantee data integrity and solve the problem of difficult balance between efficiency and reliability in traditional communication solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart for a computer communication system; Figure 2 Flowchart of a method for setting up computer communications. DETAILED DESCRIPTION

[0019] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0020] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0021] The present application discloses a communication system, a communication setting method, and a program. Referring to the accompanying drawings, a communication system is described, including: The network congestion status assessment module assesses the congestion status by monitoring key network indicators in real time. In the data collection phase, it uses the network interface to collect data such as packet loss rate, round-trip time (RTT), and bandwidth utilization in real time at 50 millisecond intervals. It obtains network path delay and loss information by sending probe packets, and obtains bandwidth utilization information from network device statistics. Next, the collected data is input into the preset method to calculate the network congestion status value C. The closer the C value is to 0, the smoother the network is; the closer it is to 1, the more severe the congestion is. For example, a local area network can calculate the packet loss rate and bandwidth utilization. , represents mild congestion. Finally, the C value is transmitted to the transmission rate control module in real time to provide a basis for rate adjustment; The transmission rate control module dynamically controls the data packet transmission rate based on the network congestion status evaluation results. After receiving the C value, according to the formula Calculate the target transmission rate, where The maximum transmission rate allowed by the system is set. Lower than the preset minimum transmission rate , then set it to ,For example , hour, ,like And the result is greater than , then press Regulation.

[0022] In addition, the current congestion level change is monitored through a sliding time window ,when ( is a smaller threshold), according to the formula Adjust the final transfer rate, is the weight coefficient, is the current transmission rate, The rate after the last adjustment, such as when congestion is relieved, , , , Finally, the determined transmission rate is sent to the data packet processing module for segment processing; The data packet processing module segments the data packets and generates a transmission sequence according to the rate provided by the transmission rate control module. According to the regulated rate, the data to be transmitted is decomposed into data packets of fixed size. In the video streaming transmission scenario, the original video data will be cut into segments that meet the network transmission capacity, encapsulated into data packets and metadata is added. Then a unique sequence flag is assigned to each data packet so that the target node can correctly parse and restore the original data to ensure orderly and complete transmission. Finally, the processed data packet is transmitted to the redundant coding control module. The channel error probability assessment module assesses the error probability of the current channel by analyzing the characteristics of the communication channel. It collects historical bit error rate, signal strength, signal-to-noise ratio and other data of the channel, such as monitoring the bit error of the received signal, and calculating the bit error rate by counting the number of bit errors within a certain period of time. Then, the preset algorithm is used to calculate the error probability P(0 A low value indicates good channel quality, and vice versa. Finally, the P value is sent to the redundant coding control module to adjust the redundant coding ratio; The channel error probability assessment module assesses the error probability of the current header by analyzing the characteristics of the communication channel. It collects data such as the channel's historical bit error rate, signal strength, and signal-to-noise ratio. For example, it monitors the bit error of the received signal and calculates the bit error rate by counting the number of bit errors within a certain period of time. It then uses a preset algorithm to calculate the error probability P(0) of the current channel based on the collected data. A low value indicates good channel quality, and vice versa. Finally, the P value is sent to the redundant coding control module to adjust the redundant coding ratio; The redundant coding control module adjusts the redundant coding ratio to enhance data integrity based on the channel error probability assessment result. After receiving the P value, according to the formula Calculate the redundant coding ratio, A and B are system set constants, if K exceeds the preset maximum ratio limit , then the limit K is ,like , , hour, ,like Finally, K is set to 0.045. Then, according to the K value, redundant information is added to the data packet using repetition code, Hamming code or forward correction technology to improve the data anti-interference ability. Finally, the data packet with redundant coding is sent to the data sending module for transmission; The data transmission module completes the communication process by sending the redundantly encoded data packet to the target device. The received data packet is first encapsulated according to the TCP / IP protocol stack specifications and then encapsulated into an HTTP response message within the socket connection. The encapsulated data packet is then sent via the network link to the designated address of the target device, ensuring accurate and complete data delivery. These modules work together to optimize the data packet transmission rate and redundant encoding ratio by dynamically assessing network congestion and channel error probability, effectively improving the data transmission performance and data integrity of the computer communication system.

