A data exchange management method and system

Abstract

The present invention discloses a data exchange management method and system, which relates to the technical field of data exchange management, including configuring a data exchange server to collect data to be exchanged; constructing a data classification model to classify the sensitivity level of the data to be exchanged; encrypting the data to be exchanged in sections based on the classification results, optimizing the data transmission path for data exchange; decrypting and storing the exchanged data, and setting access control permissions to manage the stored data. The present invention effectively improves data security by configuring the server end to collect and pre-process the data to be exchanged, constructing a data classification model to classify the data sensitivity level, and encrypting it in sections, while optimizing the data transmission path, greatly improving the data transmission efficiency, and improving the applicability of data management.

Description

Technical Field

[0001] The present invention relates to the technical field of data exchange management, and in particular to a data exchange management method and system. Background Art

[0002] With the rapid development of information technology, data exchange has become increasingly critical in many fields such as the Internet, the Internet of Things, and cloud computing. Data exchange management methods involve multiple links such as data collection, processing, encryption, transmission, storage, and permission management. Traditional data exchange methods usually focus on data transmission efficiency and encryption security, but with the surge in data volume and the diversification of data types, these methods are facing new challenges. Especially in terms of data security and privacy protection, the requirements are becoming increasingly stringent. Traditional methods have failed to fully solve the problem of balancing the security and efficiency of sensitive data during transmission. Existing technologies are insufficient in processing the classification and dynamic encryption of sensitive data, ignoring the specific security requirements of data of different sensitivity levels, and the data exchange efficiency is low in practical applications. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing data exchange management methods and systems, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the prior art is insufficient in processing the classification and dynamic encryption of sensitive data, ignores the specific security requirements of data of different sensitivity levels, and has low data exchange efficiency in practical applications.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a data exchange management method, which includes configuring a data exchange server to collect data to be exchanged; building a data classification model to classify the sensitivity level of the data to be exchanged; encrypting the data to be exchanged in segments based on the classification results, and optimizing the data transmission path for data exchange; decrypting and storing the exchanged data, and setting access control permissions to manage the stored data.

[0006] As a preferred solution of the data exchange management method described in the present invention, wherein: the configuration of the data exchange server and the collection of the data to be exchanged refers to configuring a large-capacity SSD storage server group, installing OpenVPN software on the server group to generate an SSL certificate and distributing it to all server ends to generate a data transmission channel, all server ends are connected through a network, and a data collection unit is deployed on the server end to automatically collect the data to be exchanged, and the collected data to be exchanged is automatically cleaned, filtered and formatted.

[0007] As a preferred solution of the data exchange management method of the present invention, wherein: the construction of a data classification model to classify the sensitivity level of the data to be exchanged includes:

[0008] Use the RBF kernel function to build a data classification model, set the candidate value range and initial candidate value of the regularization parameter C and the kernel function parameter γ, and the RBF kernel function is:

[0009] K(x i , x j ) = exp(-γ||x i -x j || 2 )

[0010] Where γ is the kernel function parameter, x i and x j are the i-th and j-th data input;

[0011] Label the data in the training set with data sensitivity levels and use the training set to train the data classification model:

[0012]

[0013] Where y is the model output label, y i is the i-th training data label, α i is the Lagrange multiplier of the i-th training data, b is the bias term, and n is the total number of data in the training data set;

[0014] The optimization objective function of the data classification model is defined as:

[0015]

[0016] where α i and α j are the Lagrange multipliers of the i-th and j-th training data, y i and j are the labels of the i-th training data and the j-th training data respectively;

[0017] The candidate values ​​of the regularization parameter C and the kernel function parameter γ are combined, each combination is substituted into the data classification model for model training to obtain the optimized objective function output, and the regularization parameter C and the kernel function parameter γ combination of the minimum optimized objective function is selected as the model parameter output to the data classification model;

[0018] The data classification model is deployed to all servers, and the sensitivity level of the data to be exchanged collected by the server is classified to generate data sensitivity level classification results.

