A network information protection method based on big data analysis

Abstract

The present invention relates to the field of network information protection technology based on big data analysis, and discloses a network information protection method based on big data analysis. By establishing a transmission channel identification system, the problem of unique identification of data such as transmission content type, source address, destination address and timestamp during transmission is solved, thereby providing more accurate encryption protection. A custom generation function combines these multi-dimensional information to generate a unique transmission channel identifier to ensure the uniqueness of each transmission process in a big data environment. By performing weighted mapping on string characters through a hash function, hash collisions are avoided and the uniqueness of the identifier is ensured. The use of high-dimensional mapping functions and large prime numbers further increases the unpredictability of the output value, improves encryption security, and effectively prevents man-in-the-middle attacks and replay attacks. This scheme ensures precise encryption control and optimizes security in a big data environment.

Description

Technical Field

[0001] The present invention relates to the technical field of network information protection based on big data analysis, and specifically to a network information protection method based on big data analysis. Background Art

[0002] With the rapid development of network technology, the internet has become a fundamental component of modern society. Against this backdrop, network security has become a critical issue for organizations and individuals alike. Especially in the face of big data, the protection of network information and privacy security have become pressing issues. Various threats that may arise during data transmission, such as data theft, tampering, and hijacking, require technical mitigation. Currently, however, most existing technologies face numerous challenges, including insufficient encryption, unstable data transmission, vulnerabilities in encryption algorithms, and inappropriate path selection.

[0003] Existing network information protection methods primarily focus on traditional encryption and authentication mechanisms. Many methods employ static encryption algorithms, such as AES, to protect data transmission. While these methods provide basic protection, they often fall short in the face of dynamically changing network environments and increasingly sophisticated attack vectors. Traditional encryption methods lack flexibility and adaptability in data encryption strength, and are unable to adjust encryption strength based on the real-time state of the transmission network and the sensitivity of the data. Furthermore, static encryption algorithms can experience encryption and decryption efficiency issues when dealing with large-scale data flows, impacting network performance.

[0004] While existing technologies have improved network information protection capabilities to a certain extent, they remain insufficient when dealing with dynamic network environments and complex attack methods. Traditional methods such as static encryption, path selection, and encryption strength adjustment suffer from poor flexibility and real-time performance in the face of the volatile nature of modern network environments. Therefore, this case proposes a network information protection method based on big data analysis, including dynamic encryption, path selection, and encryption strength adjustment. Summary of the Invention

[0005] The present invention provides a network information protection method based on big data analysis, which helps solve the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a network information protection method based on big data analysis, comprising:

[0007] S1. Dynamic transmission channel encryption:

[0008] S11. Establish a transmission channel identification system:

[0009] Get the type of the transmitted content, recorded as ContentType;

[0010] The types of the transmitted content include Sensitive content, Public content and General content;

[0011] Get the source address of the transmission, recorded as Source;

[0012] Get the destination address of the transmission, recorded as Destination;

[0013] Get the timestamp of the transmission, recorded as Timestamp;

[0014] Set a custom generation function, denoted as g. g is a multidimensional mapping function based on ContentType, Source, Destination, and Timestamp. Specifically:

[0015]

[0016] in, is the mapping function, specifically:

[0017]

[0018] Hash(·) is a custom hash function that converts the input string into a fixed-length numeric value. Specifically:

[0019] Set a large prime number, denoted as P1;

[0020]

[0021] Among them, X i is the ASCII value of the i-th character in string X; α i is the weight coefficient of the i-th character in string X; n is the total number of characters in string X; mod is the modular operation;

[0022] The calculation result of the mapping function is recorded as the intermediate value V;

[0023] Set the high-dimensional mapping function, specifically:

[0024] Set a large prime number, denoted as P2, and P2≠P1;

[0025]

[0026] Among them, sin(V), cos(V), and log(V+1) are three mathematical transformations of the intermediate value V, which produce nonlinear changes;

[0027] The result calculated by the high-order mapping function is used as a unique identifier, recorded as UID;

[0028] S12, analyzing the transmission content type in real time;

[0029] S13, adaptive adjustment of encryption strength;

[0030] S2, secure transmission based on blockchain;

[0031] S3, intelligent management of end-to-end encryption.

[0032] Optionally, the real-time analysis of the transmission content type specifically includes:

[0033] Based on the type of transmitted content, an encryption requirement function f(ContentType) is set. This function uses a linear weighted method, specifically:

[0034]

[0035] Among them, β1, β2 and β3 are the weight coefficients of each type of content; It is an indicator function. If the input condition is true, the value is 1, and if the input condition is false, the value is 0.

[0036] Optionally, the adaptive adjustment of encryption strength specifically includes:

[0037] Get the transmission network delay, recorded as NetworkLatency;

[0038] Get the transmission packet loss rate, recorded as PacketLoss;

[0039] Get the transmission network bandwidth, recorded as Bandwidth;

[0040] Obtain the reliability of the transmission network path, denoted as Reliability;

[0041] Set the network status risk assessment function to calculate the network status risk level and adjust the encryption strength. Specifically:

[0042] The network status risk assessment function is:

[0043]

[0044] Among them, γ1, γ2, γ3 and γ4 are network status factors that adjust the risk assessment;

[0045] Set three levels of encryption:

[0046] ψ: high-intensity encryption;

[0047] φ: medium-strength encryption;

[0048] Low-strength encryption;

[0049] Adjust the encryption strength based on the network status risk assessment function calculation results:

[0050] The result of network status risk assessment function calculation is recorded as R;

[0051]

[0052] EncryptionStrength is the encryption strength.

