Blockchain encrypted data analysis system and method based on scene evaluation

Abstract

The application relates to the technical field of blockchains, in particular to a blockchain encrypted data analysis system and method based on scene evaluation, the steps of the method comprising: acquiring resource state information of each node in a blockchain network, scene disturbance information and blockchain network topology structure information; analyzing the resource limited state of each node in the blockchain network based on the resource state information, evaluating the resource limited influence degree corresponding to different resource limited types; performing cross-node collaborative analysis based on the resource limited influence degree, combining the scene disturbance information and the blockchain network topology structure information, evaluating the node collaborative redundancy under the influence of the scene disturbance; and optimizing the blockchain encrypted data analysis strategy based on data analysis requirement information, combining the node collaborative redundancy, and executing the optimized encrypted data analysis strategy. The application quantifies the collaborative complexity between nodes across resource types under scene disturbance, thereby improving the robustness and execution performance of the blockchain network in a resource limited and disturbed environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain, in particular to a blockchain encrypted data analysis system and method based on scene evaluation. BACKGROUND

[0002] As the core carrier of distributed ledger, the blockchain technology has great potential in the fields of finance, supply chain, Internet of Things, etc. due to its decentralization and non-tamperability. However, in the actual deployment process, the blockchain network often faces complex operating environments and differentiated node resource conditions, especially in resource-constrained scenarios such as industrial Internet of Things and mobile edge computing, each node presents a non-uniform resource constraint state in the dimensions of computing power, storage, communication, etc.

[0003] The existing technology mainly focuses on single-dimensional performance improvement for blockchain resource optimization, such as reducing computing overhead by improving consensus algorithm or relieving storage pressure by using data sharding technology. Although the above methods can achieve local optimization effect in specific scenarios, they lack the ability to analyze the coordination of multiple types of resource constraints. At the same time, the existing technology ignores the differentiated impact of different resource-constrained types under specific scene disturbances, lacks quantitative evaluation of cross-resource-type coordination complexity between nodes, and leads to the fact that in actual application scenarios, the node selection and task allocation strategy often cannot balance the analysis efficiency and resource utilization, and even performance may drop sharply in a disturbed environment. SUMMARY

[0004] In order to overcome the defects and deficiencies existing in the prior art, the present application provides a blockchain encrypted data analysis system and method based on scene evaluation, which improves the robustness and execution performance of the blockchain network in resource-constrained and disturbed environments by quantifying the cross-resource-type coordination complexity between nodes under scene disturbance.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] In a first aspect, the present application provides a blockchain encrypted data analysis method based on scene evaluation, comprising: S100, obtaining resource state information of each node in the blockchain network, scene disturbance information, and blockchain network topology structure information; S200, analyzing the resource-constrained state of each node in the blockchain network based on the resource state information, and evaluating the resource-constrained impact degree corresponding to different resource-constrained types; S300, performing cross-node coordination analysis based on the resource-constrained impact degree, combining the scene disturbance information and the blockchain network topology structure information, and evaluating the node coordination redundancy under the influence of scene disturbance; S400, optimizing the blockchain encrypted data analysis strategy based on the data analysis requirement information and the node coordination redundancy, and executing the optimized encrypted data analysis strategy.

[0007] Further, the evaluating the influence degree of different resource limitation types in step S200 comprises:

[0008] S210, extracting computing resource information, storage resource information and communication resource information of each node through the resource state information and constructing a resource limitation index matrix;

[0009] S220, performing dimensionless processing on each resource limitation index in the resource limitation index matrix and calculating a proportion value of each resource limitation index under all node samples;

[0010] S230, calculating information entropy of each resource limitation index in the resource limitation index matrix through the proportion value and taking a difference value between 1 and the information entropy as an information utility value of each resource limitation index;

[0011] S240, normalizing the information utility value of each resource limitation index to obtain a resource limitation influence weight corresponding to each resource limitation type, which is used to evaluate the influence degree of different resource limitation types.

