Low-delay communication optimization method and device in secret communication and medium

Abstract

The invention discloses a low-delay communication optimization method and device in secret communication and a medium, and relates to the related technical field of secret communication, and the method comprises the steps: employing a task data set to extract encryption features and task features, and building a lightweight encryption scheme; reading a real-time link state of a communication link, and establishing a dynamic segmentation granularity; configuring the segmentation number of the encrypted communication task, and establishing a random segmentation disturbance coefficient; after granularity segmentation is carried out on the encrypted communication task, adaptive optimization is carried out according to a real-time link state and a task granularity segmentation result, and an adaptive optimization result is established; and performing granularity communication transmission of the communication task. The technical problems that in the prior art, data calculation and encryption complexity is high, and a fixed granularity segmentation strategy cannot adapt to a dynamic communication link state, so that data transmission safety is poor, efficiency is low and communication delay is high are solved, and the technical effects of improving data transmission safety and efficiency and reducing communication delay are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to confidential communications, and in particular to low-latency communication optimization methods, devices and media in confidential communications. Background Art

[0002] With the rapid development of information technology, communication technology is increasingly used in various fields of society, especially in confidential communication scenarios involving high security and real-time, such as financial transactions, medical data transmission, etc. However, these scenarios put forward strict requirements on low latency, high reliability and security of communication. How to further optimize communication efficiency and reduce communication delay under the premise of meeting the high security requirements of confidential communication has become an important problem to be solved. In confidential communication, encryption is an important means to ensure data security. Traditional encryption methods usually encrypt data through complex algorithms to prevent unauthorized access or tampering, but often introduce high computational complexity and increase communication delay. In addition, the data characteristics and encryption requirements of different tasks vary greatly. Static encryption is difficult to flexibly adapt to diverse task requirements and dynamically changing network environments. In communication networks, link status (such as bandwidth, delay, packet loss rate and jitter) will fluctuate dynamically with time and environment. When the link status is poor, data transmission may face problems such as increased delay and increased packet loss rate, resulting in reduced communication efficiency, security and reliability.

[0003] Therefore, in the current relevant technologies, there are technical problems such as high complexity of data calculation and encryption, and the inability of fixed-granularity segmentation strategies to adapt to the dynamic communication link status, resulting in poor data transmission security, low efficiency and high communication delay. Summary of the invention

[0004] The present application solves the technical problems in the prior art of high complexity of data calculation and encryption, and the inability of fixed-granularity segmentation strategies to adapt to dynamic communication link states, resulting in poor data transmission security, low efficiency and high communication delay by providing a low-latency communication optimization method, device and medium in confidential communication, thereby achieving the technical effect of improving data transmission security and efficiency and reducing communication delay.

[0005] The present application provides a low-latency communication optimization method in confidential communication, the method comprising: obtaining a task data set of a communication task, extracting encryption features and task features using the task data set, performing balanced encryption matching of an encryption database according to the encryption features and the task features, and establishing a lightweight encryption scheme; after encrypting the communication task using the lightweight encryption scheme, reading the real-time link status of the communication link, and establishing a dynamic segmentation granularity using the real-time link status; establishing a segmentation constraint based on the encryption feature, configuring the number of segmentations of the encrypted communication task using the dynamic segmentation granularity and the segmentation constraint, and establishing a random segmentation perturbation coefficient; after performing granular segmentation of the encrypted communication task using the random segmentation perturbation coefficient and the number of segmentations, performing adaptive optimization according to the real-time link status and the task granularity segmentation results, and establishing an adaptive optimization result; and performing granular communication transmission of the communication task using the adaptive optimization result.

[0006] In a possible implementation, the low-latency communication optimization method in the confidential communication also performs the following processing: extracting multidimensional link state parameters according to the real-time link state, the multidimensional link state parameters including bandwidth parameters, delay parameters, packet loss rate parameters, and jitter parameters, and establishing a link state vector according to the multidimensional link state parameters; performing a backtracking analysis on the real-time link state, and generating a trend prediction state using the backtracking analysis results and the link state vector; configuring a comprehensive utility evaluation channel, and using the comprehensive utility evaluation channel to perform a segmentation utility evaluation based on the link state vector and the trend prediction state, and establish the dynamic segmentation granularity.

[0007] In a possible implementation, the low-latency communication optimization method in the secure communication further performs the following processing: performing delay evaluation based on the delay evaluation subchannel to establish a first utility evaluation feature: Based on the delayed evaluation subchannel, delayed evaluation is performed to establish the first utility evaluation feature: ; in, Characterizes the first utility evaluation feature, L is the real-time delay, Characterizes the current segmentation granularity, is the delay sensitivity parameter; Based on the reliability evaluation sub-channel, reliability evaluation is performed to establish the second utility evaluation feature: ; in, Characterize the second utility evaluation feature, PLR is the real-time packet loss rate, J is the real-time jitter, is the reliability sensitivity parameter; Based on the segmented adaptation evaluation sub-channel, the adaptability evaluation is performed and the third utility evaluation feature is established: ; in, Characterize the third utility evaluation characteristics, The recommended granularity for trend-based state calculations is To allocate an adaptation range control parameter; a comprehensive utility evaluation channel is configured according to the first utility evaluation feature, the second utility evaluation feature, and the third utility evaluation feature.

[0008] In a possible implementation, the low-latency communication optimization method in the confidential communication also performs the following processing: establishing a minimum segmentation number table mapped to the encryption level, matching and calling the minimum segmentation number table through the encryption feature, and establishing the segmentation constraint; after constraining the encrypted communication task with the segmentation constraint, the encrypted communication task segmentation is determined through the dynamic segmentation granularity: A: if the first segmentation number generated according to the dynamic segmentation granularity is less than the segmentation constraint, the segmentation number is generated based on the segmentation constraint; B: if the first segmentation number generated according to the dynamic segmentation granularity is greater than or equal to the segmentation constraint, the segmentation number is generated based on the first segmentation number.

