Blockchain-based enterprise data processing method and system

Abstract

The application provides a kind of enterprise data processing method and system based on blockchain, it is related to data processing technology, including: block server is generated with the permission relationship chain corresponding to block node in the enterprise attribute analysis of block node, the permission relationship chain is one-way attribute and permission is set from low to high;Block server receives the data collection request of first block node after, based on data collection request determines first relationship chain, first decomposition mode and first calculation mode;Second relationship chain is obtained after first relationship chain is decomposed and handled based on first decomposition mode, and fusion data chain is generated after different second relationship chain is assembled;Generation request instruction is distributed to fusion node in fusion data chain and is based on hash encryption feedback to block server, and data processing index is obtained after block server is calculated based on first calculation mode.

Description

Technical Field

[0001] This invention relates to data processing technology, and more particularly to a blockchain-based enterprise data processing method and system. Background Technology

[0002] Blockchain (English name: blockchain or blockchain) is a decentralized distributed ledger that is stored in blocks, is immutable, secure and reliable. It combines distributed storage, peer-to-peer transmission, consensus mechanisms, cryptography and other technologies to record transactions and information through a continuously growing chain of data blocks, ensuring data security and transparency.

[0003] In the process of data processing by multiple enterprises, enterprise data is confidential and requires dual protection of security and authenticity. Blockchain has the above-mentioned advantages, but current technology cannot effectively collect and process data from multiple enterprises based on blockchain. Summary of the Invention

[0004] This invention provides a blockchain-based enterprise data processing method and system that can solve the above-mentioned technical problems, effectively collect and process data from multiple enterprises based on blockchain, achieve corresponding correlation analysis, and process and manipulate enterprise chain data while ensuring the authenticity and validity of the data.

[0005] A first aspect of this invention provides a blockchain-based enterprise data processing method, comprising:

[0006] The block server analyzes the enterprise attributes within the block node to generate a permission relationship chain corresponding to the block node. The permission relationship chain is a unidirectional attribute and the permissions are set from low to high.

[0007] After receiving the data collection request from the first block node, the block server determines the first relationship chain, the first decomposition method, and the first calculation method based on the data collection request;

[0008] The first relation chain is decomposed based on the first decomposition method to obtain the second relation chain. Different second relation chains are assembled to generate a fused data chain.

[0009] The generated request instruction is distributed to the fusion nodes within the fusion data chain and fed back to the block server based on hash encryption. The block server then calculates the data processing indicators based on the first calculation method.

[0010] Optionally, in one possible implementation of the first aspect, the block server analyzes the enterprise attributes within the block node to generate a permission relationship chain corresponding to the block node. The permission relationship chain is unidirectional and permissions are set from low to high, including:

[0011] Extract shareholder information for each block node's enterprise attributes, and generate shareholder tags and node types for each block node based on the shareholder information;

[0012] Based on shareholder tags and node types, the corresponding block nodes are merged and connected to generate the corresponding permission relationship chain.

[0013] Optionally, in one possible implementation of the first aspect, the step of extracting shareholder information of the enterprise attributes of each block node and generating shareholder tags for each block node based on the shareholder information includes:

[0014] Shareholder tags are categorized to obtain individual tags and corporate tags, with each block node corresponding to at least one individual tag and / or corporate tag;

[0015] If a block node is determined to have only a personal tag and does not correspond to the personal tags of other block nodes, then the corresponding block node will be taken as the chain termination node.

[0016] If it is determined that the personal label of a block node corresponds to the personal labels of other block nodes, then the corresponding block node is taken as the starting node of the chain;

[0017] If it is determined that the enterprise label of a block node corresponds to other block nodes or other block node enterprise labels, then the corresponding block node is taken as the starting node of the chain.

[0018] Optionally, in one possible implementation of the first aspect, if it is determined that a block node only has an enterprise label and does not correspond to the enterprise labels of other block nodes, then the corresponding block node is taken as the chain termination node.

[0019] If it is determined that the enterprise label of a block node corresponds to the enterprise label of other block nodes, then the corresponding block node is taken as the starting node of the chain.

[0020] If it is determined that the personal label of a block node corresponds to the personal labels of other block nodes, then the corresponding block node is taken as the starting node of the chain.