[0023] Next, the present invention is described to further regulate the data packet transmission rate based on the network congestion status evaluation result to reduce the delay fluctuation. First, the various steps are listed: the first step is to determine the network congestion status value C, which ranges from 0≤C≤1; the second step is to calculate the network congestion status value C according to the formula Calculate the target transmission rate, where Indicates the maximum allowed transmission rate, C indicates the network congestion status value; the third step is to judge Is the transmission rate lower than the preset minimum rate? If so, then Set as Otherwise, according to the calculated The final step is to apply the regulated transmission rate to the segmented packets.

[0024] First, when determining the network congestion status value C, its purpose is to quantify the current degree of network congestion. The closer the value is to 0, the smoother the network is; the closer the value is to 1, the more congested the network is. This setting can provide a basis for subsequent calculations to dynamically adjust the data transmission strategy. Second, in the formula for calculating the target transmission rate, is the set maximum transmission rate, C is the previously determined network congestion state value, and the optimal value depends on the specific application scenario and network load. Since the square operation can amplify the impact of lower C values ​​and reduce the rate growth corresponding to higher C values, more precise rate control can be achieved. Formula The significance of is to significantly increase the transmission rate when the network is relatively smooth (low C value), and limit the speed when the network becomes more congested (high C value), reducing the impact on delay fluctuations. In the third step, if the calculated The transmission rate is lower than the minimum specified , then set equal This measure can avoid transmission failures due to too low a rate to ensure basic transmission needs. Fourth, the regulated rate must ultimately be applied to data segmentation, because segmentation is often used to send large amounts of information in modern communications.

[0025] For example, in one embodiment, consider a computer communication scenario, assuming It is set to 100Mbps, C is 0.3 according to the test results, the minimum rate is 20Mbps. According to the formula , we can calculate the target transmission rate to be 109Mbps. Based on this, we can determine that 109Mbps is greater than , so the rate is adjusted and applied to the upload of data blocks that have been segmented. If the speed becomes 15Mbps, it will be set to 20Mbps to ensure that basic functions are not affected.

[0026] Next, we will further describe how the present invention regulates the redundant coding ratio based on the channel error probability assessment results to enhance data integrity. This process consists of four steps: obtaining the current channel error probability; calculating the redundant coding ratio according to a preset formula; limiting the ratio if it exceeds an upper limit; and using the ratio to add redundant information to protect the data.

[0027] The first step is to obtain the channel error probability P from the current communication environment. This is a real number between 0 and 1. P reflects the probability of an error in the current communication link. A lower P value indicates higher channel quality, while a higher P value indicates poorer channel quality.

[0028] The second step is based on the formula The redundant coding ratio K is calculated using the formula (A) where A and B are predefined constants. A determines the maximum allowable redundancy, while B represents the acceleration or sensitivity of the redundant coding as the channel error rate increases. For example, if A is 0.5 and B is 2, then when P equals 1, the theoretical maximum K should be close to the range of A, meaning the redundancy does not exceed 50%. This formula is designed because a higher channel error probability requires a higher proportion of redundant bits for error correction and data protection.

[0029] Then, in the third step, if the calculated ratio K is greater than the maximum ratio limit specified by the system , the redundant coding setting is fixed at this upper limit, ensuring that even under extremely adverse channel conditions, the redundant part will not significantly affect the payload. This setting ensures reasonable resource allocation and optimizes network traffic management while maintaining the necessary level of data integrity.

[0030] The final step is to use the exact ratio K obtained above to add a corresponding amount of redundant information to the data packets to be transmitted. This implementation may include repetition codes, Hamming codes, or more advanced forward error correction techniques. In one embodiment, assume that data needs to be transmitted from a server to a client in a communication environment with a channel error probability of P = 0.3. Using pre-designed parameters A = 0.5 and B = 2, K is calculated to be approximately 0.045 (i.e., 4.5% of the total data is redundant). However, considering additional constraints in real-world scenarios, such as bandwidth limitations, an upper limit of 0.1 is set. The resulting effective redundancy added to secure this batch of data is 0.045, effectively improving data accuracy at the receiving end without significantly impacting system performance.

[0031] Next, the present invention further describes the regulation of data packet transmission rate based on the network congestion status evaluation result. The method includes the following steps: first, monitoring the change of the current congestion level through a sliding time window ;Secondly define the congestion relief conditions| |<ε and set a smaller threshold ε; then when the above conditions are met, use the formula Adjust the final transmission rate; finally, apply the adjusted transmission rate to all subsequent packets.

