Data delay processing method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence data delay processing. The invention relates to a data delay processing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a source data packet, carrying out the analysis and prediction of the source data packet through a trained artificial intelligence prediction model, and obtaining the prediction delay time of the source data packet, and adjusting the processing priority and the transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet, and transmitting the processed data packet to a target address through a data packet transmission protocol. The method has the effect of improving the data transmission efficiency and stability.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence processing data delay, and in particular to a data delay processing method, device, computer equipment and storage medium. Background Art

[0002] With the rapid development of network technology, the efficiency of data processing and transmission has become particularly important. In the process of network transmission, the delay of data packets is a common problem, which may lead to unstable and inefficient data transmission. In order to solve this problem, the existing technology usually adopts fixed priority and transmission path for data transmission. This method cannot be dynamically adjusted according to actual conditions, so the transmission efficiency is not high. In order to overcome this defect, a method is needed to predict the characteristics of data packets through artificial intelligence algorithms and dynamically adjust the transmission priority and transmission path. Summary of the invention

[0003] In order to improve data transmission efficiency and stability, the present application provides a data delay processing method, device, computer equipment and storage medium.

[0004] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A data delay processing method, the data delay processing method comprising: Get the source data packet; Analyze and predict the source data packet through the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet; Adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet; The processed data packet is transmitted to a target address through a data packet transmission protocol.

[0005] By adopting the above technical solution, the processing priority and transmission path of the data packet can be dynamically adjusted by predicting the delay time. High-priority data packets can give priority to low-latency paths and reliable transmission protocols (such as TCP), thereby ensuring the real-time and reliability of data transmission, while low-priority data packets can choose more efficient transmission paths and protocols (such as UDP) to improve the overall data transmission efficiency. Adjusting the transmission strategy according to the real-time network status and data packet characteristics can effectively utilize network bandwidth and transmission resources, avoid network congestion and resource waste, and thus improve the utilization efficiency of system resources.

[0006] In a preferred example, the present application may be further configured as follows: the obtaining of the source data packet further includes: Acquire multiple historical delay times and historical data packet processing priorities of data packets, and train a preset artificial intelligence prediction model according to the multiple historical delay times and historical data packet processing priorities of data packets to obtain the trained artificial intelligence prediction model.

[0007] By adopting the above technical solution and using a trained artificial intelligence prediction model, the delay time and processing priority of historical data packets can be used to accurately predict the delay of future data packets.

[0008] In a preferred example, the present application can be further configured as follows: the source data packet is analyzed and predicted by the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet, specifically including: The source data packet is parsed to obtain a feature vector of the source data packet, wherein the feature vector of the source data packet includes the size, type, source, destination IP address, protocol type and timestamp of the data packet; Standardizing the feature vector of the source data packet to obtain a processed feature vector of the source data packet; The processed source data packet feature vector is input into the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet.

[0009] By adopting the above technical solution, by parsing the source data packet, feature vectors such as the size, type, source, destination IP address, protocol type and timestamp of the data packet are extracted. These features can fully describe the properties of the data packet and provide sufficient input information for the subsequent prediction model. Standardizing the extracted feature vectors can eliminate the dimensional differences between different features, ensure that the model can balance the influence of each feature during the training and prediction process, and improve the stability of the model and the prediction accuracy. Using the trained artificial intelligence prediction model and inputting the standardized feature vector, the delay time of each source data packet can be accurately predicted. This prediction capability enables the system to dynamically adjust the priority and path before data transmission to maximize transmission efficiency and stability. The ability to predict delay time enables the system to have real-time optimization and response capabilities. The system can dynamically adjust the processing priority and transmission path of the data packet based on the prediction results to cope with the changing network conditions and data requirements, and improve the real-time and stability of data transmission.