[0019] As a preferred solution of the data exchange management method of the present invention, the segmented encryption of the data to be exchanged based on the classification result refers to segmenting the data according to the data sensitivity level classification result obtained by the data classification model, generating labels in sequence to mark the data segments, and setting the encryption method according to the sensitivity level to encrypt the data in segments:

[0020] Q=AES m (D)

[0021] Where Q is the encrypted data, D is the data before encryption, AES m Data encryption method;

[0022] After the data is encrypted in segments, the segmented data are reassembled according to the data segment labels to form complete data.

[0023] As a preferred solution of the data exchange management method of the present invention, wherein: the optimizing the data transmission path for data exchange includes:

[0024] Collect the connection information between all servers and define the server network graph G(V,E). The servers are nodes V, and the connections between servers are edges E. Give each edge E a weight:

[0025]

[0026] Where b(E kl ) is the bandwidth of the connection between node k and node l, d(E kl ) is the network delay between node k and node l, σ and ρ are adjustment coefficients;

[0027] The server node that collects the data to be exchanged is taken as the source point s, the source point path estimate p0 is initialized to 0, all other connected nodes are infinite and an access list is formed according to the edge weight from small to large;

[0028] According to the access list, select the node s1 with the smallest edge weight for access, and update the access node path estimate to p1:

[0029] p1=p0+ω(E s，s1 )

[0030] Where ω(E s，s1 ) is the weight of the edge between source point s and node s1;

[0031] Traverse all neighboring nodes sn of node s1 and calculate the path estimate p n and the path estimate p from the source point s to the neighboring node sn s，sn For comparison,

[0032] p n=p1+ω(E s1，sn )

[0033] p s，sn =p0+ω(E s，sn )

[0034] Where ω(E s1，sn ) is the edge weight between node s1 and its neighboring node sn, ω(E s，sn ) is the edge weight between the source node s and the neighboring node sn;

[0035] If p n <p s，sn , then remove node s1 from the access list and update the shortest path estimate to p1, start from node s1 to update the path estimate of neighboring nodes, repeatedly extract nodes from the access list for access until the access list is empty, and store the calculated shortest path estimate from the source point s to all nodes;

[0036] The target server node information of data exchange is obtained, and the encrypted data is exchanged and transmitted through the shortest path estimation from the stored source point s to the node.

[0037] As a preferred solution of the data exchange management method of the present invention, the decrypting and storing the exchanged data refers to the target server node re-segmenting the data after receiving the transmitted encrypted data, and decrypting the segmented encrypted data according to the data encryption method:

[0038] D=AES m-1 (Q)

[0039] AES m-1 For AES m Data decryption method;

[0040] After the data is decrypted in segments, it is re-formed into complete data. The target server calculates the hash value of the decrypted data and compares it with the hash value of the data before encryption calculated by the data collection server to verify the data integrity. If the hash values ​​are consistent, the data is stored on the target server. If the comparison is inconsistent, the data is re-encrypted and transmitted until the hash values ​​are consistent.

[0041] As a preferred solution of the data exchange management method described in the present invention, wherein: the setting of access control permission management to store data refers to applying access permissions and access passwords to the data after the target server stores the data, defining user roles, and assigning access permission levels to each user role. When the user accesses the data, the target server verifies the user's access permissions and access passwords and monitors the entire data access process, and verifies the integrity of the accessed data after the access is completed.

[0042] Another object of the present invention is to provide a data exchange management system, which comprises:

[0043] A data collection module, used to collect and pre-process the data to be exchanged;

[0044] Data classification module, used to build a data classification model to classify the sensitivity level of the data to be exchanged;

[0045] A data encryption module is used to encrypt and decrypt data in sections according to the data sensitivity level;

[0046] A data transmission module, used to optimize the data exchange transmission path and transmit the encrypted data;

[0047] The data storage module is used to store the decrypted data and set security access control measures.

[0048] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above data exchange management method when executing the computer program.

[0049] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned data exchange management method when executed by a processor.

[0050] The beneficial effects of the present invention are as follows: the present invention configures the server side to collect and pre-process the data to be exchanged, constructs a data classification model to classify the data sensitivity level, and encrypts it in segments, thereby effectively improving data security, while optimizing the data transmission path, greatly improving data transmission efficiency, and improving the applicability of data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0052] Figure 1 The figure is a flowchart of the data exchange management method.

[0053] Figure 2 Schematic diagram of data exchange implementation.