[0053] Optionally, the three encryption strengths include:

[0054] High-strength encryption ψ is specifically:

[0055] Set a large prime number, denoted as P3, and P3≠P2;

[0056] Get the total number of data blocks during transmission, denoted as ψ n ;

[0057]

[0058] Among them, DataBit i is the number of bits of the i-th data block; k i is the weight coefficient of the i-th data block; the medium-strength encryption φ is specifically:

[0059] Set a large prime number, denoted as P4, and P4≠P3;

[0060] Set the number of data blocks required for medium encryption, denoted as φ n ;

[0061]

[0062] Low-strength encryption Specifically:

[0063] Set a large prime number, denoted as P5, and P5≠P4;

[0064] Set the minimum number of data blocks required for low-strength encryption, denoted as

[0065]

[0066] Optionally, the blockchain-based secure transmission specifically includes:

[0067] S21. Create a blockchain data transmission node:

[0068] Each time data is transmitted, a transmission block is generated, denoted as B, which contains the following content:

[0069] B=(UID,EncryptedData,Timestamp,Source,Destination,Nonce);

[0070] Among them, EncryptedData is the encrypted data; Nonce is a random value to ensure the non-repeatability of the data transmission process;

[0071] S22. Smart contract controls data transmission permissions:

[0072] Get user credentials, recorded as UserCredentials;

[0073] Set the random verification algorithm and perform transmission verification V(UID,UserCredentials), specifically:

[0074] Set a large prime number, denoted as P6, and P6≠P5;

[0075] V(UID,UserCredentials)=(h(UID,UserCredentials,Timestamp))mod P6;

[0076] Where h(·) is a hash function;

[0077] If the result of the random verification algorithm V(UID,UserCredentials) is zero, the transmission is allowed;

[0078] If the result of the random verification algorithm V(UID,UserCredentials) is not zero, the transmission is allowed;

[0079] S23. Verification and confirmation after transmission is completed:

[0080] When verifying the integrity of block transmission, the reverse hash function is used for verification:

[0081]

[0082] Among them, h(B) and h(B') are the hash values ​​of the transmitted blocks respectively. If the two are equal, it is determined that the data has not been tampered with. If the two are not equal, it is determined that the data has been tampered with. B' is the block received by the target node.

[0083] Optionally, the intelligent management of end-to-end encryption specifically includes:

[0084] S31, intelligent selection of transmission path:

[0085] Set the transmission path set, recorded as Paths;

[0086] Get all transmission paths and add them to the transmission path collection Paths;

[0087] Based on the path selection optimization function P(ContentType), the optimal transmission path is calculated:

[0088]

[0089] in, is the basic cost function of the transmission path p; is the content sensitivity function of the transmission path p;

[0090] S32, real-time encryption policy adjustment:

[0091] Based on the dynamic monitoring data transmitted over the network, set the encryption level adjustment function, specifically:

[0092] A(NetworkStatus,ContentType)=R(NetworkStatus)1+f(ContentType)·δ2;

[0093] Among them, δ1 and δ2 are the weight coefficients of encryption strength; A(NetworkStatus,ContentType is used to adjust the encryption strength;

[0094] S33, key management and update mechanism:

[0095] Get the current timestamp, recorded as Timestamp current ;

[0096] Get the currently used encryption key, denoted as K old ;

[0097] Get the risk score of the transmission path, recorded as PathRisk;

[0098] Set a large prime number, denoted as P7, and P7≠P6;

[0099] A new key is generated by combining the transmission path risk and the current timestamp. The key update function is as follows:

[0100]

[0101] Optionally, the basic cost function specifically includes:

[0102] Get the delay of transmission path p, denoted as Latency(p);

[0103] Get the packet loss rate of transmission path p, denoted as PacketLoss(p);

[0104] Get the bandwidth of transmission path p, denoted as Bandwidth(p);

[0105] The basic cost function is specifically:

[0106]

[0107] Among them, λ1, λ2 and λ3 are the weighted coefficients of each factor.

[0108] Optionally, the content sensitivity function specifically includes:

[0109]

[0110] The present invention has the following beneficial effects:

[0111] 1. By establishing a transmission channel identification system, the unique identification of data such as the transmission content type, source address, destination address, and timestamp during transmission is resolved, providing more precise encryption protection. A unique transmission channel identifier is generated through a custom generation function, combining a multi-dimensional mapping of the transmission content type, source address, destination address, and timestamp. This identifier ensures the uniqueness of each transmission channel in a big data environment, enabling precise encryption control over each transmission process. Specifically, a hash function is used to perform a weighted mapping on each character of the string, avoiding hash collisions and ensuring the uniqueness of the identifier. The combination of high-dimensional mapping functions and large prime numbers further increases the unpredictability of the output value, improving encryption security and effectively preventing man-in-the-middle and replay attacks.

[0112] 2. By analyzing the transmitted content type in real time, this approach addresses the inability of traditional encryption schemes to dynamically adjust encryption strength based on different content types, thereby improving encryption efficiency and data protection accuracy. Traditional network information protection typically uses a fixed encryption method for all transmitted data. This static encryption approach cannot adapt to the security requirements of diverse data. To address this issue, this solution designs an encryption requirement function based on the transmitted content type. This function uses a linear weighting approach, taking into account the encryption requirements and complexity of the content, and sets different encryption strength requirements for different types of content. By setting weighting coefficients, the system can automatically adjust the encryption policy based on content type, such as sensitive data, public data, and general data, ensuring that highly sensitive data receives adequate encryption protection while avoiding over-encryption of public data, thereby optimizing the use of encryption resources. The introduction of an indicator function further enhances the flexibility of encryption requirements, enabling the encryption requirement function to accurately determine the encryption strength requirements for different transmitted content. For example, for sensitive content, the indicator function will be "true", triggering a higher encryption strength; for public content, the indicator function will be "false", reducing the encryption strength accordingly, ensuring transmission efficiency is not affected. By designing this encryption requirement function, the system can intelligently and dynamically adjust encryption strategies based on the varying needs of the transmitted content. This not only improves data transmission security but also avoids the waste of resources caused by unnecessary over-encryption. This flexible encryption mechanism significantly improves data transmission efficiency and security, ensuring that different data types receive the most appropriate protection in different network environments.

[0113] 3. Adaptive encryption strength adjustment solves the problem of traditional static encryption strategies being unable to adapt to dynamic network conditions, thereby improving encryption efficiency and optimizing network performance. Traditional network encryption schemes typically use a fixed encryption strength to protect data transmission. However, fixed encryption strength cannot adapt to changing network conditions. As a result, when network conditions are poor, an excessively high encryption strength may affect transmission efficiency, while when network conditions are good, an excessively low encryption strength may not effectively protect data security. Therefore, the system needs to dynamically adjust the encryption strength based on real-time network conditions to ensure data security and transmission efficiency. In this solution, by obtaining key parameters of the transmission network—latency, packet loss rate, bandwidth, and path reliability—and combining them with a "network status risk assessment function," the network risk level is calculated and the encryption strength is adaptively adjusted accordingly. This approach allows for flexible encryption strategy adjustments based on real-time network conditions. For example, when network latency and packet loss rates are high, the system automatically reduces the encryption strength to minimize the impact of encryption on transmission performance. When network conditions are good, the system increases the encryption strength to ensure data security. Through this mechanism, the system can adjust encryption strength based on real-time network conditions, avoiding the waste of resources and data leakage risks associated with over-encryption. This dynamic encryption strategy not only improves data transmission efficiency but also ensures appropriate security protection under various network conditions, thereby optimizing the balance between security and performance during data transmission.