[0009] In a possible implementation, the low-latency communication optimization method in the confidential communication also performs the following processing: determining the basic segmentation granularity based on the number of segmentations and the communication task; using the random segmentation perturbation coefficient to perturb the basic segmentation granularity to establish a mapping segmentation granularity for each segmentation unit; adjusting the granularity of the initial segmentation unit based on the mapping segmentation granularity, and completing the encrypted communication task granularity segmentation according to the adjustment result.

[0010] In a possible implementation, the low-latency communication optimization method in the confidential communication also performs the following processing: establishing link adaptation characteristics, the link adaptation characteristics include link bandwidth utilization, link delay adaptability, packet loss rate adaptability, and jitter adaptability; establishing task adaptation characteristics, the task adaptation characteristics include complete task completion characteristics and task priority adaptation characteristics; constructing a fitness function based on the link adaptation characteristics and the task adaptation characteristics to complete adaptive optimization.

[0011] In a possible implementation, the low-latency communication optimization method in the confidential communication also performs the following processing: performing security risk self-inspection on the communication transmission and establishing a security risk self-inspection result; generating a self-adjusting granularity correction coefficient based on the security risk self-inspection result; and performing granular communication transmission after performing the dynamic segmentation granularity correction through the self-adjusting granularity correction coefficient.

[0012] The present application also provides a low-latency communication optimization device in confidential communication, including: a lightweight encryption scheme establishment module, which is used to obtain a task data set of a communication task, extract encryption features and task features using the task data set, perform balanced encryption matching of the encryption database based on the encryption features and the task features, and establish a lightweight encryption scheme; a dynamic segmentation granularity establishment module, which is used to encrypt the communication task using the lightweight encryption scheme, read the real-time link status of the communication link, and establish a dynamic segmentation granularity using the real-time link status; a task segmentation quantity configuration module, which is used to establish a segmentation constraint based on the encryption feature, configure the number of segmentations of the encrypted communication task using the dynamic segmentation granularity and the segmentation constraint, and establish a random segmentation perturbation coefficient; an adaptive optimization result establishment module, which is used to perform granular segmentation of the encrypted communication task using the random segmentation perturbation coefficient and the number of segmentations, and then perform adaptive optimization based on the real-time link status and the task granularity segmentation result to establish an adaptive optimization result; a granular communication transmission module, which is used to perform granular communication transmission of communication tasks using the adaptive optimization result.

[0013] The present application also provides a computer-readable storage medium, comprising: a computer program stored thereon, which, when executed by a processor, implements a low-latency communication optimization method in confidential communication.

[0014] The low-latency communication optimization method, device and medium in confidential communication proposed in this application are proposed to extract encryption features and task features using task data sets, establish a lightweight encryption scheme; read the real-time link status of the communication link, establish a dynamic segmentation granularity; configure the number of segmentations of the encrypted communication task, and establish a random segmentation perturbation coefficient; after performing granular segmentation of the encrypted communication task, perform adaptive optimization according to the real-time link status and task granularity segmentation results, establish an adaptive optimization result; and perform granular communication transmission of the communication task. It solves the technical problems existing in the prior art that the data calculation and encryption complexity are high, and the fixed granularity segmentation strategy cannot adapt to the dynamic communication link status, resulting in poor data transmission security, low efficiency and high communication delay, and achieves the technical effect of improving data transmission security and efficiency and reducing communication delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0016] Figure 1 A schematic diagram of a flow chart of a method for optimizing low-latency communication in secure communication provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a low-latency communication optimization device for secure communication provided in an embodiment of the present application.

[0017] Explanation of the reference numerals: lightweight encryption scheme establishing module 10 , dynamic segmentation granularity establishing module 20 , task segmentation quantity configuration module 30 , adaptive optimization result establishing module 40 , granularity communication transmission module 50 . DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of the objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server comprising a series of steps is not necessarily limited to those steps clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a low-latency communication optimization method in secure communication, such as Figure 1 As shown, the method includes: Step S100, obtaining a task data set of a communication task, extracting encryption features and task features using the task data set, performing balanced encryption matching of an encryption database according to the encryption features and the task features, and establishing a lightweight encryption scheme.

[0022] Preferably, all task data sets related to the communication task are obtained, which may include data packet content, communication protocol, task parameters, target network status, data characteristics, etc. The data in the task data set provides the basic information required for encryption and clarifies the specific requirements of the communication task (such as security, real-time, bandwidth, etc.). Then, encryption features and task features are extracted based on the task data set, that is, the content of the task data set is analyzed, including analyzing the sensitivity of the data, judging the security level requirements, determining the adaptability of the encryption algorithm, analyzing the real-time requirements, identifying the task priority, analyzing the data transmission volume and frequency, and extracting link status related features, etc., and then extracting encryption features and task features, where encryption features refer to attributes or requirements related to data security, including data sensitivity (whether the data contains sensitive information, such as personal privacy, commercial secrets or various security data), security level requirements (security level requirements during data transmission, such as whether tamper-proof and replay attack-proof are required , anti-eavesdropping), encryption algorithm adaptability (select different encryption algorithms according to the data type and processing speed requirements such as text, image, streaming data), encryption strength and computational complexity balance (determine the data transmission time and resource allocation constraints to select the appropriate encryption strength); task characteristics refer to the operational requirements and attributes related to the communication task itself, including real-time requirements (whether low-latency transmission is required, for example, voice communication or video conferencing requires strong real-time performance, while file transfer has lower real-time requirements), task priority (the priority of the task in the entire communication task, for example, urgent tasks may give priority to the use of fast and secure transmission mechanisms), data volume and transmission frequency (packet size, data transmission frequency, such as high frequency, low frequency, etc.), task environment characteristics (the actual state of the communication link, such as bandwidth, delay, packet loss rate, etc.) and redundancy and reliability requirements (whether redundancy needs to be increased to ensure data transmission reliability, for example, some tasks have a low tolerance for data loss).

