Data encryption transmission method and system applied to all-in-one machine simulation model

Abstract

The invention relates to the technical field of safety communication, and particularly discloses a data encryption transmission method and system applied to an all-in-one machine simulation model, and the method comprises the steps: obtaining dynamic data of the all-in-one machine simulation model in real time, partitioning the to-be-transmitted data based on the original encryption density ranges of all nodes in an all-in-one machine simulation model dynamic parameter tree constructed by using the dynamic data to obtain a to-be-transmitted data block set; respectively encrypting the to-be-transmitted data block set based on the multi-dimensional encryption related features of the to-be-transmitted data block set and the blocking rule to obtain an encrypted data block set; managing the encrypted data block set based on a multi-level key system to obtain security management data; transmitting the security management data to a receiving end based on a double-link communication mode, and performing multi-node cooperative verification at the receiving end through a block chain node network to obtain a data decryption verification result; according to the invention, the security, anti-attack performance and real-time performance of all-in-one machine simulation data transmission can be improved based on the security communication technology, and the to-be-transmitted data is prevented from being tampered, stolen and illegally accessed.

Description

Technical Field

[0001] The present invention relates to the field of secure communication technology, and in particular to a data encryption transmission method and system applied to an all-in-one machine simulation model. Background Art

[0002] In today's era of rapid digitalization and informatization, all-in-one simulation models play a vital role in numerous fields and are widely used in industries such as industrial manufacturing, aerospace, and scientific research. These simulation models can simulate and analyze complex systems or processes, helping researchers and engineers gain a deeper understanding of system behavior, optimize design solutions, and predict potential problems. However, the data involved in all-in-one simulation models is often highly sensitive and confidential, containing information such as key technical parameters, design concepts, and important experimental results. If this data is stolen, tampered with, or leaked during data transmission, it can cause serious losses to enterprises or research institutions, potentially leading to the loss of technological advantages, the disclosure of trade secrets, and intellectual property disputes. Therefore, ensuring the security of all-in-one simulation model data during transmission is crucial. With the continuous advancement of network technology, the data transmission environment has become increasingly complex, facing various security threats such as cyber hackers and malware. Traditional data encryption transmission methods are unable to meet the special requirements of all-in-one simulation model data and are unable to effectively combat complex and diverse network attacks. In this context, data encryption transmission methods and systems for all-in-one simulation models are of great significance for ensuring data security and promoting the stable development of related fields.

[0003] However, existing data encryption and transmission technologies applied to all-in-one simulation models struggle to properly segment transmitted data based on the dynamic data of the all-in-one simulation model, resulting in irrational data segmentation. Furthermore, these technologies fail to fully consider the characteristics of different data blocks, impacting subsequent encryption and reducing the security and relevance of encryption. In the key management, data transmission, and verification stages, the lack of effective key management, communication, and decryption verification methods prevents effective verification of data integrity and authenticity, making it difficult to ensure the security and reliability of data during transmission.

[0004] Therefore, the present invention proposes a data encryption transmission method and system applied to an all-in-one machine simulation model. Summary of the Invention

[0005] The present invention provides a data encryption transmission method and system applied to an all-in-one machine simulation model. The method specifically determines the block segmentation rules of the data to be transmitted based on the dynamic data of the all-in-one machine simulation model, and specifically determines the encryption method of the block-processed data to be transmitted in combination with the multi-dimensional encryption-related characteristics of the set of data blocks to be transmitted. This makes the encryption of the data to be transmitted highly targeted, effectively improves the security of the data to be transmitted, and further guarantees the security of the data to be transmitted by combining a multi-level key system and a dual-link communication mode, and ensures the accuracy and integrity of data transmission, thereby preventing data tampering.

[0006] The present invention provides a data encryption transmission method applied to an all-in-one machine simulation model, comprising: Step S1: acquiring dynamic data of the all-in-one machine simulation model in real time, dividing the data to be transmitted into blocks based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using the dynamic data, and obtaining a set of data blocks to be transmitted; Step S2: Encrypting the data block set to be transmitted separately based on the multi-dimensional encryption-related features and block segmentation rules of the data block set to be transmitted to obtain an encrypted data block set; Step S3: managing the encrypted data block set based on the multi-level key system to obtain security management data; Step S4: The security management data is transmitted to the receiving end based on the dual-link communication method, and multi-node collaborative verification is performed on the receiving end through the blockchain node network to obtain the data decryption verification result.

[0007] Optionally, the dynamic data of the all-in-one machine simulation model includes: system operation state parameters, data flow characteristic parameters, security environment parameters, and physical environment parameters; Among them, the system operation state parameters include resource load index values ​​and simulation process characteristic parameters; The data stream characteristic parameters include streaming attribute values ​​and data attribute change characteristic parameters; Security environment parameters include threat perception index values ​​and multi-dimensional encryption strength requirement parameters; Physical environment parameters include equipment status monitoring parameters and network topology change parameters.

[0008] Optionally, the data to be transmitted is divided into blocks based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using dynamic data to obtain a set of data blocks to be transmitted, including: Based on the hierarchy of all parameter classes contained in the dynamic data, a hierarchical structure is constructed for all parameter classes contained in the dynamic data to obtain a dynamic parameter tree of the all-in-one machine simulation model; Based on the dynamic data, the actual values ​​of all bottom-level nodes in the dynamic parameter tree of the all-in-one simulation model are determined, and based on the actual values ​​of all bottom-level nodes, and based on the calculation rules between the parameter classes of all adjacent hierarchical directly connected nodes in the dynamic parameter tree of the all-in-one simulation model and the actual values ​​of all bottom-level nodes, all non-bottom-level nodes in the dynamic parameter tree of the all-in-one simulation model are sequentially deduced and calculated upward to obtain the actual values ​​of all non-bottom-level nodes; Based on the actual value of each node in the dynamic parameter tree of the all-in-one simulation model, the encryption density list of the parameter class corresponding to the corresponding node is retrieved to determine the original encryption density range of each node; Analyzing the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, and determining the optimal data block spacing based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model and the original encryption density range of all nodes; The data to be transmitted is divided into blocks based on the optimal data block spacing to obtain a set of data blocks to be transmitted.

