Methods and systems for encrypted transmission of network communication information

Abstract

This invention relates to the field of network security protocol technology, specifically to a method and system for encrypted transmission of network communication information, comprising the following steps: collecting transmission delay and queuing delay and summing them to generate a total delay; normalizing the error to form a delay redundancy mask; adjusting the encryption level and key length based on the judgment interval; constructing a topology mapping based on node processing capabilities and communication distance; allocating tasks and generating a node task table using the HEFT algorithm; generating a control factor using weighted averaging; optimizing the encryption level and task allocation; and improving transmission security and efficiency. In this invention, by collecting delay, extracting errors and normalizing them to generate a mask, dynamically adjusting the encryption level and key length to cope with fluctuations, optimizing task allocation based on node capabilities and communication distance, improving efficiency using the HEFT algorithm, and generating a control factor using weighted averaging to avoid over- or under-encryption, the invention enhances transmission security and efficiency.

Description

Technical Field

[0001] This invention relates to the field of network security protocol technology, and in particular to a method and system for encrypting and transmitting network communication information. Background Technology

[0002] The field of network security protocol technology encompasses methods and mechanisms for ensuring secure information transmission during network communication. Its core content involves developing and applying security protocols to prevent unauthorized access, tampering, or forgery of data during transmission. This primarily covers aspects such as authentication, key distribution, data encryption, integrity verification, and communication session control. The entire network security protocol system provides multi-layered security mechanisms based on various application scenarios and transmission requirements, including data encryption and security controls at the link layer, network layer, and application layer. This ensures that both communicating parties can establish a trusted transmission channel in insecure network environments and, at the technical level, standardizes the interaction processes and information processing rules for secure communication.

[0003] The network communication information encryption transmission method and system refers to the technical solution for transmitting information through specific encryption methods in network communication. It covers encryption and decryption operations during the transmission of communication data, including key generation and distribution methods, symmetric or asymmetric encryption methods, and protocol-based encrypted handshake processes. Specifically, it uses cryptographic algorithms to encrypt and decrypt data, and verifies the identities of both communicating parties through an authentication mechanism defined by the protocol. The encrypted transmission of network communication information is completed through the steps of establishing an encrypted session, exchanging necessary key materials, and executing information encoding and transmission.

[0004] While existing technologies offer a relatively sound theoretical framework for ensuring information transmission security, they exhibit significant shortcomings in practical operation. First, existing security mechanisms are often fixed and fail to dynamically adjust encryption strategies based on the actual network environment. This results in the use of a uniform encryption level and key length even under conditions of high network load, high latency, or bandwidth fluctuations, failing to fully utilize network resources. Second, current network encryption processes typically ignore the actual processing capacity and load of routing nodes. This can lead to over-allocation of encryption tasks on nodes with limited computing resources, impacting overall system efficiency, or underutilization of computing resources on nodes with lower loads. Furthermore, existing technologies tend to have a simplistic consideration of communication distance, failing to comprehensively account for the impact of multiple topologies and geographical distributions, resulting in encryption strategies that may not be optimal in various scenarios. Finally, existing encryption control mechanisms are usually based on fixed protocols and fail to adaptively adjust according to dynamic parameters such as latency. This can easily lead to over-encryption or excessive decryption loads in high-latency network environments, thereby affecting system efficiency and communication reliability. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for encrypted transmission of network communication information, comprising the following steps:

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for encrypted transmission of network communication information, comprising the following steps:

[0007] S1: Collect the end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel and sum them to obtain the real-time total delay. Extract the error between the current summing result and the previous period's summing result, and combine the multi-server queuing delay parameters for normalization to generate a delay redundancy mask.

[0008] S2: Based on the aforementioned delay redundancy mask, determine the mask interval and divide it into an enhancement area and a buffer. Adjust the nesting level and length of the key according to the interval to generate an encryption structure parameter set.

[0009] S3: Based on the encrypted structure parameter set, collect server processing capacity and load parameters and calculate the available computing power of the server. At the same time, collect link communication distance parameters and combine them for mapping to construct a topology capability mapping table.

[0010] S4: Input the encrypted structure parameter set and topology capability mapping table into the HEFT algorithm to allocate encryption tasks, match the encryption tasks according to the available computing power of the server, and generate an encryption task table.

[0011] S5: The topology capability mapping table and the encryption task table are used to generate an encryption control factor through a weighted average model. The encryption nesting level depth and task allocation method are adjusted according to the encryption control factor and compared with the actual time consumption to generate the information encryption transmission result.

[0012] As a further aspect of the present invention, the delay redundancy mask includes a normalization coefficient and a real-time total delay; the encryption structure parameter set specifically refers to nesting level parameters, key length parameters, and interval category parameters; the topology capability mapping table includes server available computing power, link communication distance, and load parameters; the encryption task table specifically includes a task allocation matrix, server matching index, and computing resource distribution; and the information encryption transmission result includes an encryption control factor, nesting level depth, and task allocation method.

[0013] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0014] S101: Collect end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel. Summate the collected end-to-end transmission delay parameters and multiple server queuing delay parameters to generate the real-time total delay.

[0015] S102: Based on the real-time total delay, obtain the delay value obtained by summing the previous period, calculate the difference between the two and use it as the offset of the current delay change, and at the same time call the queuing delay parameters of multiple routing nodes, combine them with the offset and normalize them to obtain the normalized offset coefficient.

[0016] S103: Distribute the queuing delay parameters of the multi-node according to the normalized offset coefficient to form a corresponding mask, and map it with the currently calculated difference to generate a delay redundancy mask.

[0017] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0018] S201: Obtain the mask interval based on the time delay redundancy mask parameter, classify the multiple values ​​into enhancement area or buffer according to the interval threshold, establish the interval correspondence after classification, and generate mask interval division value.