[0010] In a preferred example, the present application can be further configured as follows: the processed source data packet feature vector is input into the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet, specifically including: The trained artificial intelligence prediction model predicts the processed source data packet feature vector using the formula: , where ω is the weight learned by the artificial intelligence prediction model, x is the input feature, b is the bias term, and y is the prediction result, i.e., the predicted delay time of the source data packet.

[0011] By adopting the above technical solutions, the trained artificial intelligence prediction model can use the processed source data packet feature vector to make accurate predictions, that is, predict the delay time of the source data packet. This accurate prediction capability enables the system to dynamically adjust the processing priority and transmission path of the data packet according to the actual situation to maximize the transmission efficiency.

[0012] In a preferred example, the present application may be further configured as follows: adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet specifically includes: Adjusting the processing priority of the source data packet according to the predicted delay time of the source data packet, and selecting the transmission path according to the predicted delay time of the source data packet; The adjusted priority and the transmission path are updated into the source data packet to obtain the processed data packet.

[0013] By adopting the above technical solution, the processing priority and transmission path of the data packet are adjusted according to the predicted delay time. High-priority data packets can choose a transmission path with low delay and higher reliability to ensure the timely transmission of important data; while low-priority data packets can choose a more economical and efficient transmission method to avoid resource waste. By dynamically adjusting the processing priority and transmission path of the data packet, the system can effectively reduce the delay of the data packet. Accurately controlling the processing priority of the data packet according to the predicted delay time can avoid congestion caused by data packets competing for resources in the network. At the same time, optimizing the transmission path can also effectively reduce the loss and retransmission of data packets and improve the stability and reliability of data transmission. By dynamically adjusting the priority and transmission path, the system can intelligently manage the use of network resources. Efficiently utilize network bandwidth and transmission capacity to avoid idle waste of resources, thereby improving the overall efficiency and economy of the system. Predicting the delay time and adjusting the transmission strategy accordingly enables the system to have the ability to respond in real time and adapt to different network conditions. The system can make adjustments at any time according to the changing network conditions and data flow requirements to maintain the stability and efficiency of data transmission.

[0014] In a preferred example, the present application may be further configured as follows: the processing priority of the source data packet is adjusted according to the predicted delay time of the source data packet, and a transmission path is selected according to the predicted delay time of the source data packet, and further includes: Acquire priority adjustment rule information, and obtain a predicted delay time priority threshold according to the priority adjustment rule information, wherein the predicted delay time priority threshold includes a high priority, a medium priority, and a low priority; Adjusting the processing priority of the source data packet according to the predicted delay time priority threshold; Real-time network status information is acquired, and the transmission path is selected according to the real-time network status information and the predicted delay time of the source data packet.

[0015] By adopting the above technical solution, the system can dynamically adjust the processing priority of data packets according to the predicted delay time priority threshold (such as high priority, medium priority, and low priority). This refined priority adjustment can ensure that important data packets are transmitted first. Combining real-time network status information and predicted delay time, the system can intelligently select the optimal transmission path. High-priority data packets can choose stable and low-latency paths to ensure real-time and reliability, while low-priority data packets can choose more economical transmission paths to optimize resource utilization. Predicting delay time and adjusting processing priority according to priority rules enables the system to respond quickly to network needs and changing conditions. This timely responsiveness helps improve data transmission efficiency and overall service quality.

[0016] In a preferred example, the present application may be further configured as follows: the method of transmitting the processed data packet to the target address through the data packet transmission protocol further includes: The data packet transmission protocol is selected according to the priority and transmission path information of the data packet, and the high-priority data packets are transmitted using TCP, and the low-priority data packets are transmitted using UDP.

[0017] By adopting the above technical solution, the use of network resources can be optimized by selecting the appropriate transmission protocol according to the priority of the data packet. The reliable TCP protocol is used for high-priority data packets to reduce retransmission and delay; the efficient UDP protocol is used for low-priority data packets to reduce network overhead and increase transmission speed. The stability of the system can be improved by distinguishing the priority of data packets and selecting the appropriate transmission protocol. TCP is used for high-priority data packets to ensure the reliable transmission of key data; UDP is used for low-priority data packets to improve the system's adaptability to network fluctuations and reduce the impact on the overall transmission stability.

