A data encryption transmission method and system applied to an all-in-one machine simulation model

Abstract

This invention relates to the field of secure communication technology, and specifically discloses a data encryption transmission method and system applied to an all-in-one machine simulation model. The method includes: 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, to obtain a set of data blocks to be transmitted; encrypting the set of data blocks to be transmitted separately based on the multi-dimensional encryption-related features and block division rules of the set of data blocks to be transmitted, to obtain a set of encrypted data blocks; managing the set of encrypted data blocks based on a multi-level key system to obtain secure management data; transmitting the secure management data to the receiving end based on a dual-link communication method, and performing multi-node collaborative verification at the receiving end through a blockchain node network to obtain the data decryption verification result. This invention can improve the security, anti-attack capability, and real-time performance of all-in-one machine simulation data transmission based on secure communication technology, preventing the data to be transmitted from being tampered with, stolen, or illegally accessed.

Description

Technical Field

[0001] This 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 Technology

[0002] In today's era of rapid digital and information technology development, integrated simulation models play a vital role in numerous fields, widely applied 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 schemes, and predict potential problems. However, the data involved in integrated simulation models is often highly sensitive and confidential, containing key technical parameters, design concepts, and important experimental results. During data transmission, theft, tampering, or leakage can cause serious losses to enterprises or research institutions, potentially leading to the loss of technological advantages, the leakage of trade secrets, and intellectual property disputes. Therefore, ensuring the security of integrated simulation model data during transmission is crucial. With the continuous development of network technology, the data transmission environment has become increasingly complex, facing various security threats from hackers, malware, and others. Traditional data encryption transmission methods are insufficient to meet the special needs of integrated simulation model data and cannot effectively cope with complex and ever-changing network attacks. Against this backdrop, data encryption transmission methods and systems applied to integrated simulation models are of great significance for ensuring data security and promoting the stable development of related fields.

[0003] However, existing data encryption transmission technologies applied to all-in-one machine simulation models struggle to properly segment the data to be transmitted based on the dynamic data of the simulation model. This results in unreasonable data block division and an inability to fully consider the characteristics of different data blocks, affecting subsequent encryption effectiveness and reducing the security and specificity of encryption. Furthermore, the lack of effective key management, communication, and decryption verification methods in the key management and data transmission and verification stages makes it impossible to effectively verify the integrity and authenticity of the data, thus hindering the security and reliability of data during transmission.

[0004] Therefore, this invention proposes a data encryption transmission method and system for use in all-in-one machine simulation models. Summary of the Invention

[0005] This invention provides a data encryption transmission method and system for an all-in-one machine simulation model. The method determines the segmentation rules of the data to be transmitted based on the dynamic data of the all-in-one machine simulation model, and combines this with the multi-dimensional encryption-related features of the data block set to be transmitted to determine the encryption method for the segmented data. This makes the encryption of the data to be transmitted highly targeted, effectively improving the security of the transmitted data. Furthermore, by combining a multi-level key system and a dual-path communication method, the security of the transmitted data is further guaranteed, and the accuracy and integrity of data transmission are ensured, preventing data tampering.

[0006] This invention provides a data encryption transmission method for an all-in-one computer simulation model, comprising:

[0007] Step S1: Real-time acquisition of dynamic data from 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 constructed using the dynamic data, the data to be transmitted is divided into blocks to obtain a set of data blocks to be transmitted.

[0008] Step S2: Based on the multi-dimensional encryption-related features and block division rules of the data block set to be transmitted, encrypt the data block set to be transmitted separately to obtain the encrypted data block set;

[0009] Step S3: Manage the encrypted data block set based on a multi-level key system to obtain security management data;

[0010] Step S4: Transmit the secure management data to the receiving end based on the dual-link communication method, and perform multi-node collaborative verification through the blockchain node network at the receiving end to obtain the data decryption verification result.

[0011] Optionally, the dynamic data of the all-in-one machine simulation model includes: system operating parameters, data flow characteristic parameters, security environment parameters, and physical environment parameters;

[0012] Among them, the system runtime parameters include resource load index values ​​and simulation process characteristic parameters;

[0013] Data stream feature parameters include streaming attribute values ​​and data attribute change feature parameters;

[0014] Security environment parameters include threat perception index values ​​and multi-dimensional encryption strength requirements.

[0015] Physical environment parameters include equipment status monitoring parameters and network topology change parameters.

[0016] Optionally, 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:

[0017] Based on the hierarchical structure of all parameter classes contained in the dynamic data, a dynamic parameter tree of the all-in-one machine simulation model is obtained by hierarchically constructing all parameter classes contained in the dynamic data.

[0018] Based on dynamic data, the actual values ​​of all bottom-level nodes in the dynamic parameter tree of the all-in-one machine simulation model are determined. 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 machine simulation model and the actual values ​​of all bottom-level nodes, the calculation is performed sequentially upwards for all non-bottom-level nodes in the dynamic parameter tree of the all-in-one machine simulation model to obtain the actual values ​​of all non-bottom-level nodes.

[0019] Based on the actual value of each node in the dynamic parameter tree of the all-in-one machine simulation model, the encryption density list of the parameter class corresponding to the node is retrieved, and the original encryption density range of each node is determined.

[0020] Based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine simulation model is analyzed, and the optimal data block spacing is determined based on the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine simulation model and the original encryption density range of all nodes.

[0021] 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.

[0022] Optionally, based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine simulation model is analyzed, including:

[0023] All paths from the root node to each bottom node in the dynamic parameter tree of the all-in-one machine simulation model are treated as extreme paths.

[0024] Calculate the jitter factor between the original encryption density ranges of every two nodes in each extreme path;

[0025] 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.

