Encryption optimization method for data communication

Abstract

The present invention relates to an encryption optimization method for data communication. An intelligent algorithm library containing multiple encryption algorithms is designed, and the encryption algorithm is selected for the current data communication environment through analysis and learning of historical communication data; data is processed in layers according to sensitivity and priority, and different encryption algorithms and keys are used in each layer; through the design of a chain-dependent structure, the encryption result of each layer affects the encryption parameter selection and optimization of the next layer; an adaptive key generation and update strategy is designed, combining the hardware characteristics of the device and the real-time network status; an optimized distributed key distribution mechanism is adopted, and the distribution path is dynamically selected in combination with the data flow on the encryption link and the load of the network node, and the key synchronization is performed through the chain encryption result; data is divided into multiple fragments, and encrypted and transmitted through different transmission channels; based on the adaptive key management strategy, the encryption path of each transmission channel is dynamically optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of data communication, and in particular to an encryption optimization method for data communication. Background Art

[0002] Although the encryption optimization methods currently used for data communication have improved the security and efficiency of data transmission to a certain extent, there are still some significant deficiencies and drawbacks. Taking the Chinese invention patent, publication number CN114417378A, as an example, the patent proposes a key exchange or public key encryption optimization method based on Mersenne numbers. By utilizing the mathematical properties of Mersenne numbers, the modulus operation is optimized, thereby reducing the computational complexity and improving the encryption and decryption speed of key exchange and public key encryption. Although this method has certain advantages in some scenarios, it still has the following deficiencies in a wider range of applications.

[0003] First, the method has high dependency. The core of the invention is to use Mersenne numbers to optimize the modulo operation, and the Mersenne number itself is a special prime number in the form of M n =2 n -1, where n is a positive integer. This feature determines that the selection range of Mersenne numbers is very limited and may not be applicable to all scenarios. Although the patent proposes two alternatives to modulo operations, in actual applications, selecting or constructing a suitable modulus to match the Mersenne number or its approximate form is still a complex and time-consuming process. Secondly, this method may have performance bottlenecks when dealing with complex communication environments. Modern communication systems often need to process multiple types of data and operate in complex network environments, such as multi-channel transmission, dynamic key management, etc. In this case, relying solely on Mersenne numbers to optimize encryption operations may lead to system performance degradation. Specifically, since the application of Mersenne numbers requires a large number of modulo operations to be replaced in key generation and encryption algorithms, although this reduces the computational complexity under certain conditions, this method may not effectively reduce the actual computational burden in an environment with high concurrency and low latency requirements. Third, this method has limited security improvements. Although the Mersenne number can optimize computational complexity, it does not fundamentally improve the system's ability to resist attacks. With the improvement of computing power, optimization methods that rely solely on specific mathematical properties may not have sufficient defense capabilities when facing high-level security threats. For example, under the potential threat of the quantum computing era, encryption methods based on traditional modular arithmetic optimization may face huge challenges. The security requirements of modern data communications are getting higher and higher, especially when it comes to the transmission of sensitive information, which requires more complex and dynamic encryption strategies. However, this optimization method based on Mersenne numbers may not be able to cope with these new security threats due to its singleness and dependence. Summary of the invention

[0004] The purpose of the present invention is to provide an encryption optimization method for data communication, thereby solving some of the drawbacks and shortcomings pointed out in the background technology.

[0005] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: an encryption optimization method for data communication, comprising: S1, construction of an intelligent encryption algorithm library:

[0006] Design an intelligent algorithm library containing multiple encryption algorithms, including symmetric encryption, asymmetric encryption and hybrid encryption; the intelligent algorithm library selects the encryption algorithm for the current data communication environment through analysis and learning of historical communication data;

[0007] S2, multi-level encryption strategy and chain optimization:

[0008] S2.1. Data is processed in layers according to sensitivity and priority, with different encryption algorithms and keys used at each layer, and a security architecture where the upper layer encrypted data depends on the lower layer keys;

[0009] S2.2, through the design of chain dependency structure, the encryption result of each layer affects the encryption parameter selection and optimization of the next layer;

[0010] S3, adaptive key management and distribution optimization:

[0011] S3.1. Based on the dynamic changes of the communication environment and the data transmission requirements, an adaptive key generation and update strategy is designed, wherein the key generation process combines the hardware characteristics of the device and the real-time network status;

[0012] S3.2, adopt an optimized distributed key distribution mechanism, combine the data flow on the encryption link and the load of the network node, dynamically select the distribution path, and synchronize the key through the chain encryption result;

[0013] S4, end-to-end encryption optimization and multi-channel transmission:

[0014] S4.1. Divide the data into multiple fragments and encrypt and transmit them through different transmission channels; each channel uses an independent encryption algorithm and key, while ensuring the integrity and independence of each data fragment;

[0015] S4.2. Based on the adaptive key management strategy, the encryption path of each transmission channel is dynamically optimized. In the data transmission process, the path and channel are selected according to the real-time monitored network status, and the chain optimization results are combined to make adjustments and optimizations.

[0016] Furthermore, the process of performing the encryption algorithm in the data communication environment includes:

[0017] S1. First, collect and store historical communication data including communication frequency, data packet size, network delay, and packet loss rate, and input them into the deep learning model; use a high-dimensional feature mapping function Ξ(α, ​​β, γ), which is in the form of:

[0018]

[0019] Among them, α(t) represents the change of communication frequency over time, β(t) represents the dynamic characteristics of network delay, and γ(t) reflects the time series change of packet size;

[0020] S2. Then, in the pattern matching and prediction stage, the adaptive prediction model Λ(ω, t) is used, which is defined as follows:

[0021]

[0022] Among them, ω n (t) represents the change of the extracted multidimensional features over time, and its differential term Capture the instantaneous rate of change of the feature, and cos(ω n (t)·t) reflects the periodic fluctuation of the feature in time; the convolution integral term Then the weighted sum of historical feature data is combined to synthesize the cumulative impact of past features on the current forecast;

[0023] S3. Finally, in the process of adaptive algorithm selection and optimization, by introducing the multidimensional encryption strategy function formula:

[0024]

[0025] in, is the predicted optimization parameter, δ(t) is the adjustment parameter of encryption strength; The prediction parameters are weighted and summed to generate an overall encryption strategy evaluation value; the strategy evaluation value is normalized by a square root operation; then the sine modulation term Used to refine the encryption strategy selection; by Real-time adjustment of functions, switching encryption schemes when the communication environment frequently fluctuates.

[0026] Furthermore, the method for data layering processing includes:

[0027] S1. According to the type, content and usage of data, the sensitivity assessment function Φ(x) is used to classify data. The formula is:

[0028]

[0029] Among them, K is the weight factor, x(t) is the dynamic attribute of the data; the sensitivity change of the data in different time periods is evaluated by integrating the time series characteristics; and the sin(K·t) term is used to capture the periodic characteristics of the data sensitivity changing over time. The term reflects the instantaneous rate of change of data sensitivity;

[0030] S2. Next, the data is assigned to different encryption levels based on the sensitivity and priority of the data, and different encryption algorithms and keys are selected for each level. The encryption strength of each level is determined by the encryption strategy function Ψ(y), which is:

[0031]

[0032] Among them, λ n is the attenuation factor of a specific encryption algorithm, y n is the encryption strength parameter; in Ψ(y), λ n Controls the decay of encryption strength over time; n The weighted sum of is used to comprehensively evaluate the strength of multiple encryption algorithms to generate an overall encryption strategy;

[0033] S3. Finally, establish the inter-layer dependency and security architecture, and make the decryption of the upper layer data dependent on the security of the lower layer key through the dependency function Γ(z); dependency function:

[0034]

[0035] Among them, z k Represents the security strength of each layer of keys, θ k is the dependency angle between keys; and It represents the cumulative value of the key security of each layer, reflecting the overall security of the entire system; and cos(θ k ) is used to quantify the dependencies between keys at each layer.