[0023] Preferably, balanced encryption matching of the encryption database is performed according to the encryption characteristics of the matching task and the task characteristics to select a suitable encryption scheme, wherein the encryption database is a database containing options of various encryption schemes, performance indicators of the algorithms (such as complexity, resource usage, etc.) and applicable scenarios. When performing balanced encryption matching, the balance between encryption strength (security) and performance requirements (such as real-time performance, computational complexity) is comprehensively considered. For example, for tasks with low latency requirements, a lightweight encryption algorithm may be selected, while for tasks with high security requirements, a more complex but more secure algorithm may be selected, and ultimately a lightweight encryption scheme is obtained. Lightweight means reducing computational complexity and resource consumption as much as possible while meeting task requirements, such as algorithm optimization, reducing redundant calculations, distributed processing, etc. A lightweight encryption scheme includes the encryption algorithm used, parameter configuration, and specific encryption strategies for data to ensure secure and efficient communication data transmission.

[0024] Step S200: After encrypting the communication task using the lightweight encryption scheme, read the real-time link status of the communication link, and establish a dynamic segmentation granularity using the real-time link status.

[0025] Preferably, a lightweight encryption scheme is used to encrypt the communication task, including processing the data in the communication task (such as segmentation and sorting), selecting an appropriate encryption algorithm (such as AES, DES, etc.) according to the task requirements and resource constraints, and reducing the computational complexity of encryption through lightweight design, so that the encryption operation can meet the security requirements and can be completed within limited computing resources and time. Then, the real-time link status of the communication link is read through network monitoring tools (such as SNMP, Ping, NetFlow), that is, the network quality is regularly detected during the communication process (such as using heartbeat packets or real-time traffic monitoring), and the parameters that change over time during the network transmission process are obtained, including bandwidth (available network transmission capacity), delay (the time from the sender to the receiver), jitter (the fluctuation range of the delay) and packet loss rate (the proportion of data packets lost during the transmission process). Finally, the real-time link status is used to establish a dynamic segmentation granularity. The encrypted communication data will be dynamically adjusted into data blocks of different sizes according to the real-time changes of the link status to optimize the transmission efficiency and reliability. Specifically, the data of the communication task often exists in the form of large data blocks, and direct transmission may not adapt to the changes in the link status. Through granular segmentation, that is, using algorithms (such as dynamic bandwidth allocation algorithms, sliding window mechanisms, etc.) to adjust the segmentation granularity based on bandwidth, delay, packet loss rate and jitter, the data is divided into small blocks suitable for the current network conditions. Among them, the dynamic segmentation granularity is the data block size dynamically determined according to the link status. For example, if the link bandwidth is high and the delay is low, larger data blocks can be divided to increase the transmission speed. If the link bandwidth is low and the packet loss rate is high, smaller data blocks are divided to reduce the data loss caused by a single transmission failure, thereby maximizing the communication efficiency, reducing delay, and reducing the transmission failure rate, thereby optimizing the transmission performance of encrypted data under different link conditions.

[0026] Furthermore, step S200 also includes step S210, extracting multidimensional link state parameters according to the real-time link state, the multidimensional link state parameters including bandwidth parameters, delay parameters, packet loss rate parameters, and jitter parameters, and establishing a link state vector according to the multidimensional link state parameters; step S220, performing a retrospective analysis on the real-time link state, and generating a trend prediction state using the retrospective analysis results and the link state vector; step S230, configuring a comprehensive utility evaluation channel, using the comprehensive utility evaluation channel to perform a segmentation utility evaluation based on the link state vector and the trend prediction state, and establishing the dynamic segmentation granularity.

[0027] Preferably, multi-dimensional link state parameters are extracted from the read real-time link state, i.e., multiple key indicators describing the network link status, including bandwidth parameters (transmission capacity of the current link, such as Mbps, affecting data transmission speed), delay parameters (time delay from the sender to the receiver, such as ms, determining the timeliness of data arrival), packet loss rate parameters (the proportion of data packets lost during data transmission, reflecting network transmission reliability) and jitter parameters (the fluctuation range of delay, affecting the transmission stability of real-time data such as voice and video), and then a link state vector is established based on the multi-dimensional link state parameters, i.e., the multi-dimensional link state parameters are organized into a vector form to represent the overall status of the link state, which can comprehensively evaluate the current network state in multiple dimensions.