[0019] S202: Adjust the key nesting level based on the mask interval division value, increase the number of levels corresponding to the enhancement area, decrease the number of levels corresponding to the buffer area, and sort out the multi-interval level parameters to obtain the level adjustment coefficient;

[0020] S203: Call the level adjustment coefficient and combine it with the mask interval division value to simultaneously correct the key length. Combine the corrected level parameters with the length parameters to generate an encryption structure parameter set.

[0021] As a further aspect of the present invention, the interval threshold is set by statistically detecting the numerical distribution of the delay redundancy mask parameter, calculating the maximum and minimum values, and then dividing the boundary proportionally according to the range interval.

[0022] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0023] S301: Based on the encrypted structure parameter set, collect the server processing capacity and load parameters, compare the collected processing capacity value with the load occupancy value, calculate the server's current available computing capacity, and generate the server computing capacity.

[0024] S302: Call the server's computing power and collect the link communication distance parameters, map the link communication distance to the computing power value, pair the two types of parameters according to the combination of the same range, and generate a communication capability mapping value;

[0025] S303: Based on the communication capability mapping value and the encrypted structure parameter set, the computing capability values ​​under multiple link distances are combined and organized to bind the corresponding structure parameter sets, and a topology capability mapping table is generated.

[0026] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0027] S401: Based on the encrypted structure parameter set, obtain the computing power of multiple servers, compare it with the available computing power of the servers, allocate tasks according to the computing resources of the servers and the encryption task requirements, and obtain the task matching quantity.

[0028] S402: Based on the task matching quantity, match it with the computational requirements of the encryption task, call the HEFT algorithm to optimize the allocation of the encryption task, generate the execution schedule of the task among multiple servers, and obtain the task schedule list.

[0029] S403: Based on the task scheduling list, calculate the latency during the execution of multiple tasks, and generate an encrypted task table by combining the network conditions and computing capabilities among multiple nodes.

[0030] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0031] S501: Based on the topology capability mapping table and the encryption task table, call the weighted average model to calculate and normalize the capability values ​​and corresponding latency values ​​of multiple nodes according to the weight ratio, and generate the encryption control factor.

[0032] S502: Based on the encryption control factor, for the encryption nesting level parameter and task allocation method, detect the influence of the value on the number of encryption nesting levels and the task division ratio, and adjust the number of nesting levels and task allocation parameters according to the differences to obtain the encryption complexity coefficient.

[0033] S503: Call the encryption complexity coefficient and compare it with the actual time consumed by the task. Update the calculation process of information encryption transmission based on the comparison deviation result and generate information encryption transmission result.

[0034] As a further aspect of the present invention, the encryption complexity coefficient is calculated and set based on the computational complexity of the encryption algorithm, the number of nesting levels, the key length, the task allocation method, and the execution delay factor.

[0035] A network communication information encryption transmission system includes:

[0036] The delay analysis module collects end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel and sums them to obtain the real-time total delay. It extracts the error between the current summation result and the summation result of the previous period, and combines the normalization of the multi-server queuing delay parameters to generate a delay redundancy mask and transmit it to the delay discrimination module.

[0037] The delay discrimination module determines the mask interval based on the delay redundancy mask and divides it into an enhancement area and a buffer. It also adjusts the nesting level and length of the key according to the interval, generates an encrypted structure parameter set, and transmits it to the routing topology module.

[0038] The routing topology module, based on the encrypted structure parameter set, collects server processing capacity and load parameters and calculates the available computing power of the server. At the same time, it collects link communication distance parameters, performs combined mapping, constructs a topology capability mapping table, and transmits it to the task allocation module.

[0039] The task allocation module inputs the encrypted structure parameter set and topology capability mapping table into the HEFT algorithm to allocate encryption tasks, matches the encryption tasks according to the available computing power of the server, generates an encryption task table, and transmits it to the encryption control module.

[0040] The encryption control module generates an encryption control factor by using a weighted average model to map the topology capability table and the encryption task table. Based on the encryption control factor, it adjusts the encryption nesting level depth and task allocation method and compares the results with the actual time consumption to generate the encrypted information transmission result.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0042] In this invention, by collecting transmission delay parameters and routing node queuing delay parameters and summing them to obtain the real-time total delay, the actual transmission delay of data in the network can be accurately reflected. Then, by performing error extraction and normalization processing with the results from the previous period, a delay redundancy mask is generated. This dynamic adjustment method can respond to network delay fluctuations and load changes in real time, thereby optimizing the allocation of encryption levels and key lengths. Based on the determination of the delay redundancy mask, the encryption control interval is divided into an enhancement zone and a buffer zone, which can effectively improve the flexibility of the encryption system, enabling encryption tasks to automatically adjust according to actual network conditions, ensuring that the encryption strength in data transmission always adapts to network conditions. Furthermore, by comprehensively analyzing and mapping multiple parameters such as routing node processing capacity, load, and communication distance, tasks can be dynamically allocated according to the specific network topology, improving the efficiency of computing resource utilization. The introduction of the HEFT algorithm, based on the actual computing capacity of nodes, not only optimizes the allocation of encryption tasks but also improves the overall processing capacity and efficiency of the network. The application of the weighted average model makes the generation of encryption control factors more accurate, further optimizing the encryption levels and task allocation methods, avoiding over-encryption or under-encryption, and ensuring that the encryption effect matches the actual time consumption. This comprehensive dynamic adjustment and task optimization method effectively enhances the security and efficiency of encrypted information transmission. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of the steps of the present invention;

[0045] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0046] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0047] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0048] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0049] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0050] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0051] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0052] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0053] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0054] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0055] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0056] Please see Figure 1 This invention provides a method for encrypted transmission of network communication information, comprising the following steps:

[0057] S1: Collect the end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel and sum them to obtain the real-time total delay. Extract the error between the current summing result and the previous period's summing result, and combine the multi-server queuing delay parameters for normalization to generate a delay redundancy mask.