[0018] The second object of the invention is achieved by the following technical solutions: A data delay processing device, the data delay processing device comprising: A data packet acquisition module, used to acquire source data packets; A model application module, used to analyze and predict the source data packet through a trained artificial intelligence prediction model to obtain a predicted delay time of the source data packet; A data packet processing module, used for adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet; The data packet transmission module is used to transmit the processed data packet to a target address through a data packet transmission protocol.

[0019] By adopting the above technical solution, the processing priority and transmission path of the data packet can be dynamically adjusted by predicting the delay time. High-priority data packets can give priority to low-latency paths and reliable transmission protocols (such as TCP), thereby ensuring the real-time and reliability of data transmission, while low-priority data packets can choose more efficient transmission paths and protocols (such as UDP) to improve the overall data transmission efficiency. Adjusting the transmission strategy according to the real-time network status and data packet characteristics can effectively utilize network bandwidth and transmission resources, avoid network congestion and resource waste, and thus improve the utilization efficiency of system resources.

[0020] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned data delay processing method when executing the computer program.

[0021] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned data delay processing method are implemented.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. By predicting the delay time, the processing priority and transmission path of the data packet can be adjusted dynamically. High-priority data packets can give priority to low-latency paths and reliable transmission protocols (such as TCP) to ensure the real-time and reliability of data transmission, while low-priority data packets can choose more efficient transmission paths and protocols (such as UDP) to improve the overall data transmission efficiency. Adjusting the transmission strategy according to the real-time network status and data packet characteristics can effectively utilize network bandwidth and transmission resources, avoid network congestion and resource waste, and thus improve the utilization efficiency of system resources; 2. Adjust the processing priority and transmission path of the data packet according to the predicted delay time. High-priority data packets can choose a transmission path with low latency and higher reliability to ensure the timely transmission of important data; while low-priority data packets can choose a more economical and efficient transmission method to avoid waste of resources. By dynamically adjusting the processing priority and transmission path of the data packet, the system can effectively reduce the delay of the data packet. Accurately controlling the processing priority of the data packet according to the predicted delay time can avoid congestion caused by data packets competing for resources in the network. At the same time, optimizing the transmission path can also effectively reduce the loss and retransmission of data packets and improve the stability and reliability of data transmission. By dynamically adjusting the priority and transmission path, the system can intelligently manage the use of network resources. Efficiently utilize network bandwidth and transmission capacity to avoid idle waste of resources, thereby improving the overall efficiency and economy of the system. Predicting the delay time and adjusting the transmission strategy accordingly enables the system to have the ability to respond in real time and adapt to different network conditions. The system can make adjustments at any time according to changing network conditions and data flow requirements to maintain the stability and efficiency of data transmission; 3. Selecting the appropriate transport protocol based on the data packet priority can optimize the use of network resources. Using the reliable TCP protocol for high-priority data packets can reduce retransmission and delay; using the efficient UDP protocol for low-priority data packets can reduce network overhead and increase transmission speed. By distinguishing the data packet priority and selecting the appropriate transport protocol, the stability of the system can be improved. Using TCP for high-priority data packets ensures reliable transmission of critical data; using UDP for low-priority data packets improves the system's adaptability to network fluctuations and reduces the impact on overall transmission stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a data delay processing method in one embodiment of the present application; Figure 2 This is a flowchart of the implementation of the data delay processing method after step S10 in one embodiment of the present application; Figure 3 This is a flowchart of the implementation of step S20 of the data delay processing method in one embodiment of the present application; Figure 4 This is a flowchart of the implementation of the data delay processing method before step S23 in an embodiment of the present application; Figure 5 This is a flowchart of the implementation of step S30 of the data delay processing method in one embodiment of the present application; Figure 6 This is a flowchart of the implementation of the data delay processing method after step S31 in one embodiment of the present application; Figure 7 This is a flowchart of the implementation of the data delay processing method before step S40 in one embodiment of the present application; Figure 8 This is a principle block diagram of a data delay processing method in one embodiment of the present application; Fig. 9 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below in conjunction with the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses a data delay processing method, which specifically includes the following steps: S10: Obtain source data packet.