[0026] The internal jitter level of the multidimensional dynamic parameters of the all-in-one machine simulation model is analyzed based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths.

[0027] Optionally, calculate the jitter factor between the original encryption density ranges of every two nodes in each extreme path, including:

[0028] The upper limit, middle limit, and lower limit of the original encryption density range of all nodes in each extreme path are connected from high to low according to the node hierarchy to obtain the original encryption density upper limit curve, original encryption density middle curve, and original encryption density lower limit curve of each extreme path.

[0029] The difference between the function value at the lowest level node and the function value at the highest level node in each pair of nodes corresponding to the first derivative function of the original encryption density upper limit curve of each extreme path is used as the ratio of the function value at the highest level node to the function value of the first derivative function at the highest level node. This ratio is used as the upper jitter factor of the corresponding two nodes in the extreme path.

[0030] The difference between the function value at the lowest level node and the function value at the highest level node of the original encryption density limit curve of each extreme path is used as the ratio of the function value at the highest level node to the function value of the corresponding first derivative function at the corresponding highest level node. This ratio is used as the jitter factor of the corresponding two nodes in the extreme path.

[0031] The difference between the function value of the first derivative of the original encryption density lower limit curve of each extreme path at the x-coordinate of the lowest level node and the function value at the x-coordinate of the highest level node in each pair of nodes is used as the ratio of the function value of the first derivative at the x-coordinate of the highest level node. This ratio is taken as the lower jitter factor of the corresponding two nodes in the extreme path.

[0032] Based on the upper, middle, and lower jitter factors of each pair of nodes in each extreme path, the jitter factor between the original encryption density ranges of each pair of nodes in each extreme path is calculated.

[0033] 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 multidimensional dynamic parameters of the all-in-one machine simulation model is analyzed, including:

[0034] The deviation between row vectors with the same ordinal number in the unit jitter factor matrix of all extreme paths is taken as the horizontal jitter factor of the corresponding layer;

[0035] The deviation between any two column vectors in the overall jitter factor matrix of all extreme paths is taken as the longitudinal jitter factor.

[0036] The magnification factor of the overall jitter factor matrix is ​​determined based on all horizontal jitter factors and all vertical horizontal jitter factors. The overall jitter factor matrix is ​​then numerically magnified based on the magnification factor to obtain the overall jitter factor consideration matrix.

[0037] The mean of all elements in the overall jitter factor consideration matrix is ​​used as the internal jitter level of the multidimensional dynamic parameters of the all-in-one machine simulation model.

[0038] Optionally, the optimal data block spacing is determined based on the internal jitter level of the multidimensional dynamic parameters of the all-in-one machine simulation model and the original encryption density range of all nodes, including:

[0039] The internal jitter of the multidimensional dynamic parameters based on the integrated machine simulation model is used to reduce the original encryption density range of each node in both directions to obtain the improved encryption density range of each node.

[0040] The median value of the intersection range with the greatest overlap among all the improved encryption density ranges is taken as the optimal encryption density;

[0041] The reciprocal of the optimal encryption density is used as the optimal data block spacing.

[0042] Optionally, S2: Based on the multi-dimensional encryption-related features and block division rules of the set of data blocks to be transmitted, encrypt the set of data blocks to be transmitted separately to obtain an encrypted data block set, including:

[0043] Based on the multidimensional encryption-related features of the set of data blocks to be transmitted, including all data types and the amount of data of each data type, the encryption level of each data block to be transmitted is determined.

[0044] 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 segmentation rules and the encryption level of each data block to be transmitted.

[0045] The encryption algorithm for each data block to be transmitted is used to encrypt all data blocks in the set of data blocks to be transmitted separately, thus obtaining a set of encrypted data blocks.

[0046] Optionally, based on all data types contained in each data block in the set of data blocks to be transmitted and the amount of data of each data type, the encryption level of each data block to be transmitted is determined, including:

[0047] Determine the initial encryption level for each data type;

[0048] The ratio of the amount of each data type in each data block to the total number of corresponding data blocks is used as the weight of the corresponding data type in the corresponding data block.

[0049] 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, summed, and rounded up to obtain the encryption level of each data block to be transmitted.

[0050] This invention provides a data encryption transmission system for an all-in-one computer simulation model, comprising:

[0051] The data segmentation module is used to acquire dynamic data of the all-in-one machine simulation model in real time. 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 segmented to obtain a set of data blocks to be transmitted.

[0052] The block data encryption module is used to encrypt the set of data blocks to be transmitted separately based on the multi-dimensional encryption-related features and block division rules of the set of data blocks to be transmitted, so as to obtain an encrypted set of data blocks.

[0053] The multi-level key management module is used to manage the set of encrypted data blocks based on a multi-level key system and obtain security management data;

[0054] The decryption and verification module is used to transmit secure management data to the receiving end based on a dual-link communication method, and to perform multi-node collaborative verification at the receiving end through a blockchain node network to obtain the data decryption and verification results.

[0055] The beneficial effects of this invention compared to existing technologies are as follows: Based on the dynamic data of the all-in-one machine simulation model, the block division rules of the data to be transmitted are determined in a targeted manner. Combined with the multi-dimensional encryption-related features of the data block set to be transmitted, the encryption method of the data to be transmitted after block processing is determined in a targeted manner. This makes the encryption of the data to be transmitted highly targeted, effectively improving the security of the data to be transmitted. Furthermore, combined with a multi-level key system and dual-path communication mode, the security of the data to be transmitted is further guaranteed, and the accuracy and integrity of data transmission are ensured, preventing data tampering.