[0009] Optionally, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model is analyzed based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, including: All paths from the root node to each bottom node in the dynamic parameter tree of the all-in-one machine simulation model are considered as extreme paths; Calculate the jitter factor between the original encryption density ranges of every two nodes in each extreme path; Based on the jitter factor between the original encryption density range of every two nodes in each extreme path, the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths are constructed; Based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model is analyzed.

[0010] Optionally, a jitter factor is calculated between the original encrypted density ranges of every two nodes in each extreme path, including: Connect the upper limit, middle value, and lower limit of the original encryption density range of all nodes in each extreme path from high to low according to the node level, and obtain the upper limit curve, middle curve, and lower limit curve of the original encryption density of each extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the original encrypted density upper limit curve of each extreme path at the horizontal coordinate corresponding to the lowest level node and the function value at the horizontal coordinate corresponding to the highest level node in each two nodes to the function value of the first derivative function at the horizontal coordinate corresponding to the highest level node is taken as the upper jitter factor corresponding to the two nodes in the corresponding extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the mid-limit curve of the original encrypted density of each extreme path at the abscissa corresponding to the lowest level node and the function value at the abscissa corresponding to the highest level node in each two nodes to the function value of the first derivative function at the abscissa corresponding to the highest level node is taken as the medium jitter factor of the two nodes corresponding to the extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the original encrypted density lower limit curve of each extreme path at the horizontal coordinate corresponding to the lowest level node and the function value at the horizontal coordinate corresponding to the highest level node in each two nodes to the function value of the first derivative function at the horizontal coordinate corresponding to the highest level node is taken as the lower jitter factor of the corresponding two nodes in the corresponding extreme path; Based on the upper jitter factor, middle jitter factor, and lower jitter factor of every two nodes in each extreme path, the jitter factor between the original encryption density range of every two nodes in each extreme path is calculated.

[0011] Optionally, based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model is analyzed, including: The deviation between the row vectors with the same ordinal number in the unit jitter factor matrix of all extreme paths is regarded as the lateral jitter factor of the corresponding layer; The deviation between the two column vectors in the overall jitter factor matrix of all extreme paths is regarded as the longitudinal jitter factor; determining a magnification factor of an overall jitter factor matrix based on all lateral jitter factors and all longitudinal lateral jitter factors, and numerically amplifying the overall jitter factor matrix based on the magnification factor to obtain an overall jitter factor consideration matrix; The mean value of all elements in the overall jitter factor consideration matrix is ​​regarded as the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model.

[0012] Optionally, the optimal data block spacing is determined based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model and the original encryption density range of all nodes, including: Based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model, the original encryption density range of each node is double-ended to obtain the improved encryption density range of each node; The middle value of the intersection range with the greatest degree of intersection among the improved encryption density ranges of all intersection points is regarded as the optimal encryption density; The inverse of the optimal encryption density is taken as the optimal data block spacing.

[0013] Optionally, S2: encrypting the set of data blocks to be transmitted separately based on the multi-dimensional encryption-related features and the block division rule of the set of data blocks to be transmitted to obtain the encrypted data block set, including: Determining an encryption level for each data block to be transmitted based on all data types and data volume of each data type contained in each data block to be transmitted in the multi-dimensional encryption-related characteristics of the set of data blocks to be transmitted; Determining an encryption algorithm for each data block to be transmitted based on an optimal encryption density corresponding to an optimal data block spacing in the block rule and an encryption level of each data block to be transmitted; All the data blocks to be transmitted in the set of data blocks to be transmitted are encrypted separately based on the encryption algorithm of each data block to obtain an encrypted data block set.

[0014] Optionally, determining the encryption level of each data block to be transmitted based on all data types and the data amount of each data type contained in each data block to be transmitted in the set of data blocks to be transmitted includes: Determine the initial encryption level for each data type; The ratio of the amount of data of each data type in each data block to be transmitted to the total number of the corresponding data blocks to be transmitted is used as the weight of the corresponding data type in the corresponding data block to be transmitted; Based on the weights of all data types in each data block to be transmitted, the initial encryption levels of all data types are weighted and rounded up to obtain the encryption level of each data block to be transmitted.

[0015] The present invention provides a data encryption transmission system applied to an all-in-one machine simulation model, comprising: A data segmentation module is used to obtain the dynamic data of the all-in-one machine simulation model in real time, and to segment the data to be transmitted based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using the dynamic data to obtain a set of data blocks to be transmitted; A block data encryption module is used to encrypt the set of data blocks to be transmitted respectively based on the multi-dimensional encryption related features and block division rules of the set of data blocks to be transmitted to obtain an encrypted data block set; A multi-level key management module is used to manage the encrypted data block set based on a multi-level key system to obtain security management data; The decryption and verification module is used to transmit security management data to the receiving end based on a dual-link communication method, and perform multi-node collaborative verification through the blockchain node network at the receiving end to obtain data decryption and verification results.

[0016] The beneficial effects of the present invention compared with the prior art are as follows: based on the dynamic data of the all-in-one machine simulation model, the block segmentation rules of the data to be transmitted are determined in a targeted manner, and combined with the multi-dimensional encryption-related characteristics of the set of data blocks to be transmitted, the encryption method of the data to be transmitted after the block processing is determined in a targeted manner, so that the encryption of the data to be transmitted is highly targeted, and the security of the data to be transmitted is effectively improved. Combined with the multi-level key system and the dual-link communication method, the security of the data to be transmitted is further guaranteed, and the accuracy and integrity of the data transmission are ensured, and data tampering is prevented.