[0012] Further, the resource limitation index in the resource limitation index matrix in step S210 comprises a computing resource limitation index, a storage resource limitation index and a communication resource limitation index, wherein the computing resource limitation index is a ratio of used computing power of a node to total computing power of the node, the storage resource limitation index is a ratio of used storage capacity of the node to total storage capacity of the node, and the communication resource limitation index is a ratio of used bandwidth of the node to total bandwidth of the node.

[0013] Further, the evaluating the node collaborative redundancy under the influence of the scene disturbance in step S300 comprises:

[0014] S310, constructing a node adjacency matrix through blockchain network topology structure information of each node in a blockchain network, and a matrix element in the node adjacency matrix representing link availability between nodes;

[0015] S320, constructing a node resource limitation vector by extracting resource limitation indexes in a resource limitation index matrix of each node and combining scene disturbance information;

[0016] S330, calculating an average resource limitation difference degree corresponding to each resource limitation type between each node and all adjacent nodes through the node adjacency matrix and the node resource limitation vector;

[0017] S340, obtaining the node collaborative redundancy by weighted summation of the average resource limitation difference degrees corresponding to each resource limitation type through the resource limitation influence weight.

[0018] Further, the constructing the node resource limitation vector in step S320 comprises:

[0019] S321, extract the resource limited index in the resource limited index matrix of each node resource, the resource limited index includes the calculation resource limited index, the storage resource limited index and the communication resource limited index;

[0020] S322, obtain the scene disturbance information of each node in the blockchain network, the scene disturbance information includes the electromagnetic interference level, the malicious attack level and the disturbance occurrence probability obtained based on the historical disturbance event statistics;

[0021] S323, the scene disturbance information is normalized to obtain the disturbance intensity vector and the influence weight matrix between the disturbance type and the resource limited type;

[0022] S324, the disturbance intensity vector and the influence weight matrix are subjected to matrix operation to obtain the disturbance influence factor of each node under different resource limited types;

[0023] S325, the disturbance influence factor is used to correct the resource limited index, and the node resource limited vector is constructed through the corrected resource limited index.

[0024] Further, the optimization of the blockchain encrypted data analysis strategy in step S400 comprises:

[0025] S410, the average node cooperative redundancy of all nodes in the blockchain network is taken as the global cooperative redundancy of the blockchain network;

[0026] S420, when the global cooperative redundancy is greater than or equal to the preset global cooperative redundancy threshold, the node whose node cooperative redundancy is greater than or equal to the preset node cooperative redundancy threshold is taken as a candidate node for multi-node cooperative analysis;

[0027] S430, when the global cooperative redundancy is less than the preset global cooperative redundancy threshold, single-node independent analysis is performed.

[0028] In a second aspect, the present application provides a blockchain encrypted data analysis system based on scene evaluation, comprising:

[0029] An information acquisition module is configured to acquire resource state information, scene disturbance information and blockchain network topology structure information of each node in the blockchain network;

[0030] A resource limited influence evaluation module is connected to the information acquisition module and configured to analyze the resource limited state of each node in the blockchain network based on the resource state information and evaluate the resource limited influence degree of different resource limited types;

[0031] a node coordination redundancy evaluation module connected to the resource limitation influence evaluation module, configured to perform cross-node coordination analysis based on the resource limitation influence degree, scene disturbance information and blockchain network topology information, and evaluate the node coordination redundancy under the influence of the scene disturbance;

[0032] a data analysis strategy optimization module connected to the node coordination redundancy evaluation module, configured to optimize the blockchain encrypted data analysis strategy based on data analysis requirement information and node coordination redundancy, and execute the optimized encrypted data analysis strategy.

[0033] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be invoked by the processor, and the processor executes the scene evaluation based blockchain encrypted data analysis method by invoking the computer program stored in the memory.

[0034] In a fourth aspect, the present application provides a computer readable storage medium storing instructions, which, when executed on a computer, cause the computer to execute the scene evaluation based blockchain encrypted data analysis method.