[0036] Furthermore, the construction method adopted by the key generation and update strategy includes:

[0037] S1. First, by real-time monitoring of the key parameters of the communication environment, including bandwidth B(t), delay L(t), packet loss rate P(t) and network load N(t), the dynamic changes of the communication environment are comprehensively evaluated using the environment evaluation function Ψ(B, L, P, N). The function form is:

[0038]

[0039] Among them, α is the weight factor of packet loss rate, represents the combined effect of bandwidth and delay, and ln(1+N(t)) is used to quantify the impact of network load;

[0040] S2. Then, in the key generation process, the hardware characteristics of the integrated device, including the computing power of the processor C p 、Memory capacity M c , Random number generator performance R g , and through the hardware characteristic function Θ(C p , M c , R g ) dynamically adjusts the complexity of key generation; the function is defined as:

[0041]

[0042] in, Reflects the combined impact of device computing power and memory, Used to adjust the impact of the performance of the random number generator on the complexity of key generation.

[0043] Furthermore, the process of establishing the dynamic selection distribution path includes:

[0044] S1. First, monitor the data flow F(t) on the encrypted link and the load of the network node L in real time. n (t), obtain the status information of each node, including the data packet transmission delay D n (t), node processing capacity C n and the current load level L n (t); Based on the information, the key distribution path is calculated, and the path optimization function Φ(P) is defined as follows:

[0045]

[0046] Among them, N is the number of nodes on the path, β is the weight factor, and Φ(P) represents the comprehensive goodness of each path;

[0047] S2. In the key distribution process, a chain encryption method is used. Each hop node will re-encrypt the key group to generate a new encryption result. The chain encryption function Ψ(K n ) is described by the following formula:

[0048]

[0049] Among them, K k is the encryption key of the kth node, θ k is the dependency angle between nodes in the encryption process, γ is the attenuation coefficient of chain encryption;

[0050] S3. Finally, the load balancing function Ω(L) is used to monitor and adjust the load of each node in real time, which is defined as follows:

[0051]

[0052] Among them, α is the load balancing adjustment parameter, and Ω(L) is used to dynamically adjust the distribution path and load distribution strategy.

[0053] Furthermore, the construction of end-to-end encryption optimization and multi-channel transmission includes the following steps:

[0054] S1. Split the original data into multiple segments according to predefined rules, dynamically adjust the segment size according to the type and sensitivity of the data, and assign a unique identifier to each segment;

[0055] S2. Transmit each fragment through different transmission channels. Each channel uses an independent encryption algorithm and key for encryption. The channels are independent of each other and do not share encryption information.

[0056] S3. During transmission, attach integrity verification information to each fragment;

[0057] S4. Verify and reassemble each fragment at the receiving end using the fragment's unique identifier and verification information.

[0058] Furthermore, the process of allocating a unique identifier includes:

[0059] S1. First, according to the data including file type, content structure and communication requirements, predefined segmentation rules are formulated; the original data is segmented into multiple logically independent segments through the rules; the segmentation rules are represented by a high-dimensional integral function Φ(x):

[0060]

[0061] Among them, x(t) is the time-dependent variable of the data, κ is the frequency factor, and α i is the adjustment factor;

[0062] S2. Then, in the data segmentation process, the size of the segment is dynamically adjusted according to the data type including text, image, video and the sensitivity including confidentiality and privacy; high-sensitivity data is segmented into smaller segments; low-sensitivity data is segmented into larger segments; the dynamic adjustment of the segment size is achieved through the function Ψ(y):

[0063]

[0064] Among them, y n (t) is the time-dependent function of the segment sensitivity, β n and λ n is the adjustment coefficient, ω n is the frequency factor;

[0065] S3. Assign a unique identifier when generating each data segment; the identifier contains the source information of the segment, the segmentation order, and the sensitivity level of the data; the unique identifier is generated by the function Γ(z):

[0066]

[0067] Among them, z k is the allocation parameter, θ k is the unique adjustment factor, δ k is the time attenuation coefficient, and ζ is the frequency adjustment factor.

[0068] Furthermore, the encryption implementation using an independent encryption algorithm and key includes the following steps:

[0069] S1. First, the data is divided into multiple fragments, and an independent transmission channel is assigned to each fragment. Each transmission channel is assigned a different encryption algorithm and key through the function Φ(T) according to the characteristics including bandwidth, delay, and security requirements. Φ(T) is expressed as:

[0070]

[0071] Among them, A(t) represents the dynamic characteristics of the channel, λ is the attenuation coefficient, and B i and ω i are encryption strength and frequency adjustment factors, respectively;

[0072] S2. Secondly, each channel uses an independent key management mechanism. When generating, distributing and updating keys, the key management strategy is dynamically adjusted based on the channel characteristics through the function Ψ(K). Ψ(K) is expressed as:

[0073]

[0074] Among them, K j is the key parameter of the channel, α j and β j The key update adjustment factor.

[0075] Beneficial effects of the present invention:

[0076] By performing multi-level segmentation and independent encryption on data, the present invention ensures that even if a transmission channel or data fragment is attacked, the overall data is still difficult to decrypt. In particular, the use of chain encryption and independent key management strategies makes the decryption of upper-layer data dependent on the security of lower-layer keys, further improving the system's defense capabilities.

[0077] According to the dynamic changes in the communication environment and data transmission requirements, the encryption algorithm, key generation and distribution strategy are adjusted in real time. This adaptive mechanism enables the system to select the optimal encryption scheme and path under complex network conditions to ensure efficient and secure transmission by monitoring key parameters such as bandwidth, delay, and packet loss rate in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 This is a flow chart of the encryption optimization method for data communication of the present invention.

[0079] Figure 2 A process flow chart of the encryption algorithm for the data communication environment of the present invention.

[0080] Figure 3 This is a flow chart of the method for data layering processing of the present invention.

[0081] Figure 4 This is a flow chart of the construction method adopted by the key generation and update strategy of the present invention. DETAILED DESCRIPTION

[0082] The specific implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0083] See attached Figure 1 The encryption optimization method for data communication of the present invention first constructs an intelligent encryption algorithm library: an intelligent algorithm library containing multiple encryption algorithms is designed, and the library covers multiple types of symmetric encryption, asymmetric encryption and hybrid encryption. Symmetric encryption algorithms such as AES and DES, asymmetric encryption algorithms such as RSA and ECC, and hybrid encryption schemes combine the advantages of symmetric and asymmetric encryption to provide efficient and secure encryption options. The intelligent algorithm library automatically identifies the characteristics of different communication environments through analysis and learning of historical communication data, and dynamically selects the most suitable encryption algorithm based on the current network conditions, data transmission requirements and security requirements. The analysis and learning process relies on a machine learning model. The system predicts the communication mode of the current environment by evaluating key indicators such as the processing of past communication data, bandwidth occupancy, delay, and packet loss rate, and intelligently selects encryption algorithms to ensure the best balance between security and performance in different communication environments.

