Block chain encrypted data analysis system and method based on scene evaluation

Abstract

The invention relates to the technical field of block chains, in particular to a block chain encrypted data analysis system and method based on scene evaluation, and the method comprises the steps: obtaining resource state information, scene disturbance information and block chain network topology structure information of each node in a block chain network; analyzing a resource restriction state of each node in the block chain network based on the resource state information, and evaluating resource restriction influence degrees corresponding to different resource restriction types; performing cross-node collaborative analysis on the basis of the resource limited influence degree in combination with the scene disturbance information and the block chain network topology structure information, and evaluating node collaborative redundancy under the scene disturbance influence; and optimizing a block chain encrypted data analysis strategy based on the data analysis demand information in combination with the node collaborative redundancy, and executing the optimized encrypted data analysis strategy. According to the method, the robustness and the execution performance of a block chain network in a resource limited and disturbed environment are improved by quantifying the collaborative complexity of cross-resource types between nodes under scene disturbance.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a blockchain encryption data analysis system and method based on scenario assessment. Background Art

[0002] As the core carrier of distributed ledgers, blockchain technology, with its decentralized and tamper-proof features, has shown tremendous potential in fields such as finance, supply chain, and the Internet of Things. However, in actual deployment, blockchain networks often face complex operating environments and differentiated node resource conditions. This is particularly true in resource-constrained scenarios such as the Industrial Internet of Things and mobile edge computing, where nodes face uneven resource constraints across computing power, storage, and communication resources.

[0003] Existing research on blockchain resource optimization primarily focuses on improving performance in a single dimension, such as reducing computational overhead through improved consensus algorithms or alleviating storage pressure through data sharding. While these approaches can achieve local optimization in specific scenarios, they lack the ability to collaboratively analyze multiple resource constraints. Furthermore, existing research ignores the differentiated impact of different resource constraint types under specific scenario perturbations and lacks a quantitative assessment of the complexity of cross-resource collaboration between nodes. As a result, in real-world application scenarios, node selection and task allocation strategies often fail to balance analysis efficiency and resource utilization, and can even lead to a sharp drop in performance under perturbations. Summary of the Invention

[0004] In order to overcome the defects and shortcomings of the existing technology, the present invention provides a blockchain encryption data analysis system and method based on scenario assessment. By quantifying the collaboration complexity between nodes across resource types under scenario disturbances, the robustness and execution performance of the blockchain network in resource-constrained and disturbed environments are improved.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a blockchain encryption data analysis method based on scenario evaluation, including: S100, obtaining resource status information, scenario disturbance information and blockchain network topology information of each node in the blockchain network; S200, analyzing the resource restriction status of each node in the blockchain network based on the resource status information, and evaluating the degree of resource restriction impact corresponding to different resource restriction types; S300, performing cross-node collaborative analysis based on the degree of resource restriction impact combined with scenario disturbance information and blockchain network topology information, and evaluating the node collaborative redundancy under the influence of scenario disturbance; S400, optimizing the blockchain encryption data analysis strategy based on data analysis demand information combined with node collaborative redundancy and executing the optimized encryption data analysis strategy.

[0006] Furthermore, the step S200 of evaluating the resource limitation impact corresponding to different resource limitation types includes: S210, extracting computing resource information, storage resource information, and communication resource information of each node through resource status information and constructing a resource constraint indicator matrix; S220, performing dimensionless processing on each resource-constrained indicator in the resource-constrained indicator matrix and calculating the proportion value of each resource-constrained indicator under all node samples; S230, calculating the information entropy of each resource-constrained indicator in the resource-constrained indicator matrix by using the proportional value and taking the difference between 1 and the information entropy as the information utility value of each resource-constrained indicator; S240 : Normalize the information utility value of each resource constraint indicator to obtain a resource constraint impact weight corresponding to each resource constraint type, which is used to evaluate the resource constraint impact degree corresponding to different resource constraint types.