[0032] The first step is to collect the congestion level information in the network in real time through a sliding time window and calculate the difference in congestion levels between the previous and next two time points. During this process, and The sliding time window represents the congestion assessment value at the current and previous time points, respectively. The length of the sliding time window is generally set to range from tens to hundreds of milliseconds, and the optimal length depends on the specific communication scenario. The second step is to evaluate the change in the congestion level to confirm whether the network is in a stable congestion-relief state. The condition is met when its absolute value is less than a minimum threshold ε. Typically, ε should be set to a small positive number that is consistent with congestion fluctuations in normal transmission. The specific value range of this threshold can be determined in an experimental environment based on historical statistical data.

[0033] If the above congestion relief state is established, then enter the third step, that is, according to the formula Dynamically adjust the transmission rate. In the formula, α and β are weighted coefficients corresponding to the current transmission rate. and the value after the last rate adjustment To ensure a stable and smooth update rate, the recommended initial parameter settings are α=0.7, β=0.3. Of course, these values ​​may be slightly modified according to the network load type. The design principle of this linear weighted formula is to comprehensively consider the short-term dynamic rate requirements. and longer time periods for steady rate reference baselines , making transmission decisions both flexible and robust.

[0034] For example, in one embodiment, it is assumed that a computer device is downloading a large number of image files. Initially, high network congestion is caused by high concurrent connections. After several data transmission adjustments, if the congestion level is gradually reduced in a recent period of time and the change is always maintained within a small value range, the mechanism of resetting the sending end rate is triggered. At this time, it is assumed that it has been previously determined that is 6Mbps, while earlier records Displayed as 5Mbps, the new target transmission speed obtained after calculation based on the aforementioned weighted parameters This approach, but with a slight increase, ensures that premature saturation is avoided while fully utilizing redundant bandwidth to improve user throughput. Ultimately, all future data packets are transmitted at the newly specified rate.

[0035] Next, the calculation logic of the formula of the present invention is described: When C is greater than or equal to the dynamic congestion threshold γ, the linear relationship is used. Instead of the original quadratic function expression, λ is used as a regulating factor to further reduce jitter under sudden high latency. The specific steps include the following: First, determine whether the congestion level value C meets the condition C ≥ γ; Second, if the condition is met, use the linear formula Replace the original quadratic function expression; third, adjust the parameter λ in the new formula to optimize performance.

[0036] In the first step, the dynamic congestion threshold γ is a variable that changes with the system state. Its range is typically set based on the network environment, for example, 0 ≤ γ ≤ 1. The optimal value can be selected based on empirical data to ensure it reflects the actual congestion state. The congestion level C measures the packet traffic load in the current communication. When C is greater than or equal to γ, the replacement logic is activated.

[0037] In the second step, Used to replace the original quadratic function expression. represents the maximum bandwidth utilization, the highest achievable performance point for the system; C is the aforementioned measure of the current congestion level; and λ is an adjustment factor, typically ranging from -1 < λ < 1. The optimal value can be determined through experimental tuning. This formula indicates that in severe congestion conditions, an exponential-based bandwidth adjustment strategy is employed to accommodate sudden high-latency scenarios. By adjusting λ, the response curve can be flexibly controlled at different congestion levels.

[0038] In one embodiment, for example, assuming that the computer network is initially set to γ ​​= 0.8, =90MBps, λ is set to 0.6, and it is assumed that the detection shows that the network C reaches 0.9 at a certain stage. At this time, according to the rules, the linear substitution mode is entered and the above exponential formula is applied to the traffic distribution problem, which can achieve a smooth transition and significantly improve latency and packet loss.

[0039] Finally, the core reason for configuring this formula is to balance the effects of handling light and heavy congestion. Compared to direct hard switching mechanisms, which are prone to side effects such as increased oscillation, this new algorithm, designed here, leverages mathematically continuous and easily predictable features to effectively curb congestion while maintaining a high level of user-perceived quality.

[0040] Next, the weighted moving average strategy of the present invention is introduced to evaluate the real-time congestion level. , after the formula is derived, update the Rtarget calculation part in the step and replace the original basic formula with ; ,θ is used to balance the relationship between convergence speed and accuracy. The specific process includes the following steps.