[0028] Preferably, a retrospective analysis is performed on the real-time link status, that is, based on the historical data of the link status parameters, the change trend of the link status is analyzed, including extracting the change data of the link status from the past time period, summarizing the change pattern of the link status parameters through statistical analysis (such as time series analysis), and obtaining the retrospective analysis results. Then, based on the retrospective analysis results and the link state vector, a prediction algorithm is used to predict the trend of the future link status and generate a trend prediction state. For example, a machine learning model (such as ARIMA, LSTM) is used to predict the future bandwidth, delay, packet loss rate and jitter. The output trend prediction state represents the trend information of the link status in a certain period of time in the future. Then, a comprehensive utility evaluation channel is configured to perform segmentation utility evaluation based on the link state vector and the trend prediction state to optimize the selection of data segmentation granularity and ensure It can adapt to the current and future link states, wherein the comprehensive utility evaluation channel includes a delay evaluation sub-channel, a reliability evaluation sub-channel and a segmentation adaptation evaluation sub-channel. The delay evaluation sub-channel evaluates the delay consumption of the data segmentation granularity under the current link state by simulation; the reliability evaluation sub-channel analyzes the relationship between the segmentation granularity and the packet loss rate to evaluate the impact of the segmentation strategy on the reliability of data transmission; the segmentation adaptation evaluation sub-channel compares the matching degree between the segmentation granularity and the predicted state vector to evaluate whether the segmentation strategy is adapted to the current and future link states; the evaluation channel is used to calculate the comprehensive utility of each segmentation granularity under the current state and the predicted state, and the dynamic segmentation granularity is selected according to the calculation results to ensure that it has a high utility under both the current and predicted states to adapt to the current and future network conditions, thereby optimizing the data communication transmission efficiency and reliability.

[0029] Furthermore, step S230 further includes step S231, performing delay evaluation based on the delay evaluation subchannel to establish a first utility evaluation feature: ; in, Characterizes the first utility evaluation feature, L is the real-time delay, Characterizes the current segmentation granularity, is the delay sensitivity parameter; Step S232, performing reliability evaluation based on the reliability evaluation sub-channel, and establishing a second utility evaluation feature: ; in, Characterize the second utility evaluation feature, PLR is the real-time packet loss rate, J is the real-time jitter, is the reliability sensitivity parameter; Step S233, perform adaptability evaluation based on the segmented adaptation evaluation subchannels to establish a third utility evaluation feature: ; in, Characterize the third utility evaluation characteristics, The recommended granularity for trend-based state calculations is To allocate adaptation range control parameters; Step S234: configuring a comprehensive utility evaluation channel according to the first utility evaluation feature, the second utility evaluation feature, and the third utility evaluation feature.

[0030] Preferably, the segmentation granularity is dynamically adjusted through comprehensive analysis of the first utility evaluation feature (delay evaluation), the second utility evaluation feature (reliability evaluation) and the third utility evaluation feature (adaptability evaluation), so that the data segmentation strategy can operate efficiently in a complex link environment, thereby optimizing the overall performance of the communication. Specifically, the first utility evaluation feature is delay evaluation. When the segmentation granularity is large, the data block needs to be transmitted for a longer time, resulting in increased delay and reduced evaluation value. By reducing the segmentation granularity or optimizing the link state, the delay evaluation feature can be improved and the task performance can be optimized. The second utility evaluation feature is reliability evaluation. When the segmentation granularity is small, the reliability of the transmission is usually Higher, because the data blocks are smaller, it is easier to transmit successfully in the link status fluctuations; the third utility evaluation feature is adaptability evaluation. If the current segmentation granularity is closer to the predicted optimal granularity, the adaptability evaluation feature is closer to 1, indicating that the current granularity has high adaptability. If the granularity deviates greatly, the evaluation value decreases, indicating that the current segmentation strategy needs to be adjusted; finally, the delay evaluation sub-channel, reliability evaluation sub-channel and segmentation adaptation evaluation sub-channel are integrated and configured as a comprehensive utility evaluation channel to score the overall utility of the current segmentation strategy, guide the adjustment of segmentation granularity, and then adapt to complex network environments, balance communication efficiency and reliability, and ensure the optimal performance of communication tasks.

[0031] Step S300: establishing a segmentation constraint based on the encryption feature, configuring the segmentation quantity of the encrypted communication task using the dynamic segmentation granularity and the segmentation constraint, and establishing a random segmentation perturbation coefficient.

[0032] Preferably, segmentation constraints are established based on encryption characteristics, that is, based on the encryption characteristics of the task data set (such as data sensitivity, algorithm adaptability, security level requirements, encryption computational complexity and performance balance between the number of segmentations, etc.), set constraints on encrypted data segmentation, such as minimum segmentation granularity (ensuring that the data block is not too small to increase encryption computational overhead), maximum segmentation granularity (limiting the data block size to enhance anti-attack capabilities), etc., to ensure that the data block size (granularity) and number can meet security and performance requirements; then use dynamic segmentation granularity and segmentation constraints to configure the number of segmentations of the encrypted communication task, where the number of segmentations is related to the task real-time and link status, and must be within the specified range, that is, the number of segmentations is calculated by the segmentation granularity and the total amount of task data, and The number of segmentations must satisfy the segmentation constraints. For example, when the security requirements of the encryption task are high, the maximum granularity may be limited to increase the number of segmentations (enhance security). At the same time, a pseudo-random number generator (such as based on Gaussian distribution or uniform distribution) is used to establish a random segmentation perturbation coefficient, that is, a random parameter is introduced based on the segmentation granularity or the number of segmentations, aiming to increase the unpredictability of data segmentation to improve communication security. The random segmentation perturbation makes the size and number of data blocks have a certain degree of uncertainty, which increases the difficulty of data interception and analysis. When the link status fluctuates slightly, the data segmentation can be fine-tuned through random perturbations to adapt to the current link changes, thereby improving the security and adaptability of data transmission, and realizing safe and efficient data transmission in complex network environments.

[0033] Furthermore, step S300 also includes step S310, establishing a minimum segmentation number table mapped to the encryption level, matching and calling the minimum segmentation number table through the encryption feature, and establishing the segmentation constraint; step S320, after constraining the encrypted communication task with the segmentation constraint, performing segmentation judgment on the encrypted communication task through the dynamic segmentation granularity: A: if the first segmentation number generated according to the dynamic segmentation granularity is less than the segmentation constraint, generating the segmentation number based on the segmentation constraint; B: if the first segmentation number generated according to the dynamic segmentation granularity is greater than or equal to the segmentation constraint, generating the segmentation number based on the first segmentation number.