[0026] In this embodiment, the source data packet refers to an initial data unit transmitted in network communication.

[0027] Specifically, the system captures data packets passing through the network interface by listening to the data stream transmitted on the interface. Key information such as source address, destination address, protocol type, data payload and timestamp are extracted from the captured data packets to form a source data packet object. The source data packet object is used in subsequent delay prediction, processing priority adjustment and transmission path optimization steps to ensure that the data packet can be efficiently transmitted to the target address.

[0028] S20: Analyze and predict the source data packet through the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet.

[0029] In this embodiment, the trained artificial intelligence prediction model refers to a machine learning model obtained by training with multiple historical data packet delay times and historical data packet processing priorities.

[0030] Specifically, a pre-trained delay prediction model is loaded into the system. Required feature information is extracted from the source data packet, such as packet size, protocol type, source address, destination address, and timestamp. The extracted features are normalized or standardized to ensure that they meet the input requirements of the model. The pre-processed feature vector is input into the trained model, and the model calculates and outputs the predicted delay time based on these features. The result output by the model is the predicted delay time of the source data packet.

[0031] S30: adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet.

[0032] In this embodiment, the processing priority of the source data packet refers to the priority processing of the data packet by the system according to its importance and urgency during the data packet transmission process. The transmission path refers to the network route that the data packet passes through from the source address to the destination address.

[0033] Specifically, the predicted delay time of the source data packet is obtained from the trained artificial intelligence prediction model.

[0034] Adjust processing priority: Set the processing priority of the data packet based on the predicted delay time. For example, if the predicted delay time exceeds the predicted delay time priority threshold, increase the priority of the data packet to reduce the actual delay. Select a transmission path based on the predicted delay time and the current network conditions. Avoid selecting congested or high-delay transmission paths, and give priority to the transmission path with the fastest transmission speed. Attach the adjusted priority and selected transmission path information to the source data packet to form a processed data packet.

[0035] S40: Transmitting the processed data packet to the target address through the data packet transmission protocol.

[0036] In this embodiment, the data packet transmission protocol refers to a set of protocols used to transmit data packets in a network.

[0037] Specifically, first, select a data packet transmission protocol (such as TCP or UDP) based on the characteristics and requirements of the data packet. For the TCP protocol, a three-way handshake is required to establish a connection before sending data, while the UDP protocol does not require a connection. Next, the processed data packet is encapsulated according to the selected protocol and the corresponding protocol header information is added. Then, the encapsulated data packet is sent to the target address through the network interface. Finally, for the TCP protocol, the confirmation information (ACK) returned by the target address is received to ensure that the data packet has been successfully received; for the UDP protocol, no confirmation is performed after the data packet is sent.

[0038] In one embodiment, if Figure 2 As shown, after step S10, the source data packet is obtained, and the following steps are also included: S101: Obtain multiple historical delay times and historical data packet processing priorities of multiple data packets, and train a preset artificial intelligence prediction model according to the multiple historical delay times and historical data packet processing priorities to obtain a trained artificial intelligence prediction model.

[0039] Specifically, collect the actual measured delay time of multiple data packets during network transmission. Collect the processing priority information of these data packets during transmission. Remove outliers and missing values ​​to ensure the accuracy and completeness of the data. Extract useful features from the data packets, such as packet size, protocol type, source address, destination address, timestamp, etc. Standardize the feature data to make them on the same scale. Divide the dataset into training set and test set, with a ratio of 80% and 20%, for model training and performance verification. Select linear regression model: Linear regression model is used to predict delay time. Build a model and use a linear regression model. Train the model using the training set data and adjust the model parameters to minimize the prediction error.