[0114] 4. By implementing different encryption strategies across three different encryption strengths, this solution addresses the inability to flexibly adjust encryption levels to meet varying network environments and data security requirements. This effectively balances the trade-off between encryption strength and transmission efficiency, enhancing the flexibility and security of data transmission. In traditional encryption schemes, encryption strength is typically statically set, meaning that all transmitted data uses the same encryption level, regardless of the data's security requirements or network environment. This one-size-fits-all approach often leads to over-encryption in some cases, wasting computing resources and potentially causing excessive network latency and bandwidth consumption. In other cases, using lower encryption strengths may not provide adequate protection, increasing security risks. This solution provides flexible encryption options for different network conditions and data types by setting three different encryption strengths: high, medium, and low. Specifically, high-strength encryption ensures maximum data security when the number of data blocks is large by using large prime numbers and a higher bit count. This is suitable for high-risk transmission scenarios, such as sensitive or high-value data. Medium-strength encryption, based on the number of data blocks and a moderate prime number, is suitable for scenarios requiring a balance between data security and transmission efficiency. This encryption strategy can not only ensure good security, but also avoid excessive consumption of computing resources when not necessary. Low-strength encryption: For low-risk transmissions, reduce the complexity of encryption, use fewer data blocks and lower prime number settings, ensure efficient transmission when network conditions are poor, and reduce the burden of encryption operations. Through this mechanism of dynamically adjusting encryption strength, the system can automatically select the appropriate encryption scheme based on actual needs and network conditions, avoiding the waste of resources and security risks brought by fixed encryption strength. This hierarchical encryption method enhances the flexibility of data transmission, ensuring that while ensuring security, it optimizes transmission efficiency. Especially in the case of network delays or limited bandwidth, it can effectively reduce the impact of encryption on performance.

[0115] 5. Blockchain-based secure transmission addresses the issue of data integrity and legitimacy during data transmission, thereby enhancing the security and immutability of data transmission. Traditional data transmission often relies on centralized authentication mechanisms, which pose single points of failure and tampering risks. Furthermore, the lack of effective verification mechanisms during transmission makes it vulnerable to man-in-the-middle attacks or malicious tampering. These issues make it difficult to ensure the trustworthiness of data transmission, especially when dealing with sensitive data, where security requirements are particularly stringent. This solution effectively addresses these issues by introducing blockchain technology. Specifically, the creation of blockchain data transmission nodes: Each data transmission generates a new transmission block containing encrypted data and a random value. The introduction of a random value ensures the non-repeatability of each data transmission, preventing data tampering or replay attacks. This immutability significantly enhances data transmission security and ensures data integrity during transmission. Smart contracts control data transmission permissions: Through the verification mechanism of smart contracts, the system ensures that only authorized users can perform data transmission. The random verification algorithm used in smart contracts further enhances the legitimacy of transmission requests, preventing intrusion by unauthorized users and unauthorized transmission operations. This automated permission control mechanism, based on smart contracts, reduces the risk of human intervention and improves the overall security of the system. Verification and confirmation after transmission: After data transmission is completed, the destination node verifies the data integrity using a reverse hash function to ensure that the data has not been tampered with during transmission. If the hash values ​​of the transmitted blocks are inconsistent, the destination node can immediately identify the data tampering and take appropriate measures. This mechanism ensures effective verification and confirmation at every step of the data transmission process, ensuring the immutability and authenticity of the data. By introducing blockchain technology, the entire data transmission process is fully verified and protected, avoiding the single point of failure inherent in traditional data transmission methods and enhancing the security and reliability of data transmission. Furthermore, the verification mechanism based on smart contracts and hash verification ensures not only a high level of security but also the timely detection of any tampering after the transmission is completed, significantly enhancing transparency and trust in the data transmission process. This blockchain-driven data transmission security solution ensures the integrity, authenticity, and immutability of sensitive data, significantly enhancing the system's protection capabilities.

[0116] 6. Through an intelligent end-to-end encryption management solution, this solution addresses the challenges of path selection, encryption strategy, and key management in data transmission, ensuring the security, flexibility, and efficiency of data transmission, thereby improving the protection and responsiveness of the entire network. Intelligent Transmission Path Selection: Traditional data transmission often relies on static configuration for path selection, which can result in inflexible and inefficient data transmission paths and an inability to adapt to changing network conditions in real time. This solution addresses this issue by setting a set of transmission paths and using a path selection optimization function to calculate the optimal transmission path. Path optimization considers factors such as the transmission path's underlying cost, content sensitivity, and security, ensuring that the selected path not only meets performance requirements but also maximizes data security during transmission. This dynamic path selection improves transmission efficiency and effectively mitigates potential network attacks and security risks. Real-time Encryption Policy Adjustment: As network transmission conditions, such as latency, bandwidth, and packet loss rate, change, traditional encryption strategies often fail to adapt flexibly to the current network environment, resulting in insufficient encryption strength or excessive waste of computing resources. This solution adjusts the encryption level in real time based on dynamic network transmission monitoring data. Using an encryption level adjustment function, it balances encryption strength with network load, ensuring data security while maximizing resource utilization. Dynamic adjustment of the encryption strategy enhances flexibility during transmission, allowing for increased encryption strength during poor network conditions and decreased encryption strength during good conditions to improve transmission efficiency. Key management and update mechanism: Key management is crucial for ensuring the security of encrypted data. Traditional static key management methods carry the risk of key leakage, while frequent key updates can increase system complexity. This solution generates new keys based on transmission path risk and the current timestamp, employing a highly random, large prime number generation function to avoid the security risks of static keys. Key updates during each transmission enhance encryption strength, ensure key timeliness and unpredictability, and thus reduce the risk of key leakage. Furthermore, regular key updates strengthen protection against modern network attacks, such as man-in-the-middle attacks, thereby enhancing system security. Through intelligent transmission path selection, dynamic real-time encryption policy adjustments, and key management and update mechanisms, this solution effectively addresses the challenges of traditional network encryption transmission, including path selection, inflexible encryption policies, and insecure key management. These innovative management measures not only improve the security and efficiency of data transmission, but also enhance the system's adaptability and responsiveness to diverse network environments, ensuring that data is consistently protected under complex and changing network conditions.