[0035] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0036] The present application quantitatively evaluates the cross-resource type coordination complexity between nodes by fusing the node resource limitation state, network topology structure and scene disturbance information, and optimizes the node coordination mode under the influence of disturbance, thereby providing more accurate and efficient strategy support for blockchain encrypted data analysis, and improving the robustness and execution performance of the blockchain network in the resource limited and disturbance environment. BRIEF DESCRIPTION OF DRAWINGS

[0037] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments, made with reference to the accompanying drawings:

[0038] Figure 1 is a flowchart of the scene evaluation based blockchain encrypted data analysis method provided by the embodiments of the present application;

[0039] Figure 2 is a structural schematic diagram of the scene evaluation based blockchain encrypted data analysis system provided by the embodiments of the present application;

[0040] Figure 3 is a structural schematic diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0041] The technical scheme of the present application will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical scheme of the present application, but not limitations thereof. In the absence of conflicts, the technical features in the embodiments and the embodiments can be combined with each other.

[0042] Please refer to Figure 1 , Figure 1 is the overall flowchart of the blockchain encrypted data analysis method based on scene evaluation provided by the embodiment of the present application, which specifically comprises the following steps:

[0043] S100, acquiring resource state information, scene disturbance information and blockchain network topology structure information of each node in the blockchain network.

[0044] S200, analyzing the resource limited state of each node in the blockchain network based on the resource state information, and evaluating the resource limited influence degree corresponding to different resource limited types;

[0045] In the blockchain network, the limited computing power mainly affects the ability of the node to perform complex computing tasks. Especially in the scene involving zero-knowledge proof, smart contract execution or large-scale data encryption analysis, insufficient computing power will significantly prolong the task execution time, increase the delay of block confirmation, and may cause task backlog. The limited storage mainly restricts the ability of the node to store the complete blockchain account book, historical transaction data or temporary analysis data. In the scene requiring large-scale data indexing, querying or cross-chain data integration, insufficient storage will cause the node to be unable to participate in complete data analysis tasks, or need to frequently request data from other nodes, increasing the communication burden. Compared with the limited computing power, the limited storage has a weaker direct impact on the computing speed, but has a long-term and stable restrictive effect on the data integrity, traceability and analysis task coverage. The limited communication mainly affects the data exchange efficiency and stability between nodes. When the bandwidth is insufficient or the link quality is poor, even if the node has sufficient computing power and storage resources, it may not be able to participate in efficient collaborative analysis due to data transmission delay or high packet loss rate. The impact of limited communication is particularly significant in encrypted data analysis that requires frequent node interaction, result aggregation and real-time synchronization, especially in network environments with complex topology structure or link stability affected by external disturbance. The weakening effect of limited communication on overall collaborative efficiency may even exceed that of limited computing power and storage. In summary, the limited computing power more affects the task execution speed and real-time performance, the limited storage affects the data availability and task coverage, and the limited communication affects the node collaborative efficiency and real-time consistency of the results. The influence degree of different resource limited types is jointly affected by the specific task type, network topology structure and external disturbance condition. Therefore, it is necessary to perform differential quantitative evaluation when calculating the influence weight of the computing resource, so as to formulate targeted optimization strategies. The evaluation of the resource limited influence degree corresponding to different resource limited types includes:

[0046] S210, extract the computing resource information, storage resource information and communication resource information of each node through the resource state information and construct a resource constraint index matrix, the resource constraint index in the resource constraint index matrix includes a computing resource constraint index, a storage resource constraint index and a communication resource constraint index, wherein the computing resource constraint index is a ratio of used computing power of a node to total computing power of the node, the storage resource constraint index is a ratio of used storage capacity of a node to total storage capacity of the node, and the communication resource constraint index is a ratio of used bandwidth of a node to total bandwidth of the node;

[0047] S220, dimensionless processing is performed on each resource constraint index in the resource constraint index matrix, and a proportion value of each resource constraint index under all node samples is calculated;

[0048] S230, information entropy of each resource constraint index in the resource constraint index matrix is calculated through the proportion value, and a difference value between 1 and the information entropy is taken as an information utility value of each resource constraint index;

[0049] S240, the information utility value of each resource constraint index is normalized to obtain a resource constraint influence weight corresponding to each resource constraint type, which is used to evaluate a resource constraint influence degree corresponding to different resource constraint types.