[0040] Furthermore, a linear regression model is fitted using the training data: , where y is the target vector. X is the feature variable matrix, each row represents a sample, and each column represents a feature. β is the regression coefficient vector (model parameter). ε is the error term. Minimize the loss function: The loss function of the linear regression model is the mean square error (MSE), , where n is the number of samples. yi is the actual value of the ith sample. Xi is the feature vector of the ith sample. The predicted value of the ith sample. The model parameters are optimized by gradient descent, which is an iterative optimization algorithm used to find parameters that minimize the loss function. Initialize the regression coefficient β to a random value or a vector of all zeros. The gradient of the loss function MSE with respect to β is: , where XT is the transpose of the feature matrix X. y is the actual value vector. Xβ is the predicted value vector. Update parameters: Update β according to the gradient, . Where η is the learning rate, which controls the step size of each update. Repeat the steps of calculating gradients and updating parameters until the loss function converges to a minimum or reaches a preset number of iterations. Calculate the error between the predicted value and the actual value, and use evaluation indicators such as mean squared error (MSE) and mean absolute error (MAE) to evaluate the model performance. MSE: The average of the squared errors between the predicted value and the actual value. The smaller the MSE, the better the prediction performance of the model.

[0041] In one embodiment, if Figure 3 As shown, in step S20, the source data packet is analyzed and predicted by the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet, which specifically includes: S21: Parse the source data packet to obtain a feature vector of the source data packet, where the feature vector of the source data packet includes the size, type, source, destination IP address, protocol type and timestamp of the data packet.

[0042] Specifically, use a network monitoring tool or library to capture source packets. For example, use Wireshark, tcpdump, or Scapy libraries. Wireshark: Suitable for real-time capture and analysis of network traffic. tcpdump: Command-line tool that captures and saves network packets. Scapy: Python library for programmatic capture and parsing of packets. Read each packet one by one from the captured packet stream. Get the size from the length field of the packet, in the Ethernet frame header or IP header. Determine the packet type (such as TCP, UDP, or ICMP) based on the protocol field in the IP header. Extract from the source IP address field of the IP header. Extract from the destination IP address field of the IP header. Based on the timestamp recorded when the packet was received.

[0043] S22: Standardize the feature vector of the source data packet to obtain a processed feature vector of the source data packet.

[0044] Specifically, first, collect and organize the feature vectors of all source packets to form a data set. Each feature vector contains the following features: feature vector = [size, type, source IP, destination IP, protocol type, timestamp]. Standardize to ensure that the feature order and type are consistent during model training.

[0045] S23: Input the processed source data packet feature vector into the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet.

[0046] Specifically, the feature vector after standardization is prepared in the format of the model training, and the trained artificial intelligence prediction model is loaded. The feature vector is input into the trained artificial intelligence prediction model, and the trained artificial intelligence prediction model returns a prediction value, which is the predicted delay time of the source data packet.

[0047] In one embodiment, if Figure 4 As shown, in step S23, the processed source data packet feature vector is input into the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet, which specifically includes: S231: The trained artificial intelligence prediction model predicts the feature vector of the processed source data packet. The formula is: , where ω is the weight learned by the AI ​​prediction model, x is the input feature, b is the bias term, and y is the prediction result, i.e., the predicted delay time of the source data packet.

[0048] Specifically, the weights (ω) and bias terms (b) are extracted from the loaded model, the weighted sum is calculated, and the input feature vector is weighted summed using the extracted weights and bias terms to obtain the predicted delay time of the source data packet.

[0049] In one embodiment, if Figure 5 As shown, in step S30, the processing priority and transmission path of the source data packet are adjusted according to the predicted delay time of the source data packet to obtain the processed data packet, which specifically includes: S31: adjusting the processing priority of the source data packet according to the predicted delay time of the source data packet, and selecting a transmission path according to the predicted delay time of the source data packet.