[0034] Encryption level Minimum number of splits Low (normal security) 5 Medium (higher security) 10 High (highest security) 20 Then, according to the encryption features of the communication task (such as security level, data sensitivity), a minimum split number table matching call is performed, that is, the corresponding minimum split number is found from the minimum split number table, including the encryption features of the input task (such as security level), and the minimum split number (lower limit of the split number) that matches the security level of the task is output. Then the minimum split number is used as the basis of the split constraint, and other factors (such as link status, task requirements) are combined to further improve the split constraint; the split number is calculated by dynamic split granularity, and judged and adjusted in combination with the split constraint. If the first split number calculated according to the dynamic split granularity is less than the split constraint (minimum split number), it means that the first split number cannot meet the security requirements. The split number is generated according to the split constraint, that is, the split number is increased to the minimum split number, where the first split number is calculated by the total amount of communication task data and the dynamic split granularity; if the first split number is greater than or equal to the split constraint, it means that the first split number has met the split constraint, and the first split number is directly used as the split number without further adjustment. By dynamically associating the encryption level with the number of segmentations, data segmentation can take into account both security and transmission efficiency. For tasks with high security requirements, segmentation constraints ensure the minimum number of segmentations to improve security. For tasks with ordinary security requirements, dynamic segmentation granularity optimizes transmission performance and improves communication efficiency.

[0035] Step S400, after performing encrypted communication task granularity segmentation using the random segmentation disturbance coefficient and the segmentation number, adaptive optimization is performed according to the real-time link state and the task granularity segmentation result to establish an adaptive optimization result.

[0036] Step S400 further includes step S410, determining the basic segmentation granularity according to the segmentation number and the communication task; step S420, using the random segmentation perturbation coefficient to perturb the basic segmentation granularity to establish the mapping segmentation granularity of each segmentation unit; step S430, adjusting the granularity of the initial segmentation unit based on the mapping segmentation granularity, and completing the encrypted communication task granularity segmentation according to the adjustment result.

[0037] Preferably, the encrypted communication task granularity is segmented using the random segmentation perturbation coefficient and the number of segments. Specifically, the basic segmentation granularity (the size of the segmentation block before the random perturbation is introduced) is determined according to the number of segments and the communication task as the starting point of the granularity segmentation. The initial size of each segmentation unit is determined by the basic segmentation granularity to ensure that the segmentation strategy can meet the basic requirements of the communication task. The basic segmentation granularity is then perturbed using the random segmentation perturbation coefficient to establish the mapping segmentation granularity of each segmentation unit, that is, uncertainty is introduced based on the random segmentation perturbation coefficient to increase the segmentation flexibility and security. The mapping segmentation granularity of each segmentation unit will vary slightly due to the perturbation, and then based on The established mapping segmentation accuracy adjusts the initial basic segmentation granularity and completes the encrypted communication task granularity segmentation according to the adjustment result. For example, in a high-bandwidth, low-latency link state, the segmentation granularity can be increased to improve transmission efficiency, and in a low-bandwidth, high-packet loss rate link state, the segmentation granularity can be reduced to improve transmission reliability, or real-time tasks may require a smaller segmentation granularity to reduce latency, and high-security tasks may require a smaller segmentation granularity to enhance the encryption effect; finally, the size of each data block is divided according to the adjusted granularity and sent after encryption, thereby realizing a safe, efficient and adaptive segmentation transmission strategy, improving transmission efficiency and enhancing communication security.

[0038] Preferably, adaptive optimization is performed according to the real-time link status and task granularity segmentation results, which means that in a dynamic network environment, according to the real-time status of the link (such as bandwidth, delay, packet loss rate, etc.) and the task segmentation (such as granularity and number), the data segmentation strategy is optimized to achieve the optimal balance between security, transmission efficiency and reliability, wherein the input conditions include the real-time link status, granularity segmentation results and communication task requirements, and minimizing delay, maximizing reliability and meeting task requirements are used as optimization goals. Specifically, according to the segmentation results and the real-time link status, dynamic programming, genetic algorithms and other methods are used to find the optimal granularity, that is, the utility of the current segmentation strategy is evaluated, the granularity is adjusted to maximize the comprehensive utility, and the adjusted number of segmentations is calculated, and finally the adaptive optimization result is output, including the optimal granularity and the corresponding number of segmentations, thereby ensuring that the communication task achieves the best balance between security, efficiency and reliability.

[0039] Furthermore, step S400 also includes step S440, establishing link adaptation characteristics, wherein the link adaptation characteristics include link bandwidth utilization, link delay adaptability, packet loss rate adaptability, and jitter adaptability; step S450, establishing task adaptation characteristics, wherein the task adaptation characteristics include complete task completion characteristics and task priority adaptation characteristics; and step S460, constructing a fitness function based on the link adaptation characteristics and the task adaptation characteristics to complete adaptive optimization.

[0040] Preferably, a link adaptation feature is established and used as a measurement indicator. The link adaptation feature is used to measure the degree of match between the current communication task and the link status, including link bandwidth utilization, link delay adaptability, packet loss rate adaptability, and jitter adaptability. Among them, link bandwidth utilization refers to the actual usage rate of the link bandwidth. The closer the actual transmission rate is to the link bandwidth, the higher the utilization rate and the better the link resource utilization efficiency. Link delay adaptability refers to the degree of adaptability between the current segmentation granularity and the link delay. The smaller the segmentation granularity, the higher the delay adaptability. Packet loss rate adaptability refers to the reliability of the segmentation strategy under the current packet loss rate. Small granularity is more adaptable under high packet loss rate and has high packet loss rate adaptability. Jitter adaptability refers to the adaptability of granular segmentation to link delay jitter. Small granularity can better adapt to links with larger jitter.