[0050] S300, based on the resource constraint influence degree, scene disturbance information and blockchain network topology structure information are combined for cross-node collaborative analysis, and node collaborative redundancy under the influence of scene disturbance is evaluated;

[0051] The resource limitation difference of a node refers to the average difference value between the limitation degree of a certain node in a certain resource type (computing power, storage, communication) and the limitation degree of its adjacent nodes in the same resource type, reflecting the imbalance degree of the node and the adjacent nodes in resource availability. The greater the difference, the more obvious the difference between the node and the adjacent nodes in the resource type. For example, if a node has a high degree of computing power limitation, and its neighbor nodes have sufficient computing power, the difference in computing power dimension is large. Conversely, if the node and the neighbor nodes have similar computing power limitation, the difference is small. The node coordination redundancy is a comprehensive index obtained by comprehensively considering the difference degrees of the three types of resources of computing power, storage and communication, and combining their respective influence weights, reflecting the potential of a node to form complementary coordination with neighbor nodes in multiple resource types. A high node coordination redundancy means that there is a significant difference between the node and the neighbor nodes in resource type, which can form complementary advantages in multi-node cooperation, making it easier for the network to improve overall performance by resource complementation when performing encrypted data analysis tasks. The greater the resource limitation difference of each node in different resource limitation types, the greater the resource complementation space between nodes, thereby increasing the node coordination redundancy. In this case, multi-node cooperation can assign different types of tasks to nodes with lighter resource limitations to undertake corresponding computing or storage tasks, thereby improving overall task completion efficiency and network fault tolerance. Conversely, if the resource limitation difference is generally small, the abilities of the nodes tend to be similar, the redundancy is low, and the cooperation advantage is not obvious. The flexibility and robustness of the network in dealing with disturbances or sudden resource shortages will decrease. The node coordination redundancy under the influence of the scene disturbance is evaluated, including:

[0052] S310, constructing a node adjacency matrix through the blockchain network topology structure information of each node in the blockchain network, the matrix elements in the node adjacency matrix representing the link availability between nodes;

[0053] S320, constructing a node resource limitation vector by extracting the resource limitation indicators in the resource limitation indicator matrix of each node and combining the scene disturbance information;

[0054] S330, calculating the average resource limitation difference between each node and all adjacent nodes in each resource limitation type through the node adjacency matrix and the node resource limitation vector:

[0055] ;

[0056] wherein, is the average resource limitation difference between the node and all adjacent nodes in the resource limitation type , is the average resource limitation difference between the node a resource-constrained type in the resource-constrained index matrix of the resource-constrained index a resource-constrained index, a node a resource-constrained type in the resource-constrained index matrix of the resource-constrained index a resource-constrained index, a node a resource-constrained type in the resource-constrained index matrix of the resource-constrained index a resource-constrained type in the resource-constrained index matrix of the resource-constrained index a node a set of adjacent nodes of the node , a number of nodes in the set of adjacent nodes

[0057] S340, weighting and summing the average resource-constrained difference degrees corresponding to each resource-constrained type by the resource-constrained influence weight to obtain the node collaborative redundancy degree;