[0041] First, the parameter ω is introduced to control the proportional relationship between the current congestion level C(n) and the weighted coefficient of the previous moment, where 0<ω≤1. When ω approaches 1, it is more sensitive to the latest measurement value and can reflect the network change status more quickly. Secondly, the calculation method of the real-time congestion level is defined. The function of this formula is to dynamically adjust the actual monitoring data C(n) of the current network and the historical information of the previous sampling moment. Through such a linear combination, it is ensured that the real situation can be smoothly reflected even in the transient disturbance of the network. For example, if a large packet loss rate or delay is measured on a certain communication link several times in a row, these values ​​will be reasonably converted and integrated into the comprehensive evaluation to form an accurate .

[0042] Then the transmission rate calculation logic was updated, and the traditional basic Replace with a better model: The important variables involved here are the maximum theoretical value of the target maximum rate limit level, And determine the trade-off factor θ. For the range of θ, a positive number not less than 0.5 is usually selected to maintain a good balance between stability and accuracy. For example, in one embodiment, assuming that the maximum allowed transmission bandwidth capacity in a certain network segment is determined Fixed at 10 kilobytes per second, based on the latest estimated congestion weight When a certain level is reached, the corresponding output rate is reconfigured to achieve the optimal allocation standard, while also balancing stability and responsiveness. Therefore, setting appropriate parameters is crucial to ensure efficient and robust system operation. Specifically, when θ = 0.8, this indicates that achieving accuracy is more important than simply pursuing extremely fast change tracking capabilities.

[0043] Next, the data integrity improvement process of the present invention is described: by continuously counting M historical error events to form a cumulative error rate , combined with the latest instant error rate To recalibrate the model parameters ), ensuring adaptive learning capabilities. This process is divided into several steps.

[0044] First, historical error events are counted for M consecutive times. Specifically, in this step, a fixed positive integer M is defined to represent the size of the statistical window. The range of M is generally between 5 and 10, and its optimal value depends on the complexity of the specific communication environment and the system's response speed requirements. By extracting relevant statistical features from M consecutive communication error records, the historical error probability of each time is recorded as , i represents the sequence number of the i-th error in the sequence.

[0045] The second is the calculation of the cumulative error rate. Each historical error rate Pi obtained by the above statistics is accumulated by summing up, and the result is defined as the cumulative error rate , where P is a weighting factor used to normalize the impact of different errors. For example, in a specific scenario, setting P = 0.1 evens out the importance of all historical data, ensuring that the calculation results are not excessively affected by a single abnormal event.

[0046] Then get the instant error rate In each new communication process, a corresponding set of latest feedback information will be generated, including the current transmission failure ratio or frequency, which is the instantaneous error rate. In one embodiment, assuming that there are three data packet losses in the network transmission within the last second and a total of 100 packets are sent, then the result is =3% as the data representation of immediate feedback.

[0047] Then use the formula to adjust the model parameters. According to the above data, substitute it into the update formula This involves modifying the original initial state A value (e.g., preset default weights) based on actual dynamic conditions. The hyperparameter γ here needs to be determined experimentally. By default, it can be set to around 0.8-1.2 to ensure a reasonable balance between new and old influences. This setting aims to maintain existing empirical values ​​while quickly adapting to new and unusual changes.

[0048] The ultimate goal is to improve the overall system's intelligent self-adjustment capabilities, enhance fault resilience, and optimize data quality and stability over the long term. Specifically, if the interconnected communications between a data center's server clusters repeatedly experience congestion and frame loss, this mechanism can gradually learn to find optimal paths, reducing the probability of recurring failures until the system achieves near-zero-loss operation.

[0049] Next, the dual-level scheduling mode proposed by the present invention controls the key priority traffic, executes the basic rate matching rule in the normal operation phase; when a sudden severe congestion detection occurs, a fast response algorithm is used. , using a short-term, high-frequency sampling mechanism to respond to abnormal conditions and quickly stabilize the link performance trend curve. Specifically, it includes the following steps: First, a two-tier scheduling model is proposed. Second, basic rate matching rules are implemented during normal operation. Third, a fast response algorithm is used when sudden severe congestion detection occurs. Fourth, a short-term, high-frequency sampling mechanism is used to respond to abnormal conditions. Finally, the link performance trend curve is quickly stabilized by adjusting parameters.