[0058] S2: Based on the time-delay redundancy mask, determine the mask interval and divide it into an enhancement area and a buffer. Adjust the nesting level and length of the key according to the interval to generate an encryption structure parameter set.

[0059] S3: Based on the encrypted structure parameter set, collect server processing capacity and load parameters and calculate the available computing capacity of the server. At the same time, collect link communication distance parameters and combine them for mapping to build a topology capability mapping table.

[0060] S4: Input the encrypted structure parameter set and topology capability mapping table into the HEFT algorithm to allocate encryption tasks, match the encryption tasks according to the available computing power of the server, and generate an encryption task table.

[0061] S5: The topology capability mapping table and the encryption task table are mapped together. An encryption control factor is generated through a weighted average model. The encryption nesting level depth and task allocation method are adjusted according to the encryption control factor and compared with the actual time consumption to generate the information encryption transmission result.

[0062] The latency redundancy mask includes the normalization coefficient and the total real-time latency. The encryption structure parameter set specifically refers to the nesting level parameter, key length parameter, and interval category parameter. The topology capability mapping table includes the available computing power of the server, the link communication distance, and the load parameter. The encryption task table specifically includes the task allocation matrix, the server matching index, and the distribution of computing resources. The information encryption transmission result includes the encryption control factor, the nesting level depth, and the task allocation method.

[0063] Please see Figure 2 The specific steps of S1 are as follows:

[0064] S101: Collect end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel. Summate the collected end-to-end transmission delay parameters and multiple server queuing delay parameters to generate the real-time total delay.

[0065] The message sending time is obtained by recording the client timestamp, for example, the message sending time is 1530 milliseconds. The message entry time is obtained by monitoring the transmission channel entry, for example, the entry time is 1535 milliseconds. The difference is calculated to obtain the end-to-end transmission delay parameter of 5 milliseconds. At the same time, the queuing delay parameters of multiple servers are collected by collecting the server kernel logs. For example, the message arrival time of server 1 is 1540 milliseconds, the processing start time is 1543 milliseconds, the difference is 3 milliseconds; the message arrival time of server 2 is 1545 milliseconds, the processing start time is 1548 milliseconds, the difference is 3 milliseconds; the message arrival time of server 3 is 1550 milliseconds, the processing start time is 1554 milliseconds, the difference is 4 milliseconds. The end-to-end transmission delay parameter of 5 milliseconds is added to the queuing delay parameters of server 1 (3 milliseconds), server 2 (3 milliseconds), and server 3 (4 milliseconds). The summation process is 5 + 3 = 8 milliseconds, 8 + 3 = 11 milliseconds, 11 + 4 = 15 milliseconds, generating a real-time total delay of 15 milliseconds.

[0066] S102: Based on the real-time total latency, obtain the latency value obtained by summing the previous period, calculate the difference between the two and use it as the offset of the current latency change, and at the same time call the queuing delay parameters of multiple routing nodes, combine them with the offset and normalize them to obtain the normalized offset coefficient.

[0067] Based on a total real-time latency of 15 milliseconds, the latency value of 12 milliseconds for the same service path in the previous cycle is retrieved from the database. The difference is calculated, and a subtraction operation is performed to subtract 12 milliseconds from 15 milliseconds to obtain an offset of 3 milliseconds. At the same time, the queuing latency parameters of the routing nodes are called, including the latency of node A (2.1 milliseconds), node B (1.8 milliseconds), and node C (3.3 milliseconds). The offset of 3 milliseconds is added to the latency of node A (2.1 milliseconds) to get 5.1 milliseconds, and added to the latency of node B (1.8 milliseconds) to get 4.8 milliseconds. Adding the delay of node A to node C (3.3 milliseconds) gives 6.3 milliseconds. Summing all the sums gives 5.1 + 4.8 + 6.3 = 16.2 milliseconds. Performing a normalization operation on node A (5.1 / 16.2) gives 0.315, on node B (4.8 / 16.2) gives 0.296, and on node C (6.3 / 16.2) gives 0.389. The normalized offset coefficients are generated as follows: Node A: 0.315, Node B: 0.296, Node C: 0.389.

[0068] Table 1: Router Node Delay Parameter Table

[0069]

[0070] S103: Distribute the adjustment of the queuing delay parameters of the multi-node according to the normalized offset coefficient to form the corresponding mask, and map it with the difference in the current calculation to generate a delay redundancy mask.

[0071] Based on the normalized offset coefficients: node A: 0.315, node B: 0.296, node C: 0.389, perform a multiplication operation on the queuing delay parameter of node A (2.1 milliseconds): 2.1 × 0.315 = 0.6615 milliseconds; perform a multiplication operation on the queuing delay parameter of node B (1.8 milliseconds): 1.8 × 0.296 = 0.5328 milliseconds; and perform a multiplication operation on the queuing delay parameter of node C (3.3 milliseconds): 3.3 × 0.389 = 1.2837 milliseconds, forming a mask vector [0.6615, 0.5328, ...]. [1.2837] Perform a multiplication operation on the mask elements and the difference amount of 3 milliseconds. The mapping calculation for node A is 0.6615 × 3 = 1.9845 milliseconds, the mapping calculation for node B is 0.5328 × 3 = 1.5984 milliseconds, and the mapping calculation for node C is 1.2837 × 3 = 3.8511 milliseconds. Generate a delay redundancy mask for node A: 1.9845 milliseconds, node B: 1.5984 milliseconds, and node C: 3.8511 milliseconds.