[0017] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a data encryption transmission method applied to an all-in-one machine simulation model in an embodiment of the present invention; Figure 2 This is a block processing flowchart in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0021] refer to Figure 1 The present invention provides an implementation of a data encryption transmission method applied to an all-in-one machine simulation model, comprising: Step S1: acquiring dynamic data of the all-in-one machine simulation model in real time, dividing the data to be transmitted into blocks based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using the dynamic data, and obtaining a set of data blocks to be transmitted; Step S2: Encrypting the data block set to be transmitted separately based on the multi-dimensional encryption-related features and block segmentation rules of the data block set to be transmitted to obtain an encrypted data block set; Step S3: managing the encrypted data block set based on the multi-level key system to obtain security management data; Step S4: The security management data is transmitted to the receiving end based on the dual-link communication method, and multi-node collaborative verification is performed on the receiving end through the blockchain node network to obtain the data decryption verification result.

[0022] Among them, all-in-one simulation models are widely used in secure communications technology, such as industrial manufacturing, aerospace, and scientific research, to simulate and analyze complex systems or processes. The data involved is highly sensitive and confidential, including key technical parameters, design concepts, and important experimental results.

[0023] Multi-dimensional encryption-related characteristics of a set of data blocks to be transmitted: These characteristics refer to the multiple dimensions of encryption-related characteristics of the data blocks to be transmitted. Specifically, they include the total data types and the amount of each data type contained in each data block to be transmitted. For example, if a data block to be transmitted contains a large amount of sensitive user authentication data, its encryption level may be relatively high based on these multi-dimensional encryption-related characteristics, requiring a more complex and advanced encryption algorithm to ensure data security.

[0024] Block rule: The criterion for dividing the data to be transmitted into blocks based on the dynamic data of the all-in-one simulation model, that is, the optimal block spacing.

[0025] Encrypted Data Block Set: All data blocks to be transmitted are encrypted individually according to multi-dimensional encryption characteristics and block segmentation rules. These encrypted data blocks collectively constitute an encrypted data block set. This represents an intermediate state during the data encryption process. After subsequent multi-level key system management, dual-link communication transmission, and verification, the data ultimately reaches the receiving end securely. For example, data blocks of different encryption levels may be encrypted using different encryption algorithms (such as symmetric encryption or asymmetric encryption), forming an encrypted data block set.

[0026] A multi-level key system is a hierarchical key management approach. Using this system to manage a collection of encrypted data blocks, different levels of keys are responsible for encryption, decryption, and access control operations at different levels or for different data blocks. This management approach further ensures the security of encrypted data and the rationality of access control, ultimately generating security management data. For example, high-level keys may be used to control access to the entire encrypted data, while lower-level keys are used to decrypt specific data blocks. The different levels of keys work together to ensure data security and efficient management. Security management data, managed through the multi-level key system, comprises encrypted data and related key management information, providing the basis for subsequent data transmission and receiver authentication. For example, a multi-level key system includes a device root key, a session master key, and data block ephemeral keys, where the ephemeral keys are dynamically generated using a chaotic sequence.

[0027] Security management data is transmitted to the receiving end via a dual-link communication method. At the receiving end, multi-node collaborative verification is performed through the blockchain node network to obtain the data decryption verification result. Dual-link communication utilizes two different communication links for data transmission, which improves data transmission reliability and reduces the risk of errors or loss during data transmission. After the security management data is transmitted to the receiving end via the dual-link method, the receiving end performs multi-node collaborative verification using the blockchain node network. The blockchain node network is distributed and tamper-proof. Multiple nodes jointly verify the received data, verifying its integrity, authenticity, and decryption accuracy through comparison and calculation. The final result is a data decryption verification result. If verification passes, it indicates that the data has not been tampered with during transmission and that decryption is correct, ensuring the security and accuracy of data transmission. If verification fails, it indicates a potential data issue and requires appropriate action. For example, each node in the blockchain node network stores partial data information and verification rules. Through information exchange and collaborative computation among multiple nodes, comprehensive verification of received data is achieved.

[0028] In an alternative embodiment, the dynamic data of the all-in-one machine simulation model includes: system operating state parameters, data flow characteristic parameters, security environment parameters, and physical environment parameters; Among them, the system operation state parameters include resource load index values ​​and simulation process characteristic parameters; The data stream characteristic parameters include streaming attribute values ​​and data attribute change characteristic parameters; Security environment parameters include threat perception index values ​​and multi-dimensional encryption strength requirement parameters; Physical environment parameters include equipment status monitoring parameters and network topology change parameters.

[0029] Resource load indicators, part of the system's operational parameters within the all-in-one simulation model's dynamic data, reflect the occupancy and utilization of various resources (such as CPU, memory, and storage) during the model's operation. Specific values, such as CPU utilization and memory usage, can help determine resource availability during model runtime and are crucial for assessing the system's carrying capacity during data transmission.

[0030] Simulation process characteristic parameters: These parameters also represent system operational parameters and describe the characteristics of each simulation process within the all-in-one simulation model, such as process startup time, runtime, inter-process dependencies, and process execution priority. These parameters help understand the overall flow and status of the simulation model's operation and may influence the timing and sequence of data transmission.

[0031] Streaming attribute values ​​are one of the data flow characteristic parameters used to characterize the data flow characteristics within the all-in-one simulation model, such as whether the data flows continuously or intermittently, and the flow rate and direction. Understanding streaming attribute values ​​can help better understand the dynamic characteristics of data and provide a basis for developing block segmentation and encryption strategies during encrypted data transmission.

[0032] Data attribute change characteristic parameters: These are also data flow characteristic parameters and primarily reflect how data attributes change during the simulation model's execution, such as changes in data format, increases and decreases in data volume, and the frequency of data content updates. Understanding these parameters can make encrypted transmission methods more adaptable to dynamic data changes and ensure data security.

[0033] Threat Perception Index: This is included in the security environment parameters and is used to measure the security threat level faced by the all-in-one simulation model's environment. This may include network attack detection indicators and risk assessments of potential vulnerabilities. Based on these indicators, the strength and method of data encryption can be adjusted to address varying levels of security threats.

[0034] Multi-dimensional encryption strength requirement parameters: As part of the security environment parameters, these parameters describe the requirements for data encryption strength from multiple perspectives, such as confidentiality, integrity, and availability. Different types of data may have different encryption strength requirements across these dimensions. These parameters guide the selection of appropriate encryption algorithms and strategies to ensure data security during transmission.