[0050] In the first step, a two-tiered scheduling model was proposed to handle critical priority traffic in a layered manner, enabling the system to better optimize bandwidth allocation and improve network performance. The upper-tier scheduling model is responsible for overall policy planning, while the lower-tier model is responsible for detailed operations and resource allocation. This ensures that priority traffic is always properly managed. For example, real-time video streams can be placed in a high-priority scheduling queue to ensure smooth transmission.

[0051] In the second step, the basic rate matching rules implemented during normal operation are designed to ensure relative stability between packets and avoid unnecessary congestion or idleness. The basic rate should be set within a standard range of values ​​that neither causes excessive utilization nor significantly limits efficiency. In one embodiment, for standard network file exchange, the default rate is set to 80% of the actual available capacity utilization.

[0052] In the third step, when a sudden severe congestion is detected, the application of the fast response algorithm is triggered Where μ represents an adjustment coefficient, which generally ranges from 0 to 2, with an optimal value of 1.5; ν is a weight normalization factor greater than zero; is the sum of all baseline rates; ζ is used to compensate for the additional error term and is typically a small positive value, recommended to be approximately half the initial rate basis. This formula comprehensively measures the distance between the current load and the ideal target and determines the magnitude of the required acceleration.

[0053] Entering the fourth step, short-term, high-frequency sampling allows for more accurate capture of rapidly changing conditions. High frequency shortens the time interval, increasing feedback sensitivity. For example, during a large-scale DDoS attack, precise measurements of incoming traffic can be taken every few microseconds.

[0054] The final step involves adjusting relevant parameters to quickly restore the entire system to a stable, balanced state of operation. By carefully adjusting the aforementioned values, the link is ultimately brought back to the desired functional state. Specifically, based on previous experience, a default parameter set table corresponding to different conditions may be established for immediate use, further enhancing the practical feasibility and flexibility of the solution.

[0055] A computer communication setting method of the present invention includes: effectively improving network communication performance and improving data transmission quality and efficiency through a series of dynamic evaluation and optimization technologies. The specific steps are as follows: First, based on the current network congestion status, the data packet transmission rate is regulated to reduce the delay fluctuation caused by the network load. Secondly, the data packet is segmented according to the regulated transmission rate, and a transmission sequence is generated in a certain order to ensure that the data transmission process is orderly and efficient. Next, the error probability of the communication channel is comprehensively analyzed, and the proportion of redundant coding is adjusted according to the results to enhance the data's resistance to damage in a high-interference environment, thereby further improving data integrity. Finally, after the above optimization, the data packet with redundant coding is sent to the target device to complete the overall communication process.

[0056] This method provides solutions to two key technical problems in existing communication schemes: how to regulate the packet transmission rate according to the network congestion status to reduce delay fluctuations, and how to adjust the redundant coding ratio according to the channel error probability to solve the problem of damaged data integrity.

[0057] To address the first issue (latency fluctuations during network congestion), this invention continuously assesses the real-time network congestion status and employs an adaptive adjustment mechanism to rationally control the packet transmission rate. This avoids sending excessively high traffic rates when the network is highly loaded, thereby preventing queue backlogs and excessive latency. Conversely, during periods of low load, the transmission rate is increased to fully utilize network bandwidth. This approach significantly reduces latency jitter caused by congestion, ensuring more stable and smooth data transmission.

[0058] To address the second issue (protecting data integrity), this invention introduces a redundant coding strategy based on channel error probability assessment. The communication system dynamically calculates the required redundancy based on the error probability characteristics of the specific application scenario and increases or decreases the coding ratio accordingly. This approach achieves both resource conservation and reliable recovery of damaged information, ensuring data consistency and accuracy throughout the entire communication chain.

[0059] A communication setting method, comprising: Step 1: Based on the network congestion status assessment results, the packet transmission rate is adjusted to reduce delay fluctuations; Step 2: Segment the data packets according to the adjusted transmission rate and generate a transmission sequence; Step 3: Adjust the redundant coding ratio based on the channel error probability assessment result to enhance data integrity; Step 4: Send the data packet with redundant coding to the target device to complete the communication process.

[0060] A program includes computer-readable codes, and when the computer-readable codes are run on a computer device, the computer device is caused to execute all the steps of a computer communication system and method.