[0058] In a blockchain network, scenario disturbances (such as electromagnetic interference, malicious attacks, disturbance occurrence probability) directly or indirectly change the resource availability of nodes, and their effects on different resource-constrained types differ significantly in terms of mode and intensity. For computing power constraints, electromagnetic interference may cause node hardware to run unstably and increase the error rate of calculations, thereby reducing effective computing power; malicious attacks (such as denial-of-service attacks) may directly occupy node computing power resources or force nodes to perform additional defensive calculations, further exacerbating computing power constraints. In areas with a high probability of disturbance, even if a node has sufficient computing power, it may exhibit a high degree of computing power constraints due to frequent responses to disturbances. 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; electromagnetic interference may have a smaller direct impact on storage capacity, but it can cause read / write errors in storage devices, leading to an increase in data checksum and redundancy backup requirements, thereby indirectly increasing storage resource pressure. In environments with frequent disturbances, nodes need to maintain more redundant storage to prevent data loss, which will increase the degree of storage constraints in the long term. For communication constraints, electromagnetic interference has the most direct impact on communication links, reducing data transmission rates, increasing packet loss rates, and delaying communication; malicious attacks (such as network congestion attacks and link hijacking) can directly reduce the available bandwidth and stability of inter-node communication. In areas with a high probability of disturbance, nodes may need to use higher communication redundancy and encryption verification mechanisms to ensure data transmission security, which not only reduces communication efficiency but also amplifies communication constraints. Therefore, different types of scenario disturbances have different effects on computing power, storage, and communication constraints: electromagnetic interference has a more significant impact on computing power and communication, malicious attacks pose a more significant threat to storage and communication, and the probability of disturbance amplifies the cumulative effects of various influences in the time dimension. Differentiated effects are an important basis for constructing an influence weight matrix between disturbance types and resource-constrained types, and are a key step in subsequent correction of resource-constrained indicators to accurately reflect the true state of nodes. The node resource-constrained vector includes:

[0059] S321, extracting resource-constrained indicators in the node resource-constrained indicator matrix, including computing resource-constrained indicators, storage resource-constrained indicators, and communication resource-constrained indicators;

[0060] S322, obtaining scenario disturbance information of each node in the blockchain network, including electromagnetic interference level, malicious attack level, and disturbance occurrence probability based on historical disturbance event statistics;

[0061] S323, normalizing the scenario disturbance information to obtain a disturbance intensity vector and establishing an influence weight matrix between disturbance types and resource-constrained types;

[0062] S324, matrix operation of the disturbance intensity vector and the influence weight matrix to obtain the disturbance influence factor of each node on different resource limited types;

[0063] S325, correcting the resource limited index by using the disturbance influence factor and constructing a node resource limited vector by using the corrected resource limited index.

[0064] S400, optimizing the encrypted data analysis strategy of the blockchain based on the data analysis requirement information and the node collaborative redundancy, and executing the optimized encrypted data analysis strategy;

[0065] By calculating the global collaborative redundancy, i.e. the average collaborative redundancy level of all nodes, the overall resource complementarity and collaborative potential of the blockchain network can be comprehensively reflected. The global collaborative redundancy integrates the differences and weights of multiple resource types, and objectively quantifies the feasibility and effectiveness of the collaboration between nodes in the network. When the global collaborative redundancy reaches or exceeds the preset threshold, it indicates that the overall network has strong resource complementarity and collaborative analysis capability. Therefore, selecting nodes with high collaborative redundancy as candidates for multi-node collaborative analysis can fully utilize resource advantages, realize task sharing and collaboration, speed up 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 resource distribution is relatively uniform or the overall resource is limited, and the collaborative advantage between nodes is insufficient. Therefore, forcibly performing multi-node collaboration may lead to excessive collaboration overhead and reduced efficiency. Therefore, using a single node independent analysis strategy is more suitable for the current network resource state, which helps to reduce communication overhead and complexity, ensure stable completion of analysis tasks, and optimize the blockchain encrypted data analysis strategy, including:

[0066] S410, taking the average node collaborative redundancy of all nodes in the blockchain network as the global collaborative redundancy of the blockchain network;

[0067] S420, when the global collaborative redundancy is greater than or equal to the preset global collaborative redundancy threshold, nodes with node collaborative redundancy greater than or equal to the preset node collaborative redundancy threshold are selected as candidate nodes for multi-node collaborative analysis;

[0068] S430, when the global collaborative redundancy is less than the preset global collaborative redundancy threshold, single node independent analysis is performed.

[0069] In the embodiment of the present application, the determination manner of the set parameters such as the preset global collaborative redundancy threshold and the preset node collaborative redundancy threshold can be: constructing a data set by acquiring resource state information, scene disturbance information and blockchain network topology structure information, substituting into the calculation of global collaborative redundancy and node collaborative redundancy, simultaneously acquiring the judgment result of experts on global collaborative redundancy and node collaborative redundancy, introducing the calculated global collaborative redundancy, node collaborative redundancy and judgment result into fitting software, and outputting the preset global collaborative redundancy threshold and the preset node collaborative redundancy threshold meeting the maximum judgment accuracy.