[0021] Optionally, in one possible implementation of the first aspect, the step of generating the corresponding permission relationship chain by fusing and connecting the corresponding block nodes based on shareholder tags and node types includes:

[0022] If it is determined that one block node has the same enterprise tag as another block node, then the two block nodes are connected, and the chain of the block node with the corresponding enterprise tag is set to face the corresponding block node.

[0023] If it is determined that the personal label of one block node is the same as the personal label of another block node, a personal relay node is generated, and the personal relay node is connected to the two block nodes respectively, with the chains of the two block nodes pointing towards the corresponding personal relay node.

[0024] If it is determined that there are other chain start nodes before any chain start node, then the corresponding chain start node is taken as a chain relay node;

[0025] The permission relationship chain is obtained by merging the chain start node, chain relay node, chain end node and personal tag.

[0026] Optionally, in one possible implementation of the first aspect, the fusion of the chain start node, chain relay node, chain end node, and personal tag to obtain the permission relationship chain includes:

[0027] By performing secondary connections on pairs of connected nodes, a chain of permission relationships is obtained, consisting of all directly or indirectly connected nodes.

[0028] The system determines the nodes corresponding to individual tags within the permission relationship chain, generates individual relationship nodes, and extracts the share information of each shareholder in the corresponding chain.

[0029] Optionally, in one possible implementation of the first aspect, after receiving a data collection request from the first block node, the block server determines a first relationship chain, a first decomposition method, and a first calculation method based on the data collection request, including:

[0030] When the block server receives a data collection request from the first block node, it retrieves the permission relationship chain corresponding to the first block node to obtain the first relationship chain.

[0031] Extract the attributes of the data collection request to determine the corresponding first decomposition method. Different attributes have preset first decomposition methods.

[0032] The first calculation method includes a combination of different machine learning algorithms, and the different machine learning algorithms have different preset fusion weights.

[0033] Optionally, in one possible implementation of the first aspect, the step of decomposing the first relation chain based on the first decomposition method to obtain a second relation chain, and assembling different second relation chains to generate a fused data chain, includes:

[0034] If it is determined that there are multiple first relationship chains, then the first relationship chain with the most nodes is selected as the main chain and the other first relationship chains are selected as slave chains;

[0035] The chain node corresponding to the first block node in the chain is removed as the decomposition point to obtain the decomposition chain. The other chain nodes in the decomposition chain connected to the decomposition point are connected to the chain node corresponding to the first block node in the main chain to obtain the fused data chain.

[0036] Optionally, in one possible implementation of the first aspect, the generation request instruction is distributed to the fusion node within the fusion data chain and fed back to the block server based on hash encryption. The block server calculates the data processing indicators based on the first calculation method, including:

[0037] Identify enterprise information at different chain dimensions corresponding to the data collection request, decompose the fused data chain based on the enterprise information at different chain dimensions, perform hash calculation, and obtain multi-level hash keys for different fused nodes;

[0038] Enterprise information at different fusion nodes is encrypted using multi-level hash keys, with each fusion node having different dimensions within the fusion data chain;

[0039] After receiving enterprise information, the block server inputs it into a combination of different machine learning algorithms, and calculates data processing indicators based on different preset fusion weights.

[0040] A second aspect of this invention provides a blockchain-based enterprise data acquisition and processing system, comprising:

[0041] The generation module is used to enable the block server to analyze the enterprise attributes within the block node and generate a permission relationship chain corresponding to the block node. The permission relationship chain is a unidirectional attribute and the permissions are set from low to high.

[0042] The determination module is used to enable the block server to determine the first relationship chain, the first decomposition method, and the first calculation method based on the data collection request after receiving the data collection request from the first block node.

[0043] The fusion module is used to decompose the first relation chain based on the first decomposition method to obtain the second relation chain, and to assemble different second relation chains to generate a fused data chain;

[0044] The calculation module is used to distribute the generation request instruction to the fusion node in the fusion data chain and feed it back to the block server based on hash encryption. The block server calculates the data processing indicators based on the first calculation method. Attached Figure Description

[0045] Figure 1 A flowchart of a blockchain-based enterprise data processing method;

[0046] Figure 2This is a structural diagram of a blockchain-based enterprise data acquisition and processing system. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0049] It should be understood that in the various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0050] It should be understood that in this invention, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0051] It should be understood that in this invention, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "Contains A, B, and C", "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0052] It should be understood that in this invention, "B corresponding to A", "B corresponding to A", "A and B correspond", or "B and A correspond" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information. Matching A and B is defined as a similarity between A and B that is greater than or equal to a preset threshold.