[0035] Device status monitoring parameters: These are physical environment parameters. They reflect the status of the devices that the all-in-one simulation model relies on, such as temperature, humidity, and hardware fault indicators. Device status affects the stability and security of data transmission. These parameters help identify potential device issues in advance and ensure smooth data transmission.

[0036] Network topology change parameters: These parameters describe changes in the network topology, such as the addition or removal of network nodes and changes in the connection status of network links. Network topology changes can affect data transmission paths and speeds. Understanding these parameters allows for better data planning and ensures accurate and timely data transmission.

[0037] In an alternative embodiment, reference Figure 2 , based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using dynamic data, the data to be transmitted is divided into blocks to obtain a set of data blocks to be transmitted, including: Based on the hierarchy of all parameter classes contained in the dynamic data, a hierarchical structure is constructed for all parameter classes contained in the dynamic data to obtain a dynamic parameter tree of the all-in-one machine simulation model; Based on the dynamic data, the actual values ​​of all bottom-level nodes in the dynamic parameter tree of the all-in-one simulation model are determined, and based on the actual values ​​of all bottom-level nodes, and based on the calculation rules between the parameter classes of all adjacent hierarchical directly connected nodes in the dynamic parameter tree of the all-in-one simulation model and the actual values ​​of all bottom-level nodes, all non-bottom-level nodes in the dynamic parameter tree of the all-in-one simulation model are sequentially deduced and calculated upward to obtain the actual values ​​of all non-bottom-level nodes; Based on the actual value of each node in the dynamic parameter tree of the all-in-one simulation model, the encryption density list of the parameter class corresponding to the corresponding node is retrieved to determine the original encryption density range of each node; Analyzing the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, and determining the optimal data block spacing based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model and the original encryption density range of all nodes; The data to be transmitted is divided into blocks based on the optimal data block spacing to obtain a set of data blocks to be transmitted.

[0038] Based on the hierarchy of all parameter classes contained in the dynamic data, a hierarchical structure is constructed for all parameter classes contained in the dynamic data to obtain the dynamic parameter tree of the all-in-one simulation model: the various parameters in the dynamic data of the all-in-one simulation model are organized according to their hierarchical relationships to construct a tree-like structure. For example, the root node is a dynamic parameter, and the next layer of nodes connected to the root node include different types of parameters such as system operating state parameters and data flow characteristic parameters. The next layer of nodes linked to the system operating parameters include resource load index values ​​and simulation process characteristic parameters. Following this rule, according to their respective hierarchies and mutual relationships, a tree structure that can clearly display the hierarchical relationship of parameters is constructed, namely the dynamic parameter tree of the all-in-one simulation model.

[0039] Determine the actual values ​​of all bottom-level nodes in the dynamic parameter tree of the appliance simulation model based on dynamic data: In the constructed dynamic parameter tree, find the nodes at the bottom level and, using the acquired dynamic data of the appliance simulation model, determine the actual parameter values ​​corresponding to these bottom-level nodes. For example, if the bottom-level nodes represent specific parameters such as device temperature or network bandwidth, extract the actual measured values ​​of these parameters from the dynamic data.

[0040] Based on the actual values ​​of all bottom-level nodes, and based on the calculation rules between the parameter classes of all adjacent directly connected nodes in the dynamic parameter tree of the all-in-one simulation model and the actual values ​​of all bottom-level nodes, all non-bottom-level nodes in the dynamic parameter tree of the all-in-one simulation model are sequentially deduced and calculated upward to obtain the actual values ​​of all non-bottom-level nodes: using the actual values ​​already determined for the bottom-level nodes, according to the preset calculation rules between the directly connected nodes at adjacent levels in the parameter tree, the actual values ​​of the non-bottom-level nodes are calculated step by step from the bottom up. For example, the value of a node in the previous layer may be obtained by applying existing calculation rules (such as weighted sum after normalization) to the values ​​of several related nodes in the next layer. By deducing in this way, the actual values ​​of all non-bottom-level nodes can be obtained, thereby completing the information of each node in the entire parameter tree.

[0041] Encryption density list for parameter classes corresponding to nodes: A predefined list of parameter classes corresponding to each node in the parameter tree, which records the original encryption density range corresponding to the parameter class at different trend ranges. Encryption density can be understood as the inverse of the block spacing when block encryption is performed on the data to be transmitted.

[0042] Based on the actual value of each node in the all-in-one simulation model's dynamic parameter tree, the encryption density list of the corresponding parameter class is retrieved to determine the original encryption density range of each node. Based on the actual value of each node in the parameter tree, matching information is searched in the encryption density list of its corresponding parameter class to determine the original encryption density range applicable to the node's data. For example, if a node represents a specific type of data flow parameter, based on its actual flow rate, the encryption density range suitable for that flow data is found in the corresponding encryption density list. This range is the original encryption density range, providing a basis for subsequent determination of encryption strength.

[0043] The internal jitter of the multi-dimensional dynamic parameters of the all-in-one simulation model is determined by analyzing the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one simulation model. This value reflects the severity or consistency of changes between multi-dimensional dynamic parameters. A high degree of internal jitter indicates complex parameter changes and may require a more detailed data segmentation strategy.

[0044] Optimal data block spacing: This is determined by combining the internal jitter of the multi-dimensional dynamic parameters of the integrated appliance simulation model and the original encryption density range of all nodes. This is the interval used to segment data for transmission. For example, if the internal jitter and encryption density range indicate significant data fluctuations in certain areas or high security requirements, a smaller optimal data block spacing may be determined, allowing for finer data block division, facilitating targeted encryption and improving data security.

[0045] The data to be transmitted is segmented based on the optimal data segment spacing to obtain a set of data blocks to be transmitted: The data to be transmitted is divided into multiple data blocks according to the determined optimal data segment spacing. These data blocks together constitute the set of data blocks to be transmitted. The size and content of each data block are determined based on the optimal data segment spacing. These data blocks are subsequently encrypted based on their characteristics to ensure data security during transmission.