[0054] Figure 3 This is a structural diagram of the data exchange management system. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0058] Example 1

[0059] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a data exchange management method, the data exchange management method comprising:

[0060] S1. Configure the data exchange server and collect the data to be exchanged;

[0061] Specifically, configuring a data exchange server and collecting data to be exchanged means configuring a large-capacity SSD storage server group, installing OpenVPN software on the server group to generate an SSL certificate and distributing it to all server ends to generate a data transmission channel. All server ends are connected through a network, and a data collection unit is deployed on the server end to automatically collect the data to be exchanged, and the collected data to be exchanged is automatically cleaned, filtered and formatted.

[0062] Automated data collection reduces the demand for human resources and improves the speed and accuracy of data collection. Automatically collected data can be processed immediately to provide support for real-time data analysis and decision-making. It is particularly suitable for application scenarios that require real-time monitoring and rapid response, such as financial market analysis, network security monitoring, etc. By automatically cleaning, filtering and formatting data, the present invention ensures the quality and consistency of the data. The cleaning process removes invalid, erroneous or duplicate data, while formatting ensures the compatibility of data between different systems, which is particularly critical for big data analysis and can ensure the accuracy and reliability of the analysis results. In addition, standardized data formats are also necessary for data sharing and exchange.

[0063] S2. Build a data classification model to classify the sensitivity level of the data to be exchanged;

[0064] Specifically, building a data classification model to classify the sensitivity level of data to be exchanged includes:

[0065] Use the RBF kernel function to build a data classification model, set the candidate value range and initial candidate value of the regularization parameter C and the kernel function parameter γ, and the RBF kernel function is:

[0066] K(x i , x j ) = exp(-γ||x i -x j || 2 )

[0067] Where γ is the kernel function parameter, x i and x j is the i-th and j-th data input;

[0068] Label the data in the training set with data sensitivity levels and use the training set to train the data classification model:

[0069]

[0070] Where y is the model output label, y i is the i-th training data label, α i is the Lagrange multiplier of the i-th training data, b is the bias term, and n is the total number of data in the training data set;

[0071] The optimization objective function of the data classification model is defined as:

[0072]

[0073] where α i and α j are the Lagrange multipliers of the i-th and j-th training data, y i and j are the labels of the i-th training data and the j-th training data respectively;

[0074] The candidate values ​​of the regularization parameter C and the kernel function parameter γ are combined, each combination is substituted into the data classification model for model training to obtain the optimized objective function output, and the regularization parameter C and the kernel function parameter γ combination of the minimum optimized objective function is selected as the model parameter output to the data classification model;

[0075] The data classification model is deployed to all servers, and the sensitivity level of the data to be exchanged collected by the server is classified to generate data sensitivity level classification results.

[0076] The RBF kernel function can handle the classification problem of nonlinear features and has a good classification effect for complex data distribution. When processing multi-type data, especially when the relationship between data features is not obvious, the RBF kernel provides a powerful ability to discover the deep structural relationship between data. It is suitable for scenarios where sensitive data needs to be accurately classified, such as financial information processing, personal privacy data management, etc. Reasonable selection of C and γ values ​​can significantly improve the generalization ability of the model. By adjusting these parameters, the model can achieve a better balance on complex data sets and avoid overfitting or underfitting problems. By adjusting parameters, the model can adapt to different data environments and needs, especially in application scenarios with large data volumes and complex data features. A clearly defined optimization objective function allows the model to clearly define the optimization goal during the training process, that is, minimizing the error and maximizing the classification interval, thereby improving the classification performance of the model. The optimization objective function makes the parameter adjustment during the model training process more efficient and clear-cut. It is suitable for technical fields that require high-precision classification. Deployment of precisely tuned data classification models to all servers can classify data when it initially enters the system, which not only improves the efficiency of data processing, but also enhances the system's ability to respond immediately to sensitive data.

[0077] S3, segmenting and encrypting the data to be exchanged based on the classification results, and optimizing the data transmission path for data exchange;

[0078] Specifically, segmented encryption of the exchanged data based on the classification results refers to segmenting the data according to the data sensitivity level classification results obtained by the data classification model, generating labels in sequence to mark the data segments, and setting the encryption method according to the sensitivity level to encrypt the data segmented:

[0079] Q=AES m (D)

[0080] Where Q is the encrypted data, D is the data before encryption, AES m Data encryption method;

[0081] After the data is encrypted in segments, the segmented data are reassembled according to the data segment labels to form complete data.