[0041] Preferably, task adaptation features are established and used as measurement indicators. Task adaptation features are used to evaluate the degree to which the segmentation strategy meets task requirements, and usually include complete task completion features and task priority adaptation features, wherein the complete task completion feature refers to meeting the time requirements for task completion. The shorter the remaining time, the higher the adaptability of the task completion. The task priority adaptation feature is used to measure whether the current segmentation strategy meets the priority requirements of the task, and high-priority tasks will receive higher adaptability evaluations. A fitness function is constructed by integrating link adaptation features and task adaptation features to evaluate the overall adaptability of the current segmentation strategy to complete adaptive optimization. Specifically, the fitness function is a weighted combination of link adaptation features and task adaptation features, and the weights are determined according to actual task requirements and link environment. Adaptive optimization dynamically adjusts the segmentation granularity and quantity by maximizing the fitness function value, and finally outputs the optimal segmentation strategy (granularity and quantity) and the corresponding maximum fitness function value to achieve an efficient, secure, and reliable communication segmentation transmission strategy.

[0042] Step S500: Using the adaptation optimization result to perform granular communication transmission of the communication task.

[0043] Preferably, the adaptive optimization result is used to perform granular communication transmission of the communication task. Specifically, the data of the communication task is divided into multiple data blocks based on the optimal segmentation granularity, and each data block is encrypted according to the encryption requirements of the communication task (such as encryption algorithm, encryption key), and then the encrypted data blocks are communicated and transmitted one by one through the network link. The transmission order and control parameters (such as timeout period) of each data block can be dynamically adjusted according to the link status. During the transmission process, the link status (such as bandwidth, delay, etc.) is continuously monitored. For example, if the link status deteriorates (such as reduced bandwidth, increased packet loss rate), the transmission rate can be dynamically adjusted or smaller data blocks can be transmitted first; if the link status improves, the number of concurrent transmissions can be increased or larger data blocks can be transmitted; thereby improving the efficiency and reliability of communication data transmission and enhancing the security of data communication.

[0044] Furthermore, step S500 also includes step S510, performing security risk self-inspection on communication transmission and establishing security risk self-inspection results; step S520, generating a self-adjusting granularity correction coefficient based on the security risk self-inspection results; and step S530, performing granular communication transmission after performing the dynamic segmentation granularity correction through the self-adjusting granularity correction coefficient.

[0045] Preferably, the security risk self-check is to dynamically evaluate the security of the communication task, identify potential security risks in the current transmission process to ensure the confidentiality, integrity and availability of data transmission, and promptly discover and respond to possible security risks, including using encryption integrity checks (such as hash functions) to verify the integrity of data blocks, and combining link status monitoring tools to analyze whether there are any abnormalities in the link. Specifically, perform self-check assessments of data security (detecting whether the data may be eavesdropped or tampered with, and evaluating whether the current encryption algorithm meets the security level requirements), link security (detecting abnormal activities in the link, such as packet loss, frequent retransmissions, abnormal bandwidth fluctuations, and checking whether there are any attack behaviors on the link, such as man-in-the-middle attacks or denial of service attacks), and transmission security (checking whether the segmented data blocks are damaged or lost, and detecting whether the order of packet arrival is abnormal), establish security risk self-check results, such as security risk scores and detected security risks, such as high packet loss rates, potential attacks, and packet tampering, thereby improving the security of communication data transmission. Preferably, a self-adjusting granularity correction coefficient is generated according to the security risk self-check result, wherein the self-adjusting granularity correction coefficient is a dynamic adjustment factor used to correct the current segmentation granularity according to the security risk assessment result, so as to enhance the security or adaptability of data transmission, that is, if the risk score is high, it indicates that the security risk is large, and the segmentation granularity needs to be reduced to enhance security; if the risk score is low, it indicates that the security risk is small, and the granularity can be appropriately increased; then the self-adjusting granularity correction coefficient is generated in combination with the risk type, for example, for high packet loss rate risk, the self-adjusting granularity correction coefficient tends to reduce the segmentation granularity, and then the current segmentation granularity is dynamically adjusted and corrected according to the correction coefficient. If the security risk is high, the correction coefficient reduces the granularity and increases the number of segmentations to enhance the security of data segmentation and reduce the risk of exposure of a single data block; if the security risk is low, the correction coefficient increases the granularity and reduces the number of segmentations to improve transmission efficiency and reduce the management and encryption overhead of data blocks; finally, the communication task data is re-segmented using the corrected granularity, and data encryption and transmission are completed to ensure that the segmentation strategy can adapt to different network environments and task requirements while taking into account both security and efficiency.

[0046] In the above, refer to Figure 1 The low-latency communication optimization method in the confidential communication according to the embodiment of the present invention is described in detail. Figure 2A low-latency communication optimization device in secure communication according to an embodiment of the present invention is described.

[0047] The low-latency communication optimization device in the confidential communication according to the embodiment of the present invention is used to solve the technical problems existing in the prior art, such as high complexity of data calculation and encryption, and the inability of fixed granularity segmentation strategy to adapt to the dynamic communication link state, resulting in poor data transmission security, low efficiency and high communication delay, thereby achieving the technical effect of improving data transmission security and efficiency and reducing communication delay. Figure 2 As shown, the low-latency communication optimization device in confidential communication includes: a lightweight encryption scheme establishing module 10, a dynamic segmentation granularity establishing module 20, a task segmentation quantity configuration module 30, an adaptation optimization result establishing module 40, and a granular communication transmission module 50.