[0007] Furthermore, the resource-constrained indicators in the resource-constrained indicator matrix in step S210 include a computing resource-constrained indicator, a storage resource-constrained indicator, and a communication resource-constrained indicator, wherein the computing resource-constrained indicator is the ratio of the node's used computing power to the node's total computing power, the storage resource-constrained indicator is the ratio of the node's used storage capacity to the node's total storage capacity, and the communication resource-constrained indicator is the ratio of the node's used bandwidth to the node's total bandwidth.

[0008] Furthermore, the step S300 of evaluating the node coordination redundancy under the influence of the scene disturbance includes: S310. Constructing a node adjacency matrix using blockchain network topology information of each node in the blockchain network, where matrix elements in the node adjacency matrix represent link availability between nodes; S320, constructing a node resource constraint vector by extracting resource constraint indicators from the resource constraint indicator matrix of each node and combining them with scene disturbance information; S330, calculating the average resource restriction difference corresponding to each resource restriction type between each node and all adjacent nodes using the node adjacency matrix and the node resource restriction vector; S340 , performing weighted summation of average resource constraint differences corresponding to each resource constraint type using resource constraint impact weights to obtain node coordination redundancy.

[0009] Furthermore, the step S320 of constructing the node resource restriction vector includes: S321, extracting resource-constrained indicators from the resource-constrained indicator matrix of each node, where the resource-constrained indicators include computing resource-constrained indicators, storage resource-constrained indicators, and communication resource-constrained indicators; S322. Obtaining scene disturbance information of each node in the blockchain network, where the scene disturbance information includes electromagnetic interference level, malicious attack level, and disturbance occurrence probability obtained based on historical disturbance event statistics; S323: Normalize the scene disturbance information to obtain a disturbance intensity vector and establish an influence weight matrix between the disturbance type and the resource limitation type; S324, performing a matrix operation on the disturbance intensity vector and the influence weight matrix to obtain the disturbance influence factor of each node on different resource restriction types; S325: Use the disturbance impact factor to correct the resource constraint index and construct a node resource constraint vector using the corrected resource constraint index.

[0010] Furthermore, the optimization of the blockchain encryption data analysis strategy in step S400 includes: S410. Taking the average node coordination redundancy of all nodes in the blockchain network as the global coordination redundancy of the blockchain network; S420: When the global coordination redundancy is greater than or equal to the preset global coordination redundancy threshold, nodes whose node coordination redundancy is greater than or equal to the preset node coordination redundancy threshold are selected as candidate nodes for multi-node coordination analysis; S430: When the global coordination redundancy is less than the preset global coordination redundancy threshold, perform a single-node independent analysis.

[0011] In a second aspect, the present invention provides a blockchain encryption data analysis system based on scenario assessment, comprising: The information acquisition module is used to obtain resource status information, scene disturbance information, and blockchain network topology information of each node in the blockchain network; A resource constraint impact assessment module, connected to the information acquisition module, is used to analyze the resource constraint status of each node in the blockchain network based on the resource status information and assess the degree of resource constraint impact corresponding to different resource constraint types; A node collaboration redundancy assessment module, connected to the resource constraint impact assessment module, is used to perform cross-node collaboration analysis based on the degree of resource constraint impact combined with scenario disturbance information and blockchain network topology information, and to assess the node collaboration redundancy under the influence of scenario disturbance; The data analysis strategy optimization module is connected to the node collaborative redundancy evaluation module and is used to optimize the blockchain encryption data analysis strategy based on the data analysis demand information and the node collaborative redundancy and execute the optimized encryption data analysis strategy.

[0012] In a third aspect, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a scenario-assessment-based blockchain encryption data analysis method by calling the computer program stored in the memory.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a scenario-based assessment-based blockchain encryption data analysis method.