[0072] Please see Figure 3 The specific steps of S2 are as follows:

[0073] S201: Obtain the mask interval based on the time delay redundancy mask parameter, classify the multiple values ​​into the enhancement area or the buffer according to the interval threshold, and establish the interval correspondence after the classification is completed to generate the mask interval division value.

[0074] Based on the generated delay redundancy mask parameters (Node A: 1.9845 ms, Node B: 1.5984 ms, Node C: 3.8511 ms), the mask parameter values ​​are called one by one to check the value range. For Node A, the value of 1.9845 ms is compared with the lower limit of the preset interval threshold of 1.5 ms; 1.9845 > 1.5 is true. It is also compared with the upper limit of 2.5 ms; 1.9845 < 2.5 is true, indicating it is in the buffer interval. For Node B, the value of 1.5984 ms is compared; 1.5984 > 1.5 and 1.5984 < 2.5 are true, indicating it is in the buffer interval. For Node C, the value of 3.8511 ms is compared; 3.8511 > 2.5 is true, indicating it is in the enhancement interval. The interval threshold is set with reference to delay data statistics. A baseline value is obtained by calculating the average value of the mask parameters over 10 historical cycles, for example, a mask value sequence [1.2, 1.8, 2.0, 1]. [5, 2.3, 3.0, 3.5, 1.7, 2.1, 3.2] milliseconds, sum all values: 1.2 + 1.8 = 3.0, 3.0 + 2.0 = 5.0, 5.0 + 1.5 = 6.5, 6.5 + 2.3 = 8.8, 8.8 + 3.0 = 11.8, 11.8 + 3.5 = 15.3, 15.3 + 1.7 = 17.0, 17.0 + 2.1 = 19.1, 19.1 + 3.2 = 22.3 milliseconds, divide by the period. The average value of 10 is 2.23 milliseconds. Based on the average value of 2.23 milliseconds, the buffer threshold range is set to [1.5 milliseconds, 2.5 milliseconds], and the enhancement zone threshold range is (2.5 milliseconds, ∞). According to the judgment result, nodes A and B are classified as buffers, and node C is classified as enhancement zone. The interval correspondence is established, and the buffer is mapped to the identifier 0, and the enhancement zone is mapped to the identifier 1. The mask interval division values ​​are generated as follows: node A: 0, node B: 0, node C: 1.

[0075] Table 2: Threshold Table for Mask Parameter Range

[0076]

[0077] S202: Adjust the key nesting level based on the mask interval division value, increase the number of levels corresponding to the enhancement area, decrease the number of levels corresponding to the buffer area, and sort out the multi-interval level parameters to obtain the level adjustment coefficient;

[0078] Based on the mask interval partition values ​​of node A: 0, node B: 0, and node C: 1, the key nesting level is adjusted. Node A is partitioned with value 0, indicating it belongs to the buffer zone; the number of levels is reduced by 1 (initial level 3, reduced to 2). Node B is partitioned with value 0, indicating it belongs to the buffer zone; the number of levels is reduced by 1 (2). Node C is partitioned with value 1, indicating it belongs to the enhancement zone; the number of levels is increased by 1 (4). The multi-interval level parameters are then organized: node A has a level value of 2, node B has a level value of 2, and node C has a level value of 4. The level adjustment coefficients are calculated: 2 / initial level 3 is divided by node A, resulting in 0.6667; 2 / 3 is divided by node B, resulting in 0.6667; and 4 / 3 is divided by node C, resulting in 1.3333. The resulting level adjustment coefficients are: Node A: 0.6667, Node B: 0.6667, and Node C: 1.3333.

[0079] S203: Call the level adjustment coefficient and combine it with the mask interval division value to simultaneously correct the key length. Combine the corrected level parameter with the length parameter to generate an encrypted structure parameter set.

[0080] The system calls the hierarchical adjustment coefficients at nodes A (0.6667), B (0.6667), and C (1.3333) and combines them with the mask interval division values ​​at nodes A (0), B (0), and C (1) to synchronously correct the key length. Based on the division values, node A (value 0) is used, indicating a buffer. The initial key length is 128 bits, and a length reduction operation is performed. The reduction ratio is based on a coefficient of 0.6667, calculating the corrected length as 128 × 0.6667 = 85.3376 bits. Rounding down, we get 85 bits. Node B (value 0) is then called, again indicating a buffer. The 128 × 0.6667 = 85.3376 bits are rounded down to 85 bits. Finally, node C (value 1) is called. If the region is identified as an enhancement area, a length increase operation is performed. The increase ratio is based on a reference coefficient of 1.3333. The corrected length is calculated as 128 × 1.3333 = 170.6624 bits, which is rounded down to 171 bits. The corrected hierarchical parameters node A (level 2), node B (level 2), and node C (level 4) are combined with the length parameters node A (length 85 bits), node B (length 85 bits), and node C (length 171 bits). Node A is combined into a tuple (2, 85), node B into a tuple (2, 85), and node C into a tuple (4, 171). This generates the encrypted structure parameter set A: (2, 85), B: (2, 85), and C: (4, 171).

[0081] Please see Figure 4 The specific steps of S3 are as follows:

[0082] S301: Collect server processing capacity and load parameters based on encrypted structure parameter set, compare the collected processing capacity value with the load occupancy value, calculate the server's current available computing capacity, and generate the server computing capacity.