[0117] 7. Calculating the optimized cost of transmission paths using a basic cost function addresses issues such as inefficient path selection and unpredictable transmission quality during network transmission, ensuring efficient, stable, and reliable data transmission. Obtaining latency, packet loss rate, and bandwidth of transmission paths: In traditional data transmission, path selection often ignores actual network quality. This can lead to high latency and frequent packet loss in selected paths in certain situations, impacting overall transmission performance. This solution provides accurate data support for path selection by obtaining three key metrics: latency, packet loss rate, and bandwidth. This ensures a comprehensive understanding of current network conditions. This approach addresses the issue of path selection failing to consider actual network load. Weighted Basic Cost Function Calculation: By introducing weighting coefficients, a weighted summation of latency, packet loss rate, and bandwidth is calculated to form a comprehensive "basic cost function." This function dynamically adjusts path selection preferences based on network changes, ensuring that the selected path not only considers factors such as latency and bandwidth but also adjusts weights based on different network conditions, enhancing the flexibility and adaptability of path selection. This feature addresses the problem that traditional static path selection methods are unable to adapt to dynamic network environments. Improve the quality and efficiency of transmission path selection: A basic cost function that comprehensively considers network latency, bandwidth, and packet loss rate helps select the best path among multiple transmission paths. By minimizing weighted costs, the system can intelligently select paths with low latency, sufficient bandwidth, and low packet loss rates, thereby improving the efficiency and stability of data transmission. This not only increases data transmission speeds, but also reduces the data loss rate caused by unstable transmission, thereby reducing the risks posed by the network. Optimize resource utilization and reduce unnecessary load: During network transmission, selecting a low-cost path not only speeds up data transmission, but also optimizes the utilization of bandwidth and computing resources, reduces unnecessary load, and improves the overall performance of the system. By accurately calculating the basic cost of each path, the system can automatically optimize resource usage when selecting a path, thereby improving the operational efficiency of the system.

[0118] 8. Quantifying the sensitivity of transmitted content using a content sensitivity function addresses the issue of failing to implement appropriate protection measures based on different data types during data transmission, improving data security and the relevance of encryption policies. Introducing a content sensitivity function: In traditional data encryption, all data is often treated as equal, ignoring the differences in privacy and security between different types of data. By setting a content sensitivity function, it is possible to tailor encryption and protection policies to different data contents. For example, sensitive personal information such as ID numbers and bank account numbers is much more sensitive than non-sensitive data. The content sensitivity function flexibly determines the encryption strength and transmission policy for data based on factors such as data type and privacy requirements, ensuring that sensitive data is more securely protected. This step effectively addresses the issue of inappropriately tailoring security measures based on data type. Accurately quantifying content sensitivity: By designing a sensitivity assessment function for different content types, the privacy, confidentiality, and risk of leakage of the content can be quantified. This function dynamically adjusts encryption strength and protection measures based on the varying nature of the content, such as personal information or commercial secrets, thereby avoiding the performance burden of over-encryption. This not only increases data protection flexibility, but also reduces unnecessary resource waste and improves system performance. Enhanced targeted data protection strategies: In traditional methods without sensitivity functions, the encryption strength of all data is usually uniform, which may lead to over-encryption of some non-sensitive data, increasing the system burden, while sensitive data may be exposed due to insufficient protection. By introducing content sensitivity functions, the system can automatically adjust the encryption strategy based on the type of data and privacy requirements, ensuring that sensitive data receives optimal protection while avoiding over-encryption of less sensitive data. This strategy effectively improves the security of data transmission while optimizing the efficiency of computing and bandwidth resources. Improved data transmission efficiency and security: By flexibly adjusting the encryption strategy based on the sensitivity of the content, the system can maximize the efficiency of data transmission while ensuring data security. Sensitive data will receive higher protection, while non-sensitive data can adopt appropriate protection measures, avoiding unnecessary encryption burden. This strategy optimizes data transmission efficiency, allowing the system to improve overall performance while ensuring security. BRIEF DESCRIPTION OF THE DRAWINGS

[0119] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0120] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0121] Example, see Figure 1 , a network information protection method based on big data analysis, comprising:

[0122] S1. Dynamic transmission channel encryption:

[0123] S11. Establish a transmission channel identification system:

[0124] Get the type of the transmitted content, recorded as ContentType;

[0125] The types of the transmitted content include Sensitive content, Public content and General content;

[0126] Get the source address of the transmission, recorded as Source;

[0127] Get the destination address of the transmission, recorded as Destination;

[0128] Get the timestamp of the transmission, recorded as Timestamp;

[0129] Set a custom generation function, denoted as g. g is a multidimensional mapping function based on ContentType, Source, Destination, and Timestamp. Specifically:

[0130]

[0131] in, is the mapping function, specifically:

[0132]

[0133] Hash(·) is a custom hash function that converts an input string into a fixed-length numeric value. This hash function is designed to combine the mapping of numbers and characters, generating values ​​by weighted combination of different types of data sources to avoid hash collisions. Specifically:

[0134] Set a large prime number to ensure the unpredictability of the output value, denoted as P1;

[0135]

[0136] Among them, X i is the ASCII value of the i-th character in string X; α i is the weight coefficient of the i-th character in string X, which is set by the system during initialization, taking into account the frequency and security of the character; n is the total number of characters in string X; mod is the modular operation;

[0137] The calculation result of the mapping function is recorded as the intermediate value V;

[0138] Set the high-dimensional mapping function, specifically:

[0139] Set a large prime number to ensure the randomness and unpredictability of the output value, denoted as P2, and P2≠P1;

[0140]

[0141] Among them, sin(V), cos(V), and log(V+1) are three mathematical transformations of the intermediate value V, which produce nonlinear changes;

[0142] The result calculated by the high-order mapping function is used as a unique identifier, recorded as UID;

[0143] S12, analyzing the transmission content type in real time;

[0144] S13, adaptive adjustment of encryption strength;

[0145] S2, secure transmission based on blockchain;

[0146] S3, intelligent management of end-to-end encryption.

[0147] By establishing a transmission channel identification system, the unique identification problem of data such as the transmission content type, source address, destination address, and timestamp during transmission is solved, and more precise encryption protection is provided. A unique transmission channel identifier is generated through a custom generation function combined with a multi-dimensional mapping of the transmission content type, source address, destination address, and timestamp. This identifier ensures the uniqueness of each transmission channel in a big data environment, enabling precise encryption control over each transmission process. Specifically, a hash function is used to perform a weighted mapping on each character of the string, avoiding the problem of hash collisions and ensuring the uniqueness of the identifier. The combination of high-dimensional mapping functions and large prime numbers further increases the unpredictability of the output value, improving the security of the encryption and effectively preventing man-in-the-middle and replay attacks.