[0046] In an alternative embodiment, reference Figure 2 Based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one simulation model, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model is analyzed, including: All paths from the root node to each bottom node in the dynamic parameter tree of the all-in-one machine simulation model are considered as extreme paths; Calculate the jitter factor between the original encryption density ranges of every two nodes in each extreme path; Based on the jitter factor between the original encryption density range of every two nodes in each extreme path, the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths are constructed; Based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model is analyzed.

[0047] The element in the i-th row and j-th column of the unit jitter factor matrix of each extreme path represents the jitter factor between the original encryption density ranges of the i-th node and the j-th node on the extreme path.

[0048] The element in the i-th row and j-th column of the overall jitter factor matrix of all extreme paths represents the average jitter factor between the i-th node of the j-th extreme path and the original encrypted density range of all ground nodes in the corresponding extreme path.

[0049] The above data provides comprehensive data support for analyzing the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model.

[0050] In an alternative embodiment, reference Figure 2, calculate the jitter factor between the original encrypted density ranges of every two nodes in each extreme path, including: Connect the upper limit, middle value, and lower limit of the original encryption density range of all nodes in each extreme path from high to low according to the node level, and obtain the upper limit curve, middle curve, and lower limit curve of the original encryption density of each extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the original encrypted density upper limit curve of each extreme path at the horizontal coordinate corresponding to the lowest level node and the function value at the horizontal coordinate corresponding to the highest level node in each two nodes to the function value of the first derivative function at the horizontal coordinate corresponding to the highest level node is taken as the upper jitter factor corresponding to the two nodes in the corresponding extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the mid-limit curve of the original encrypted density of each extreme path at the abscissa corresponding to the lowest level node and the function value at the abscissa corresponding to the highest level node in each two nodes to the function value of the first derivative function at the abscissa corresponding to the highest level node is taken as the medium jitter factor of the two nodes corresponding to the extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the original encrypted density lower limit curve of each extreme path at the horizontal coordinate corresponding to the lowest level node and the function value at the horizontal coordinate corresponding to the highest level node in each two nodes to the function value of the first derivative function at the horizontal coordinate corresponding to the highest level node is taken as the lower jitter factor of the corresponding two nodes in the corresponding extreme path; Based on the upper jitter factor, middle jitter factor, and lower jitter factor of every two nodes in each extreme path, the jitter factor between the original encryption density range of every two nodes in each extreme path is calculated.

[0051] In the dynamic parameter tree of the all-in-one simulation model, for the ultimate path from the root node to each underlying node, the upper limits of the original encryption density range of all nodes in this path are connected from high to low node level. The resulting curve is the original encryption density upper limit curve. This curve shows the trend of the upper limit of the encryption density along the ultimate path as the node level changes.

[0052] Similarly, the middle values ​​of the original encryption density range of all nodes are connected from high to low according to the node level to obtain the middle curve of the original encryption density, which reflects the changing trend of the middle value of the encryption density.

[0053] By connecting the lower limits of the original density ranges for all nodes from high to low node levels, we can generate curves of the original density lower limits, which reflect the changes in the lower limits of density. These curves help analyze the changing characteristics of density along the extreme paths and provide a basis for the subsequent calculation of jitter factors.

[0054] Based on the upper jitter factor, middle jitter factor, and lower jitter factor of every two nodes in each extreme path, the jitter factor between the original encryption density range of every two nodes in each extreme path is calculated: The upper, middle, and lower jitter factors measure the change in the density range between each two nodes on the extreme path, based on the rate of change of the upper, middle, and lower limits of the original density curves, respectively. These three jitter factors are taken into account when calculating the jitter factor between the original density ranges of each two nodes on each extreme path.

[0055] Possible calculation methods include weighted summation, where the upper, middle, and lower jitter factors are assigned different weights (w1, w2, and w3) based on their importance to the overall jitter (w1 + w2 + w3 = 1). The resulting jitter factor is then calculated using the formula: jitter factor = w1 × upper jitter factor + w2 × middle jitter factor + w3 × lower jitter factor. This combined jitter factor more comprehensively reflects the overall jitter level within the original encryption density range between each two nodes and is used for subsequent analysis of the internal jitter of the multi-dimensional dynamic parameters of the all-in-one simulation model.

[0056] In an alternative embodiment, reference Figure 2 Based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model is analyzed, including: The deviation between the row vectors with the same ordinal number in the unit jitter factor matrix of all extreme paths is regarded as the lateral jitter factor of the corresponding layer; The deviation between the two column vectors in the overall jitter factor matrix of all extreme paths is regarded as the longitudinal jitter factor; determining a magnification factor of an overall jitter factor matrix based on all lateral jitter factors and all longitudinal lateral jitter factors, and numerically amplifying the overall jitter factor matrix based on the magnification factor to obtain an overall jitter factor consideration matrix; The mean value of all elements in the overall jitter factor consideration matrix is ​​regarded as the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model.

[0057] Among them, the deviation between row vectors with the same ordinal number in the unit jitter factor matrix of all extreme paths (lateral jitter factor) is: Each extreme path has a unit jitter factor matrix. Within these matrices, row vectors with the same ordinal number (i.e., corresponding to the same node pair) are calculated to measure the jitter factor variation between nodes at the same location on different extreme paths and those at the same location on the same extreme path. Deviation can be calculated using methods such as Euclidean distance and Manhattan distance. This measure reflects the degree of variation in density at the same node pair position across different extreme paths, helping to analyze the jitter of multidimensional dynamic parameters from a horizontal perspective (between different extreme paths).

[0058] The deviation between two column vectors in the overall jitter factor matrix of all extreme paths (vertical jitter factor): The overall jitter factor matrix combines the jitter factor information for all extreme paths. Calculating the deviation between two column vectors can also be done using methods such as Euclidean distance. This deviation, known as the longitudinal jitter factor, provides another perspective on the inconsistency of encryption density changes and aids in analyzing the jitter characteristics of multi-dimensional dynamic parameters.