[0082] By classifying and segmenting data according to sensitivity levels, the present invention ensures that data encryption measures are targeted and differentiated, improves data security, and provides stronger protection for sensitive data, while processing non-sensitive data more efficiently. The present invention is suitable for large organizations that need to process information containing multiple sensitivity levels, such as financial institutions, healthcare providers, and government agencies, where data security and compliance are the highest priorities. By selecting appropriate encryption methods to encrypt data segments of different sensitivity levels, protection strength and resource consumption can be balanced according to security requirements and processing capabilities. This method not only enhances data security, but also improves the efficiency of the overall system. Data segment labels ensure the integrity and consistency of encrypted data, so that data can be quickly located and correctly reorganized when received and decrypted, which not only improves the accuracy of data processing, but also simplifies data management and error detection processes. The present invention is particularly suitable for distributed systems and multi-node network environments, such as multinational corporations and supply chain systems, where data transmission and synchronization are particularly critical.

[0083] Further, optimizing the data transmission path for data exchange includes,

[0084] Collect the connection information between all servers and define the server network graph G(V,E). The servers are nodes V, and the connections between servers are edges E. Give each edge E a weight:

[0085]

[0086] Where b(E kl ) is the bandwidth of the connection between node k and node l, d(E kl ) is the network delay between node k and node l, σ and ρ are adjustment coefficients;

[0087] The server node that collects the data to be exchanged is taken as the source point s, the source point path estimate p0 is initialized to 0, all other connected nodes are infinite and an access list is formed according to the edge weight from small to large;

[0088] According to the access list, select the node s1 with the smallest edge weight for access, and update the access node path estimate to p1:

[0089] p1=p0+ω(E s，s1 )

[0090] Where ω(E s，s1 ) is the weight of the edge between source point s and node s1;

[0091] Traverse all neighboring nodes sn of node s1 and calculate the path estimate p n and the path estimate p from the source point s to the neighboring node sn s，sn For comparison,

[0092] p n =p1+ω(E s1，sn )

[0093] p s，sn =p0+ω(E s，sn )

[0094] Where ω(E s1，sn ) is the edge weight between node s1 and its neighboring node sn, ω(E s，sn ) is the edge weight between the source node s and the neighboring node sn;

[0095] If p n <p s，sn , then remove node s1 from the access list and update the shortest path estimate to p1, start from node s1 to update the path estimate of neighboring nodes, repeatedly extract nodes from the access list for access until the access list is empty, and store the calculated shortest path estimate from the source point s to all nodes;

[0096] The target server node information of data exchange is obtained, and the encrypted data is exchanged and transmitted through the shortest path estimation from the stored source point s to the node.

[0097] By accurately mapping the connections between servers and calculating weights, bottlenecks and optimization points in the network can be effectively identified, thereby providing an accurate basis for optimizing data transmission paths, and helping network administrators understand and optimize the performance of the entire network. Especially in environments that process large amounts of data exchange, by starting from the source point and gradually updating the shortest path estimate of the connecting nodes, this method can gradually find the optimal data transmission path. This step-by-step approximation strategy reduces computational complexity and improves the efficiency of path optimization. It is suitable for real-time data transmission systems, such as real-time financial trading platforms, where data transmission speed directly affects the execution and efficiency of transactions. Continuously updating path estimates and selecting the minimum weight node for access can ensure that the global optimal path is found. This iterative optimization process ensures that the optimal data transmission efficiency is always maintained under dynamically changing network conditions. It is particularly suitable for cloud computing environments and distributed data processing scenarios, where servers often need to adjust data transmission strategies based on changing network conditions. By applying the calculated shortest path estimate for data exchange, the total delay of data transmission can be significantly reduced and the reliability of data transmission can be improved.

[0098] S4. Decrypt and store the exchanged data, and set access control permissions to manage the stored data;

[0099] Specifically, decrypting and storing the exchanged data means that the target server node re-segmentally processes the data after receiving the transmitted encrypted data, and decrypts the segmented encrypted data according to the data encryption method:

[0100] D=AES m-1 (Q)

[0101] AES m-1 For AES m Data decryption method;

[0102] After the data is decrypted in segments, it is re-formed into complete data. The target server calculates the hash value of the decrypted data and compares it with the hash value of the data before encryption calculated by the data collection server to verify the data integrity. If the hash values ​​are consistent, the data is stored on the target server. If the comparison is inconsistent, the data is re-encrypted and transmitted until the hash values ​​are consistent.