[0056] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0057] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0058] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0059] Figure 1 This is a flowchart of a data encryption transmission method applied to an all-in-one machine simulation model according to an embodiment of the present invention;

[0060] Figure 2This is a flowchart of a block processing embodiment of the present invention. Detailed Implementation

[0061] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0062] refer to Figure 1 This invention provides an implementation method for a data encryption transmission method applied to an all-in-one machine simulation model, comprising:

[0063] Step S1: Real-time acquisition of dynamic data from 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 constructed using the dynamic data, the data to be transmitted is divided into blocks to obtain a set of data blocks to be transmitted.

[0064] Step S2: Based on the multi-dimensional encryption-related features and block division rules of the data block set to be transmitted, encrypt the data block set to be transmitted separately to obtain the encrypted data block set;

[0065] Step S3: Manage the encrypted data block set based on a multi-level key system to obtain security management data;

[0066] Step S4: Transmit the secure management data to the receiving end based on the dual-link communication method, and perform multi-node collaborative verification through the blockchain node network at the receiving end to obtain the data decryption verification result.

[0067] Among them, the all-in-one simulation model: In the field of secure communication technology, the all-in-one simulation model is widely used in industries such as industrial manufacturing, aerospace, and scientific research to simulate and analyze complex systems or processes. The data it involves is highly sensitive and confidential, containing information such as key technical parameters, design concepts, and important experimental results.

[0068] Multidimensional encryption-related characteristics of the data block set to be transmitted: These refer to the multi-dimensional characteristics related to the encryption of the data blocks to be transmitted. Specifically, this is reflected in all the data types contained in each data block and the amount of each data type. For example, if a data block to be transmitted contains a large number of sensitive user authentication data types and the data volume is large, according to the multidimensional encryption-related characteristics, its encryption level may be relatively high, requiring more complex and higher-level encryption algorithms to ensure data security.

[0069] Segmentation rules: Criteria for segmenting the data to be transmitted based on the dynamic data of the all-in-one machine simulation model, i.e., the optimal segmentation spacing.

[0070] Encrypted data block set: All data blocks to be transmitted are encrypted individually according to multi-dimensional encryption features and block division rules. These encrypted data blocks together constitute the encrypted data block set. It represents an intermediate state of data during the encryption process. After subsequent multi-level key system management, dual-link communication transmission, and verification, the data is ultimately ensured to arrive securely at the receiving end. For example, data blocks with different encryption levels may be encrypted using different encryption algorithms (such as symmetric encryption, asymmetric encryption, etc.), forming the encrypted data block set.

[0071] A multi-level key system is a hierarchical key management approach. Managing a set of encrypted data blocks using this system means that keys at different levels are responsible for encryption, decryption, and access control operations at different levels or for different data blocks. This management method further ensures the security of encrypted data and the rationality of access control, ultimately generating secure management data. For example, higher-level keys might be used for access control of the overall encrypted data, while lower-level keys are used for decryption operations on specific data blocks. The different levels of keys work together to ensure data security and efficient management. Secure management data, after being managed by the multi-level key system, is a collection of encrypted data and related key management information, providing a foundation for subsequent data transmission and receiver verification. For example, a multi-level key system might include a device root key, a session master key, and temporary keys for data blocks, where the temporary keys are dynamically generated using chaotic sequences.

[0072] Secure 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 a blockchain node network to obtain the data decryption verification result. The dual-link communication method uses two different communication links for data transmission, which improves the reliability of data transmission and reduces the risk of errors or data loss during transmission. After the secure management data is transmitted to the receiving end via the dual links, the receiving end uses the blockchain node network for multi-node collaborative verification. The blockchain node network has characteristics such as being distributed and tamper-proof. Multiple nodes jointly participate in the verification of the received data, verifying its integrity, authenticity, and the correctness of decryption through comparison and calculation, ultimately obtaining the data decryption verification result. If the verification passes, it means that the data has not been tampered with during transmission and the decryption is correct, ensuring the security and accuracy of data transmission. If the verification fails, it indicates that the data may have problems and appropriate measures need to be taken. For example, each node in the blockchain node network stores partial information about the data and verification rules. Through information exchange and collaborative calculation among multiple nodes, comprehensive verification of the received data is achieved.

[0073] In an alternative implementation, the dynamic data of the all-in-one machine simulation model includes: system operating parameters, data flow characteristic parameters, security environment parameters, and physical environment parameters;

[0074] Among them, the system runtime parameters include resource load index values ​​and simulation process characteristic parameters;

[0075] Data stream feature parameters include streaming attribute values ​​and data attribute change feature parameters;

[0076] Security environment parameters include threat perception index values ​​and multi-dimensional encryption strength requirements.

[0077] Physical environment parameters include equipment status monitoring parameters and network topology change parameters.

[0078] Among them, the resource load index value is part of the system operating parameters in the dynamic data of the all-in-one simulation model. It reflects the degree of occupation and utilization of various resources (such as CPU, memory, storage, etc.) during the operation of the all-in-one simulation model. For example, specific values ​​such as CPU utilization and memory usage can help understand the resource tension or sufficiency status during model operation and are of great reference significance for judging the system's carrying capacity during data transmission.

[0079] Simulation process characteristic parameters: These are also system runtime parameters, describing the characteristics of each simulation process within the integrated machine simulation model, such as process startup time, runtime, inter-process dependencies, and process execution priority. These parameters help to grasp the overall flow and state of the simulation model's operation and may influence the timing and order of data transmission.

[0080] Streaming attribute values ​​are a type of data flow characteristic parameter used to characterize the data flow properties in the all-in-one machine simulation model. These properties include whether the data flows continuously or intermittently, and the speed and direction of the data flow. Understanding streaming attribute values ​​allows for a better understanding of the dynamic characteristics of the data, providing a basis for the formulation of data segmentation and encryption strategies in encrypted data transmission.