[0084] The second step is multi-level encryption strategy and chain optimization: First, the data is processed in layers according to sensitivity and priority, and different encryption algorithms and keys are used at each layer. Specifically, high-sensitivity and high-priority data are assigned to higher layers, using stronger encryption algorithms and longer keys, while low-sensitivity and low-priority data are assigned to lower layers, using relatively simple encryption strategies. The data encryption of each layer is not only carried out independently, but also depends on the security of the lower-level keys. This dependence constitutes a security superposition protection structure, that is, the integrity and security of the upper-level encryption must depend on the validity and security of the lower-level keys. Then, through the design of the chain dependency structure, the encryption results of each layer directly affect the selection and optimization of the encryption parameters of the next layer. Specifically, the output of the upper-level encryption will be used as the input of the lower-level encryption, so that the strength, algorithm selection and key length of the lower-level encryption are all related to the results of the upper-level encryption. In this way, through the progressive encryption optimization layer by layer, the system can realize dynamic encryption strategy adjustment according to the data requirements of different levels while ensuring data security.

[0085] The third step is adaptive key management and distribution optimization: First, based on the dynamic changes of the communication environment and the data transmission requirements, an adaptive key generation and update strategy is designed, in which the key generation process combines the hardware characteristics of the device with the real-time network status. When the communication environment changes, the system monitors key parameters such as network bandwidth, delay, and packet loss rate in real time, and dynamically adjusts the complexity and update frequency of key generation based on these parameters. For example, when the network conditions are good and the data transmission requirements are low, the system extends the key update cycle to reduce the computing load; while in a high-load or high-risk environment, the system shortens the update cycle and increases the key complexity. In addition, the key generation process also combines the hardware characteristics of the device, such as processor capabilities, memory capacity, and the performance of the hardware random number generator, making key generation more targeted and efficient, ensuring that different devices can obtain the best key protection under different network conditions. Then, an optimized distributed key distribution mechanism is adopted, combining the data traffic on the encryption link with the load of the network node, dynamically selecting the distribution path, and performing key synchronization through the chain encryption result. The system dynamically evaluates the security and efficiency of the distribution path by monitoring the load and data traffic of each network node in real time. On nodes with high load or congestion, the system will automatically adjust the key distribution path to avoid overloaded nodes to reduce delays and improve transmission efficiency. At the same time, the system also uses a chain encryption structure to synchronize keys between nodes, ensuring that even if a node has problems or is hacked, other nodes can still complete key updates and synchronization through chain-dependent encryption results.

[0086] The last step is end-to-end encryption optimization and multi-channel transmission: the system first divides the data into multiple fragments, each of which represents a part of the original data, and is assigned to different transmission channels according to predefined rules. During the transmission process, each channel uses an independent encryption algorithm and key to ensure that the encrypted information between channels is independent of each other and not shared. This means that even if a channel is breached or intercepted, the attacker cannot infer or decrypt the content of other channels through the data of that channel. At the same time, each data fragment is given a unique identifier when it is generated, and integrity verification information (such as hash value or digital signature) is attached to ensure the integrity and independence of the fragment. These measures ensure that even if a fragment is damaged or lost during transmission, other fragments can still be independently decrypted and reassembled, thereby maintaining the integrity and security of the overall data. Then during the data transmission process, the system dynamically optimizes the encryption path of each transmission channel based on an adaptive key management strategy. The optimization process takes into account the characteristics of the channel and the sensitivity of the data, combined with the real-time monitored network status, such as bandwidth, delay, packet loss rate, etc. The system selects the optimal path from multiple available paths through an intelligent path selection algorithm to minimize delays, improve transmission efficiency and reduce security risks. At the same time, the system combines the chain encryption results to adjust the encryption parameters of the channel in real time to ensure data security during transmission. As the network environment changes, the system will also dynamically adjust the encryption strategy and path selection based on the chain dependency to ensure the best balance between security and efficiency throughout the transmission process.

[0087] Embodiment 1:

[0088] Combination Figure 2 In this example, an international financial institution needs to transmit highly sensitive customer data between multiple branches around the world. This data includes transaction records, customer information, and financial statements. Since the data involved is extremely sensitive, any leakage will lead to serious financial losses and legal problems, so it is necessary to ensure the secure transmission of data.

[0089] First, the system collects and stores communication data from the past few months. This includes important indicators such as communication frequency, packet size, network delay, and packet loss rate. For example, in the past 6 months, the average communication frequency α(t) collected by the system fluctuated between 0.5Hz and 2Hz; the packet size γ(t) was usually between 128 bytes and 1024 bytes; and the network delay β(t) varied between 50ms and 200ms, and the packet loss rate also fluctuated between 0.1% and 1%.

[0090] Next, the system inputs this data into the deep learning model and processes it through the high-dimensional feature mapping function Ξ(α, ​​β, γ). The form of this function is:

[0091]

[0092] In this formula, α(t) represents the dynamic change of communication frequency, β(t) represents the characteristics of network delay, and γ(t) reflects the time series change of data packet size. For example, when α(t) = 1 Hz, β(t) = 100 ms, and γ(t) = 512 bytes, the value of the feature mapping function can be expressed as:

[0093]

[0094] This value reflects the characteristics of the communication environment within a specific time range T. If T = 10 seconds, the calculation result of Ξ(α, ​​β, γ) is 9.048.

[0095] Through this feature mapping function, the system is able to capture the complex dynamic characteristics of the communication environment. Subsequently, the deep learning model uses these features for analysis and predicts fluctuations in future communication processes. For example, based on the above calculation results, the system predicts that network latency will increase by 50% during a certain period of time next month, so a stronger encryption algorithm is needed.

[0096] In the case of increased network latency, the system can select a more computationally efficient encryption algorithm to reduce the impact of latency on transmission speed. At the same time, the dynamic adjustment of data packet size will also be optimized based on the prediction results to ensure that the integrity and efficiency of data transmission are not affected in the case of high latency.

[0097] Then in the pattern matching and prediction stage, international financial institutions transmit highly sensitive customer data between multiple branches around the world. Assuming that in the communication data collected in the past six months, the communication frequency α(t) fluctuates between 0.5Hz and 2Hz, the network delay β(t) varies between 50ms and 200ms, and the packet size γ(t) is usually between 128 bytes and 1024 bytes. The system needs to further optimize the encryption strategy through the adaptive prediction model Λ(ω, t).

[0098] The adaptive prediction model Λ(ω, t) is defined as follows:

[0099]

[0100] In this model, ω n (t) represents the change of the extracted multidimensional features over time, and its differential term Capture the instantaneous rate of change of the feature, and cos(ω n (t)·t) reflects the periodic fluctuation of the feature in time; the convolution integral term The weighted sum of historical feature data is then combined to synthesize the cumulative impact of past features on the current prediction.

[0101] In the application, set ω n (t) represents the characteristics of communication frequency, packet size and network delay, corresponding to three dimensions: ω 1 (t),ω 2 (t) and ω 3 (t). For example, at a certain moment, ω 1 (t) (communication frequency) is 1 Hz, ω 2 (t) (packet size) is 512 bytes, ω 3 (t) (network delay) is 100ms. Substitute these data into the prediction model for calculation.