[0014] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention integrates the node resource constraint status, network topology and scenario disturbance information to achieve a quantitative assessment of the complexity of cross-resource type collaboration between nodes, and optimizes the node collaboration mode under the influence of disturbance, thereby providing more accurate and efficient strategy support for blockchain encryption data analysis, and improving the robustness and execution performance of the blockchain network in resource-constrained and disturbed environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 This is a flowchart of a scenario-based blockchain encryption data analysis method provided by an embodiment of the present invention; Figure 2 Schematic diagram of the structure of a blockchain encryption data analysis system based on scenario assessment provided by an embodiment of the present invention; Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0017] See Figure 1 , Figure 1 The figure is a schematic diagram of the overall process of the blockchain encryption data analysis method based on scenario assessment provided by an embodiment of the present invention, which specifically includes the following steps: S100: Obtain resource status information, scenario disturbance information, and blockchain network topology information of each node in the blockchain network.

[0018] S200, analyzing the resource constraint status of each node in the blockchain network based on the resource status information, and evaluating the degree of resource constraint impact corresponding to different resource constraint types; In blockchain networks, limited computing power primarily affects a node's ability to perform complex computing tasks, especially when it comes to zero-knowledge proofs, smart contract execution, or large-scale data encryption analysis. Insufficient computing power can significantly prolong task execution time, increase block confirmation latency, and potentially cause task backlogs. Limited storage primarily restricts a node's ability to store complete blockchain ledgers, historical transaction data, or temporary analytical data. In scenarios requiring large-scale data indexing, querying, or cross-chain data integration, insufficient storage can prevent a node from participating in complete data analysis tasks or require frequent data requests from other nodes, increasing the communication burden. While limited storage has a less direct impact on computing speed than limited computing power, it has a long-term and stable impact on data integrity, traceability, and the coverage of analytical tasks. Communication limitations primarily affect the efficiency and stability of data exchange between nodes. When bandwidth is insufficient or link quality is poor, even if a node has sufficient computing power and storage resources, it may be unable to participate in efficient collaborative analysis due to data transmission delays or high packet loss rates. The impact of communication limitations is particularly significant in encrypted data analysis that requires frequent node interactions, result aggregation, and real-time synchronization. In particular, in network environments with complex topologies or where link stability is significantly affected by external disturbances, communication limitations may weaken overall collaborative efficiency more than computing power and storage limitations. In summary, computing power limitations more significantly affect task execution speed and real-time performance, storage limitations affect data availability and task coverage, and communication limitations affect node collaborative efficiency and the real-time consistency of results. The degree of impact of different resource limitation types is affected by the specific task type, network topology, and external disturbance conditions. Therefore, it is necessary to conduct differentiated quantitative evaluations when calculating the impact weights of resource limitations in order to formulate targeted optimization strategies and evaluate the degree of resource limitation impact corresponding to different resource limitation types, including: S210. Extract computing resource information, storage resource information, and communication resource information of each node through resource status information and construct a resource constraint indicator matrix. The resource constraint indicators in the resource constraint indicator matrix include a computing resource constraint indicator, a storage resource constraint indicator, and a communication resource constraint indicator. The computing resource constraint indicator is the ratio of the node's used computing power to the node's total computing power, the storage resource constraint indicator is the ratio of the node's used storage capacity to the node's total storage capacity, and the communication resource constraint indicator is the ratio of the node's used bandwidth to the node's total bandwidth. S220, performing dimensionless processing on each resource-constrained indicator in the resource-constrained indicator matrix and calculating the proportion value of each resource-constrained indicator under all node samples; S230, calculating the information entropy of each resource-constrained indicator in the resource-constrained indicator matrix by using the proportional value and taking the difference between 1 and the information entropy as the information utility value of each resource-constrained indicator; S240 : Normalize the information utility value of each resource constraint indicator to obtain a resource constraint impact weight corresponding to each resource constraint type, which is used to evaluate the resource constraint impact degree corresponding to different resource constraint types.