[0059] Based on all lateral jitter factors and all longitudinal lateral jitter factors, the magnification of the overall jitter factor matrix is ​​determined, and the overall jitter factor matrix is ​​numerically amplified based on the magnification to obtain the overall jitter factor consideration matrix: the square root of the sum of the square values ​​of the mean values ​​of all lateral jitter factors and the square values ​​of the mean values ​​of all longitudinal jitter factors and a preset multiple (for example, 0.2) is multiplied to determine a magnification. After obtaining the magnification, each element of the overall jitter factor matrix is ​​multiplied by the magnification, and the matrix is ​​numerically amplified to obtain the overall jitter factor consideration matrix. The amplified matrix more prominently reflects the jitter characteristics of the multidimensional dynamic parameters, so that in subsequent analysis, the internal jitter degree of the multidimensional dynamic parameters of the all-in-one simulation model can be more clearly determined based on the matrix, providing a more valuable reference data basis for subsequent operations such as data segmentation.

[0060] In an alternative embodiment, reference Figure 2 , based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model and the original encryption density range of all nodes, the optimal data block spacing is determined, including: Based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model, the original encryption density range of each node is double-ended to obtain the improved encryption density range of each node; The middle value of the intersection range with the greatest degree of intersection among the improved encryption density ranges of all intersection points is regarded as the optimal encryption density; The inverse of the optimal encryption density is taken as the optimal data block spacing.

[0061] Among them, the original encryption density range of each node is double-ended reduced based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model to obtain the improved encryption density range of each node: The internal jitter of the multi-dimensional dynamic parameters of the all-in-one simulation model reflects the severity of parameter changes. High internal jitter indicates complex parameter changes and relatively poor data stability. Based on this internal jitter, the original encryption density range for each node is adjusted.

[0062] "Dual-end reduction" refers to simultaneously reducing both the upper and lower limits of the original encryption density range. For example, if a node's original encryption density range is [a, b], a reduction x is calculated based on the internal jitter level (x is the product of the difference between b and a and the internal jitter level of the multi-dimensional dynamic parameters of the all-in-one simulation model, divided by 2). The improved encryption density range becomes [a+x,b-x]. This is done to better align the encryption density range with actual data fluctuations, improving the relevance and effectiveness of encryption. This ensures that each node receives an improved encryption density range, paving the way for subsequent determination of the optimal encryption density.

[0063] The optimal density is the median value of the intersection range that has the greatest degree of intersection among all the improved density ranges of the intersection points. The improved density ranges of different nodes may intersect. First, find the intersection points of all the improved density ranges, which form multiple intersection ranges.

[0064] The maximum degree of intersection is determined by the total number of intersection points across all the improved density ranges that form that intersection. Then, from these intersections, find the intersection range with the maximum degree of intersection. For example, there are three improved density ranges: [1,5], [3,7], [4,6], and [6.5,7]. The intersections formed by any two of these three ranges are [3,5], [4,5], [4,6], and [6.5,7]. The intersection range with the maximum degree of intersection is assumed to be [4,5] (i.e., the total number of intersection points across all the improved density ranges that form the intersection [4,5] is 3, the highest among all the intersections).

[0065] Finally, the middle value of this maximum intersection range is taken, such as (4 + 5) ÷ 2 = 4.5, and is considered the optimal encryption density. This optimal encryption density takes into account the common characteristics of the improved encryption density range of all nodes. It can subsequently be used to determine the data block spacing and select the appropriate encryption algorithm to ensure the security and rationality of data encryption transmission.

[0066] In an alternative embodiment, S2: encrypting the set of data blocks to be transmitted separately based on the multi-dimensional encryption-related characteristics and the block segmentation rule of the set of data blocks to be transmitted to obtain the encrypted data block set, including: Determining an encryption level for each data block to be transmitted based on all data types and data volume of each data type contained in each data block to be transmitted in the multi-dimensional encryption-related characteristics of the set of data blocks to be transmitted; Determining an encryption algorithm for each data block to be transmitted based on an optimal encryption density corresponding to an optimal data block spacing in the block rule and an encryption level of each data block to be transmitted; All the data blocks to be transmitted in the set of data blocks to be transmitted are encrypted separately based on the encryption algorithm of each data block to obtain an encrypted data block set.

[0067] Among them, all data types contained in each data block to be transmitted and the data volume of each data type: During encrypted data transmission, the data to be transmitted is divided into blocks. Each block contains different types of data, known as data types. For example, it may contain text data types, numeric data types, image data types, and so on. Furthermore, the amount of data each data type occupies within the block varies, and this amount can be measured in bytes.

[0068] Encryption level for each data block to be transmitted: The encryption level indicates the strength and complexity required to encrypt the data block to be transmitted. A higher encryption level means a more advanced and complex encryption algorithm is required to ensure data security.

[0069] The encryption algorithm for each data block to be transmitted is determined based on the optimal encryption density corresponding to the optimal data block spacing in the block rule and the encryption level of each data block to be transmitted: The optimal data block spacing determined by the block partitioning rule corresponds to an optimal encryption density, which reflects the appropriate degree of encryption for each data block. These two factors, combined with the encryption level of each data block to be transmitted, are used to select an appropriate encryption algorithm. Generally speaking, data blocks with high encryption levels and high optimal encryption densities may use encryption algorithms with higher security and greater computational complexity, such as the RSA algorithm in asymmetric encryption. Data blocks with lower encryption levels and lower optimal encryption densities may use relatively simpler and more computationally efficient symmetric encryption algorithms, such as the AES algorithm. In this way, the most appropriate encryption algorithm is selected based on the specific characteristics of the data block, ensuring both data security and encryption efficiency.