[0148] The real-time analysis of the transmission content type specifically includes:

[0149] Based on the type of transmitted content, an encryption requirement function f(ContentType) is set. This function is designed based on a new model, taking into account the encryption requirements and complexity of the content, and adopts a linear weighted method. Specifically:

[0150]

[0151] Among them, β1, \2 and β3 are the weight coefficients of each type of content; It is an indicator function. If the input condition is true, the value is 1, and if the input condition is false, the value is 0.

[0152] γ1 represents the encryption requirement weight for Sensitive content. For sensitive content, a larger weight coefficient β1 indicates that it requires stronger encryption protection. Typically, this value is set to a higher number, such as β1 = 10. β2 represents the encryption requirement weight for Public content. For public content, a smaller weight coefficient β2 indicates that it requires less encryption. Typically, this value is set to a lower number, such as β2 = 1. β3 represents the encryption requirement weight for General content. For content with encryption requirements between sensitive and public content, the weight coefficient β3 can be set to a medium number, such as β3 = 5.

[0153] By analyzing the transmitted content type in real time, this approach addresses the inability of traditional encryption schemes to dynamically adjust encryption strength based on different content types, thereby improving encryption efficiency and data protection accuracy. Traditional network information protection typically uses a fixed encryption method for all transmitted data. This static encryption approach cannot adapt to the security needs of diverse data. To address this issue, this solution designs an encryption requirement function based on the type of transmitted content. This function uses a linear weighting approach, taking into account the encryption requirements and complexity of the content, and sets different encryption strength requirements for different types of content. By setting weighting coefficients, the system can automatically adjust the encryption policy based on content type, such as sensitive data, public data, and general data, ensuring that highly sensitive data receives adequate encryption protection while avoiding over-encryption of public data, thereby optimizing the use of encryption resources. The introduction of an indicator function further enhances the flexibility of encryption requirements, enabling the encryption requirement function to accurately determine the encryption strength requirements for different transmitted content. For example, for sensitive content, the indicator function will be "true," triggering a higher encryption strength; for public content, the indicator function will be "false," reducing the encryption strength accordingly, ensuring transmission efficiency is not affected. By designing this encryption requirement function, the system can intelligently and dynamically adjust encryption strategies based on the varying needs of the transmitted content. This not only improves data transmission security but also avoids the waste of resources caused by unnecessary over-encryption. This flexible encryption mechanism significantly improves data transmission efficiency and security, ensuring that different data types receive the most appropriate protection in different network environments.

[0154] The adaptive adjustment of encryption strength specifically includes:

[0155] Get the transmission network delay, recorded as NetworkLatency;

[0156] Get the transmission packet loss rate, recorded as PacketLoss;

[0157] Get the transmission network bandwidth, recorded as Bandwidth;

[0158] Obtain the reliability of the transmission network path, denoted as Reliability;

[0159] Set the network status risk assessment function to calculate the network status risk level and adjust the encryption strength. Specifically:

[0160] The network status risk assessment function is:

[0161]

[0162] Among them, γ1, γ2, γ3 and γ4 are network status factors that adjust the risk assessment;

[0163] Set three levels of encryption:

[0164] ψ: high-intensity encryption;

[0165] φ: medium-strength encryption;

[0166] Low-strength encryption;

[0167] Adjust the encryption strength based on the network status risk assessment function calculation results:

[0168] The result of network status risk assessment function calculation is recorded as R;

[0169]

[0170] EncryptionStrength is the encryption strength.

[0171] Adaptive encryption strength adjustment solves the problem of traditional static encryption strategies being unable to adapt to dynamic network conditions, thereby improving encryption efficiency and optimizing network performance. Traditional network encryption schemes typically use a fixed encryption strength to protect data transmission. However, fixed encryption strength cannot adapt to changing network conditions. Consequently, when network conditions are poor, an excessively high encryption strength can affect transmission efficiency, while when network conditions are good, an excessively low encryption strength can ineffectively protect data security. Therefore, the system needs to dynamically adjust encryption strength based on real-time network conditions to ensure data security and transmission efficiency. In this solution, by analyzing key network parameters—latency, packet loss rate, bandwidth, and path reliability—and combining them with a "network status risk assessment function," the network risk level is calculated and adaptively adjusted based on this risk. This approach allows for flexible encryption strategy adjustments based on real-time network conditions. For example, when network latency and packet loss rates are high, the system automatically reduces encryption strength to minimize the impact of encryption on transmission performance. When network conditions are good, the system increases encryption strength to ensure data security. This mechanism allows the system to adjust encryption strength based on real-time network conditions, avoiding the waste of resources and data leakage risks associated with over-encryption. This dynamic encryption strategy not only improves the efficiency of data transmission, but also ensures appropriate security protection under various network conditions, thereby optimizing the balance between security and performance during data transmission.

[0172] The three types of encryption strength specifically include:

[0173] High-strength encryption ψ is specifically:

[0174] Set a large prime number, denoted as P3, and P3≠P2;

[0175] Get the total number of data blocks during transmission, denoted as ψ n ;

[0176]

[0177] Among them, k i is the weight coefficient of the i-th data block; DataBit i is the number of bits of the i-th data block; k i is the weight coefficient of the i-th data block, reflecting the complexity of different data blocks;

[0178] Medium-strength encryption φ is specifically:

[0179] Set a large prime number, denoted as P4, and P4≠P3;

[0180] Set the number of data blocks required for medium encryption, denoted as φ n ;

[0181]

[0182] Low-strength encryption Specifically:

[0183] Set a large prime number, denoted as P5, and P5≠P4;

[0184] Set the minimum number of data blocks required for low-strength encryption, denoted as

[0185]