[0102] Set the instantaneous rate of change of the feature 0.2Hz / s, 50 bytes / s, is 10ms / s, and ω is set 1 (t) periodic fluctuation cos(ω 1 The amplitude of (t)·t) is 0.8, and the calculation result is:

[0103]

[0104] After substituting the actual data, the convolution integral terms are set to 0.5, 0.3, and 0.4 respectively, and the sum is:

[0105] Λ(ω,t)=(0.2·0.8+0.5)+(50·0.7+0.3)+(10·0.6+0.4)≈11.14

[0106] Based on this prediction value, the system determines that the changes in network delay and packet size in the current environment have a greater impact on the communication frequency. Therefore, the system will choose to use encryption algorithms that are more sensitive to delay, such as block-based encryption algorithms, to avoid data loss or retransmission caused by delay. In addition, the system can also adjust the packet size segmentation strategy based on the prediction results, transmitting larger packets when the network status improves, and segmenting the packets into smaller ones when the delay or packet loss rate increases to ensure transmission efficiency.

[0107] After the pattern matching and prediction stage, the system has obtained the current state evaluation value of the communication environment through the adaptive prediction model Λ(ω, t). On this basis, the system has entered the key stage of adaptive algorithm selection and optimization. By introducing the multi-dimensional encryption strategy function to select and adjust the encryption scheme.

[0108] The multidimensional encryption strategy function is defined as follows:

[0109]

[0110] in, is the predicted optimization parameter, and δ(t) is the adjustment parameter of encryption strength. The formula consists of two main parts: the first part is the integral term The overall encryption strategy evaluation value is generated by weighted summing of the prediction parameters, and then normalized by square root operation; the second part is the sinusoidal modulation term Used to refine encryption strategy selection.

[0111] Set the current forecast parameters Mainly based on communication frequency, packet size and network latency. is 2.5, and δ(t) is 0.3. The integral term calculated by the system in practice is:

[0112]

[0113] Considering the sinusoidal modulation term, set δ k The value range is from 0.1 to 0.5. The maximum value in the current communication environment is 1.2, and the calculation of the sinusoidal modulation term is:

[0114]

[0115] final, The calculation result is:

[0116]

[0117] This result shows that in the current communication environment, the system needs to select a higher-strength encryption algorithm. For example, the system will choose to use a more complex asymmetric encryption scheme and increase the key length to cope with the potential network fluctuations and security threats shown in the forecast. At the same time, according to the adjustment of the sinusoidal modulation term, the system can also reduce the encryption strength in certain periods to improve transmission efficiency. This dynamic encryption strategy selection and optimization mechanism ensures that the system can flexibly adjust the encryption scheme when dealing with different network conditions to achieve the best balance between security and performance.

[0118] Embodiment 2:

[0119] like Figure 3 As shown, in this embodiment, an international financial institution needs to classify different types of data according to their sensitivity to ensure that an appropriate encryption strategy is selected during transmission. The data processed by the institution includes customer personal information (high sensitivity), transaction records (medium sensitivity) and general business reports (low sensitivity).

[0120] The system first uses the sensitivity evaluation function Φ(x) to classify the data. The function is defined as follows:

[0121]

[0122] Among them, K is a weight factor ranging from 0.1 to 1.0, which is used to adjust the impact of the periodic characteristics of the data; x(t) is the dynamic attribute of the data, representing the changes of different types of data over time.

[0123] Set the data dynamic attribute x(t) of customer personal information to 3 (indicating high sensitivity), the data dynamic attribute of transaction records to 2, and the data dynamic attribute of general business reports to 1. Set K to 0.9 for customer personal information, 0.5 for transaction records, and 0.2 for general business reports. Substitute these data into the sensitivity evaluation function for calculation.

[0124] For customer personal information, the calculation result of Φ(x) is:

[0125]

[0126] Assuming T = 10 seconds, the integral calculation result is 27.3. Similarly, the calculation result for transaction records is 12.8, and the calculation result for general business reports is 5.5.

[0127] Through these calculations, the system can derive a sensitivity assessment value for each data type. Customer personal information is assigned to the highest priority level due to its high sensitivity, transaction records are assigned to the medium priority level, and general business reports are assigned to the lowest priority level. These assessment values ​​directly affect the subsequent encryption algorithm selection and key strength setting, ensuring that highly sensitive data is protected most strictly during transmission.

[0128] The institution has classified the data according to the sensitivity assessment function Φ(x), and assigned customer personal information, transaction records and general business reports to three sensitivity levels: high, medium and low. Now, the system will assign the data to different encryption levels based on these sensitivity levels and priority results, and select different encryption algorithms and keys for each level. The encryption strength of each level is determined by the encryption policy function Ψ(y).

[0129] The encryption strategy function Ψ(y) is defined as follows:

[0130]

[0131] Among them, λ n is the attenuation factor of a specific encryption algorithm, ranging from 0.01 to 0.1, used to control the attenuation of encryption strength over time; nIt is an encryption strength parameter, ranging from 1 to 5, which is used to indicate the encryption strength required for different data types.

[0132] Set highly sensitive customer personal information 1 The value is 5, the attenuation factor λ 1 0.02; medium sensitivity transaction record y 2 The value is 3, the attenuation factor λ 2 0.05; low sensitivity general business report y 3 The value is 1, the attenuation factor λ 3 is 0.1. Substitute these data into the encryption strategy function for calculation:

[0133] For highly sensitive customer personal information:

[0134] Ψ(y 1 )=5·e -0.02·t

[0135] For medium sensitivity transactions:

[0136] Ψ(y 2 )=3·e -0.05·t

[0137] For general business reporting of low sensitivity:

[0138] Ψ(y 3 )=1·e -0.1·t

[0139] Set the time t = 10 seconds, and the calculation results are as follows:

[0140] Ψ(y 1 )=5·e -0.2 ≈4.08

[0141] Ψ(y 2 )=3·e -0.5 ≈1.82

[0142] Ψ(y 3 )=1·e -1.0 ≈0.37

[0143] Based on the calculation results, the system can determine the encryption strength of each layer. For customer personal information, the system will select a more complex encryption algorithm (such as 4096-bit RSA or advanced AES algorithm) and assign a longer key to ensure the highest security. For transaction records, the system selects a medium-strength encryption algorithm (such as 2048-bit RSA or standard AES) to balance security and performance. For general business reports, the system can select a relatively simple encryption algorithm (such as 128-bit AES) to improve transmission efficiency.

[0144] The institution ensures the security of different data layers by conducting sensitivity assessments and selecting encryption strategies for customer personal information, transaction records, and general business reports. Now, the system needs to further establish inter-layer dependencies and security architectures, and ensure that the decryption of upper-layer data depends on the security of the lower-layer keys through the dependency function Γ(z).

[0145] The dependency function Γ(z) is defined as follows:

[0146]

[0147] Among them, z k Represents the security strength of each layer of keys, θ k is the dependency angle between keys, It represents the cumulative value of the key security of each layer, reflecting the overall security of the entire system, cos(θ k ) is used to quantify the dependencies between keys at each layer.