[0081] Data attribute change characteristic parameters: These are also data flow characteristic parameters, mainly reflecting the changes in data attributes during the simulation model's operation, such as changes in data format, trends in data volume, and the frequency of data content updates. Mastering these parameters enables encrypted transmission methods to better adapt to dynamic data changes, ensuring data security.

[0082] Threat perception metrics: Included in the security environment parameters, these measures the level of security threats faced by the environment in which the all-in-one simulation model exists. These may include indicators for detecting network attacks, risk assessments of potential vulnerabilities, etc. Based on these metrics, the strength and method of data encryption can be adjusted to address different levels of security threats.

[0083] Multidimensional Encryption Strength Requirements: As part of the security environment parameters, these parameters describe the requirements for data encryption strength from multiple dimensions, such as confidentiality, integrity, and availability. Different types of data may have different encryption strength requirements across different dimensions. These parameters guide the selection of appropriate encryption algorithms and strategies to ensure data security during transmission.

[0084] Equipment status monitoring parameters: These are physical environment parameters. They reflect the status information of the equipment on which the all-in-one simulation model depends for operation, such as equipment temperature, humidity, and hardware fault indicators. Equipment status affects the stability and security of data transmission. These parameters help to detect potential equipment problems in advance and ensure the normal operation of data transmission.

[0085] Network topology change parameters: These are physical environment parameters that describe changes in the network topology, such as the addition or removal of network nodes or changes in the connection status of network links. Changes in network topology can affect data transmission paths and speeds. Understanding these parameters allows for better planning of data transmission methods, ensuring accurate and timely data transmission.

[0086] In an alternative implementation, refer to 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:

[0087] Based on the hierarchical structure of all parameter classes contained in the dynamic data, a dynamic parameter tree of the all-in-one machine simulation model is obtained by hierarchically constructing all parameter classes contained in the dynamic data.

[0088] Based on dynamic data, the actual values ​​of all bottom-level nodes in the dynamic parameter tree of the all-in-one machine simulation model are determined. 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 machine simulation model and the actual values ​​of all bottom-level nodes, the calculation is performed sequentially upwards for all non-bottom-level nodes in the dynamic parameter tree of the all-in-one machine simulation model to obtain the actual values ​​of all non-bottom-level nodes.

[0089] Based on the actual value of each node in the dynamic parameter tree of the all-in-one machine simulation model, the encryption density list of the parameter class corresponding to the node is retrieved, and the original encryption density range of each node is determined.

[0090] Based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine simulation model is analyzed, and the optimal data block spacing is determined based on the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine simulation model and the original encryption density range of all nodes.

[0091] 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.

[0092] Specifically, based on the hierarchical structure of all parameter classes contained in the dynamic data, a dynamic parameter tree for the all-in-one machine simulation model is obtained. This involves organizing the various parameters in the dynamic data of the all-in-one machine simulation model according to their hierarchical relationships, constructing a tree-like structure. For example, the root node represents the dynamic parameters, and the next-level nodes connected to the root node include different types of parameters such as system runtime parameters and data flow characteristic parameters. The next-level nodes linked to the system runtime parameters include resource load index values ​​and simulation process characteristic parameters. Following this rule, a tree structure that clearly displays the hierarchical relationships of the parameters—the dynamic parameter tree of the all-in-one machine simulation model—is built according to their respective levels and interrelationships.

[0093] The actual values ​​of all bottom-level nodes in the dynamic parameter tree of the all-in-one machine simulation model are determined based on dynamic data: In the constructed dynamic parameter tree, the nodes at the bottom level are found, and the actual parameter values ​​corresponding to these bottom-level nodes are determined by acquiring the dynamic data of the all-in-one machine simulation model. For example, if the bottom-level nodes represent specific parameters such as device temperature and network bandwidth, the actual measured values ​​of these parameters are extracted from the dynamic data.

[0094] Based on the actual values ​​of all bottom-level nodes, and according to the calculation rules between parameter classes of all directly connected nodes at adjacent levels in the dynamic parameter tree of the integrated machine simulation model, and the actual values ​​of all bottom-level nodes, the actual values ​​of all non-bottom-level nodes in the dynamic parameter tree of the integrated machine simulation model are calculated sequentially upwards. Using the determined actual values ​​of the bottom-level nodes, and according to the preset calculation rules between directly connected nodes at adjacent levels in the parameter tree, the actual values ​​of non-bottom-level nodes are calculated progressively upwards from the bottom. For example, the value of a node at a higher level may be obtained from the values ​​of several related nodes at the lower level through existing calculation rules (such as weighted summation after normalization). By deriving in this way, the actual values ​​of all non-bottom-level nodes can be obtained, thus completing the information of each node in the entire parameter tree.

[0095] Encryption density list for the parameter class corresponding to each node: A pre-defined list for the parameter class corresponding to each node in the parameter tree. This list records the original encryption density range corresponding to the parameter class under different trend ranges. Encryption density can be understood as the reciprocal of the block spacing when encrypting data in blocks.

[0096] Based on the actual value of each node in the dynamic parameter tree of the integrated machine simulation model, the encryption density list of the corresponding parameter class for that node is retrieved to determine the original encryption density range for each node. Specifically, 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 applicable original encryption density range for that node's data. For example, if a node represents a specific type of data traffic parameter, the appropriate encryption density range for that traffic data is found from the corresponding encryption density list based on its actual traffic volume. This range is the original encryption density range, providing a basis for subsequently determining the encryption strength.