[0070] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a blockchain encrypted data analysis system based on scene evaluation provided by the embodiment of the present application, comprising:

[0071] The information acquisition module 210 is configured to acquire resource state information, scene disturbance information and blockchain network topology structure information of each node in the blockchain network.

[0072] The resource limitation influence evaluation module 220 is connected with the information acquisition module 210 and is configured to analyze the resource limitation state of each node in the blockchain network based on the resource state information, and evaluate the resource limitation influence degree corresponding to different resource limitation types.

[0073] The node collaborative redundancy evaluation module 230 is connected with the resource limitation influence evaluation module 220 and is configured to perform cross-node collaborative analysis based on the resource limitation influence degree, the scene disturbance information and the blockchain network topology structure information, and evaluate the node collaborative redundancy degree under the influence of scene disturbance.

[0074] The data analysis strategy optimization module 240 is connected with the node collaborative redundancy evaluation module 230 and is configured to optimize the blockchain encrypted data analysis strategy based on data analysis demand information in combination with the node collaborative redundancy degree, and execute the optimized encrypted data analysis strategy.

[0075] In the embodiment of the present application, the resource limitation influence evaluation module 220 is configured to analyze the resource limitation state of each node in the blockchain network based on the resource state information, and evaluate the resource limitation influence degree corresponding to different resource limitation types, comprising:

[0076] The computing resource information, the storage resource information and the communication resource information of each node are extracted through the resource state information, and a resource-constrained index matrix is constructed, wherein the resource-constrained indexes in the resource-constrained index matrix include a computing resource-constrained index, a storage resource-constrained index and a communication resource-constrained index, the computing resource-constrained index is a ratio of used computing power of a node to total computing power of the node, the storage resource-constrained index is a ratio of used storage capacity of the node to total storage capacity of the node, and the communication resource-constrained index is a ratio of used bandwidth of the node to total bandwidth of the node;

[0077] The dimensionless processing is performed on each resource-constrained index in the resource-constrained index matrix, and the proportion value of each resource-constrained index under all node samples is calculated;

[0078] The information entropy of each resource-constrained index in the resource-constrained index matrix is calculated through the proportion value, and the difference value between 1 and the information entropy is taken as the information utility value of each resource-constrained index;

[0079] The information utility value of each resource-constrained index is normalized to obtain a resource-constrained influence weight corresponding to each resource-constrained type, which is used to evaluate the resource-constrained influence degree corresponding to different resource-constrained types.

[0080] In the embodiment of the application, the node cooperative redundancy evaluation module 230 is used for performing cross-node cooperative analysis based on the resource-constrained influence degree, combining scene disturbance information and blockchain network topology structure information, evaluating the node cooperative redundancy degree under the influence of scene disturbance, including:

[0081] A node adjacency matrix is constructed through the blockchain network topology structure information of each node in the blockchain network, and the matrix elements in the node adjacency matrix represent the link availability between nodes;

[0082] A node resource-constrained vector is constructed by extracting the resource-constrained indexes in the resource-constrained index matrix of each node and combining the scene disturbance information, including:

[0083] The resource-constrained indexes in the resource-constrained index matrix of each node are extracted, and the resource-constrained indexes include the computing resource-constrained index, the storage resource-constrained index and the communication resource-constrained index;

[0084] The scene disturbance information of each node in the blockchain network is obtained, and the scene disturbance information includes an electromagnetic interference level, a malicious attack level and a disturbance occurrence probability obtained based on historical disturbance event statistics;

[0085] The disturbance intensity vector is obtained by normalizing the scene disturbance information, and an influence weight matrix between disturbance types and resource-constrained types is established;

[0086] The disturbance influence factor of each node on different resource-constrained types is obtained by performing matrix operation on the disturbance intensity vector and the influence weight matrix.