[0061] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A communication system, characterized in that: include: A network congestion status evaluation module, used to evaluate the network congestion status; The transmission rate control module adjusts the data packet transmission rate based on the network congestion status assessment results to reduce delay fluctuations; The data packet processing module segments the data packets according to the regulated transmission rate and generates a transmission sequence; A channel error probability assessment module is used to assess the error probability of the current channel; Redundant coding control module, which adjusts the redundant coding ratio based on the channel error probability assessment result to enhance data integrity; The data sending module sends the data packet with redundant coding to the target device to complete the communication process.

2. The communication system according to claim 1, wherein: The transmission rate control module controls the data packet transmission rate based on the network congestion status evaluation result to reduce the delay fluctuation operation, specifically including: Determine the network congestion state value C, 0≤C≤1; Based on the formula Calculate the target transmission rate, where Indicates the maximum allowed transmission rate, and C indicates the network congestion status; like Lower than the preset minimum transmission rate , then set equal Otherwise, follow Performs throttling; applies the regulated transmission rate to the fragmented packets.

3. The communication system according to claim 2, wherein: The redundant coding control module regulates the redundant coding ratio based on the channel error probability to enhance data integrity, specifically including obtaining the error probability P of the current channel, 0≤P≤1: According to the formula Calculate the redundant coding ratio, where A and B are constants set by the system, and P is the channel error probability; If K exceeds the preset maximum ratio limit , then the limit K is equal to ; The calculated K is used to add redundant information to the data packet for protection.

4. The communication system according to claim 3, wherein: The operation of the transmission rate control module regulating the data packet transmission rate based on the network congestion status evaluation result further includes: Monitor the current congestion level changes through a sliding time window ,in is the congestion status value at the current moment, The congestion state value at the previous moment defines the congestion relief condition And set is a smaller threshold; If the above conditions are met, then according to the final target rate ,in is the weight coefficient, is the current transmission rate weight, is the historical transmission rate weight, and 1. Adjust the final transmission rate, is the current transmission rate, is the historical reference rate.

5. The communication system according to claim 4, wherein: When C is greater than or equal to the dynamic congestion threshold When the transmission rate control module uses the linear relationship Instead of the original quadratic function expression, As an exponential decay coefficient adjustment factor, it further reduces jitter under sudden high latency. e is the base of a natural constant, which is used as the base of the exponential function. The maximum allowed transmission rate in a bursty high congestion state.

6. The communication system according to claim 5, characterized in that The network congestion status evaluation module introduces a weighted moving average strategy to evaluate the real-time congestion level. , ω is the time smoothing factor, C(n) is the original congestion value measured at the current moment, and C(n-1) is the congestion weighted value at the previous moment; The transmission rate control module updates In the calculation part, replace the original basic formula with , Used to balance the relationship between convergence speed and accuracy, for The weighted congestion value at the time, Indicates the maximum theoretical transmission rate allowed by the system.

7. The communication system according to claim 6, wherein: The process of optimizing the redundant coding ratio includes: calculating the cumulative error rate by continuously counting the historical error events of the latest M transmissions , is the error statistics window size, where is the error probability of the i-th transmission, combined with the latest instantaneous error rate To recalibrate the model parameters , ensuring adaptive learning capabilities, is the error sensitivity factor, Cumulative error rate is the instantaneous error rate, is the historical parameter value.

8. The communication system according to claim 7, wherein: The transmission rate control module proposes a dual-level scheduling mode to control key priority traffic, and implements basic rate matching rules in the normal operation phase; when sudden severe congestion detection occurs, a fast response algorithm is used. , where μ is the aggregation coefficient, ν is the load distribution factor, and ζ is the compensation constant, A guaranteed rate set for basic services.

9. A communication setting method, characterized in that: include: Step 1: Based on the network congestion status assessment results, the packet transmission rate is adjusted to reduce delay fluctuations; Step 2: Segment the data packets according to the adjusted transmission rate and generate a transmission sequence; Step 3: Adjust the redundant coding ratio based on the channel error probability assessment result to enhance data integrity; Step 4: Send the data packet with redundant coding to the target device to complete the communication process.

10. A program, characterized in that The program includes computer-readable codes, and when the computer-readable codes are run on a computer device, the computer device is caused to execute all the steps of the computer communication system and method according to any one of claims 1 to 9.

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