[0097] The degree of internal jitter in the multidimensional dynamic parameters of the all-in-one machine simulation model is determined by analyzing the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model. It reflects the degree of drastic changes or consistency among the multidimensional dynamic parameters. If the internal jitter is large, it indicates that the parameter changes are more complex, and a more refined data partitioning strategy may be required.

[0098] Optimal data block spacing: Determined by the internal jitter level of the multidimensional dynamic parameters of the integrated machine simulation model and the original encryption density range of all nodes. It is a numerical interval used to block the data to be transmitted. For example, when the internal jitter level and encryption density range indicate that certain data areas vary greatly or have high security requirements, a smaller optimal data block spacing may be determined to allow for finer data block division, facilitating targeted encryption and improving data security.

[0099] 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: The data to be transmitted is divided into multiple data blocks according to the determined optimal data block 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 block spacing, and subsequently, each data block will be encrypted separately based on its characteristics to ensure data security during transmission.

[0100] In an alternative implementation, refer to 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, the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine simulation model is analyzed, including:

[0101] All paths from the root node to each bottom node in the dynamic parameter tree of the all-in-one machine simulation model are treated as extreme paths.

[0102] Calculate the jitter factor between the original encryption density ranges of every two nodes in each extreme path;

[0103] 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.

[0104] The internal jitter level of the multidimensional dynamic parameters of the all-in-one machine simulation model is analyzed based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths.

[0105] In this context, the element in the i-th row and j-th column of the unit jitter factor matrix for each extreme path represents the jitter factor between the original encryption density range of the i-th node and the j-th node on the extreme path.

[0106] The element in the i-th row and j-th column of the overall jitter factor matrix for all extreme paths represents the mean of the jitter factor between the i-th node of the j-th extreme path and the original encryption density range of all grounding points in the corresponding extreme path.

[0107] The above data provides comprehensive data support for analyzing the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine simulation model.

[0108] In an alternative implementation, refer to Figure 2 Calculate the jitter factor between the original encryption density ranges of every two nodes in each extreme path, including:

[0109] The upper limit, middle limit, and lower limit of the original encryption density range of all nodes in each extreme path are connected from high to low according to the node hierarchy to obtain the original encryption density upper limit curve, original encryption density middle curve, and original encryption density lower limit curve of each extreme path.

[0110] The difference between the function value at the lowest level node and the function value at the highest level node in each pair of nodes corresponding to the first derivative function of the original encryption density upper limit curve of each extreme path is used as the ratio of the function value at the highest level node to the function value of the first derivative function at the highest level node. This ratio is used as the upper jitter factor of the corresponding two nodes in the extreme path.

[0111] The difference between the function value at the lowest level node and the function value at the highest level node of the original encryption density limit curve of each extreme path is used as the ratio of the function value at the highest level node to the function value of the corresponding first derivative function at the corresponding highest level node. This ratio is used as the jitter factor of the corresponding two nodes in the extreme path.

[0112] The difference between the function value of the first derivative of the original encryption density lower limit curve of each extreme path at the x-coordinate of the lowest level node and the function value at the x-coordinate of the highest level node in each pair of nodes is used as the ratio of the function value of the first derivative at the x-coordinate of the highest level node. This ratio is taken as the lower jitter factor of the corresponding two nodes in the extreme path.

[0113] Based on the upper, middle, and lower jitter factors of each pair of nodes in each extreme path, the jitter factor between the original encryption density ranges of each pair of nodes in each extreme path is calculated.

[0114] In the dynamic parameter tree of the integrated machine simulation model, for the extreme path from the root node to each bottom node, the upper limit of the original encryption density range of all nodes in the path is connected from high to low node level, and the resulting curve is the original encryption density upper limit curve. It shows the trend of the encryption density upper limit value changing with the node level on the extreme path.

[0115] Similarly, by connecting the median values ​​of the original encryption density range of all nodes from high to low according to the node hierarchy, we obtain the median curve of the original encryption density, which reflects the changing trend of the median encryption density value.

[0116] By connecting the lower limits of the original encryption density range for all nodes from high to low node hierarchy, we obtain the original encryption density lower limit curve, which reflects the variation of the encryption density lower limit. These curves help analyze the variation characteristics of encryption density on extreme paths, providing a basis for subsequent calculation of the jitter factor.

[0117] Based on the upper, middle, and lower jitter factors of every two nodes in each extreme path, the jitter factor between the original encryption density ranges of every two nodes in each extreme path is calculated:

[0118] The upper, middle, and lower jitter factors measure the change in the encryption density range between every two nodes on the extreme path from the perspective of the rate of change of the original upper limit curve, middle curve, and lower limit curve, respectively. When calculating the jitter factor between every two nodes in each extreme path, all three jitter factors are taken into account.

[0119] Possible calculation methods include weighted summation, assigning different weights w1, w2, and w3 (w1+w2+w3=1) to the upper, middle, and lower jitter factors based on their relative importance to the overall jitter. The jitter factor is then calculated using the formula: Jitter Factor = w1 × Upper Jitter Factor + w2 × Middle Jitter Factor + w3 × Lower Jitter Factor. This comprehensive jitter factor more comprehensively reflects the overall jitter level within the original encryption density range between each pair of nodes, and is used for subsequent analysis of the internal jitter of the multi-dimensional dynamic parameters of the integrated machine simulation model.

[0120] In an alternative implementation, refer to 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 multidimensional dynamic parameters of the all-in-one machine simulation model is analyzed, including:

[0121] The deviation between row vectors with the same ordinal number in the unit jitter factor matrix of all extreme paths is taken as the horizontal jitter factor of the corresponding layer;

[0122] The deviation between any two column vectors in the overall jitter factor matrix of all extreme paths is taken as the longitudinal jitter factor.