[0083] Based on the encrypted structure parameter set A: (2, 85), B: (2, 85), C: (4, 171), server processing capacity parameters are collected. For example, the processing capacity of node A is obtained through system performance monitoring tools, showing a peak CPU computing power of 1000 GFlops. Load utilization parameters are collected through the operating system's real-time interface, showing a current CPU utilization of 70%. The processing capacity value of 1000 GFlops and the load utilization value of 70% are compared. The load utilization value is compared with a preset high load threshold of 70%. The threshold is set with reference to a 10-cycle load data sequence [65, 68, 72, 75, 70, 69, 73, 71, 74, 70]%, and all values ​​are summed: 65 + 68 = 133, 133 + 72. =205, 205+75=280, 280+70=350, 350+69=419, 419+73=492, 492+71=563, 563+74=637, 637+70=707%, divided by the number of periods (10), approximately equals the average value of 70%. Setting the high load threshold to 70%, 70% is considered a high load range. Calculate the server's current available computing power: perform subtraction (1 - 70%) divided by 100, resulting in 0.3; perform multiplication (1000 GFlops multiplied by 0.3), resulting in 300 GFlops. Similarly, node B has a data acquisition and processing capacity of 900 GFlops and a load of 65%. Determining that 65% is less than 70% indicates a low load range, calculate the 900... (1-0.65)=315GFlops, node C has a data acquisition and processing capacity of 1200GFlops, and the load is 50%. Since 50% is less than 70% (low load), calculate 1200... (1-0.5)=600GFlops, generating server computing power: Node A: 300GFlops, Node B: 315GFlops, Node C: 600GFlops.

[0084] S302: Call the server's computing power and collect the link communication distance parameters, map the link communication distance to the computing power value, pair the two types of parameters according to the combination of the same range, and generate the communication capability mapping value.

[0085] The system calls upon the server's computing power {A: 300 GFlops, B: 315 GFlops, C: 600 GFlops}, and collects link communication distance parameters. For example, the distance from node A to the data center is calculated using GPS coordinates to be 50 kilometers, node B is 150 kilometers away, and node C is 300 kilometers away. The system then maps the link communication distance to the computing power values, setting distance range thresholds. Referring to international network standards, distances less than 100 kilometers are considered short-range, 100 to 500 kilometers are mid-range, and greater than 500 kilometers are long-range. The mapping rule defines a multiplier of 1.0 for short-range, 0.8 for mid-range, and 0.5 for long-range. Node A, with a distance of 50 kilometers (less than 100), is classified as short-range, and the mapped value is calculated to be 300. 1.0 = 300, node B is 150 km away, falling between 100 and 500, classified as medium range, mapping value calculated as 315. 0.8 = 252, node C is 300 km away, the mapping value is calculated to be 600. 0.8 = 480, generating communication capability mapping values: Node A: 300, Node B: 252, Node C: 480.

[0086] S303: Based on the communication capability mapping value and the encrypted structure parameter set, the computing capability values ​​under multiple link distances are combined and organized to bind the corresponding structure parameter sets to generate a topology capability mapping table.

[0087] Based on the communication capability mapping values ​​{A: 300, B: 252, C: 480} and the encryption structure parameter set A: (2, 85), B: (2, 85), C: (4, 171), the values ​​are combined and organized. The communication capability mapping value 300 of node A and the encryption structure parameter level 2 length of 85 bits are bound into a tuple (300, 2, 85). The mapping value 252 of node B and the level 2 length of 85 bits are bound into a tuple (252, 2, 85). The mapping value 480 of node C and the level 4 length of 171 bits are bound into a tuple (480, 4, 171). The computing capability values ​​under multi-link distance are bound to the corresponding structure parameter sets to generate a topology capability mapping table.

[0088] Table 3: Topology Capability Mapping Table

[0089]

[0090] Please see Figure 5 The specific steps of S4 are as follows:

[0091] S401: Based on the encrypted structure parameter set, obtain the computing power of multiple servers, compare it with the available computing power of the servers, allocate tasks according to the computing resources of the servers and the encryption task requirements, and obtain the task matching quantity.

[0092] Based on the encrypted structure parameter set A: (2, 85), B: (2, 85), C: (4, 171), the computing power of multiple servers is obtained. The computing power parameters of node A are collected through the server performance monitoring interface, showing a peak CPU processing capacity of 1000 GFlops for node A, 900 GFlops for node B, and 1200 GFlops for node C. The available computing power parameters of the servers are obtained from the S301 results, showing available capacity of 300 GFlops for node A, 315 GFlops for node B, and 600 GFlops for node C. A comparison operation is performed, and the resource utilization value is calculated using the formula: available capacity divided by computing power multiplied by 100%. For node A, the calculation is 300 / 1000 * 100 = 30%; for node B, it is 315 / 900 * 100 = 35%; and for node C, it is 600 / 1200 * 1000 * 1000 = 35%. 100 = 50%, set the load threshold range, referencing the 10-cycle utilization data sequence [25, 30, 40, 45, 35, 38, 42, 48, 32, 28]%, summing all values: 25+30=55, 55+40=95, 95+45=140, 140+35=175, 175+38=213, 213+42=255, 255+48=303, 303+32=335, 335+28= 363%, divided by the number of periods (10), yields an average of 36.3%. A low-load threshold range is set [0%, 40%], and a high-load threshold range is set (40%, 100%). Node A is considered low-load if its value is 30% less than 40%, node B is low-load if its value is 35% less than 40%, and node C is high-load if its value is 50% greater than 40%. Tasks are allocated based on computing resources and encryption task requirements, defining task requirement parameters: Task 1 size 200 GFlops, Task 2... The task size is 150 GFlops, the task size is 300 GFlops, the available capacity of node A is 300 GFlops, the requirement of task 1 is 200 GFlops which is less than 300 GFlops, so the allocation operation is performed. Node A allocates task 1, and the remaining capacity is 300 - 200 = 100 GFlops. The available capacity of node B is 315 GFlops, the requirement of task 2 is 150 GFlops which is less than 315 GFlops, so task 2 is allocated, and the remaining capacity is 315 - 150 = 165 GFlops. The available capacity of node C is 600 GFlops, the requirement of task 3 is 300 GFlops which is less than 600 GFlops, so task 3 is allocated, and the remaining capacity is 600 - 300 = 300 GFlops. The task matching quantity is calculated, and the total allocated task size is summed to 200 + 150 + 300 = 650 GFlops, resulting in a total task matching quantity of 650 GFlops.