[0148] The system is set to encrypt three layers of data: the first layer is highly sensitive customer personal information, the second layer is medium-sensitive transaction records, and the third layer is low-sensitivity general business reports. Set the security strength of each layer's key 1 、z 2 and z 3 are 0.9, 0.8 and 0.7 respectively, depending on the angle θ 1 ,θ 2 and θ 3 They are 30°, 45° and 60° respectively.

[0149] Substitute these data into the dependent function for calculation:

[0150]

[0151] First, calculate the cosine of each angle:

[0152] Cos(30°)=0.866, cos(45°)=0.707, cos(60°)=0.5

[0153] Therefore, the cumulative value of the key dependency is:

[0154]

[0155] Next, take the cube root of the result:

[0156]

[0157] According to the calculation results, Γ(z)≈0.58 means that under the current key dependency structure, the overall security of the system is at a medium level. At this point, the system will enhance the strength of the underlying key or adjust the dependency structure to improve the overall encryption security.

[0158] Embodiment 3:

[0159] like Figure 4 As shown, in this embodiment, in order to ensure that the key used in the communication process can adapt to the changes in the network environment, the system first monitors the key parameters of the communication environment in real time, including bandwidth B(t), delay L(t), packet loss rate P(t) and network load N(t). These parameters will be input into the environment evaluation function Ψ(B, L, P, N) to comprehensively evaluate the dynamic changes of the communication environment.

[0160] The formula of the environmental assessment function is as follows:

[0161]

[0162] Among them, α is the weight factor of packet loss rate, ranging from 0.1 to 0.5; Represents the combined effect of bandwidth and delay, and ln(1+N(t)) is used to quantify the impact of network load.

[0163] Assume that in a certain period of time, the communication bandwidth B(t) is 100Mbps, the delay L(t) is 50ms, the packet loss rate P(t) is 0.01, and the network load N(t) is 0.8. Set the weight factor α of the packet loss rate to 0.3 and calculate the environmental assessment value.

[0164] First, calculate the combined effect of bandwidth and latency:

[0165]

[0166] Then calculate the attenuation term e of the packet loss rate -αP(t) :

[0167] e -αP(t) =e -0.3·0.01 ≈0.997

[0168] At the same time, the logarithmic value of the network load is:

[0169] ln(1+N(t))=ln(1+0.8)≈0.587

[0170] Substituting the formula for integration, setting the time T = 10 seconds, the calculation result of the environmental assessment value is:

[0171]

[0172] This evaluation value represents the comprehensive status of the current communication environment. In the application, if the value of Ψ(B, L, P, N) is high, the system will consider the current communication environment to be relatively stable, and a higher-strength encryption algorithm and a longer key can be used for data encryption. If the value of Ψ(B, L, P, N) is low, indicating that the communication environment is unstable, the system will select an encryption algorithm with higher computational efficiency but relatively lower security to reduce delays and increase the speed of data transmission.

[0173] To ensure that the key generation process can fully utilize the computing resources of the device, the system will integrate the hardware characteristics of the device, including the computing power of the processor C p 、Memory capacity M c and random number generator performance R g These parameters will be input into the hardware characterization function Θ(C p , M c , R g ) to dynamically adjust the complexity of key generation.

[0174] The formula for the hardware characteristic function is as follows:

[0175]

[0176] in, Reflects the combined impact of device computing power and memory, Used to adjust the impact of the performance of the random number generator on the complexity of key generation. Set the processor computing power C p The range is 10 to 100 GFlops, and the memory capacity is M c The range is 4 to 64GB, and the random number generator performance R g The range is 0.1 to 1.0.

[0177] Set on a certain device, the processor computing power C p 50GFlops, memory capacity M c For 16GB, the random number generator performance R g is 0.5. Substitute it into the formula for calculation:

[0178] First calculate the combined impact of processor and memory:

[0179]

[0180] Then calculate the adjustment item of the random number generator:

[0181]

[0182] Finally, the calculation result of the hardware characteristic function is:

[0183] Θ(Cp , M c , R g )=200·3=600

[0184] This result indicates that the device can support a more complex key generation process under the current configuration. Therefore, the system can select a stronger encryption algorithm (such as a longer key or more rounds of encryption operations) to ensure data security.

[0185] Set the computing power of another device to be lower, C p 20GFlops, memory M c For 8GB, the random number generator performance R g is 0.2. Substituting into the same formula:

[0186] Calculate the compound impact of processors and memory:

[0187]

[0188] Calculate the adjustment term for the random number generator:

[0189]

[0190] The final calculation result is:

[0191] Θ(C p , M c , R g )=56.6·6≈339.6

[0192] The result shows that the hardware resources of the device are relatively limited. The system selects an encryption algorithm with less computational effort to reduce the occupation of system resources while ensuring the efficiency of data transmission.

[0193] Embodiment 4:

[0194] In this embodiment, the system optimizes the transmission of the key by dynamically selecting the distribution path. This process includes real-time monitoring of the data flow F(t) on the encrypted link and the load of the network node L n (t), obtain the status information of each node, such as the data packet transmission delay D n (t), node processing capacity C n and the current load level L n (t). Based on this information, the system calculates the best key distribution path and uses the path optimization function Φ(P) to evaluate the goodness of each path.

[0195] The definition of the path optimization function is as follows:

[0196]

[0197] Among them, N is the number of nodes on the path, β is the weight factor, ranging from 0.1 to 0.5, and Φ(P) represents the comprehensive goodness of each path.

[0198] In a certain actual scenario, the system needs to transmit the key through five nodes N = 5. The following is the status information of each node:

[0199] Node 1: Processing capacity C 1 =100Gbps, delay D 1 (t) = 10ms, load L 1 (t) = 0.4

[0200] Node 2: Processing capacity C 2 =150Gbps, delay D 2 (t) = 15ms, load L 2 (t) = 0.3

[0201] Node 3: Processing capacity C 3 =200Gbps, delay D 3 (t) = 12ms, load L 3 (t) = 0.5

[0202] Node 4: Processing capacity C 4 =120Gbps, delay D 4 (t) = 20ms, load L 4 (t) = 0.6

[0203] Node 5: Processing capacity C 5 =180Gbps, delay D 5 (t) = 10ms, load L 5 (t) = 0.2

[0204] Assume that the data flow F(t) is currently 50 Gbps and β = 0.3. Substitute these data into the path optimization function for calculation:

[0205] First, calculate the contribution value of each node

[0206] Node 1:

[0207] Node 2:

[0208] Node 3:

[0209] Node 4:

[0210] Node 5:

[0211] Then calculate the total contribution of each node and consider the impact of data traffic:

[0212]

[0213] Then calculate the attenuation term e considering data traffic -βF(t) :

[0214] e- 0.3·50 ≈e -15 ≈3.06×10 -7

[0215] The final path optimization value is:

[0216]

[0217] According to the calculation results, the goodness of this path is very low, indicating that this path is not suitable for key distribution under the current high traffic situation. The system can select other paths or adjust the transmission time based on this result to reduce the impact of traffic on key distribution.

[0218] After selecting the appropriate path, the system uses a chain encryption method to ensure the security of key transmission. In this method, each hop node will re-encrypt the key group to generate a new encryption result. The chain encryption function Ψ(K n ) is used to describe this process, and the formula is as follows:

[0219]

[0220] Among them, K k is the encryption key of the kth node, θ k is the dependency angle between nodes in the encryption process, and γ is the attenuation coefficient of chain encryption, ranging from 0.01 to 0.1.