[0048] A lightweight encryption scheme establishment module 10 is used to obtain a task data set of a communication task, extract encryption features and task features using the task data set, perform balanced encryption matching of the encryption database based on the encryption features and the task features, and establish a lightweight encryption scheme; a dynamic segmentation granularity establishment module 20 is used to read the real-time link status of the communication link after encrypting the communication task using the lightweight encryption scheme, and establish a dynamic segmentation granularity using the real-time link status; a task segmentation quantity configuration module 30 is used to establish a segmentation constraint based on the encryption feature, configure the segmentation quantity of the encrypted communication task using the dynamic segmentation granularity and the segmentation constraint, and establish a random segmentation perturbation coefficient; an adaptive optimization result establishment module 40 is used to perform adaptive optimization based on the real-time link status and the task granularity segmentation result after performing granular segmentation of the encrypted communication task using the random segmentation perturbation coefficient and the segmentation quantity, and establish an adaptive optimization result; a granular communication transmission module 50 is used to perform granular communication transmission of communication tasks using the adaptive optimization result.

[0049] Below, the specific configuration of the dynamic segmentation granularity establishment module 20 will be described in detail. The dynamic segmentation granularity establishment module 20 further includes: extracting multidimensional link state parameters according to the real-time link state, the multidimensional link state parameters include bandwidth parameters, delay parameters, packet loss rate parameters, and jitter parameters, and establishing a link state vector according to the multidimensional link state parameters; performing a retrospective analysis on the real-time link state, and generating a trend prediction state using the retrospective analysis results and the link state vector; configuring a comprehensive utility evaluation channel, and using the comprehensive utility evaluation channel to perform a segmentation utility evaluation based on the link state vector and the trend prediction state, and establish the dynamic segmentation granularity. Below, the specific configuration of the dynamic segmentation granularity establishment module 20 will continue to be described in detail. The dynamic segmentation granularity establishment module 20 further includes: performing a delay evaluation based on the delay evaluation sub-channel, and establishing a first utility evaluation feature: ; in, Characterizes the first utility evaluation feature, L is the real-time delay, Characterizes the current segmentation granularity, is the delay sensitivity parameter; Based on the reliability evaluation sub-channel, reliability evaluation is performed to establish the second utility evaluation feature: ; in, Characterize the second utility evaluation feature, PLR is the real-time packet loss rate, J is the real-time jitter, is the reliability sensitivity parameter; Based on the segmented adaptation evaluation sub-channel, the adaptability evaluation is performed and the third utility evaluation feature is established: ; in, Characterize the third utility evaluation characteristics, The recommended granularity for trend-based state calculations is To allocate adaptation range control parameters; The comprehensive utility evaluation channel is configured according to the first utility evaluation feature, the second utility evaluation feature, and the third utility evaluation feature. Below, the specific configuration of the task segmentation quantity configuration module 30 will be described in detail. The task segmentation quantity configuration module 30 further includes: establishing a minimum segmentation quantity table mapped with the encryption level, matching and calling the minimum segmentation quantity table through the encryption feature, and establishing the segmentation constraint; after constraining the encrypted communication task with the segmentation constraint, the encrypted communication task segmentation is determined through the dynamic segmentation granularity: A: If the first segmentation quantity generated according to the dynamic segmentation granularity is less than the segmentation constraint, the segmentation quantity is generated based on the segmentation constraint; B: If the first segmentation quantity generated according to the dynamic segmentation granularity is greater than or equal to the segmentation constraint, the segmentation quantity is generated based on the first segmentation quantity.

[0050] The specific configuration of the adaptive optimization result establishment module 40 will be described in detail below. The adaptive optimization result establishment module 40 further includes: determining the basic segmentation granularity according to the segmentation quantity and the communication task; using the random segmentation perturbation coefficient to perturb the basic segmentation granularity to establish the mapping segmentation granularity of each segmentation unit; adjusting the granularity of the initial segmentation unit based on the mapping segmentation granularity, and completing the encrypted communication task granularity segmentation according to the adjustment result.

[0051] The specific configuration of the adaptive optimization result establishment module 40 will be described in detail below. The adaptive optimization result establishment module 40 further includes: establishing link adaptation features, the link adaptation features include link bandwidth utilization, link delay adaptability, packet loss rate adaptability, and jitter adaptability; establishing task adaptation features, the task adaptation features include complete task completion features and task priority adaptation features; constructing a fitness function based on the link adaptation features and the task adaptation features to complete adaptive optimization.

[0052] The specific configuration of the granular communication transmission module 50 will be described in detail below. The granular communication transmission module 50 further includes: performing a security risk self-check on the communication transmission and establishing a security risk self-check result; generating a self-adjusting granularity correction coefficient based on the security risk self-check result; and performing granular communication transmission after performing the dynamic segmentation granularity correction by the self-adjusting granularity correction coefficient.

[0053] The low-latency communication optimization device in confidential communication provided by an embodiment of the present invention can execute the low-latency communication optimization method in confidential communication provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0054] Based on the foregoing embodiments, an embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the low-latency communication optimization method in confidential communication as described in any of the previous embodiments.

[0055] Although the present application makes various references to certain modules in the devices according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0056] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A low-latency communication optimization method in secure communication, characterized in that: The method comprises: Acquire a task data set of a communication task, extract encryption features and task features using the task data set, perform balanced encryption matching of an encryption database according to the encryption features and the task features, and establish a lightweight encryption scheme; After encrypting the communication task using the lightweight encryption scheme, reading the real-time link state of the communication link, and using the real-time link state to establish a dynamic segmentation granularity; Establishing a segmentation constraint based on the encryption feature, configuring the segmentation quantity of the encrypted communication task using the dynamic segmentation granularity and the segmentation constraint, and establishing a random segmentation perturbation coefficient; After performing encrypted communication task granularity segmentation using the random segmentation perturbation coefficient and the segmentation number, adaptive optimization is performed according to the real-time link state and the task granularity segmentation result to establish an adaptive optimization result; The adaptation optimization result is used to perform granular communication transmission of the communication task.