[0123] The magnification factor of the overall jitter factor matrix is ​​determined based on all horizontal jitter factors and all vertical horizontal jitter factors. The overall jitter factor matrix is ​​then numerically magnified based on the magnification factor to obtain the overall jitter factor consideration matrix.

[0124] The mean of all elements in the overall jitter factor consideration matrix is ​​used as the internal jitter level of the multidimensional dynamic parameters of the all-in-one machine simulation model.

[0125] Among them, the deviation (lateral jitter factor) between row vectors with the same ordinal number in the unit jitter factor matrix of all extreme paths:

[0126] 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 considered. Calculating the deviation between these ordinal row vectors measures the change in jitter factor between nodes at the same position on different extreme paths and nodes at the same position on the same extreme path. Methods for calculating the deviation include Euclidean distance and Manhattan distance. It reflects the degree of difference in encryption density changes at the same node pair position across different extreme paths, helping to analyze the jitter of multi-dimensional dynamic parameters from a lateral perspective (between different extreme paths).

[0127] The deviation between any two column vectors in the overall jitter factor matrix of all extreme paths (vertical jitter factor):

[0128] The overall jitter factor matrix integrates jitter factor information from all extreme paths. The deviation between any two column vectors can be calculated using methods such as Euclidean distance. This deviation, known as the longitudinal jitter factor, reveals the inconsistency in encryption density changes from another perspective, aiding in the analysis of jitter characteristics of multidimensional dynamic parameters.

[0129] Based on all horizontal and vertical jitter factors, the magnification factor of the overall jitter factor matrix is ​​determined. The overall jitter factor matrix is ​​then numerically magnified based on this magnification factor to obtain the overall jitter factor consideration matrix: a magnification factor is determined by multiplying the square root of the sum of the squares of the means of all horizontal and vertical jitter factors by a preset factor (e.g., 0.2). After obtaining the magnification factor, each element of the overall jitter factor matrix is ​​multiplied by this factor to numerically magnify the matrix, thus obtaining the overall jitter factor consideration matrix. The magnified matrix more prominently reflects the jitter characteristics of multidimensional dynamic parameters, enabling a clearer determination of the internal jitter degree of the multidimensional dynamic parameters of the all-in-one machine simulation model in subsequent analysis, providing a more valuable data foundation for subsequent operations such as data segmentation.

[0130] In an alternative implementation, refer to Figure 2 The optimal data block spacing is determined based on the internal jitter level of the multidimensional dynamic parameters of the all-in-one machine simulation model and the original encryption density range of all nodes, including:

[0131] The internal jitter of the multidimensional dynamic parameters based on the integrated machine simulation model is used to reduce the original encryption density range of each node in both directions to obtain the improved encryption density range of each node.

[0132] The median value of the intersection range with the greatest overlap among all the improved encryption density ranges is taken as the optimal encryption density;

[0133] The reciprocal of the optimal encryption density is used as the optimal data block spacing.

[0134] Specifically, the internal jitter level of the multidimensional dynamic parameters based on the integrated machine simulation model is used to reduce the original encryption density range of each node in both directions, resulting in an improved encryption density range for each node:

[0135] The degree of internal jitter in the multidimensional dynamic parameters of the all-in-one machine simulation model reflects the drastic nature of parameter changes. A high degree of internal jitter indicates complex parameter variations and relatively poor data stability. Based on this degree of internal jitter, the original encryption density range for each node is adjusted.

[0136] "Dual-end reduction" refers to simultaneously reducing both the upper and lower limits of the original encryption density range. For example, if the original encryption density range of a node is [a, b], and a reduction amount 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 multidimensional dynamic parameters of the integrated machine simulation model, divided by 2), then the improved encryption density range becomes [a+x, b−x]. The purpose of this is to make the encryption density range more closely reflect the actual changes in the data, improving the targeting and effectiveness of encryption. In this way, each node obtains an improved encryption density range, preparing for the subsequent determination of the optimal encryption density.

[0137] The optimal encryption density is determined by taking the median of the intersection ranges with the greatest overlap among all the improved encryption density ranges: there will be overlaps between the improved encryption density ranges of different nodes. First, find the intersections of all the improved encryption density ranges; these intersections form multiple intersection ranges.

[0138] The maximum degree of intersection is defined as the maximum total number of intersection points among all improved encryption density ranges that form this intersection range. Then, the intersection range with the maximum degree of intersection is found from these intersections. For example, there are three improved encryption density ranges [1,5], [3,7], [4,6], and [6.5,7]. The intersections formed by each pair of these three ranges include [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] (that is, the total number of intersection points among all improved encryption density ranges that form the intersection of [4,5] is 3, which is the maximum among all intersections).

[0139] Finally, the median value of the intersection range with the maximum overlap is taken, such as (4+5)÷2=4.5, and this value is taken as the optimal encryption density. This optimal encryption density takes into account the common characteristics of all nodes improving the encryption density range, and can be used to determine the data block spacing and select a suitable encryption algorithm to ensure the security and rationality of encrypted data transmission.

[0140] In an alternative implementation, S2: Based on the multi-dimensional encryption-related features and block division rules of the set of data blocks to be transmitted, the set of data blocks to be transmitted is encrypted separately to obtain an encrypted data block set, including:

[0141] Based on the multidimensional encryption-related features of the set of data blocks to be transmitted, including all data types and the amount of data of each data type, the encryption level of each data block to be transmitted is determined.

[0142] 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 segmentation rules and the encryption level of each data block to be transmitted.

[0143] The encryption algorithm for each data block to be transmitted is used to encrypt all data blocks in the set of data blocks to be transmitted separately, thus obtaining a set of encrypted data blocks.