[0221] In practical applications, the key setting needs to be transmitted through five nodes:

[0222] Node 1's key K 1 =256 bits

[0223] Node 2's key K 2 =512 bits

[0224] Node 3's key K 3 =768 bits

[0225] Node 4's key K 4 =1024 bits

[0226] Node 5's key K 5 =2048 bits

[0227] Dependence angle θ k They are 30°, 45°, 60°, 75° and 90° respectively, and the attenuation coefficient γ is set to 0.05.

[0228] First, calculate the cumulative multiplication of the key of each node and the cosine value of the dependent angle:

[0229] Node 1: cos(30°)≈0.866

[0230] Node 2: cos(45°)≈0.707

[0231] Node 3: cos(60°)≈0.5

[0232] Node 4: cos(75°)≈0.258

[0233] Node 5: cos(90°)=0

[0234] The calculation of the key multiplication value and the influence of the dependency angle is as follows:

[0235]

[0236] Since the dependency angle of the 5th node is 90° and its cosine value is 0, the result of the entire chain encryption is 0, which means that the path completely loses its security at the 5th node. Therefore, the system will exclude this path and consider reselecting the dependency angle or changing the path.

[0237] If the dependency angle of the fifth node is set to 60°, the calculation is as follows:

[0238]

[0239] Consider the attenuation term e of chain encryption -γ·n :

[0240] e -0.05·5 =e -0.25 ≈0.7788

[0241] The final chain encryption result is:

[0242] Ψ(K n )=1.46×10 10 0.7788≈1.14×10 10

[0243] Through this calculation, the system confirms that the key transmission under the current path and dependency structure has high security. This chain encryption method ensures that even if the key of a certain node is compromised, it is difficult for the attacker to use the key of the previous node to decrypt the key of the next node, thereby enhancing the overall security.

[0244] On the basis of chain encryption, in order to further improve the load balance and transmission efficiency between network nodes, the system introduces a load balancing function Ω(L) to monitor and adjust the load of each node in real time. The definition of the load balancing function is as follows:

[0245]

[0246] Among them, L n (t) represents the real-time load of the nth node, C n is the processing capacity of the node, α is the load balancing adjustment parameter, and its value range is 0.1 to 1.0. This function is used to evaluate the load status of the node and dynamically adjust the key distribution path and load distribution strategy based on the results.

[0247] Assume that the system needs to transmit the key through four nodes, and the load of each node is L n (t) and processing capacity C n The details are as follows:

[0248] Node 1: Load L 1 (t) = 50%, processing capacity C 1 =100Gbps

[0249] Node 2: Load L 2 (t) = 70%, processing capacity C 2 =150Gbps

[0250] Node 3: Load L 3 (t) = 30%, processing capacity C 3 =200Gbps

[0251] Node 4: Load L 4 (t) = 80%, processing capacity C 4 =120Gbps

[0252] The load balancing adjustment parameter α is set to 0.5. The system evaluates the load balance of each node by calculating the load balancing function.

[0253] First, calculate the load ratio of each node:

[0254] Node 1:

[0255] Node 2:

[0256] Node 3:

[0257] Node 4:

[0258] Next, consider the impact of load balancing adjustment parameters and calculate the load balancing contribution value of each node:

[0259] Node 1: 0.5 sin(0.5 t)

[0260] Node 2: 0.467·sin(0.5·t)

[0261] Node 3: 0.15 sin(0.5 t)

[0262] Node 4: 0.667·sin(0.5·t)

[0263] The sum of the load balancing function Ω(L) is:

[0264] Ω(L)=[0.5+0.467+0.15+0.667]·sin(0.5·t)≈1.784·sin(0.5·t)

[0265] when seconds, sin(1)≈0.841, so:

[0266] Ω(L)≈1.784·0.841≈1.5

[0267] The result shows that at the current moment, the load of each node is relatively balanced, but there is still some room for adjustment. The system will dynamically adjust the path according to the result of the load balancing function, reduce the task allocation of high-load nodes, and give priority to using nodes with lighter loads (such as node 3) for key distribution. This dynamic adjustment mechanism ensures the load balance of the entire network, thereby improving the efficiency and security of key transmission.

[0268] Embodiment 5:

[0269] In this embodiment, to ensure the security of data transmission, the system needs to segment the original data into multiple logically independent segments and assign a unique identifier to each segment. The segmentation rule is formulated according to the file type, content structure and communication requirements of the data, and is represented by a high-dimensional integral function Φ(x).

[0270] The definition of the high-dimensional integral function is as follows:

[0271]

[0272] Among them, x(t) is the time-dependent variable of the data, κ is the frequency factor, ranging from 0.5 to 2.0, and α i It is the adjustment coefficient, and its value range is 0.01 to 0.1.

[0273] Assume that the system is processing a document containing customer financial information. The document needs to be segmented according to its content structure and communication requirements. The time-dependent variable x(t) of the document data changes over time, and the frequency factor K is set to 1.5. In order to ensure the security and integrity of the data, the system sets 3 adjustment coefficients α for the document. 1 , α 2 and α 3 , which are 0.02, 0.05 and 0.08 respectively.

[0274] First, the system calculates the integral term of the segmentation rule based on the characteristics of the data. The initial value of the document data x(t) is set to 100, and its time change rate is a constant, and the calculation is as follows:

[0275]

[0276] Then, calculate the sum of the adjustments:

[0277]

[0278] Set the time T to 10 seconds and substitute these data into the high-dimensional integral function Φ(x):

[0279]

[0280] After integral calculation, the result is approximately:

[0281] Φ(x)≈650

[0282] Based on this result, the system determines the rules for data segmentation and divides the original data into multiple fragments. Each fragment is assigned a unique identifier based on its content, sensitivity and transmission requirements, ensuring that these fragments can be accurately tracked, encrypted and reassembled during data transmission.

[0283] The system further optimizes the data segmentation process to ensure that data of different sensitivities and types can be transmitted more efficiently and securely by dynamically adjusting the segment size. This process includes the processing of data types such as text, images, and videos, while taking into account the confidentiality and privacy of the data. Highly sensitive data is segmented into smaller segments, while low-sensitivity data is segmented into larger segments. The dynamic adjustment of the segment size is achieved through the function Ψ(y).

[0284] The definition of the dynamic adjustment function is as follows:

[0285]

[0286] Among them, y n (t) is the time-dependent function of the segment sensitivity, β n and λ nis the adjustment coefficient, the value range is 0.01 to 0.1 and 0.1 to 1.0, ω n is the frequency factor, ranging from 0.5 to 2.0.

[0287] Assume that the system is processing a mixed data set containing text, images, and videos. The confidentiality of the text data is high, and the sensitivity is set to y. 1 (t) = 5, adjustment coefficient β 1 =0.02, frequency factor ω 1 =1.0. The confidentiality of the image data is medium, and the sensitivity is set to y 2 (t) = 3, adjustment coefficient β 2 =0.05, frequency factor ω 2 =1.5. The confidentiality of video data is relatively low, and the sensitivity is set to y 3 (t) = 1, adjustment coefficient β 3 =0.1, frequency factor ω 3 =2.0.