[0019] S300: Based on the degree of resource restriction impact, combined with scenario disturbance information and blockchain network topology information, cross-node collaboration analysis is performed to evaluate the node collaboration redundancy under the influence of scenario disturbance; A node's resource constraint variance refers to the average difference between a node's constraint on a specific resource type (computing power, storage, or communication) and the constraint on the same resource type of its neighboring nodes. This reflects the imbalance in resource availability between the node and its neighbors. A greater variance indicates a more significant capacity gap between the node and its neighbors in that resource type. For example, if a node is highly constrained in computing power while its neighbors have sufficient computing power, the variance in computing power is large. Conversely, if the computing power constraints of the node and its neighbors are similar, the variance is small. Node collaborative redundancy is a comprehensive metric derived by comprehensively considering the variance in computing power, storage, and communication resources, combined with their respective influence weights. It reflects a node's potential for complementary synergy with its neighbors across multiple resource types. A high degree of node collaborative redundancy indicates significant differences in resource types between the node and its neighbors. This difference can create complementary advantages in multi-node collaboration, making it easier for the network to improve overall performance through resource complementarity when performing cryptographic data analysis tasks. The greater the difference in resource constraints between nodes in different resource constraint types, the greater the space for resource complementarity between nodes, which increases the node collaborative redundancy. In this case, multi-node collaboration can improve overall task completion efficiency and network fault tolerance by assigning different types of tasks, allowing nodes with less resource constraints to take on corresponding types of computing or storage tasks. On the contrary, if the difference in resource constraints is generally small, the capabilities between nodes will converge, the redundancy will be low, and the collaborative advantage will not be obvious. The network's flexibility and robustness in responding to disturbances or sudden resource shortages will decrease. Evaluating node collaborative redundancy under the influence of scenario disturbances includes: S310. Constructing a node adjacency matrix using blockchain network topology information of each node in the blockchain network, where matrix elements in the node adjacency matrix represent link availability between nodes; S320, constructing a node resource constraint vector by extracting resource constraint indicators from the resource constraint indicator matrix of each node and combining them with scene disturbance information; S330. Calculate the average resource restriction difference corresponding to each resource restriction type between each node and all adjacent nodes using the node adjacency matrix and the node resource restriction vector: ; in, For resource-constrained types ,node The average resource constraint difference between all neighboring nodes, For nodes Resource constraint type in the resource constraint indicator matrix Resource-constrained indicators, For nodes Resource constraint type in the resource constraint indicator matrix Resource-constrained indicators, for and The absolute value of the difference in resource constraints between For nodes The set of adjacent nodes of , is the number of nodes in the adjacent node set; S340: Calculate the node coordination redundancy by weighting and summing the average resource constraint differences corresponding to each resource constraint type using the resource constraint impact weight; In blockchain networks, disturbances (such as electromagnetic interference, malicious attacks, and the probability of disturbances) can directly or indirectly alter node resource availability. The manner and intensity of these impacts vary significantly across different resource constraints. For computationally constrained networks, electromagnetic interference can cause unstable node hardware and increase computational errors, thereby reducing effective computing power. Malicious attacks (such as denial-of-service attacks) can directly occupy node computing resources or force them to perform additional defensive calculations, further exacerbating computational constraints. In areas with a high probability of disturbances, even nodes with sufficient computing power may experience significant computational constraints due to frequent disturbance responses. For storage constraints, malicious attacks (such as data tampering and malicious data injection) can cause node storage space to be occupied by abnormal data, reducing the effective storage capacity available for blockchain data. While electromagnetic interference has a minimal direct impact on storage capacity, it can trigger read and write errors on storage devices, increasing the need for data verification and redundant backups, thereby indirectly increasing storage resource pressure. In environments with frequent disturbances, nodes need to maintain more redundant storage to prevent data loss, which can increase storage constraints over the long term. For communication constraints, electromagnetic interference has the most direct impact on communication links, which will reduce data transmission rates, increase packet loss rates and delays; malicious attacks (such as network congestion attacks and link hijacking) will directly reduce the available bandwidth and stability of communication between nodes. When the probability of disturbances is high, nodes may need to adopt higher communication redundancy and encryption verification mechanisms to ensure data transmission security, which not only reduces communication efficiency, but also amplifies the degree of communication constraints. Therefore, different types of scenario disturbances have different effects on computing power, storage, and communication constraints: electromagnetic interference has a more prominent impact on computing power and communication, malicious attacks pose a more significant threat to storage and communication, and the probability of disturbances amplifies the cumulative effect of various influences in the time dimension. Differentiated impacts are an important basis for constructing the influence weight matrix of disturbance types and resource constraint types. It is also a key step in subsequently correcting resource constraint indicators and accurately reflecting the true status of nodes. Constructing a node resource constraint vector includes: S321, extracting resource-constrained indicators from the resource-constrained indicator matrix of each node, where the resource-constrained indicators include computing resource-constrained indicators, storage resource-constrained indicators, and communication resource-constrained indicators; S322. Obtaining scene disturbance information of each node in the blockchain network, where the scene disturbance information includes electromagnetic interference level, malicious attack level, and disturbance occurrence probability obtained based on historical disturbance event statistics; S323: Normalize the scene disturbance information to obtain a disturbance intensity vector and establish an influence weight matrix between the disturbance type and the resource limitation type; S324, performing a matrix operation on the disturbance intensity vector and the influence weight matrix to obtain the disturbance influence factor of each node on different resource restriction types; S325: Use the disturbance impact factor to correct the resource constraint index and construct a node resource constraint vector using the corrected resource constraint index.