[0103] By re-segmenting and decrypting the encrypted data after receiving it at the target server node, the present invention not only ensures the security of the data, but also enhances the flexibility of data processing. Segmented decryption allows the system to restore the data part by part, which helps to quickly locate possible errors or data corruption. By calculating the hash value of the decrypted data and comparing it with the hash value of the original data at the sender, the present invention ensures the integrity and consistency of the data during transmission. This hash verification mechanism provides a double guarantee for the security of the data and effectively prevents the tampering of the data during transmission. When the hash value comparison is inconsistent, the present invention stipulates that the data is re-encrypted and transmitted until the hash value comparison is consistent. This automatic retransmission mechanism greatly improves the reliability of data exchange, ensures that the received data is completely correct and can be safely stored, and is suitable for occasions with extremely high requirements for data integrity, such as remote medical diagnosis, data exchange between multinational companies, etc., ensuring the accuracy and reliability of the data.

[0104] Furthermore, setting access control permissions to manage stored data means that after the target server stores the data, it applies access permissions and access passwords to the data, defines user roles, and assigns access permission levels to each user role. When the user accesses the data, the target server verifies the user's access permissions and access passwords and monitors the entire data access process, and verifies the integrity of the accessed data after the access is completed.

[0105] By setting precise access rights and requiring access passwords, the present invention effectively improves the data protection level and reduces the risk of data leakage and illegal access. This method ensures the confidentiality and security of data and makes data management more strict and orderly. By granting specific access rights to each user role, the present invention makes data management more in line with the principle of minimizing permissions, that is, only providing the necessary minimum permissions, thereby greatly improving the security and effectiveness of operations. Monitoring the data access process can detect and record data access activities in real time, which helps to track potential illegal access behaviors and internal threats, and increases the transparency of data security management. It is suitable for scenarios with extremely high data security requirements, such as cloud service providers and data centers, who need to ensure that customer data is absolutely secure during access and processing. By performing integrity verification after data access is completed, the present invention ensures that the integrity of the data is not destroyed, thereby providing an effective data integrity protection mechanism.

[0106] Example 2

[0107] Reference Figure 3 , which is a second embodiment of the present invention, and which is different from the previous embodiment, provides a data exchange management system, which includes:

[0108] A data collection module, used to collect and pre-process the data to be exchanged;

[0109] Data classification module, used to build a data classification model to classify the sensitivity level of the data to be exchanged;

[0110] A data encryption module is used to encrypt and decrypt data in sections according to the data sensitivity level;

[0111] A data transmission module, used to optimize the data exchange transmission path and transmit the encrypted data;

[0112] The data storage module is used to store the decrypted data and set security access control measures.