[0070] All data blocks in the set are encrypted based on the encryption algorithm for each block to be transmitted, resulting in an encrypted data block set. Each block in the set is encrypted using the previously determined encryption algorithm. Each block is encrypted independently, ensuring the security of each block is specifically protected. For example, block A may be encrypted using the RSA algorithm, while block B may be encrypted using the AES algorithm. After these encryption steps are complete, all encrypted blocks together constitute the encrypted data block set. Even if some of the blocks in this set are intercepted during transmission, the encryption algorithms used are tailored to their specific characteristics, making it difficult for attackers to decipher the data content. This ensures data security during transmission.

[0071] In an alternative embodiment, determining the encryption level of each data block to be transmitted based on all data types and the amount of data of each data type contained in each data block to be transmitted in the set of data blocks to be transmitted includes: Determine the initial encryption level for each data type; The ratio of the amount of data of each data type in each data block to be transmitted to the total number of the corresponding data blocks to be transmitted is used as the weight of the corresponding data type in the corresponding data block to be transmitted; Based on the weights of all data types in each data block to be transmitted, the initial encryption levels of all data types are weighted and rounded up to obtain the encryption level of each data block to be transmitted.

[0072] Among them, determine the initial encryption level of each data type: This step is to pre-set a basic encryption level for each data type based on the inherent characteristics and security requirements of the data type. Generally speaking, factors such as the sensitivity and importance of the data type determine its initial encryption level. For example, data types involving user privacy information, such as ID card numbers and bank card numbers, will be set to a higher initial encryption level due to their high sensitivity; while some public, general descriptive text data types may have a relatively low initial encryption level. These initial encryption levels provide the basic numerical value for the subsequent determination of the encryption level of each data block to be transmitted.

[0073] Based on the weights of all data types in each data block to be transmitted, the initial encryption levels of all data types are weighted and rounded up to obtain the encryption level of each data block to be transmitted: Weighted sum: Multiply the initial encryption levels of all data types in each data block to be transmitted by their corresponding weights, and then add these products. For example, suppose a data block contains three data types, A, B, and C, with initial encryption levels of 3, 2, and 1, respectively, and weights of 0.5, 0.3, and 0.2, respectively. The weighted sum is 3 × 0.5 + 2 × 0.3 + 1 × 0.2 = 1.5 + 0.6 + 0.2 = 2.3.

[0074] Rounding up: The weighted sum is rounded up to determine the final encryption level for the data block to be transmitted. For example, in the example above, after rounding up 2.3, the encryption level for the data block is 3. This method comprehensively considers the characteristics and proportions of each data type within the data block to determine the encryption level that best meets actual needs, providing a basis for subsequent selection of an appropriate encryption algorithm.

[0075] The present invention provides an embodiment of a data encryption transmission system applied to an all-in-one machine simulation model, comprising: A data segmentation module is used to obtain the dynamic data of the all-in-one machine simulation model in real time, and to segment the data to be transmitted based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using the dynamic data to obtain a set of data blocks to be transmitted; A block data encryption module is used to encrypt the set of data blocks to be transmitted respectively based on the multi-dimensional encryption related features and block division rules of the set of data blocks to be transmitted to obtain an encrypted data block set; A multi-level key management module is used to manage the encrypted data block set based on a multi-level key system to obtain security management data; The decryption and verification module is used to transmit security management data to the receiving end based on a dual-link communication method, and perform multi-node collaborative verification through the blockchain node network at the receiving end to obtain data decryption and verification results.

[0076] The above system specifically determines the block segmentation rules of the data to be transmitted based on the dynamic data of the all-in-one simulation model, and combines the multi-dimensional encryption-related characteristics of the set of data blocks to be transmitted to specifically determine the encryption method of the data to be transmitted after the block processing, so that the encryption of the data to be transmitted is highly targeted, effectively improving the security of the data to be transmitted. Combined with the multi-level key system and dual-link communication method, the security of the data to be transmitted is further guaranteed, and the accuracy and integrity of data transmission are ensured to prevent data tampering.

[0077] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. A data encryption transmission method applied to an all-in-one machine simulation model, characterized in that: include: Step S1: acquiring dynamic data of the all-in-one machine simulation model in real time, dividing the data to be transmitted into blocks based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using the dynamic data, and obtaining a set of data blocks to be transmitted; Step S2: Encrypting the data block set to be transmitted separately based on the multi-dimensional encryption-related features and block segmentation rules of the data block set to be transmitted to obtain an encrypted data block set; Step S3: managing the encrypted data block set based on the multi-level key system to obtain security management data; Step S4: The security management data is transmitted to the receiving end based on the dual-link communication method, and multi-node collaborative verification is performed on the receiving end through the blockchain node network to obtain the data decryption verification result.

2. The data encryption transmission method applied to the all-in-one machine simulation model according to claim 1 is characterized in that: Dynamic data of the all-in-one simulation model, including: system operating parameters, data flow characteristic parameters, security environment parameters, and physical environment parameters; Among them, the system operation state parameters include resource load index values ​​and simulation process characteristic parameters; The data stream characteristic parameters include streaming attribute values ​​and data attribute change characteristic parameters; Security environment parameters include threat perception index values ​​and multi-dimensional encryption strength requirement parameters; Physical environment parameters include equipment status monitoring parameters and network topology change parameters.

3. The data encryption transmission method applied to the all-in-one machine simulation model according to claim 1 is characterized in that: The data to be transmitted is divided into blocks based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using dynamic data to obtain a set of data blocks to be transmitted, including: Based on the hierarchy of all parameter classes contained in the dynamic data, a hierarchical structure is constructed for all parameter classes contained in the dynamic data to obtain a dynamic parameter tree of the all-in-one machine simulation model; Based on the dynamic data, the actual values ​​of all bottom-level nodes in the dynamic parameter tree of the all-in-one simulation model are determined, and based on the actual values ​​of all bottom-level nodes, and based on the calculation rules between the parameter classes of all adjacent hierarchical directly connected nodes in the dynamic parameter tree of the all-in-one simulation model and the actual values ​​of all bottom-level nodes, all non-bottom-level nodes in the dynamic parameter tree of the all-in-one simulation model are sequentially deduced and calculated upward to obtain the actual values ​​of all non-bottom-level nodes; Based on the actual value of each node in the dynamic parameter tree of the all-in-one simulation model, the encryption density list of the parameter class corresponding to the corresponding node is retrieved to determine the original encryption density range of each node; Analyzing the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, and determining the optimal data block spacing based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one machine simulation model and the original encryption density range of all nodes; The data to be transmitted is divided into blocks based on the optimal data block spacing to obtain a set of data blocks to be transmitted.