[0050] Specifically, data packets are assigned to different processing priorities based on the predicted delay time. The processing priority determines the order in which data packets are processed in the network device. Generally, the delay time can be divided into several intervals, and a priority is set for each interval. According to the different intervals of delay time, data packets are divided into three priorities: high, medium and low. For example, high priority: delay time is less than 10ms. Medium priority: delay time is between 10ms and 50ms. Low priority: delay time is higher than 50ms. According to the processing priority, a suitable data packet transmission path is selected, and high priority data packets choose the path with the least delay. Medium priority data packets choose the path with moderate delay. Low priority data packets choose the regular path.

[0051] S32: Update the adjusted priority and transmission path into the source data packet to obtain a processed data packet.

[0052] Specifically, first, the source data packet is acquired and parsed to extract its header information. According to the predicted delay time, the processing priority of the data packet is adjusted and the appropriate transmission path is selected. The priority and transmission path selection strategy is divided based on different intervals of delay time. For example, low delay time corresponds to high priority and low delay path, medium delay time corresponds to medium priority and medium delay path, and high delay time corresponds to low priority and regular path. Then, the adjusted priority and selected transmission path are updated to the header field of the data packet to generate a processed data packet.

[0053] In one embodiment, if Figure 6 As shown, after step S31, the processing priority of the source data packet is adjusted according to the predicted delay time of the source data packet, and the transmission path is selected according to the predicted delay time of the source data packet, and the method further includes: S33: Obtain priority adjustment rule information, and obtain a predicted delay time priority threshold according to the priority adjustment rule information. The predicted delay time priority threshold includes a high priority, a medium priority, and a low priority.

[0054] In this embodiment, the priority adjustment rule information refers to a set of predefined rules and thresholds for determining the priority of a data packet. The predicted delay time priority threshold refers to dividing the predicted delay time of a data packet into value ranges of different priorities according to predefined rules and policies.

[0055] Specifically, by obtaining the priority adjustment rule information, the priority threshold of the predicted delay time can be determined, including high priority, medium priority and low priority. Specifically, according to different intervals of the delay time, the corresponding priority threshold is set, for example, the high priority corresponds to the delay time less than 10 milliseconds, the medium priority corresponds to the delay time between 10 and 50 milliseconds, and the low priority corresponds to the delay time greater than 50 milliseconds. According to these thresholds, the predicted delay time is evaluated and the priority of the data packet is adjusted.

[0056] Furthermore, the priority threshold can be dynamically adjusted according to actual needs.

[0057] S34: Adjust the processing priority of the source data packet according to the predicted delay time priority threshold.

[0058] Specifically, the specific steps for adjusting the processing priority of the source data packet according to the predicted delay time priority threshold are: first, obtain the predicted delay time of the source data packet through the trained artificial intelligence model. Then, according to the pre-defined priority threshold (for example, high priority is a delay time less than 10 milliseconds, medium priority is a delay time between 10 and 50 milliseconds, and low priority is a delay time greater than 50 milliseconds), adjust the processing priority of the source data packet. If the predicted delay time is less than 10 milliseconds, the data packet is set to high priority; if it is between 10 and 50 milliseconds, it is set to medium priority; if it is greater than 50 milliseconds, it is set to low priority.

[0059] S35: Acquire real-time network status information, and select a transmission path according to the real-time network status information and the predicted delay time of the source data packet.

[0060] In this embodiment, real-time network status information refers to dynamic parameters and conditions in the current network environment, including network bandwidth, network delay, network congestion, packet loss rate, jitter, routing path, network node status, and network security status.