[0288] First, calculate the fragment resizing value for the text data:

[0289]

[0290] The upper limit of the integral is set to 10 seconds. After calculation, the result is approximately:

[0291] Ψ(y 1 )≈1.87

[0292] Similarly, calculate the adjustment values ​​for image data and video data:

[0293] Ψ(y 2 )≈1.25,Ψ(y 3 )≈0.98

[0294] Based on the calculation results, the system determines the segmentation strategy for different types of data. Since text data has the highest sensitivity, the system divides it into smaller segments to ensure that each segment can be fully encrypted and protected during transmission. Image data and video data have relatively large segments due to their lower sensitivity to improve transmission efficiency.

[0295] To further enhance security, each data fragment needs to be assigned a unique identifier when it is generated. The identifier contains not only the source information of the fragment, the segmentation order, but also the sensitivity level of the data. The unique identifier is generated by the function Γ(z), ensuring that each fragment can be accurately identified and processed during transmission.

[0296] The unique identifier generation function Γ(z) is defined as follows:

[0297]

[0298] Among them, z k is the allocation parameter, θ k is the unique adjustment factor, δ k is the time attenuation coefficient, ranging from 0.01 to 0.1, and ζ is the frequency adjustment factor, ranging from 0.5 to 2.0.

[0299] Assume that the system is processing highly sensitive text data fragments. To generate a unique identifier, the system first determines the allocation parameter z k is an integer value from 1 to 3, the uniqueness adjustment factor θ k Corresponding to 30°, 45° and 60°, the time attenuation coefficient δ k They are 0.02, 0.05 and 0.08 respectively, and the frequency adjustment factor ζ is 1.5.

[0300] First, calculate the first part of the identifier generation function:

[0301]

[0302] Calculate the value of each term:

[0303] 1·0.866·e -0.02t +2 0.707 e -0.05t +3 0.5 e -0.08t

[0304] Set t = 5 seconds, the calculation result is:

[0305]

[0306] Substituting this into the cube root part of the function:

[0307]

[0308] Next, calculate the second integral term of the identifier generation function:

[0309]

[0310] If you set z(t) to a constant value of 1, the integral result will be a finite value. Set this value to 0.57.

[0311] Finally, the value of the identifier generation function Γ(z) is:

[0312] Γ(z)=1.43+0.57=2.00

[0313] This identifier value ensures the uniqueness of the data fragment, and can be dynamically adjusted according to the fragment's source information, sensitivity, and time changes, providing a reliable basis for the transmission, decryption, and reassembly of subsequent data fragments.

[0314] Embodiment 6:

[0315] In this embodiment, the system uses independent encryption algorithms and keys to ensure the security of data in different transmission channels. First, the data is divided into multiple fragments, and an independent transmission channel is assigned to each fragment. Each transmission channel is assigned a different encryption algorithm and key through the function Φ(T) according to its characteristics (such as bandwidth, delay, and security requirements).

[0316] The function Φ(T) is defined as follows:

[0317]

[0318] Among them, A(t) represents the dynamic characteristics of the channel, λ is the attenuation coefficient, ranging from 0.01 to 0.1, and B i and ω i They are encryption strength and frequency adjustment factor, and their value ranges are 1 to 10 and 0.5 to 2.0 respectively.

[0319] Assume that the system needs to transmit three data fragments through three transmission channels. The following are the characteristics of each channel:

[0320] Channel 1: bandwidth is 100Mbps, latency is 20ms, and security requirements are high

[0321] Channel 2: Bandwidth is 200 Mbps, latency is 10 ms, and security requirements are medium

[0322] Channel 3: 50 Mbps bandwidth, 30 ms latency, low security requirements

[0323] According to these characteristics, the system assigns different dynamic characteristics A(t), attenuation coefficient λ, encryption strength B to each channel. i and frequency adjustment factor ω i The details are as follows:

[0324] Channel 1: A(t)=1.5t, λ=0.05, B 1 =8,ω 1 =1.0

[0325] Channel 2: A(t)=1.0t, λ=0.02, B 2 =5,ω 2 =1.5

[0326] Channel 3: A(t)=0.5t, λ=0.08, B 3=3,ω 3 =2.0

[0327] First calculate the encrypted allocation value of channel 1:

[0328]

[0329] Set T = 10 seconds, the calculation result is:

[0330] Φ 1 (10)≈11.2

[0331] Then calculate the encrypted distribution values ​​of channel 2 and channel 3:

[0332] Φ 2 (10)≈8.5,Φ 3 (10)≈5.4

[0333] Based on these calculation results, the system selects the appropriate encryption algorithm and key for each channel. For example, channel 1 has the highest encryption assignment value, so a strong encryption algorithm (such as 4096-bit RSA) is selected and a long key is used to ensure high data security. The encryption algorithm for channels 2 and 3 is relatively weak, using 2048-bit or 128-bit AES algorithms to strike a balance between security and transmission efficiency.

[0334] To further optimize data communication security and efficiency, the system introduces an independent key management mechanism. This mechanism dynamically adjusts the key management strategy through the function Ψ(K) based on channel characteristics during key generation, distribution and update.

[0335] The function Ψ(K) is defined as follows:

[0336]

[0337] Among them, K j is the key parameter of the channel, α j and β j is the key update adjustment coefficient, and its value ranges are 0.01 to 0.1 and 0.5 to 2.0 respectively.

[0338] Assume that the system needs to transmit data fragments through three transmission channels, and the key management strategy of each channel needs to be adjusted according to its bandwidth, latency and security requirements. The following are the key management parameters for each channel:

[0339] Channel 1: Key parameter K 1 =1024,α 1 =0.05, β 1 =1.0

[0340] Channel 2: Key parameter K 2=2048,α 2 =0.02, β 2 =1.5

[0341] Channel 3: Key parameter K 3 =4096,α 3 =0.08, β 3 =2.0

[0342] First, calculate the key management policy value for channel 1:

[0343] Ψ 1 (K) = 1024·e -0.05t ·sin(1.0t)

[0344] Assuming t = 10 seconds, the calculation result is:

[0345] Ψ 1 (K)≈1024·e -0.5 ·sin(10)≈1024·0.6065·-0.544≈-339.2

[0346] Next, calculate the key management policy values ​​for channel 2 and channel 3:

[0347] Ψ 2 (K)≈2048·0.8187·-0.998≈-1675.6,Ψ 3 (K)≈4096·0.4493·0.909≈1667.7

[0348] Through these calculations, the system can determine the key management strategy for each channel. For example, the calculation result of channel 1 is small and negative, indicating that the current key management strategy needs to be adjusted to increase the key update frequency and enhance data security. The result of channel 2 shows that the key update frequency is high and can be appropriately reduced to balance the computing load. The result of channel 3 is close to the optimal value, indicating that the current key management strategy is optimal and can be maintained as it is.

[0349] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for optimizing encryption of data communication, characterized in that The following steps are involved: S1. Construction of intelligent encryption algorithm library: Design an intelligent algorithm library containing multiple encryption algorithms, covering symmetric encryption, asymmetric encryption and hybrid encryption; Use machine learning models to analyze historical communication data, automatically identify characteristics under different communication environments, and dynamically select the most suitable encryption algorithm based on current network conditions, data transmission needs, and security requirements; S2, multi-level encryption strategy and chain optimization: S2.

1. Data is processed in layers according to sensitivity and priority. Different encryption algorithms and keys are used in each layer. Inter-layer dependencies and security architecture are established. Decryption of upper-layer data is ensured to be dependent on the security of the lower-layer keys through dependency functions. S2.