[0186] By implementing three different encryption strategies across three different strengths, this solution addresses the inability to flexibly adjust encryption levels to meet varying network environments and data security requirements. This effectively balances the trade-off between encryption strength and transmission efficiency, enhancing the flexibility and security of data transmission. In traditional encryption schemes, encryption strength is typically statically set, meaning that all transmitted data uses the same encryption level, regardless of the data's security requirements or network environment. This one-size-fits-all approach often leads to over-encryption in some cases, wasting computing resources and potentially causing excessive network latency and bandwidth consumption. In other cases, using lower encryption strengths may not provide adequate protection, increasing security risks. This solution provides flexible encryption options for different network conditions and data types by setting three different encryption strategies—high, medium, and low—to accommodate different network conditions and data types. Specifically, high-strength encryption ensures maximum data security when the number of data blocks is large by using large prime numbers and a higher bit count. This is suitable for high-risk transmission scenarios, such as sensitive or high-value data. Medium-strength encryption, based on the number of data blocks and a moderate prime number, is suitable for scenarios requiring a balance between data security and transmission efficiency. This encryption strategy can not only ensure good security, but also avoid excessive consumption of computing resources when not necessary. Low-strength encryption: For low-risk transmissions, reduce the complexity of encryption, use fewer data blocks and lower prime number settings, ensure efficient transmission when network conditions are poor, and reduce the burden of encryption operations. Through this mechanism of dynamically adjusting encryption strength, the system can automatically select the appropriate encryption scheme based on actual needs and network conditions, avoiding the waste of resources and security risks brought by fixed encryption strength. This hierarchical encryption method enhances the flexibility of data transmission, ensuring that while ensuring security, it optimizes transmission efficiency. Especially in the case of network delays or limited bandwidth, it can effectively reduce the impact of encryption on performance.

[0187] The secure transmission based on blockchain specifically includes:

[0188] The blockchain is an existing technology;

[0189] S21. Create a blockchain data transmission node:

[0190] Each time data is transmitted, a transmission block is generated, denoted as B, which contains the following content:

[0191] B=(UID,EncryptedData,Timestamp,Source,Destination,Nonce);

[0192] Among them, EncryptedData is the encrypted data; Nonce is a random value to ensure the non-repeatability of the data transmission process;

[0193] S22. Smart contract controls data transmission permissions:

[0194] Get user credentials, recorded as UserCredentials;

[0195] Set the random verification algorithm and perform transmission verification V(UID,UserCredentials), specifically:

[0196] Set a large prime number, denoted as P6, and P6≠P5;

[0197] V(UID,UserCredentials)=(h(UID,UserCredentials,Timestamp))mod P6;

[0198] Where h(·) is a hash function;

[0199] If the result of the random verification algorithm V(UID,UsserCredentials) is zero, the transmission is allowed;

[0200] If the result of the random verification algorithm V(UID,UserCredentials) is not zero, the transmission is allowed;

[0201] S23. Verification and confirmation after transmission is completed:

[0202] When verifying the integrity of block transmission, the reverse hash function is used for verification:

[0203]

[0204] Among them, h(B) and h(B') are the hash values ​​of the transmitted blocks respectively. If the two are equal, it is determined that the data has not been tampered with. If the two are not equal, it is determined that the data has been tampered with. B' is the block received by the target node. After receiving the data, the target node will check the content in B and the hash value from the source node to ensure that the data has not been tampered with.

[0205] This blockchain-based secure transmission resolves the trustworthiness issue during data transmission, ensuring data integrity and legitimacy, thereby enhancing the security and immutability of data transmission. Traditional data transmission processes often rely on centralized authentication mechanisms, which pose single points of failure and tampering risks. Furthermore, the lack of effective verification mechanisms during transmission makes it vulnerable to man-in-the-middle attacks or malicious tampering. These issues make it difficult to ensure the trustworthiness of data transmission, especially when dealing with sensitive data, where security requirements are particularly stringent. This solution effectively addresses these issues by introducing blockchain technology. Specifically, the creation of blockchain data transmission nodes: Each data transmission generates a new transmission block containing encrypted data and a random value. The introduction of a random value ensures the non-repeatability of each data transmission, preventing data tampering or replay attacks. This immutability significantly enhances data transmission security and ensures data integrity during transmission. Smart contracts control data transmission permissions: Through the smart contract's verification mechanism, the system ensures that only authorized users can perform data transmission. The random verification algorithm used in the smart contract further enhances the legitimacy of transmission requests, preventing intrusion by unauthorized users and unauthorized transmission operations. This automated permission control mechanism, based on smart contracts, reduces the risk of human intervention and improves the overall security of the system. Verification and confirmation after transmission: After data transmission is completed, the destination node verifies the data integrity using a reverse hash function to ensure that the data has not been tampered with during transmission. If the hash values ​​of the transmitted blocks are inconsistent, the destination node can immediately identify the data tampering and take appropriate measures. This mechanism ensures effective verification and confirmation at every step of the data transmission process, ensuring the immutability and authenticity of the data. By introducing blockchain technology, the entire data transmission process is fully verified and protected, avoiding the single point of failure inherent in traditional data transmission methods and enhancing the security and reliability of data transmission. Furthermore, the verification mechanism based on smart contracts and hash verification ensures not only a high level of security but also the timely detection of any tampering after the transmission is completed, significantly enhancing transparency and trust in the data transmission process. This blockchain-driven data transmission security solution ensures the integrity, authenticity, and immutability of sensitive data, significantly enhancing the system's protection capabilities.

[0206] The intelligent management of end-to-end encryption specifically includes:

[0207] S31, intelligent selection of transmission path:

[0208] Set the transmission path set, recorded as Paths;

[0209] Get all transmission paths and add them to the transmission path collection Paths;

[0210] Based on the path selection optimization function P(ContentType), the optimal transmission path is calculated:

[0211]

[0212] in, is the basic cost function of the transmission path p; is the content sensitivity function of the transmission path p, taking into account the encryption requirements of the content, the wind direction weight, and the security of the path;

[0213] S32, real-time encryption policy adjustment:

[0214] Based on the dynamic monitoring data transmitted over the network, set the encryption level adjustment function, specifically:

[0215] A(NetworkStatus,ContentType)=R(NetworkStatus)·δ1+f(ContentType)·δ2;

[0216] Among them, δ1 and δ2 are the weight coefficients of encryption strength; A(NetworkStatus,ContentType is used to adjust the encryption strength;

[0217] S33, key management and update mechanism:

[0218] Get the current timestamp, recorded as Timestamp current ;

[0219] Get the currently used encryption key, denoted as K old ;

[0220] Get the risk score of the transmission path, recorded as PathRisk;

[0221] Set a large prime number, denoted as P7, to ensure that the generated key is highly random and secure, and P7≠P6;

[0222] A new key is generated by combining the transmission path risk and the current timestamp. The key update function is as follows:

[0223]