[0061] Specifically, the transmission path of the data packet is determined based on the real-time network status information and the predicted delay time. If the network condition is good and the predicted delay time is short, a low-latency path can be selected to improve the transmission speed and real-time performance. If the network is congested or the delay is high, an alternative path or a more stable transmission route may need to be selected to ensure stable transmission of the data packet and unaffected transmission quality. Changes in network status information are monitored in real time, and the transmission path is adjusted according to the changes.

[0062] In one embodiment, if Figure 7 As shown, before step S40, that is, by using a data packet transmission protocol, the processed data packet is transmitted to the target address, and further includes: S401: According to the priority and transmission path information of the data packet, a data packet transmission protocol is selected, and high-priority data packets are transmitted using TCP, and low-priority data packets are transmitted using UDP.

[0063] Specifically, the priority of each data packet is determined according to the preset priority rules. For example, high priority, medium priority and low priority. The transmission path of each data packet is determined according to the priority of the data packet and other path selection rules (such as real-time network conditions). For example, high-priority data packets choose low-latency paths, and low-priority data packets choose regular paths. Select the appropriate transmission protocol based on the priority and path information. TCP (Transmission Control Protocol): Suitable for situations with high requirements for data transmission reliability, such as high-priority data packets, because it provides functions such as in-order transmission of data packets and retransmission of lost data packets. UDP (User Datagram Protocol): Suitable for situations with high requirements for real-time data transmission and allowance for data packet loss, such as low-priority data packets, because it has a fast transmission speed but does not guarantee data integrity or in-order transmission.

[0064] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0065] In one embodiment, a data delay processing device is provided, and the data delay processing device corresponds one-to-one to the data delay processing method in the above embodiment. Figure 8 As shown, the data delay processing device includes a data packet acquisition module, a model application module, a data packet processing module and a data packet transmission module. The detailed description of each functional module is as follows: A data packet acquisition module, used to acquire source data packets; The model application module is used to analyze and predict the source data packet through the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet; A data packet processing module, used to adjust the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet; The data packet transmission module is used to transmit the processed data packet to the target address through the data packet transmission protocol.

[0066] Optionally, the data packet acquisition module also includes: The model training module is used to obtain the historical delay time and historical data packet processing priority of multiple data packets, and train the preset artificial intelligence prediction model according to the historical delay time and historical data packet processing priority of multiple data packets to obtain a trained artificial intelligence prediction model.

[0067] Optionally, the model application module includes: The feature acquisition submodule is used to parse the source data packet to obtain the feature vector of the source data packet. The feature vector of the source data packet includes the size, type, source, destination IP address, protocol type and timestamp of the data packet. A standard processing module, used for standardizing the feature vector of the source data packet to obtain a processed feature vector of the source data packet; The result acquisition submodule is used to input the processed source data packet feature vector into the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet.

[0068] Optionally, the result acquisition submodule includes: The prediction delay time unit is used to predict the feature vector of the processed source data packet using the trained artificial intelligence prediction model. The formula is: , where ω is the weight learned by the AI ​​prediction model, x is the input feature, b is the bias term, and y is the prediction result, i.e., the predicted delay time of the source data packet.

[0069] Optionally, the data packet processing module includes: A priority and path submodule, used to adjust the processing priority of the source data packet according to the predicted delay time of the source data packet, and select a transmission path according to the predicted delay time of the source data packet; The processing submodule is used to update the adjusted priority and transmission path into the source data packet to obtain a processed data packet.

[0070] Optionally, the data packet processing module further includes: A threshold confirmation submodule is used to obtain priority adjustment rule information, and obtain a predicted delay time priority threshold according to the priority adjustment rule information. The predicted delay time priority threshold includes high priority, medium priority and low priority. A priority submodule, used for adjusting the processing priority of the source data packet according to the predicted delay time priority threshold; The transmission path submodule is used to obtain real-time network status information and select a transmission path based on the real-time network status information and the predicted delay time of the source data packet.

[0071] Optionally, the data packet transmission module also includes: The protocol submodule is used to select the data packet transmission protocol according to the priority and transmission path information of the data packet. High-priority data packets are transmitted using TCP, and low-priority data packets are transmitted using UDP.