[0087] The resource-constrained index is corrected by using the disturbance influence factor, and a node resource-constrained vector is constructed by using the corrected resource-constrained index;

[0088] The average resource-constrained difference degree corresponding to each resource-constrained type between each node and all adjacent nodes is calculated by using the node adjacency matrix and the node resource-constrained vector;

[0089] The node collaborative redundancy is obtained by performing weighted summation on the average resource-constrained difference degrees corresponding to each resource-constrained type by using the resource-constrained influence weight.

[0090] In the embodiment of the present application, the data analysis strategy optimization module 240 is used to optimize the blockchain encrypted data analysis strategy based on the data analysis requirement information and the node collaborative redundancy, and execute the optimized encrypted data analysis strategy, which comprises the following steps:

[0091] The average node collaborative redundancy of all nodes in the blockchain network is taken as the global collaborative redundancy of the blockchain network, and when the global collaborative redundancy is greater than or equal to a preset global collaborative redundancy threshold, the nodes with the node collaborative redundancy greater than or equal to a preset node collaborative redundancy threshold are taken as candidate nodes for multi-node collaborative analysis, and when the global collaborative redundancy is less than the preset global collaborative redundancy threshold, single-node independent analysis is performed.

[0092] The steps of implementing the corresponding functions of each parameter and each unit module in the above-mentioned blockchain encrypted data analysis system based on scenario evaluation of the present application can refer to each parameter and step in the above-mentioned embodiments of the blockchain encrypted data analysis method based on scenario evaluation, and will not be repeated here.

[0093] Please refer to Figure 3 The embodiment of the present application also provides an electronic device 300, which comprises a memory 310, a processor 320 and a communication bus 330; the memory 310 and the processor 320 are connected through the communication bus 330. The memory 310 stores the blockchain encrypted data analysis method based on scenario evaluation provided by the above-mentioned embodiments, which can be loaded and executed by the processor 320.

[0094] The memory 310 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 310 can include a storage program area and a storage data area, wherein the storage program area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the above-mentioned blockchain encrypted data analysis method based on scenario evaluation provided by the embodiments, etc.; the storage data area can store data involved in the above-mentioned blockchain encrypted data analysis method based on scenario evaluation provided by the embodiments, etc.

[0095] The processor 320 can include one or more processing cores. The processor 320 performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 310, calling data stored in the memory 310. The processor 320 can 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 can be understood that, for different devices, the electronic device for implementing the functions of the processor 320 described above can also be other, and the embodiments of the present application are not specifically limited.

[0096] The communication bus 330 can include a path for transmitting information between the above components. The communication bus 330 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 330 can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 3 Only one double-headed arrow is used in the middle, but it does not mean that there is only one bus or one type of bus.

[0097] The embodiments of the present application provide a computer readable storage medium, which stores a computer program capable of being loaded and executed by a processor to perform the scene evaluation based blockchain encrypted data analysis method provided by the above embodiments.

[0098] In the embodiments of the present application, the computer readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. The computer readable storage medium can be, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer readable storage medium can be a portable computer diskette, a hard disk, a U disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination thereof.

[0099] 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 does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0100] The above description is merely preferred embodiments of the present application and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above application concept. For example, the technical solutions formed by replacing the above features with technical features having similar functions applied in the present application (but not limited to) with each other.