[0144] Each data block to be transmitted contains all data types and the amount of data for each data type:

[0145] During encrypted data transmission, the data to be transmitted is divided into blocks, each containing different types of data, i.e., data types. For example, it may contain text data types, numeric data types, image data types, etc. Furthermore, the amount of data occupied by each data type within the block varies, and the data size can be measured in bytes.

[0146] Encryption level for each block of data to be transmitted: The encryption level indicates the strength and complexity required to encrypt the blocks of data to be transmitted. A higher encryption level means that a more advanced and complex encryption algorithm is needed to ensure data security.

[0147] 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 segmentation rules and the encryption level of each data block to be transmitted:

[0148] The optimal data block spacing determined in the block partitioning rules corresponds to an optimal encryption density, reflecting the suitable encryption level after data block partitioning. Combining the encryption level of each data block to be transmitted, these two factors are considered to select a suitable encryption algorithm. Generally, data blocks with high encryption levels and high optimal encryption densities may choose more secure but computationally complex encryption algorithms, such as the RSA algorithm in asymmetric encryption. Conversely, 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 suitable encryption algorithm is selected based on the specific characteristics of the data blocks, ensuring both data security and encryption efficiency.

[0149] Each data block in the set to be transmitted is encrypted separately using an encryption algorithm tailored to its specific characteristics, resulting in an encrypted data block set. For each data block in this set, an encryption operation is performed according to its pre-determined encryption algorithm. Each data block is encrypted independently, ensuring that the security of each block is specifically protected. For example, data block A might be encrypted using the RSA algorithm, while data block B might be encrypted using the AES algorithm. After separate encryption, all encrypted data blocks together constitute the encrypted data block set. Even if some data blocks in this set are intercepted during transmission, the use of encryption algorithms suitable for their respective characteristics makes it difficult for attackers to decipher the data content, thus ensuring data security during transmission.

[0150] In an alternative implementation, the encryption level of each data block to be transmitted is determined based on all data types contained in each data block in the set of data blocks to be transmitted and the amount of data of each data type, including:

[0151] Determine the initial encryption level for each data type;

[0152] The ratio of the amount of each data type in each data block to the total number of corresponding data blocks is used as the weight of the corresponding data type in the corresponding data block.

[0153] 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, summed, and rounded up to obtain the encryption level of each data block to be transmitted.

[0154] The first step involves determining the initial encryption level for each data type. This step pre-determines a basic encryption level for each data type based on its inherent characteristics and security requirements. Generally, 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 have a higher initial encryption level due to their high sensitivity; while some public, general descriptive text data types may have a relatively lower initial encryption level. These initial encryption levels provide the basis for subsequently determining the encryption level for each block of data to be transmitted.

[0155] 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, summed, and rounded up to obtain the encryption level of each data block to be transmitted.

[0156] Weighted summation: For each data type in the block to be transmitted, the initial encryption level is multiplied by its corresponding weight, and then these products are summed. For example, if 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, then the weighted summation result is 3 × 0.5 + 2 × 0.3 + 1 × 0.2 = 1.5 + 0.6 + 0.2 = 2.3.

[0157] Rounding up: The weighted sum is rounded up to obtain the final encryption level of the data block to be transmitted. In the example above, after rounding up 2.3, the encryption level of the data block is 3. This method comprehensively considers the characteristics and proportions of various data types within the data block to determine an encryption level that better suits the actual needs, thus providing a basis for selecting a suitable encryption algorithm.

[0158] This invention provides an implementation method for a data encryption transmission system applied to an all-in-one computer simulation model, comprising:

[0159] The data segmentation module is used to acquire dynamic data of the all-in-one machine simulation model in real time. 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 segmented to obtain a set of data blocks to be transmitted.

[0160] The block data encryption module is used to encrypt the set of data blocks to be transmitted separately based on the multi-dimensional encryption-related features and block division rules of the set of data blocks to be transmitted, so as to obtain an encrypted set of data blocks.

[0161] The multi-level key management module is used to manage the set of encrypted data blocks based on a multi-level key system and obtain security management data;

[0162] The decryption and verification module is used to transmit secure management data to the receiving end based on a dual-link communication method, and to perform multi-node collaborative verification at the receiving end through a blockchain node network to obtain the data decryption and verification results.

[0163] The above system uses dynamic data from the integrated machine simulation model to determine the segmentation rules of the data to be transmitted. Combined with the multi-dimensional encryption-related features of the data block set to be transmitted, it determines the encryption method of the segmented data to be transmitted. This makes the encryption of the data to be transmitted highly targeted, effectively improving the security of the data to be transmitted. Furthermore, the combination of a multi-level key system and dual-path communication further ensures the security of the data to be transmitted, and also ensures the accuracy and integrity of data transmission, preventing data tampering.

[0164] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A data encryption transmission method applied to an all-in-one computer simulation model, characterized in that, include: Step S1: Real-time acquisition of dynamic data from 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 constructed using the dynamic data, the data to be transmitted is divided into blocks to obtain a set of data blocks to be transmitted. Step S2: Based on the multi-dimensional encryption-related features and block division rules of the data block set to be transmitted, encrypt the data block set to be transmitted separately to obtain the encrypted data block set; Step S3: Manage the encrypted data block set based on a multi-level key system to obtain security management data; Step S4: Transmit the secure management data to the receiving end based on the dual-link communication method, and perform multi-node collaborative verification through the blockchain node network at the receiving end to obtain the data decryption verification result; Specifically, 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 construction is performed on all parameter classes contained in the dynamic data to obtain the dynamic parameter tree of the all-in-one machine simulation model; Based on dynamic data, the actual values ​​of all bottom-level nodes in the dynamic parameter tree of the all-in-one machine simulation model are determined. 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 machine simulation model and the actual values ​​of all bottom-level nodes, the calculation is performed sequentially upwards for all non-bottom-level nodes in the dynamic parameter tree of the all-in-one machine simulation model 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 machine simulation model, the encryption density list of the parameter class corresponding to the node is retrieved, and the original encryption density range of each node is determined. Based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine simulation model is analyzed, and the optimal data block spacing is determined based on the internal jitter 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. The optimal data block spacing is determined by the internal jitter level of the multidimensional dynamic parameters based on the integrated machine simulation model and the original encryption density range of all nodes, including: The internal jitter of the multidimensional dynamic parameters based on the integrated machine simulation model is used to reduce the original encryption density range of each node in both directions to obtain the improved encryption density range of each node. The median value of the intersection range with the greatest overlap among all the improved encryption density ranges is taken as the optimal encryption density; The reciprocal of the optimal encryption density is used as the optimal data block spacing.