2. Design a chain dependency structure so that the strength, algorithm selection and key length of the lower-level encryption are all related to the results of the upper-level encryption; S3, adaptive key management and distribution optimization: S3.

1. Based on the dynamic changes of the communication environment and the data transmission requirements, an adaptive key generation and update strategy is designed, wherein the key generation process combines the hardware characteristics of the device and the real-time network status; S3.2, adopt an optimized distributed key distribution mechanism, combine the data flow on the encryption link and the load of the network node, dynamically select the distribution path, and synchronize the key through the chain encryption result; S4, end-to-end encryption optimization and multi-channel transmission: S4.

1. Divide the data into multiple fragments and encrypt and transmit them through different transmission channels; each channel uses an independent encryption algorithm and key, while ensuring the integrity and independence of each data fragment; S4.

2. Based on the adaptive key management strategy, the encryption path of each transmission channel is dynamically optimized. During the data transmission process, the path and channel are selected according to the real-time monitored network status and adjusted and optimized in combination with the chain optimization results.

2. The encryption optimization method for data communication according to claim 1, characterized in that The process of performing the encryption algorithm in the data communication environment includes: S1. First, collect and store historical communication data including communication frequency, packet size, network delay, and packet loss rate, and input them into the deep learning model; S2. Then, in the pattern matching and prediction stage, the adaptive prediction model Λ(ω,t) is used, which is defined as follows: Among them, ω n (t) represents the change of the extracted multidimensional features over time, and its differential term Capture the instantaneous rate of change of the feature, and cos(ω n (t)·t) reflects the periodic fluctuation of the feature in time; the convolution integral term Then the weighted sum of historical feature data is combined to synthesize the cumulative impact of past features on the current forecast; S3. Finally, in the process of adaptive algorithm selection and optimization, by introducing the multidimensional encryption strategy function formula: in, is the predicted optimization parameter, δ(t) is the adjustment parameter of encryption strength; The prediction parameters are weighted and summed to generate an overall encryption strategy evaluation value; the strategy evaluation value is normalized by a square root operation; then the sine modulation term Used to refine encryption strategy selection; Through Real-time adjustment of functions, switching encryption schemes when the communication environment frequently fluctuates.

3. The encryption optimization method for data communication according to claim 1, characterized in that The method for data layering processing includes: S1. According to the type, content and usage of data, the sensitivity assessment function Φ(x) is used to classify data. The formula is: Among them, κ is the weight factor, x(t) is the dynamic attribute of the data; the sensitivity change of the data in different time periods is evaluated by integrating the time series characteristics; and the sin(κ·t) term is used to capture the periodic characteristics of the data sensitivity changing over time. The term is the instantaneous rate of change of data sensitivity; S2. Next, the data is assigned to different encryption levels based on the sensitivity and priority of the data, and different encryption algorithms and keys are selected for each level. S3. Finally, establish the inter-layer dependency and security architecture, and make the decryption of the upper layer data dependent on the security of the lower layer key through the dependency function Γ(z); dependency function: Among them, z k Represents the security strength of each layer of keys, θ k is the dependency angle between keys; and Represents the cumulative value of the key security of each layer; and cos(θ k ) is used to quantify the dependencies between keys at each layer.

4. The encryption optimization method for data communication according to claim 1, characterized in that The construction method adopted by the key generation and update strategy includes: S1. First, by real-time monitoring of the key parameters of the communication environment, including bandwidth B(t), delay L(t), packet loss rate P(t) and network load N(t), the dynamic changes of the communication environment are comprehensively evaluated using the environment evaluation function Ψ(B, L, P, N). The function form is: Among them, α is the weight factor of packet loss rate, represents the combined effect of bandwidth and delay, and ln(1+N(t)) is used to quantify the impact of network load; S2. Then, in the key generation process, the hardware characteristics of the integrated device, including the computing power of the processor C p 、Memory capacity M c , Random number generator performance R g , and through the hardware characteristic function Θ(C p ,M c ,R g ) dynamically adjusts the complexity of key generation; the function is defined as: in, Reflects the combined impact of device computing power and memory, Used to adjust the impact of the performance of the random number generator on the complexity of key generation.

5. The encryption optimization method for data communication according to claim 1, characterized in that The establishment process of dynamically selecting the distribution path includes: S1. First, monitor the data flow F(t) on the encrypted link and the load of the network node L in real time. n (t), obtain the status information of each node, including the data packet transmission delay D n (t), node processing capacity C n and the current load level L n (t); Based on the information, the key distribution path is calculated, and the path optimization function Φ(P) is defined as follows: Among them, N is the number of nodes on the path, β is the weight factor, and Φ(P) represents the comprehensive goodness of each path; S2. In the key distribution process, a chain encryption method is used. Each hop node will re-encrypt the key group to generate a new encryption result. The chain encryption function Ψ(K n ) is described by the following formula: Among them, K k is the encryption key of the kth node, θ k is the dependency angle between nodes in the encryption process, γ is the attenuation coefficient of chain encryption; S3. Finally, the load balancing function Ω(L) is used to monitor and adjust the load of each node in real time, which is defined as follows: Among them, α is the load balancing adjustment parameter, and Ω(L) is used to dynamically adjust the distribution path and load distribution strategy.

6. The encryption optimization method for data communication according to claim 1, characterized in that The construction of end-to-end encryption optimization and multi-channel transmission includes the following steps: S1. Split the original data into multiple segments according to predefined rules, dynamically adjust the segment size according to the type and sensitivity of the data, and assign a unique identifier to each segment; S2. Transmit each fragment through different transmission channels. Each channel uses an independent encryption algorithm and key for encryption. The channels are independent of each other and do not share encryption information. S3. During transmission, attach integrity verification information to each fragment; S4. Verify and reassemble each fragment at the receiving end using the fragment's unique identifier and verification information.

7. The encryption optimization method for data communication according to claim 6, characterized in that The process of allocating a unique identifier includes: S1. First, according to the data including file type, content structure and communication requirements, predefined segmentation rules are formulated; the original data is segmented into multiple logically independent segments through the rules; S2. Then, in the data segmentation process, the size of the segments is dynamically adjusted according to the data type including text, image, video and the sensitivity including confidentiality and privacy; high-sensitivity data is segmented into small segments; low-sensitivity data is segmented into large segments; S3. Assign a unique identifier when generating each data segment; the identifier contains the source information of the segment, the segmentation order, and the sensitivity level of the data.

8. The encryption optimization method for data communication according to claim 6, characterized in that The encryption implementation using independent encryption algorithms and keys includes the following steps: S1. First, the data is divided into multiple fragments, and an independent transmission channel is assigned to each fragment. Each transmission channel is assigned a different encryption algorithm and key through the function Φ(T) according to the characteristics including bandwidth, delay, and security requirements. Φ(T) is expressed as: Among them, A(t) represents the dynamic characteristics of the channel, λ is the attenuation coefficient, and B i and ω i are encryption strength and frequency adjustment factors, respectively; S2. Secondly, each channel uses an independent key management mechanism. When generating, distributing and updating keys, the key management strategy is dynamically adjusted based on the channel characteristics through the function Ψ(J). Ψ(K) is expressed as: Among them, K j is the key parameter of the channel, α j and β j The key update adjustment factor.

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