[0020] S400, optimizing the blockchain encryption data analysis strategy based on the data analysis demand information and node collaborative redundancy and executing the optimized encryption data analysis strategy; By calculating global collaborative redundancy, that is, the average collaborative redundancy level of all nodes, the resource complementarity and collaborative potential of the blockchain network as a whole can be comprehensively reflected. Global collaborative redundancy integrates the differences and weights of multiple resource types and objectively quantifies the feasibility and effectiveness of collaboration between nodes in the network. When the global collaborative redundancy reaches or exceeds the preset threshold, it indicates that the network as a whole has strong resource complementarity and collaborative analysis capabilities. Therefore, selecting nodes with higher collaborative redundancy as candidates and conducting multi-node collaborative analysis can fully utilize resource advantages, achieve task sharing and collaboration, accelerate the speed of encrypted data analysis, and improve the robustness and efficiency of the system. On the contrary, when the global collaborative redundancy is lower than the threshold, it indicates that the network resources are relatively evenly distributed or the overall resources are limited, and the collaborative advantages between nodes are insufficient. Forcing multi-node collaboration may lead to excessive collaborative overhead and reduced efficiency. Therefore, adopting a single-node independent analysis strategy is more in line with the current network resource status, helps reduce communication overhead and complexity, ensures the stable completion of analysis tasks, and optimizes blockchain encrypted data analysis strategies, including: S410. Taking the average node coordination redundancy of all nodes in the blockchain network as the global coordination redundancy of the blockchain network; S420: When the global coordination redundancy is greater than or equal to the preset global coordination redundancy threshold, nodes whose node coordination redundancy is greater than or equal to the preset node coordination redundancy threshold are selected as candidate nodes for multi-node coordination analysis; S430: When the global coordination redundancy is less than the preset global coordination redundancy threshold, perform a single-node independent analysis.

[0021] In an embodiment of the present invention, setting parameters such as a preset global collaborative redundancy threshold and a preset node collaborative redundancy threshold may be determined by constructing a data set by acquiring resource status information, scenario disturbance information, and blockchain network topology information, substituting the information into the data set for calculating global collaborative redundancy and node collaborative redundancy, and simultaneously acquiring expert judgment results on global collaborative redundancy and node collaborative redundancy, importing the calculated global collaborative redundancy, node collaborative redundancy, and judgment results into the fitting software, and outputting the preset global collaborative redundancy threshold and the preset node collaborative redundancy threshold that meet the maximum judgment accuracy.