[0093] Table 4: Server Capacity and Task Allocation Table

[0094]

[0095] S402: Based on the task matching quantity, match it with the computational requirements of the encryption task, call the HEFT algorithm to optimize the allocation of the encryption task, generate the execution schedule of the task among multiple servers, and obtain the task schedule list.

[0096] Based on a task matching capacity of 650 GFlops, the computational requirements of the encryption tasks are matched. The task requirement parameters are: Task 1 size 200 GFlops, Task 2 size 150 GFlops, and Task 3 size 300 GFlops. The estimated execution time of each task on the server is calculated using the formula: task size divided by the available server capacity. Node A has an available capacity of 300 GFlops, so Task 1 execution time is approximately 200 / 300 ≈ 0.6667 hours. Node B has an available capacity of 315 GFlops, so Task 2 execution time is approximately 150 / 315 ≈ 0.4762 hours. Node C has an available capacity of 600 GFlops, so Task 3 execution time is approximately 300 / 600 ≈ 0.5 hours. The HEFT algorithm is then used to optimize the allocation, calculating task priorities and sorting them in ascending order of execution time. Task 2 (0.4762 hours) has the lowest priority and highest priority, followed by Task 1 (0.6667 hours) and then Task 3 (0.5 hours). The sorted task order is then determined. Given the list [Task 2, Task 3, Task 1], assign tasks to the server to minimize the total completion time. The initial time is 0 hours. Assign Task 2 to node B with a start time of 0 hours and an end time of 0 + 0.4762 = 0.4762 hours. Assign Task 3 to node C with a start time of 0 hours (parallel) and an end time of 0 + 0.5 = 0.5 hours. Assign Task 1 to node A with a start time of 0 hours and an end time of 0 + 0.6667 = 0.6667 hours. Calculate the total completion time. Take the maximum value max(0.4762, 0.5, 0.6667) = 0.6667 hours, organize the above content, node B executes task 2 for time period [0, 0.4762], node C executes task 3 for time period [0, 0.5], node A executes task 1 for time period [0, 0.6667], and get the task scheduling list: task 2, B, [0, 0.4762], task 3, C, [0, 0.5], task 1, A, [0, 0.6667].

[0097] S403: Based on the task scheduling list, calculate the latency during the execution of multiple tasks, and generate an encrypted task table by combining the network conditions and computing capabilities among multiple nodes.

[0098] Based on the task scheduling list, Task 2, B, [0, 0.4762], Task 3, C, [0, 0.5], Task 1, A, [0, 0.6667], the latency during multi-task execution is calculated. The task execution time periods are called. The latency of Task 2 is equal to the end time minus the start time: 0.4762 - 0 = 0.4762 hours. The latency of Task 3 is 0.5 - 0 = 0.5 hours. The latency of Task 1 is 0.6667 - 0 = 0.6667 hours. Considering the network conditions and computing power between multiple nodes, the latency between nodes is collected as a network condition parameter. The latency from node A to B is 0.02 hours obtained through ping testing. The latency from node B to C is 0.03 hours. Node C... The delay to A is 0.025 hours. Calculate the total task delay, including processing delay and network delay. The total delay for Task 2 is 0.4762 + 0 (no dependent network delay) = 0.4762 hours. The total delay for Task 3 is 0.5 + 0 = 0.5 hours. The total delay for Task 1 is 0.6667 + 0 = 0.6667 hours. Bind the task identifier, server node, and total delay value. Task 2 entry (Task 2, B, 0.4762), Task 3 entry (Task 3, C, 0.5), Task 1 entry (Task 1, A, 0.6667). Generate an encrypted task table (Task 2: B: 0.4762), (Task 3: C: 0.5), (Task 1: A: 0.6667).

[0099] Please see Figure 6 The specific steps of S5 are as follows:

[0100] S501: Based on the topology capability mapping table and the encryption task table, call the weighted average model to calculate and normalize the capability values ​​and corresponding latency values ​​of multiple nodes according to the weight ratio, and generate the encryption control factor.

[0101] Based on the topology capability mapping table (Table 3: Node A computing power 300 GFlops, encryption level 2, key length 85 bits; Node B 252 GFlops, level 2, key length 85 bits; Node C 480 GFlops, level 4, key length 171 bits) and the encryption task table (Node A task 1 latency 0.6667 hours, Node B task 2 latency 0.4762 hours, Node C task 3 latency 0.5 hours), a weighted average model is called, and capability value weights are set. (Referring to security requirements specifications, a weight of 0.7 is assigned to higher security levels when the encryption level is ≥3, and 0.5 is assigned to levels <3; Node A at level 2 receives a weight of 0.5, Node B at level 2 receives a weight of 0.5, and Node C at level 4 receives a weight of 0.7). Delay value weight. (Based on real-time requirements, the latency threshold is set to 0.6 hours. Nodes exceeding the threshold are assigned a weight of 0.4, and those within the threshold are assigned 0.2. Node A's latency is 0.6667 (>0.6), so it's assigned 0.4; Node B's latency is 0.4762 (<0.6), so it's assigned 0.2; Node C's latency is 0.5 (<0.6), so it's assigned 0.2.) Calculate the node weights. Node A: Node B: Node C: Sum of weighted values Normalized node A: Node B: Node C: The encryption control factors are generated as follows: Node A: 0.2982, Node B: 0.2429, and Node C: 0.4589.