2. The low-latency communication optimization method in confidential communication according to claim 1, characterized in that: The using the real-time link state to establish a dynamic segmentation granularity includes: Extracting multidimensional link state parameters according to the real-time link state, the multidimensional link state parameters including bandwidth parameters, delay parameters, packet loss rate parameters, and jitter parameters, and establishing a link state vector according to the multidimensional link state parameters; Performing a backtracking analysis on the real-time link state, and generating a trend prediction state using the backtracking analysis result and the link state vector; A comprehensive utility evaluation channel is configured, and the comprehensive utility evaluation channel is used to perform segmentation utility evaluation based on the link state vector and the trend prediction state, so as to establish the dynamic segmentation granularity.

3. The low-latency communication optimization method in confidential communication according to claim 2, characterized in that: The configuration comprehensive utility evaluation channel includes: Based on the delayed evaluation subchannel, delayed evaluation is performed to establish the first utility evaluation feature: ； in, Characterizes the first utility evaluation feature, L is the real-time delay, Characterizes the current segmentation granularity, is the delay sensitivity parameter; Based on the reliability evaluation sub-channel, reliability evaluation is performed to establish the second utility evaluation feature: ； in, Characterize the second utility evaluation feature, PLR is the real-time packet loss rate, J is the real-time jitter, is the reliability sensitivity parameter; Based on the segmented adaptation evaluation sub-channel, the adaptability evaluation is performed and the third utility evaluation feature is established: ； in, Characterize the third utility evaluation characteristics, The recommended granularity for trend-based state calculations is To allocate adaptation range control parameters; A comprehensive utility evaluation channel is configured according to the first utility evaluation feature, the second utility evaluation feature, and the third utility evaluation feature.

4. The low-latency communication optimization method in confidential communication according to claim 1, characterized in that: The establishing of the segmentation constraint based on the encryption feature and configuring the segmentation quantity of the encrypted communication task by using the dynamic segmentation granularity and the segmentation constraint include: Establishing a minimum segmentation quantity table mapped to the encryption level, performing a matching call on the minimum segmentation quantity table through the encryption feature, and establishing the segmentation constraint; After constraining the encrypted communication task with the segmentation constraint, the encrypted communication task segmentation is determined by the dynamic segmentation granularity: A: if the first number of segments generated according to the dynamic segmentation granularity is smaller than the segmentation constraint, generating the number of segments based on the segmentation constraint; B: If the first segmentation number generated according to the dynamic segmentation granularity is greater than or equal to the segmentation constraint, the segmentation number is generated based on the first segmentation number.

5. The low-latency communication optimization method in confidential communication according to claim 1, characterized in that: The encrypted communication task granularity segmentation using the random segmentation disturbance coefficient and the segmentation quantity includes: Determine a basic segmentation granularity according to the segmentation quantity and the communication task; Using the random segmentation perturbation coefficient to perturb the basic segmentation granularity, and establishing a mapping segmentation granularity for each segmentation unit; The granularity of the initial segmentation unit is adjusted based on the mapping segmentation granularity, and the encrypted communication task granularity segmentation is completed according to the adjustment result.

6. The low-latency communication optimization method in confidential communication according to claim 1, characterized in that: The performing adaptation and optimization according to the real-time link status and the task granularity segmentation result to establish the adaptation and optimization result includes: Establishing link adaptation characteristics, wherein the link adaptation characteristics include link bandwidth utilization, link delay adaptability, packet loss rate adaptability, and jitter adaptability; Establishing task adaptation features, wherein the task adaptation features include complete task completion features and task priority adaptation features; A fitness function is constructed according to the link adaptation characteristics and the task adaptation characteristics to complete the adaptation optimization.

7. The low-latency communication optimization method in confidential communication according to claim 1, characterized in that: The method further comprises: Conduct security risk self-inspection on communication transmission and establish security risk self-inspection results; Generate a self-adjusting granularity correction coefficient based on the safety risk self-check result; After the dynamic segmentation granularity correction is performed by the self-adjusting granularity correction coefficient, granularity communication transmission is performed.

8. Low-latency communication optimization device in confidential communication, characterized in that: The device is used to implement the low-latency communication optimization method in confidential communication according to any one of claims 1 to 7, and the device includes: A lightweight encryption scheme establishment module is used to obtain a task data set of a communication task, extract encryption features and task features using the task data set, perform balanced encryption matching of an encryption database according to the encryption features and the task features, and establish a lightweight encryption scheme; A dynamic segmentation granularity establishment module, used to encrypt the communication task using the lightweight encryption scheme, read the real-time link state of the communication link, and establish a dynamic segmentation granularity using the real-time link state; A task segmentation quantity configuration module, used to establish segmentation constraints based on the encryption feature, configure the segmentation quantity of the encrypted communication task using the dynamic segmentation granularity and the segmentation constraints, and establish a random segmentation perturbation coefficient; An adaptive optimization result establishment module is used to perform adaptive optimization according to the real-time link state and the task granularity segmentation result after performing encrypted communication task granularity segmentation using the random segmentation perturbation coefficient and the segmentation number, and establish an adaptive optimization result; The granular communication transmission module is used to perform granular communication transmission of communication tasks using the adaptation optimization result.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the low-latency communication optimization method in confidential communication as described in any one of claims 1 to 7.

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