[0022] See Figure 2 , Figure 2: This is a schematic diagram of the structure of a blockchain encryption data analysis system based on scenario assessment provided by an embodiment of the present invention, including: Information acquisition module 210, used to obtain resource status information, scene disturbance information and blockchain network topology information of each node in the blockchain network; A resource constraint impact assessment module 220, connected to the information acquisition module 210, is configured to analyze the resource constraint status of each node in the blockchain network based on the resource status information and assess the degree of resource constraint impact corresponding to different resource constraint types; The node coordination redundancy assessment module 230 is connected to the resource limitation impact assessment module 220 and is used to perform cross-node coordination analysis based on the degree of resource limitation impact combined with scenario disturbance information and blockchain network topology information to assess the node coordination redundancy under the influence of scenario disturbance; The data analysis strategy optimization module 240 is connected to the node collaborative redundancy evaluation module 230 and is used to optimize the blockchain encryption data analysis strategy based on the data analysis requirement information and the node collaborative redundancy and execute the optimized encryption data analysis strategy.

[0023] In an embodiment of the present invention, the resource constraint impact assessment module 220 is used to analyze the resource constraint status of each node in the blockchain network based on the resource status information and assess the degree of resource constraint impact corresponding to different resource constraint types, including: The computing resource information, storage resource information, and communication resource information of each node are extracted through the resource status information and a resource constraint indicator matrix is ​​constructed. The resource constraint indicators in the resource constraint indicator matrix include the computing resource constraint indicator, the storage resource constraint indicator, and the communication resource constraint indicator. Among them, the computing resource constraint indicator is the ratio of the node's used computing power to the node's total computing power, the storage resource constraint indicator is the ratio of the node's used storage capacity to the node's total storage capacity, and the communication resource constraint indicator is the ratio of the node's used bandwidth to the node's total bandwidth. Perform dimensionless processing on each resource-constrained indicator in the resource-constrained indicator matrix and calculate the proportion value of each resource-constrained indicator under all node samples; The information entropy of each resource-constrained indicator in the resource-constrained indicator matrix is ​​calculated by the proportional value, and the difference between 1 and the information entropy is used as the information utility value of each resource-constrained indicator; The information utility value of each resource constraint indicator is normalized to obtain the resource constraint impact weight corresponding to each resource constraint type, which is used to evaluate the degree of resource constraint impact corresponding to different resource constraint types.

[0024] In an embodiment of the present invention, the node coordination redundancy assessment module 230 is used to perform cross-node coordination analysis based on the degree of resource limitation impact combined with scenario disturbance information and blockchain network topology information, and assess the node coordination redundancy under the influence of scenario disturbance, including: A node adjacency matrix is ​​constructed using the blockchain network topology information of each node in the blockchain network. The matrix elements in the node adjacency matrix represent the link availability between nodes. The node resource constraint vector is constructed by extracting the resource constraint indicators from the resource constraint indicator matrix of each node and combining them with the scene disturbance information, including: Extracting resource-constrained indicators from the resource-constrained indicator matrix of each node, where the resource-constrained indicators include computing resource-constrained indicators, storage resource-constrained indicators, and communication resource-constrained indicators; Obtaining scenario disturbance information for each node in the blockchain network, including electromagnetic interference level, malicious attack level, and the probability of disturbance occurrence based on historical disturbance event statistics; Normalize the scene disturbance information to obtain the disturbance intensity vector and establish the influence weight matrix between the disturbance type and the resource limitation type; Perform matrix operations on the disturbance intensity vector and the influence weight matrix to obtain the disturbance impact factor of each node under different resource restriction types; The resource constraint index is modified by using the disturbance impact factor and the node resource constraint vector is constructed based on the modified resource constraint index. The average resource constraint difference corresponding to each resource constraint type between each node and all adjacent nodes is calculated through the node adjacency matrix and the node resource constraint vector; The node collaborative redundancy is obtained by weighting and summing the average resource constraint difference corresponding to each resource constraint type using the resource constraint impact weight.