[0224] This intelligent end-to-end encryption management solution addresses the challenges of path selection, encryption strategy, and key management in data transmission, ensuring secure, flexible, and efficient data transmission, thereby enhancing the overall network's protection and responsiveness. Intelligent Transmission Path Selection: Traditional data transmission often relies on static configuration for path selection, which can result in inflexible and inefficient data transmission paths and inability to adapt to changing network conditions in real time. This solution addresses this issue by setting a set of transmission paths and using a path selection optimization function to calculate the optimal transmission path. Path optimization considers factors such as the transmission path's underlying cost, content sensitivity, and security, ensuring that the selected path not only meets performance requirements but also maximizes data security during transmission. This dynamic path selection improves transmission efficiency and effectively mitigates potential network attacks and security risks. Real-time Encryption Policy Adjustment: As network transmission conditions, such as latency, bandwidth, and packet loss rate, change, traditional encryption strategies often fail to adapt flexibly to the current network environment, resulting in insufficient encryption strength or excessive waste of computing resources. This solution adjusts the encryption level in real time based on dynamic network transmission monitoring data. Using an encryption level adjustment function, the solution balances encryption strength with network load, ensuring data security while maximizing resource utilization. Dynamic adjustment of encryption policies enhances transmission flexibility, enabling increased encryption strength in poor network conditions and reduced encryption strength in favorable environments to improve transmission efficiency. Key management and update mechanisms: Key management is crucial for ensuring the security of encrypted data. Traditional static key management methods carry the risk of key leakage, while frequent key updates can increase system complexity. This solution generates new keys by combining transmission path risk with the current timestamp, using a highly random, large prime number generator function to avoid the security risks of static keys. Key updates during each transmission enhance encryption strength, ensuring key timeliness and unpredictability, thereby reducing the risk of key leakage. Furthermore, regular key updates strengthen protection against modern network attacks, such as man-in-the-middle attacks, and enhance system security. Through intelligent transmission path selection, dynamic real-time encryption policy adjustment, and key management and update mechanisms, this solution effectively addresses the challenges of traditional network encryption transmission, including inflexible path selection, inflexible encryption policies, and insecure key management. These innovative management measures not only improve the security and efficiency of data transmission, but also enhance the adaptability and responsiveness of the entire system in different network environments, ensuring that data is always effectively protected under complex and changing network conditions.

[0225] The basic cost function specifically includes:

[0226] Get the delay of transmission path p, denoted as Latency(p);

[0227] Get the packet loss rate of transmission path p, denoted as PacketLoss(p);

[0228] Get the bandwidth of transmission path p, denoted as Bandwidth(p);

[0229] The basic cost function is specifically:

[0230]

[0231] Among them, λ1, λ2 and λ3 are the weighted coefficients of each factor.

[0232] Calculating the optimal cost of transmission paths using a basic cost function addresses issues such as inefficient path selection and unpredictable transmission quality during network transmission, ensuring efficient, stable, and reliable data transmission. Obtaining latency, packet loss rate, and bandwidth of transmission paths: In traditional data transmission, path selection often ignores actual network quality. This can lead to high latency and frequent packet loss in selected paths in certain situations, impacting overall transmission performance. This solution provides accurate data support for path selection by obtaining three key metrics: latency, packet loss rate, and bandwidth. This ensures a comprehensive understanding of current network conditions. This approach addresses the issue of path selection failing to consider actual network load. Weighted calculation of the basic cost function: By introducing weighting coefficients, a weighted summation of latency, packet loss rate, and bandwidth is calculated to form a comprehensive "basic cost function." This function dynamically adjusts path selection preferences based on network changes, ensuring that the selected path not only considers factors such as latency and bandwidth but also adjusts weights based on different network conditions, enhancing the flexibility and adaptability of path selection. This feature addresses the problem that traditional static path selection methods are unable to adapt to dynamic network environments. Improve the quality and efficiency of transmission path selection: A basic cost function that comprehensively considers network latency, bandwidth, and packet loss rate helps select the best path among multiple transmission paths. By minimizing weighted costs, the system can intelligently select paths with low latency, sufficient bandwidth, and low packet loss rates, thereby improving the efficiency and stability of data transmission. This not only increases data transmission speeds, but also reduces the data loss rate caused by unstable transmission, thereby reducing the risks posed by the network. Optimize resource utilization and reduce unnecessary load: During network transmission, selecting a low-cost path not only speeds up data transmission, but also optimizes the utilization of bandwidth and computing resources, reduces unnecessary load, and improves the overall performance of the system. By accurately calculating the basic cost of each path, the system can automatically optimize resource usage when selecting a path, thereby improving the operational efficiency of the system.

[0233] The content sensitivity function specifically includes:

[0234]

[0235] Quantifying the sensitivity of transmitted content using a content sensitivity function addresses the issue of inappropriate protection measures for different data types during data transmission, improving data security and the relevance of encryption policies. Introducing a content sensitivity function: In traditional data encryption, all data is often treated as equal, ignoring the differences in privacy and security between different types of data. By setting a content sensitivity function, it is possible to tailor encryption and protection policies to different data contents. For example, sensitive personal information such as ID numbers and bank account numbers is much more sensitive than non-sensitive data. The content sensitivity function flexibly determines the encryption strength and transmission policy for data based on factors such as data type and privacy requirements, ensuring that sensitive data is more securely protected. This step effectively addresses the issue of inappropriate security measures tailored to data type. Accurately quantifying content sensitivity: By designing a sensitivity assessment function for different content types, it is possible to quantify the privacy, confidentiality, and risk of leakage of the content. This function dynamically adjusts encryption strength and protection measures based on the varying nature of the content, such as personal information or commercial secrets, thereby avoiding the performance burden of over-encryption. This approach not only increases data protection flexibility, but also reduces unnecessary resource waste and improves system performance. Enhanced targeted data protection strategies: In traditional methods without sensitivity functions, the encryption strength of all data is usually uniform, which may lead to over-encryption of some non-sensitive data, increasing the system burden, while sensitive data may be exposed due to insufficient protection. By introducing content sensitivity functions, the system can automatically adjust the encryption strategy based on the type of data and privacy requirements, ensuring that sensitive data receives optimal protection while avoiding over-encryption of less sensitive data. This strategy effectively improves the security of data transmission while optimizing the efficiency of computing and bandwidth resources. Improved data transmission efficiency and security: By flexibly adjusting the encryption strategy based on the sensitivity of the content, the system can maximize the efficiency of data transmission while ensuring data security. Sensitive data will receive higher protection, while non-sensitive data can adopt appropriate protection measures, avoiding unnecessary encryption burden. This strategy optimizes data transmission efficiency, allowing the system to improve overall performance while ensuring security.