[0113] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0114] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0115] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0116] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data exchange management method, characterized in that: include, Configure the data exchange server and collect the data to be exchanged; Build a data classification model to classify the sensitivity level of data to be exchanged; Based on the classification results, the data to be exchanged is encrypted in sections, and the data transmission path is optimized for data exchange; Decrypt and store the exchanged data, and set access control permissions to manage the stored data; Configuring a data exchange server and collecting data to be exchanged refers to configuring a large-capacity SSD storage server group, installing OpenVPN software on the server group to generate an SSL certificate and distributing it to all server ends to generate a data transmission channel, connecting all server ends through a network, deploying a data collection unit on the server end to automatically collect the data to be exchanged, and automatically cleaning, filtering and formatting the collected data to be exchanged; The construction of the data classification model to classify the sensitivity level of the data to be exchanged includes: Use the RBF kernel function to build a data classification model, set the candidate value range and initial candidate value of the regularization parameter C and the kernel function parameter γ, and the RBF kernel function is: K(x i ,x j )=exp(-γ||x i -x j || 2 ) Where γ is the kernel function parameter, x i and x j is the i-th and j-th data input; Label the data in the training set with data sensitivity levels and use the training set to train the data classification model: Where y is the model output label, y i is the i-th training data label, α i is the Lagrange multiplier of the i-th training data, b is the bias term, and n is the total number of data in the training data set; The optimization objective function of the data classification model is defined as: where α i and α j are the Lagrange multipliers of the i-th and j-th training data, y i and j are the labels of the i-th training data and the j-th training data respectively; The candidate values ​​of the regularization parameter C and the kernel function parameter γ are combined, each combination is substituted into the data classification model for model training to obtain the optimized objective function output, and the regularization parameter C and the kernel function parameter γ combination of the minimum optimized objective function is selected as the model parameter output to the data classification model; Deploy the data classification model to all servers, classify the sensitivity level of the data to be exchanged collected by the server, and generate data sensitivity level classification results; The segmented encryption of the data to be exchanged based on the classification result refers to segmenting the data according to the data sensitivity level classification result obtained by the data classification model, generating labels in sequence to mark the data segments, and setting the encryption method according to the sensitivity level to encrypt the data in segments: Q=AES m (D ) Where Q is the encrypted data, D is the data before encryption, AES m Data encryption method; After encrypting the data in segments, the segmented data are reassembled into complete data according to the data segment labels; The optimizing of the data transmission path for data exchange comprises: Collect the connection information between all servers and define the server network graph G(V,E). The servers are nodes V, and the connections between servers are edges E. Give each edge E a weight: Where b(E kl ) is the bandwidth of the connection between node k and node l, d(E kl ) is the network delay between node k and node l, σ and ρ are adjustment coefficients; The server node that collects the data to be exchanged is taken as the source point s, the source point path estimate p0 is initialized to 0, all other connected nodes are infinite and an access list is formed according to the edge weight from small to large; According to the access list, select the node s1 with the smallest edge weight for access, and update the access node path estimate to p1: p1=p0+ω(E s，s1 ) Where ω(E s，s1 ) is the weight of the edge between source point s and node s1; Traverse all neighboring nodes sn of node s1 and calculate the path estimate p n and the path estimate p from the source point s to the neighboring node sn s，sn For comparison, p n =p1+ω(E s1，sn ) p s，sn =p0+ω(E s，sn ) Where ω(E s1，sn ) is the edge weight between node s1 and its neighboring node sn, ω(E s，sn ) is the edge weight between the source node s and the neighboring node sn; If p n <p s，sn , then remove node s1 from the access list and update the shortest path estimate to p1, start from node s1 to update the path estimate of neighboring nodes, repeatedly extract nodes from the access list for access until the access list is empty, and store the calculated shortest path estimate from the source point s to all nodes; The target server node information of data exchange is obtained, and the encrypted data is exchanged and transmitted through the shortest path estimation from the stored source point s to the node.

2. The data exchange management method according to claim 1, characterized in that: Decrypting and storing the exchanged data means that the target server node re-segmentally processes the data after receiving the transmitted encrypted data, and decrypts the segmented encrypted data according to the data encryption method: in For AES m Data decryption method; After the data is decrypted in segments, it is re-formed into complete data. The target server calculates the hash value of the decrypted data and compares it with the hash value of the data before encryption calculated by the data collection server to verify the data integrity. If the hash values ​​are consistent, the data is stored on the target server. If the comparison is inconsistent, the data is re-encrypted and transmitted until the hash values ​​are consistent.

3. The data exchange management method according to claim 2, characterized in that: The setting of access control rights to manage stored data refers to applying access rights and access passwords to the data after the target server stores the data, defining user roles, and assigning access rights levels to each user role. When the user accesses the data, the target server verifies the user's access rights and access passwords and monitors the entire data access process, and verifies the integrity of the accessed data after the access is completed.

4. A data exchange management system based on the data exchange management method according to any one of claims 1 to 3, characterized in that: include, A data collection module, used to collect and pre-process the data to be exchanged; Data classification module, used to build a data classification model to classify the sensitivity level of the data to be exchanged; A data encryption module is used to encrypt and decrypt data in sections according to the data sensitivity level; A data transmission module, used to optimize the data exchange transmission path and transmit the encrypted data; The data storage module is used to store the decrypted data and set security access control measures.

5. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of the data exchange management method according to any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data exchange management method according to any one of claims 1 to 3 are implemented.

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