[0025] In an embodiment of the present invention, the data analysis strategy optimization module 240 is used to optimize the blockchain encryption data analysis strategy based on the data analysis requirement information and node collaborative redundancy and execute the optimized encryption data analysis strategy, including: The average node collaborative redundancy of all nodes in the blockchain network is taken as the global collaborative redundancy of the blockchain network; when the global collaborative redundancy is greater than or equal to the preset global collaborative redundancy threshold, the nodes whose node collaborative redundancy is greater than or equal to the preset node collaborative redundancy threshold are taken as candidate nodes for multi-node collaborative analysis; when the global collaborative redundancy is less than the preset global collaborative redundancy threshold, a single node independent analysis is performed.

[0026] For the above-mentioned parameters and steps for each unit module to implement corresponding functions in the scenario-assessment-based blockchain encrypted data analysis system of the present invention, reference can be made to the parameters and steps in the embodiment of the scenario-assessment-based blockchain encrypted data analysis method, and no further details will be given here.

[0027] Please refer to Figure 3An embodiment of the present invention further provides an electronic device 300, comprising a memory 310, a processor 320, and a communication bus 330. The memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores a method for analyzing blockchain encrypted data based on scenario assessment as provided in the above embodiment, which can be loaded and executed by the processor 320.

[0028] The memory 310 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the scenario-based evaluation blockchain encrypted data analysis method provided in the above embodiment; the data storage area may store data involved in the scenario-based evaluation blockchain encrypted data analysis method provided in the above embodiment.

[0029] The processor 320 may include one or more processing cores. The processor 320 executes instructions, programs, code sets, or instruction sets stored in the memory 310, calls data stored in the memory 310, and performs various functions and processes data of the present invention. The processor 320 may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that for different devices, the electronic components used to implement the above-mentioned functions of the processor 320 may also be other, and the embodiments of the present invention are not specifically limited thereto.

[0030] The communication bus 330 may include a path for transmitting information between the above components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus 330 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3Only one double arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0031] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the scenario-assessment-based blockchain encryption data analysis method provided in the above embodiment.

[0032] In embodiments of the present invention, a computer-readable storage medium may be a tangible device that retains and stores instructions used by an instruction execution device. A computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a rostrum random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, an optical disc, a magnetic disk, a mechanical encoding device, or any combination thereof.

[0033] The terms "comprises," "comprising," or any other variations thereof are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or that are inherent to such process, method, article, or apparatus.

[0034] The above description is merely a preferred embodiment of the present invention and an illustration of the underlying technical principles. Those skilled in the art should understand that the scope of application of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned application concepts. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions claimed in the present invention.

Claims

1. A blockchain encryption data analysis method based on scenario assessment, characterized in that: include: S100: Obtain resource status information, scenario disturbance information, and blockchain network topology information of each node in the blockchain network; S200, analyzing the resource constraint status of each node in the blockchain network based on the resource status information, and evaluating the degree of resource constraint impact corresponding to different resource constraint types; S300: Based on the degree of resource restriction impact, combined with scenario disturbance information and blockchain network topology information, cross-node collaboration analysis is performed to evaluate the node collaboration redundancy under the influence of scenario disturbance; S400. Optimize the blockchain encryption data analysis strategy based on the data analysis demand information and node collaborative redundancy and execute the optimized encryption data analysis strategy.

2. The blockchain encryption data analysis method based on scenario assessment according to claim 1 is characterized in that: The evaluation of the resource limitation impact corresponding to different resource limitation types in step S200 includes: S210, extracting computing resource information, storage resource information, and communication resource information of each node through resource status information and constructing a resource constraint indicator matrix; S220, performing dimensionless processing on each resource-constrained indicator in the resource-constrained indicator matrix and calculating the proportion value of each resource-constrained indicator under all node samples; S230, calculating the information entropy of each resource-constrained indicator in the resource-constrained indicator matrix by using the proportional value and taking the difference between 1 and the information entropy as the information utility value of each resource-constrained indicator; S240 : Normalize the information utility value of each resource constraint indicator to obtain a resource constraint impact weight corresponding to each resource constraint type, which is used to evaluate the resource constraint impact degree corresponding to different resource constraint types.