[0102] S502: Based on the encryption control factor, the influence of the encryption nesting level parameters and task allocation method on the encryption nesting level and task division ratio is detected, and the nesting level and task allocation parameters are adjusted according to the differences to obtain the encryption complexity coefficient.

[0103] Based on encryption control factors (Node A: 0.2982, Node B: 0.2429, Node C: 0.4589), and considering the encryption nesting level parameters (Node A: Level 2, Node B: Level 2, Node C: Level 4) and task allocation ratios (Node A: Task quantity 200 / 650 ≈ 0.3077, Node B: 150 / 650 ≈ 0.2308, Node C: 300 / 650 ≈ 0.4615), the difference between the control factors and the task ratios is detected. Node B: Node C: Set a difference threshold of 0.01 (calculated with reference to the average difference over 10 periods: sum of the sequence [0.008, 0.013, 0.009, 0.012, 0.007, 0.011, 0.010, 0.014, 0.006, 0.009] to 0.099, average 0.0099 ≈ 0.01). Determine if node A's difference is 0.0095 < 0.01 and no level adjustment is needed; if node B's difference is 0.0121 > 0.01 and a level increase is required; if node C's difference is 0.0026 < 0.01 and no level adjustment is needed, node B's original level 2 is increased by 1 to 3 levels. Simultaneously, adjust the task allocation ratio, keeping node B's original ratio unchanged. Multiplying 0.2308 by the level adjustment factor 3 / 2 = 1.5 gives 0.3462. Renormalizing the total ratio gives 0.3077 + 0.3462 + 0.4615 = 1.1154. The new ratios for node A are 0.3077 / 1.1154 ≈ 0.2759, for node B 0.3462 / 1.1154 ≈ 0.3104, and for node C 0.4615 / 1.1154 ≈ 0.4137. This yields the encryption complexity coefficients for nodes A (level 2, ratio 0.2759), B (level 3, ratio 0.3104), and C (level 4, ratio 0.4137).

[0104] Table 5: Complexity Coefficient Adjustment Table

[0105]

[0106] Table 5 shows the adjusted task ratio and encryption level.

[0107] S503: Call the encryption complexity coefficient and compare it with the actual time consumed by the task. Update the calculation process of information encryption transmission based on the comparison deviation result and generate the information encryption transmission result.

[0108] The encryption complexity coefficients for nodes A (2, 0.2759), B (3, 0.3104), and C (4, 0.4137) are compared with the actual task times (task 1 time for node A: 0.6667 hours, task 2 time for node B: 0.4762 hours, and task 3 time for node C: 0.5 hours) to calculate the expected time. The formula is: task size × level coefficient / available capacity. The baseline value for the level coefficient is 1.0 for level 2. The expected time for node A is: Hourly, node B level 3 coefficient 1.5, expected: Hourly, node C level 4 coefficient 2.0, expected: Hourly, calculate the deviation at node A: Node B: Node C: Set a deviation threshold of 0.3 (based on the communication standard protocol, the maximum allowed latency fluctuation rate is 30%). Node B's deviation of 0.2381 < 0.3 does not need to be updated, while node C's deviation of 0.5 > 0.3 needs to be updated. Node C adopts a new hierarchical coefficient of 1.8 (reduced by 0.2 from the baseline of 2.0), and recalculates the task time. Hours, deviation Still exceeding the threshold, the second update coefficient is 1.6, and the time consumed is... Hours, deviation The time taken to generate encrypted transmission results is equal to the threshold: Node A: 0.6667 hours, Node B: 0.4762 hours, Node C: 0.8 hours.

[0109] Please see Figure 7 A network communication information encryption transmission system, including:

[0110] The delay analysis module collects end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel and sums them to obtain the real-time total delay. It extracts the error between the current summation result and the summation result of the previous period, and combines the normalization of the multi-server queuing delay parameters to generate a delay redundancy mask and transmit it to the delay discrimination module.

[0111] The delay discrimination module determines the mask interval based on the delay redundancy mask and divides it into an enhancement area and a buffer. It also adjusts the nesting level and length of the key according to the interval, generates an encrypted structure parameter set, and passes it to the routing topology module.

[0112] The routing topology module, based on the encrypted structure parameter set, collects server processing capacity and load parameters and calculates the available computing power of the server. At the same time, it collects link communication distance parameters, combines and maps them, constructs a topology capability mapping table, and transmits it to the task allocation module.

[0113] The task allocation module inputs the encrypted structure parameter set and topology capability mapping table into the HEFT algorithm to allocate encryption tasks, matches the encryption tasks according to the available computing power of the server, generates an encryption task table, and transmits it to the encryption control module.

[0114] The encryption control module maps the topology capability table and the encryption task table, generates an encryption control factor through a weighted average model, adjusts the encryption nesting level depth and task allocation method according to the encryption control factor, and compares it with the actual time consumption to generate the information encryption transmission result.

[0115] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for encrypted transmission of network communication information, characterized in that, Includes the following steps: S1: Collect the end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel and sum them to obtain the real-time total delay. Extract the error between the current summing result and the previous period's summing result, and combine the multi-server queuing delay parameters for normalization to generate a delay redundancy mask. S2: Based on the aforementioned delay redundancy mask, determine the mask interval and divide it into an enhancement area and a buffer. Adjust the nesting level and length of the key according to the interval to generate an encryption structure parameter set. S3: Based on the encrypted structure parameter set, collect server processing capacity and load parameters and calculate the available computing power of the server. At the same time, collect link communication distance parameters and combine them for mapping to construct a topology capability mapping table. S4: Input the encrypted structure parameter set and topology capability mapping table into the HEFT algorithm to allocate encryption tasks, match the encryption tasks according to the available computing power of the server, and generate an encryption task table. S5: The topology capability mapping table and the encryption task table are used to generate an encryption control factor through a weighted average model. The encryption nesting level depth and task allocation method are adjusted according to the encryption control factor and compared with the actual time consumption to generate the information encryption transmission result.