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

3. The data encryption transmission method applied to an all-in-one machine simulation model according to claim 1, characterized in that, Based on the original encryption density range of all nodes in the dynamic parameter tree of the all-in-one machine simulation model, the internal jitter of the multi-dimensional dynamic parameters of the all-in-one machine 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 treated 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. The internal jitter level of the multidimensional dynamic parameters of the all-in-one machine simulation model is analyzed based on the unit jitter factor matrix of each extreme path and the overall jitter factor matrix of all extreme paths.

4. The data encryption transmission method applied to an all-in-one machine simulation model according to claim 3, characterized in that, Calculate the jitter factor between the original encryption density ranges of every two nodes in each extreme path, including: The upper limit, middle limit, and lower limit of the original encryption density range of all nodes in each extreme path are connected from high to low according to the node hierarchy to obtain the original encryption density upper limit curve, original encryption density middle curve, and original encryption density lower limit curve of each extreme path. The difference between the function value at the lowest level node and the function value at the highest level node in each pair of nodes corresponding to the first derivative function of the original encryption density upper limit curve of each extreme path is used as the ratio of the function value at the highest level node to the function value of the first derivative function at the highest level node. This ratio is used as the upper jitter factor of the corresponding two nodes in the extreme path. The difference between the function value at the lowest level node and the function value at the highest level node of the original encryption density limit curve of each extreme path is used as the ratio of the function value at the highest level node to the function value of the corresponding first derivative function at the corresponding highest level node. This ratio is used as the jitter factor of the corresponding two nodes in the extreme path. The difference between the function value of the first derivative of the original encryption density lower limit curve of each extreme path at the x-coordinate of the lowest level node and the function value at the x-coordinate of the highest level node in each pair of nodes is used as the ratio of the function value of the first derivative at the x-coordinate of the highest level node. This ratio is taken as the lower jitter factor of the corresponding two nodes in the extreme path. Based on the upper, middle, and lower jitter factors of each pair of nodes in each extreme path, the jitter factor between the original encryption density ranges of each pair of nodes in each extreme path is calculated.

5. The data encryption transmission method applied to an all-in-one machine simulation model according to claim 3, 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 multidimensional dynamic parameters of the all-in-one machine simulation model is analyzed, including: The deviation between row vectors with the same ordinal number in the unit jitter factor matrix of all extreme paths is taken as the horizontal jitter factor of the corresponding layer; The deviation between any two column vectors in the overall jitter factor matrix of all extreme paths is taken as the longitudinal jitter factor. The magnification factor of the overall jitter factor matrix is ​​determined based on all horizontal jitter factors and all vertical horizontal jitter factors. The overall jitter factor matrix is ​​then numerically magnified based on the magnification factor to obtain the overall jitter factor consideration matrix. The mean of all elements in the overall jitter factor consideration matrix is ​​used as the internal jitter level of the multidimensional dynamic parameters of the all-in-one machine simulation model.

6. The data encryption transmission method applied to an all-in-one machine simulation model according to claim 1, characterized in that, S2: Based on the multi-dimensional encryption-related features and block division rules of the data block set to be transmitted, encrypt the data block set separately to obtain an encrypted data block set, including: Based on the multi-dimensional encryption-related features of the set of data blocks to be transmitted, including all data types and the amount of data of each data type, the encryption level of each data block to be transmitted is determined. 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 segmentation rules and the encryption level of each data block to be transmitted. The encryption algorithm for each data block to be transmitted is used to encrypt all data blocks in the set of data blocks to be transmitted separately, thus obtaining a set of encrypted data blocks.

7. The data encryption transmission method applied to an all-in-one machine simulation model according to claim 6, characterized in that, Based on all data types contained in each data block in the set of data blocks to be transmitted and the amount of data of each data type, the 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 each data type in each data block to the total number of corresponding data blocks is used as the weight of the corresponding data type in the corresponding data block. 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, summed, and rounded up to obtain the encryption level of each data block to be transmitted.

8. A data encryption transmission system applied to an all-in-one computer simulation model, characterized in that, The data encryption transmission method for applying an all-in-one machine simulation model according to any one of claims 1 to 7 includes: The data segmentation module is used to acquire dynamic data of the all-in-one machine simulation model in real time. 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 segmented to obtain a set of data blocks to be transmitted. The block data encryption module is used to encrypt the set of data blocks to be transmitted separately based on the multi-dimensional encryption-related features and block division rules of the set of data blocks to be transmitted, so as to obtain an encrypted set of data blocks. The multi-level key management module is used to manage the set of encrypted data blocks based on a multi-level key system and obtain security management data; The decryption and verification module is used to transmit secure management data to the receiving end based on a dual-link communication method, and to perform multi-node collaborative verification at the receiving end through a blockchain node network to obtain the data decryption and verification result.

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