[0072] For the specific definition of the data delay processing device, please refer to the definition of the data delay processing method above, which will not be repeated here. Each module in the above-mentioned data delay processing device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0073] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for a Python library. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data delay processing method is implemented.

[0074] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Get the source data packet; The source data packets are analyzed and predicted through the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packets; Adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet; The processed data packets are transmitted to the target address through the data packet transmission protocol.

[0075] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Get the source data packet; The source data packets are analyzed and predicted through the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packets; Adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet; The processed data packets are transmitted to the target address through the data packet transmission protocol.

[0076] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0077] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0078] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A data delay processing method, characterized in that: The data delay processing method comprises: Get the source data packet; Analyze and predict the source data packet through the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet; Adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet; The processed data packet is transmitted to a target address through a data packet transmission protocol.

2. The data delay processing method according to claim 1, characterized in that: The obtaining of the source data packet further comprises: Acquire multiple historical delay times and historical data packet processing priorities of data packets, and train a preset artificial intelligence prediction model according to the multiple historical delay times and historical data packet processing priorities of data packets to obtain the trained artificial intelligence prediction model.

3. The data delay processing method according to claim 1, characterized in that: The method of analyzing and predicting the source data packet by using the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet specifically includes: The source data packet is parsed to obtain a feature vector of the source data packet, wherein the feature vector of the source data packet includes the size, type, source, destination IP address, protocol type and timestamp of the data packet; Standardizing the feature vector of the source data packet to obtain a processed feature vector of the source data packet; The processed source data packet feature vector is input into the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet.

4. The data delay processing method according to claim 3, characterized in that: The step of inputting the processed source data packet feature vector into the trained artificial intelligence prediction model to obtain the predicted delay time of the source data packet specifically includes: The trained artificial intelligence prediction model predicts the processed source data packet feature vector using the formula: , where ω is the weight learned by the artificial intelligence prediction model, x is the input feature, b is the bias term, and y is the prediction result, i.e., the predicted delay time of the source data packet.

5. The data delay processing method according to claim 1, characterized in that: The step of adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet specifically includes: Adjusting the processing priority of the source data packet according to the predicted delay time of the source data packet, and selecting the transmission path according to the predicted delay time of the source data packet; The adjusted priority and the transmission path are updated into the source data packet to obtain the processed data packet.

6. The data delay processing method according to claim 5, characterized in that: The step of adjusting the processing priority of the source data packet according to the predicted delay time of the source data packet and selecting a transmission path according to the predicted delay time of the source data packet further includes: Acquire priority adjustment rule information, and obtain a predicted delay time priority threshold according to the priority adjustment rule information, wherein the predicted delay time priority threshold includes a high priority, a medium priority, and a low priority; Adjusting the processing priority of the source data packet according to the predicted delay time priority threshold; Real-time network status information is acquired, and the transmission path is selected according to the real-time network status information and the predicted delay time of the source data packet.

7. The data delay processing method according to claim 1, characterized in that: The method of transmitting the processed data packet to a target address through a data packet transmission protocol further comprises: The data packet transmission protocol is selected according to the priority and transmission path information of the data packet, and the high-priority data packets are transmitted using TCP, and the low-priority data packets are transmitted using UDP.

8. A data delay processing device, characterized in that: The data delay processing device comprises: A data packet acquisition module, used to acquire source data packets; A model application module, used to analyze and predict the source data packet through a trained artificial intelligence prediction model to obtain a predicted delay time of the source data packet; A data packet processing module, used for adjusting the processing priority and transmission path of the source data packet according to the predicted delay time of the source data packet to obtain a processed data packet; The data packet transmission module is used to transmit the processed data packet to a target address through a data packet transmission protocol.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the data delay processing method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the data delay processing method according to any one of claims 1 to 7 are implemented.

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