2. The method for encrypted transmission of network communication information according to claim 1, characterized in that, The delay redundancy mask includes a normalization coefficient and a real-time total delay. The encryption structure parameter set specifically refers to nesting level parameters, key length parameters, and interval category parameters. The topology capability mapping table includes available server computing power, link communication distance, and load parameters. The encryption task table specifically includes a task allocation matrix, server matching index, and computing resource distribution. The information encryption transmission result includes an encryption control factor, nesting level depth, and task allocation method.

3. The method for encrypted transmission of network communication information according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collect end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel. Summate the collected end-to-end transmission delay parameters and multiple server queuing delay parameters to generate the real-time total delay. S102: Based on the real-time total delay, obtain the delay value obtained by summing the previous period, calculate the difference between the two and use it as the offset of the current delay change, and at the same time call the queuing delay parameters of multiple routing nodes, combine them with the offset and normalize them to obtain the normalized offset coefficient. S103: Distribute the queuing delay parameters of the multi-node according to the normalized offset coefficient to form a corresponding mask, and map it with the currently calculated difference to generate a delay redundancy mask.

4. The method for encrypted transmission of network communication information according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Obtain the mask interval based on the time delay redundancy mask parameter, classify the mask interval into an enhancement area or a buffer according to the interval threshold, establish the interval correspondence after classification, and generate the mask interval division value. S202: Adjust the key nesting level based on the mask interval division value, increase the number of levels corresponding to the enhancement area, decrease the number of levels corresponding to the buffer area, and sort out the multi-interval level parameters to obtain the level adjustment coefficient; S203: Call the level adjustment coefficient and combine it with the mask interval division value to simultaneously correct the key length. Combine the corrected level parameters with the length parameters to generate an encryption structure parameter set.

5. The method for encrypted transmission of network communication information according to claim 4, characterized in that, The interval threshold is set by statistically detecting the numerical distribution of the delay redundancy mask parameter, calculating the maximum and minimum values, and then dividing the range interval proportionally.

6. The method for encrypted transmission of network communication information according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the encrypted structure parameter set, collect the server processing capacity and load parameters, compare the collected processing capacity value with the load occupancy value, calculate the server's current available computing capacity, and generate the server computing capacity. S302: Call the server's computing power and collect the link communication distance parameters, map the link communication distance to the computing power value, pair the two types of parameters according to the combination of the same range, and generate a communication capability mapping value; S303: Based on the communication capability mapping value and the encrypted structure parameter set, the computing capability values ​​under multiple link distances are combined and organized to bind the corresponding structure parameter sets, and a topology capability mapping table is generated.

7. The method for encrypted transmission of network communication information according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the encrypted structure parameter set, obtain the computing power of multiple servers, compare it with the available computing power of the servers, allocate tasks according to the computing resources of the servers and the encryption task requirements, and obtain the task matching quantity. S402: Based on the task matching quantity, match it with the computational requirements of the encryption task, call the HEFT algorithm to optimize the allocation of the encryption task, generate the execution schedule of the task among multiple servers, and obtain the task schedule list. S403: Based on the task scheduling list, calculate the latency during the execution of multiple tasks, and generate an encrypted task table by combining the network conditions and computing capabilities among multiple nodes.

8. The method for encrypted transmission of network communication information according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the topology capability mapping table and the encryption task table, call the weighted average model to calculate and normalize the capability values ​​and corresponding latency values ​​of multiple nodes according to the weight ratio, and generate the encryption control factor. S502: Based on the encryption control factor, for the encryption nesting level parameters and task allocation method, detect the impact on the number of encryption nesting levels and the task division ratio, and adjust the number of nesting levels and task allocation parameters according to the differences to obtain the encryption complexity coefficient. S503: Call the encryption complexity coefficient and compare it with the actual time consumed by the task. Update the calculation process of information encryption transmission based on the comparison deviation result and generate information encryption transmission result.

9. The method for encrypted transmission of network communication information according to claim 8, characterized in that, The encryption complexity coefficient is calculated and set based on the computational complexity of the encryption algorithm, the number of nesting levels, the key length, the task allocation method, and the execution delay factor.

10. A network communication information encryption transmission system, characterized in that, The system is used to implement the network communication information encryption transmission method according to any one of claims 1-9, and the system comprises: The delay analysis module collects end-to-end transmission delay parameters and server queuing delay parameters before the message enters the transmission channel and sums them to obtain the real-time total delay. It extracts the error between the current summation result and the summation result of the previous period, and combines the normalization of the multi-server queuing delay parameters to generate a delay redundancy mask and transmit it to the delay discrimination module. The delay discrimination module determines the mask interval based on the delay redundancy mask and divides it into an enhancement area and a buffer. It also adjusts the nesting level and length of the key according to the interval, generates an encrypted structure parameter set, and transmits it to the routing topology module. The routing topology module, based on the encrypted structure parameter set, collects server processing capacity and load parameters and calculates the available computing power of the server. At the same time, it collects link communication distance parameters, performs combined mapping, constructs a topology capability mapping table, and transmits it to the task allocation module. The task allocation module inputs the encrypted structure parameter set and topology capability mapping table into the HEFT algorithm to allocate encryption tasks, matches the encryption tasks according to the available computing power of the server, generates an encryption task table, and transmits it to the encryption control module. The encryption control module generates an encryption control factor by using a weighted average model to map the topology capability table and the encryption task table. Based on the encryption control factor, it adjusts the encryption nesting level depth and task allocation method and compares the results with the actual time consumption to generate the encrypted information transmission result.

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