4. The data encryption transmission method applied to the all-in-one machine simulation model according to claim 3 is characterized in that: Based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one simulation model, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model is analyzed, including: All paths from the root node to each bottom node in the dynamic parameter tree of the all-in-one machine simulation model are considered as extreme paths; Calculate the jitter factor between the original encryption density ranges of every two nodes in each extreme path; Based on the jitter factor between the original encryption density range of every two nodes in each extreme path, the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths are constructed; Based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model is analyzed.

5. The data encryption transmission method applied to the all-in-one machine simulation model according to claim 4 is characterized in that: Calculate the jitter factor between the original encrypted density ranges of every two nodes in each extreme path, including: Connect the upper limit, middle value, and lower limit of the original encryption density range of all nodes in each extreme path from high to low according to the node level, and obtain the upper limit curve, middle curve, and lower limit curve of the original encryption density of each extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the original encrypted density upper limit curve of each extreme path at the horizontal coordinate corresponding to the lowest level node and the function value at the horizontal coordinate corresponding to the highest level node in each two nodes to the function value of the first derivative function at the horizontal coordinate corresponding to the highest level node is taken as the upper jitter factor corresponding to the two nodes in the corresponding extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the mid-limit curve of the original encrypted density of each extreme path at the abscissa corresponding to the lowest level node and the function value at the abscissa corresponding to the highest level node in each two nodes to the function value of the first derivative function at the abscissa corresponding to the highest level node is taken as the medium jitter factor of the two nodes corresponding to the extreme path; The ratio of the difference between the function value of the first derivative function represented by the function corresponding to the original encrypted density lower limit curve of each extreme path at the horizontal coordinate corresponding to the lowest level node and the function value at the horizontal coordinate corresponding to the highest level node in each two nodes to the function value of the first derivative function at the horizontal coordinate corresponding to the highest level node is taken as the lower jitter factor of the corresponding two nodes in the corresponding extreme path; Based on the upper jitter factor, middle jitter factor, and lower jitter factor of every two nodes in each extreme path, the jitter factor between the original encryption density range of every two nodes in each extreme path is calculated.

6. The data encryption transmission method applied to the all-in-one machine simulation model according to claim 4 is characterized in that: Based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths, the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model is analyzed, including: The deviation between the row vectors with the same ordinal number in the unit jitter factor matrix of all extreme paths is regarded as the lateral jitter factor of the corresponding layer; The deviation between the two column vectors in the overall jitter factor matrix of all extreme paths is regarded as the longitudinal jitter factor; determining a magnification factor of an overall jitter factor matrix based on all lateral jitter factors and all longitudinal lateral jitter factors, and numerically amplifying the overall jitter factor matrix based on the magnification factor to obtain an overall jitter factor consideration matrix; The mean value of all elements in the overall jitter factor consideration matrix is ​​regarded as the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model.

7. The data encryption transmission method applied to the all-in-one machine simulation model according to claim 3 is characterized in that: The optimal data block spacing is determined based on the internal jitter level of the multi-dimensional dynamic parameters of the all-in-one simulation model and the original encryption density range of all nodes, including: Based on the internal jitter degree of the multi-dimensional dynamic parameters of the all-in-one simulation model, the original encryption density range of each node is double-ended to obtain the improved encryption density range of each node; The middle value of the intersection range with the greatest degree of intersection among the improved encryption density ranges of all intersection points is regarded as the optimal encryption density; The inverse of the optimal encryption density is taken as the optimal data block spacing.

8. The data encryption transmission method applied to the all-in-one machine simulation model according to claim 1 is characterized in that: S2: Encrypting the set of data blocks to be transmitted based on the multi-dimensional encryption-related features and the block division rules of the set of data blocks to be transmitted to obtain an encrypted data block set, including: Determining an encryption level for each data block to be transmitted based on all data types and data volume of each data type contained in each data block to be transmitted in the multi-dimensional encryption-related characteristics of the set of data blocks to be transmitted; Determining an encryption algorithm for each data block to be transmitted based on an optimal encryption density corresponding to an optimal data block spacing in the block rule and an encryption level of each data block to be transmitted; All the data blocks to be transmitted in the set of data blocks to be transmitted are encrypted separately based on the encryption algorithm of each data block to obtain an encrypted data block set.

9. The data encryption transmission method applied to the all-in-one machine simulation model according to claim 8, characterized in that: Based on all data types and the amount of data of each data type contained in each data block to be transmitted in the set of data blocks to be transmitted, an encryption level of each data block to be transmitted is determined, including: Determine the initial encryption level for each data type; The ratio of the amount of data of each data type in each data block to be transmitted to the total number of the corresponding data blocks to be transmitted is used as the weight of the corresponding data type in the corresponding data block to be transmitted; Based on the weights of all data types in each data block to be transmitted, the initial encryption levels of all data types are weighted and rounded up to obtain the encryption level of each data block to be transmitted.

10. A data encryption transmission system applied to an all-in-one machine simulation model, characterized in that: include: A data segmentation module is used to obtain the dynamic data of the all-in-one machine simulation model in real time, and to segment the data to be transmitted based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model constructed using the dynamic data to obtain a set of data blocks to be transmitted; A block data encryption module is used to encrypt the set of data blocks to be transmitted respectively based on the multi-dimensional encryption related features and block division rules of the set of data blocks to be transmitted to obtain an encrypted data block set; A multi-level key management module is used to manage the encrypted data block set based on a multi-level key system to obtain security management data; The decryption and verification module is used to transmit security management data to the receiving end based on a dual-link communication method, and perform multi-node collaborative verification through the blockchain node network at the receiving end to obtain data decryption and verification results.

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