Claims

1. A blockchain encrypted data analysis method based on scenario evaluation, characterized in that, include: S100: Obtain resource status information, scene disturbance information, and blockchain network topology information of each node in the blockchain network; S200: Analyze the resource-constrained status of each node in the blockchain network based on resource status information, and assess the degree of impact of resource constraints corresponding to different resource-constrained types. S300: Based on the degree of impact of resource constraints, combined with scene disturbance information and blockchain network topology information, cross-node collaborative analysis is performed to evaluate the redundancy of node collaboration under the influence of scene disturbance. S400: Optimize the blockchain encrypted data analysis strategy based on data analysis requirements and node collaborative redundancy, and execute the optimized encrypted data analysis strategy. The evaluation of node cooperative redundancy under the influence of scenario disturbances in step S300 includes: S310. Construct a node adjacency matrix 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. S320. Construct a node resource-constrained vector by extracting resource-constrained indicators from the resource-constrained indicator matrix of each node and combining it with scene disturbance information. S330. Calculate the average resource constraint difference between each node and all its neighboring nodes for each resource constraint type using the node adjacency matrix and node resource constraint vector. S340. The node collaborative redundancy is obtained by weighting and summing the average resource constraint differences corresponding to each resource constraint type using the resource constraint impact weight. The construction of the node resource-constrained vector in step S320 includes: S321. Extract the resource-constrained indicators from the resource-constrained indicator matrix of each node. The resource-constrained indicators include computing resource-constrained indicators, storage resource-constrained indicators, and communication resource-constrained indicators. S322. Obtain scene disturbance information for each node in the blockchain network. 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 the disturbance intensity vector and establish the influence weight matrix between the disturbance type and the resource-constrained type; S324. Perform matrix operations on the disturbance intensity vector and the influence weight matrix to obtain the disturbance influence factor of each node under different resource-constrained types; S325. Use the disturbance impact factor to correct the resource-constrained index and construct the node resource-constrained vector through the corrected resource-constrained index.

2. The blockchain encrypted data analysis method based on scenario evaluation according to claim 1, characterized in that, The assessment of the impact of resource constraints corresponding to different types of resource constraints in step S200 includes: S210. Extract the computing resource information, storage resource information and communication resource information of each node through resource status information and construct a resource-constrained index matrix; S220. Perform dimensionless processing on each resource-constrained index in the resource-constrained index matrix and calculate the proportion of each resource-constrained index under all node samples. S230. Calculate the information entropy of each resource-constrained indicator in the resource-constrained indicator matrix using the ratio value, and use the difference between 1 and the information entropy as the information utility value of each resource-constrained indicator. S240. Normalize the information utility values ​​of each resource-constrained indicator to obtain the resource-constrained impact weights corresponding to each resource-constrained type, which are used to assess the degree of resource-constrained impact corresponding to different resource-constrained types.

3. The blockchain encrypted data analysis method based on scenario evaluation according to claim 2, characterized in that, The resource-constrained indicators in the resource-constrained indicator matrix mentioned in step S210 include computing resource-constrained indicators, storage resource-constrained indicators, and communication resource-constrained indicators. 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 encrypted data analysis method based on scenario evaluation according to claim 1, characterized in that, The optimized blockchain encrypted data analysis strategy described in step S400 includes: S410. The average node collaborative redundancy of all nodes in the blockchain network is taken as the global collaborative redundancy of the blockchain network. S420. When the global collaborative redundancy is greater than or equal to the preset global collaborative redundancy threshold, the nodes with a node collaborative redundancy greater than or equal to the preset node collaborative redundancy threshold are selected as candidate nodes for multi-node collaborative analysis. S430. When the global collaborative redundancy is less than the preset global collaborative redundancy threshold, single-node independent analysis is performed.

5. A blockchain encrypted data analysis system based on scenario evaluation, used to implement the blockchain encrypted data analysis method based on scenario evaluation as described in any one of claims 1-4, characterized in that, The system includes: The information acquisition module is used to acquire resource status information, scene disturbance information, and blockchain network topology information of each node in the blockchain network. The 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 resource status information, and assess the degree of resource constraint impact corresponding to different resource constraint types. The node collaborative redundancy assessment module, connected to the resource-constrained impact assessment module, is used to perform cross-node collaborative analysis based on the degree of resource-constrained impact, combined with scenario disturbance information and blockchain network topology information, to assess the node collaborative redundancy under the influence of scenario disturbance. The data analysis strategy optimization module is connected to the node collaborative redundancy assessment module. It is used to optimize the blockchain encrypted data analysis strategy based on data analysis requirements and node collaborative redundancy, and then execute the optimized encrypted data analysis strategy.

6. 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 blockchain cryptographic data analysis method based on scenario evaluation as described in any one of claims 1-4 by calling the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the blockchain cryptographic data analysis method based on scenario evaluation as described in any one of claims 1-4.

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