[0236] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0237] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A network information protection method based on big data analysis, characterized in that: include: S1. Dynamic transmission channel encryption: S11. Establish a transmission channel identification system: Get the type of the transmitted content, recorded as ContentType; The types of the transmitted content include sensitive content, public content and general content; Get the source address of the transmission, recorded as Source; Get the destination address of the transmission, recorded as Destination; Get the timestamp of the transmission, recorded as Timestamp; Set a custom generation function, denoted as g. g is a multidimensional mapping function based on ContentType, Source, Destination, and Timestamp. Specifically: in, is the mapping function, specifically: Hash(·) is a custom hash function that converts the input string into a fixed-length numeric value. Specifically: Set a large prime number, denoted as P1; Among them, X i is the ASCII value of the i-th character in string X; α i is the weight coefficient of the i-th character in string X; n is the total number of characters in string X; mod is the modular operation; The calculation result of the mapping function is recorded as the intermediate value V; Set the high-dimensional mapping function, specifically: Set a large prime number, denoted as P2, and P2≠P1; Among them, sin(V), cos(V), and log(V+1) are three mathematical transformations of the intermediate value V, which produce nonlinear changes; The result calculated by the high-order mapping function is used as a unique identifier, recorded as UID; S12, analyzing the transmission content type in real time; S13, adaptive adjustment of encryption strength; S2, secure transmission based on blockchain; S3, intelligent management of end-to-end encryption.

2. A network information protection method based on big data analysis according to claim 1, characterized in that: The real-time analysis of the transmission content type specifically includes: Based on the type of transmitted content, an encryption requirement function f(ContentType) is set. This function uses a linear weighted method, specifically: Among them, β1, β2 and β3 are the weight coefficients of each type of content; It is an indicator function. If the input condition is true, the value is 1, and if the input condition is false, the value is 0.

3. A network information protection method based on big data analysis according to claim 2, characterized in that: The adaptive adjustment of encryption strength specifically includes: Get the transmission network delay, recorded as NetworkLatency; Get the transmission packet loss rate, recorded as PacketLoss; Get the transmission network bandwidth, recorded as Bandwidth; Obtain the reliability of the transmission network path, denoted as Reliability; Set the network status risk assessment function to calculate the network status risk level and adjust the encryption strength. Specifically: The network status risk assessment function is: Among them, γ1, γ2, γ3 and γ4 are network status factors that adjust the risk assessment; Set three levels of encryption: ψ: high-intensity encryption; φ: medium-strength encryption; Low-strength encryption; Adjust the encryption strength based on the network status risk assessment function calculation results: The result of network status risk assessment function calculation is recorded as R; EncryptionStrength is the encryption strength.

4. A network information protection method based on big data analysis according to claim 3, characterized in that: The three types of encryption strength specifically include: High-strength encryption ψ is specifically: Set a large prime number, denoted as P3, and P3≠P2; Get the total number of data blocks during transmission, recorded as ψ n ; Among them, DataBit i is the number of bits of the i-th data block; k i is the weight coefficient of the i-th data block; Medium-strength encryption φ is specifically: Set a large prime number, denoted as P4, and P4≠P3; Set the number of data blocks required for medium encryption, denoted as φ n ; Low-strength encryption Specifically: Set a large prime number, denoted as P5, and P5≠P4; Set the minimum number of data blocks required for low-strength encryption, denoted as 5. A network information protection method based on big data analysis according to claim 4, characterized in that: The secure transmission based on blockchain specifically includes: S21. Create a blockchain data transmission node: Each time data is transmitted, a transmission block is generated, denoted as B, which contains the following content: B=(UID,EncryptedData,Timestamp,Source,Destination,Nonce); Among them, EncryptedData is the encrypted data; Noce is a random value to ensure the non-repeatability of the data transmission process; S22. Smart contract controls data transmission permissions: Get user credentials, recorded as userCredentials; Set the random verification algorithm and perform transmission verification V(UID,UserCredentials), specifically: Set a large prime number, denoted as P6, and P6≠P5; V(UID,UserCredentials)=(h(UID,UserCredentials,Timestamp)) mode P6; Where h(·) is a hash function; If the result of the random verification algorithm V(UID,UserCredentials) is zero, the transmission is allowed; If the result of the random verification algorithm V(UID,UserCredentials) is not zero, the transmission is allowed; S23. Verification and confirmation after transmission is completed: When verifying the integrity of block transmission, the reverse hash function is used for verification: Among them, h(B) and h(B') are the hash values ​​of the transmitted blocks respectively. If the two are equal, it is determined that the data has not been tampered with. If the two are not equal, it is determined that the data has been tampered with. B' is the block received by the target node.

6. A network information protection method based on big data analysis according to claim 5, characterized in that: The intelligent management of end-to-end encryption specifically includes: S31, intelligent selection of transmission path: Set the transmission path set, recorded as Paths; Get all transmission paths and add them to the transmission path collection Paths; Based on the path selection optimization function P(ContentType), the optimal transmission path is calculated: in, is the basic cost function of the transmission path p; is the content sensitivity function of the transmission path p; S32, real-time encryption policy adjustment: Based on the dynamic monitoring data transmitted over the network, set the encryption level adjustment function, specifically: A(NetworkStatus,ContentType)=R(NetworkStatus)·δ1+f(ContentType)·δ2; Among them, δ1 and δ2 are the weight coefficients of encryption strength; A(NetworkStatus, ContentType) is used to adjust the encryption strength; S33, key management and update mechanism: Get the current timestamp, recorded as Timestamp current ; Get the currently used encryption key, denoted as K old ; Get the risk score of the transmission path, recorded as PathRisk; Set a large prime number, denoted as P7, and P7≠P6; A new key is generated by combining the transmission path risk and the current timestamp. The key update function is as follows:

7. A network information protection method based on big data analysis according to claim 6, characterized in that: The basic cost function specifically includes: Get the delay of transmission path p, denoted as Latency(p); Get the packet loss rate of transmission path p, denoted as PacketLoss(p); Get the bandwidth of transmission path p, denoted as Bandwidth(p); The basic cost function is specifically: Among them, λ1, λ2 and λ3 are the weighted coefficients of each factor.

8. The network information protection method based on big data analysis according to claim 6 is characterized in that: The content sensitivity function specifically includes:

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