3. The blockchain encryption data analysis method based on scenario assessment according to claim 2 is characterized in that: The resource-constrained indicators in the resource-constrained indicator matrix in step S210 include a computing resource-constrained indicator, a storage resource-constrained indicator, and a communication resource-constrained indicator, wherein the computing resource-constrained indicator is the ratio of the node's used computing power to the node's total computing power, the storage resource-constrained indicator is the ratio of the node's used storage capacity to the node's total storage capacity, and the communication resource-constrained indicator is the ratio of the node's used bandwidth to the node's total bandwidth.

4. The blockchain encryption data analysis method based on scenario assessment according to claim 1 is characterized in that: The step S300 of evaluating node coordination redundancy under the influence of scene disturbance includes: S310. Constructing a node adjacency matrix using blockchain network topology information of each node in the blockchain network, where matrix elements in the node adjacency matrix represent link availability between nodes; S320, constructing a node resource constraint vector by extracting resource constraint indicators from the resource constraint indicator matrix of each node and combining them with scene disturbance information; S330, calculating the average resource restriction difference corresponding to each resource restriction type between each node and all adjacent nodes using the node adjacency matrix and the node resource restriction vector; S340 , performing weighted summation of average resource constraint differences corresponding to each resource constraint type using resource constraint impact weights to obtain node coordination redundancy.

5. The blockchain encryption data analysis method based on scenario assessment according to claim 4 is characterized in that: The step S320 of constructing the node resource restriction vector includes: S321, extracting resource-constrained indicators from the resource-constrained indicator matrix of each node, where the resource-constrained indicators include computing resource-constrained indicators, storage resource-constrained indicators, and communication resource-constrained indicators; S322. Obtaining scene disturbance information of each node in the blockchain network, where the scene disturbance information includes electromagnetic interference level, malicious attack level, and disturbance occurrence probability obtained based on historical disturbance event statistics; S323: Normalize the scene disturbance information to obtain a disturbance intensity vector and establish an influence weight matrix between the disturbance type and the resource limitation type; S324, performing a matrix operation on the disturbance intensity vector and the influence weight matrix to obtain the disturbance influence factor of each node on different resource restriction types; S325: Use the disturbance impact factor to correct the resource constraint index and construct a node resource constraint vector using the corrected resource constraint index.

6. The blockchain encryption data analysis method based on scenario assessment according to claim 1 is characterized in that: The optimization of the blockchain encryption data analysis strategy in step S400 includes: S410. Taking the average node coordination redundancy of all nodes in the blockchain network as the global coordination redundancy of the blockchain network; S420: When the global coordination redundancy is greater than or equal to the preset global coordination redundancy threshold, nodes whose node coordination redundancy is greater than or equal to the preset node coordination redundancy threshold are selected as candidate nodes for multi-node coordination analysis; S430: When the global coordination redundancy is less than the preset global coordination redundancy threshold, perform a single-node independent analysis.

7. A blockchain encryption data analysis system based on scenario assessment, used to implement the blockchain encryption data analysis method based on scenario assessment according to any one of claims 1 to 6, characterized in that: The system comprises: The information acquisition module is used to obtain resource status information, scene disturbance information, and blockchain network topology information of each node in the blockchain network; A resource constraint impact assessment module, connected to the information acquisition module, is used to analyze the resource constraint status of each node in the blockchain network based on the resource status information and assess the degree of resource constraint impact corresponding to different resource constraint types; A node collaboration redundancy assessment module, connected to the resource constraint impact assessment module, is used to perform cross-node collaboration analysis based on the degree of resource constraint impact combined with scenario disturbance information and blockchain network topology information, and to assess the node collaboration redundancy under the influence of scenario disturbance; The data analysis strategy optimization module is connected to the node collaborative redundancy evaluation module and is used to optimize the blockchain encryption data analysis strategy based on the data analysis demand information and the node collaborative redundancy and execute the optimized encryption data analysis strategy.

8. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the scenario-assessment-based blockchain encryption data analysis method as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the scenario-assessment-based blockchain encryption data analysis method as described in any one of claims 1 to 6.

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