[0053] Depending on the context, "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection."

[0054] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0055] This invention provides a blockchain-based enterprise data processing method, such as... Figure 1 As shown, it includes:

[0056] The block server analyzes the enterprise attributes within a block node to generate a permission relationship chain corresponding to the block node. This permission relationship chain is unidirectional, with permissions set from low to high. The technical solution provided by this invention first analyzes the enterprise attributes and determines the corresponding permission relationship chain based on the different permissions of the enterprises. It should be noted that the permission relationship chain is unidirectional, with permissions set from low to high. For example, if company A is a shareholder of company B, and company B is a shareholder of company C, then company A has the permission to view company B's enterprise data, and company B has the permission to view company C's enterprise data. In this case, the invention can obtain the corresponding permission relationship chain: C→B→A.

[0057] In one possible implementation, the block server analyzes the enterprise attributes within a block node to generate a permission relationship chain corresponding to the block node. This permission relationship chain is unidirectional and permissions are set from low to high, including:

[0058] The invention extracts shareholder information from the enterprise attributes of each block node, and generates shareholder tags and node types for each block node based on this information. This invention will obtain shareholder information for the enterprise attributes of each block node; enterprises in different block nodes may have the same or different shareholder information.

[0059] This invention generates corresponding permission relationship chains by fusing and connecting relevant block nodes based on shareholder tags and node types. The permission relationship chains generated by shareholder tags of enterprises and individuals are likely to be different. For example, while Company B is a shareholder of Company C, Company A can also be a shareholder of Company B. If individual Zhang San is also a shareholder of Company C, but Zhang San has no other shareholders, then Zhang San's corresponding chain might be B→Zhang San.

[0060] In one possible implementation, the step of extracting shareholder information of the enterprise attributes of each block node and generating shareholder tags for each block node based on the shareholder information includes:

[0061] Shareholder tags are categorized into individual tags and enterprise tags, with each block node corresponding to at least one individual tag and / or enterprise tag. It should be noted that each enterprise has corresponding shareholders, therefore this invention categorizes shareholders based on their specific characteristics.

[0062] If a block node is determined to have only a personal tag and does not correspond to the personal tags of other block nodes, then that block node is designated as the chain termination node. It should be noted that in this case, the corresponding enterprise has only one or more natural persons as shareholders, but these natural persons are not shareholders of other enterprises, and the chain will not continue to extend. Therefore, this invention will designate the corresponding block node as the chain termination node. That is, the shareholder structure corresponding to this block is relatively simple.

[0063] If a block node's personal tag is determined to correspond to the personal tags of other block nodes, then the corresponding block node is taken as the starting node of the chain. In this case, at least one natural person is a shareholder of multiple companies, and the chain may continue to extend. At this point, the present invention will take the corresponding block node as the starting node of the chain, and form a combined chain between multiple companies through the corresponding natural person.

[0064] If the enterprise label of a block node is determined to correspond to other block nodes or enterprise labels of other block nodes, then the corresponding block node is taken as the starting node of the chain. At this time, at least one enterprise is identified as a shareholder corresponding to that block node, and the chain may continue to extend. Therefore, this invention will take the corresponding block node as the starting node of the chain.

[0065] In one possible implementation, if a block node is determined to have only an enterprise label and does not correspond to the enterprise labels of other block nodes, then the corresponding block node is designated as the chain termination node. In this case, the enterprise that is a shareholder is not a shareholder of other enterprises, and the chain will not continue to extend. Therefore, this invention will designate the corresponding block node as the chain termination node.

[0066] In one possible implementation, if it is determined that the enterprise label of a block node corresponds to the enterprise labels of other block nodes, then the corresponding block node is taken as the starting node of the chain. In this case, the enterprise that is a shareholder is also a shareholder of the block nodes corresponding to other enterprises, and the chain will continue to extend. Therefore, in this case, the present invention will take the corresponding block node as the starting node of the chain.

[0067] In one possible implementation, if it is determined that the personal tag of a block node corresponds to the personal tags of other block nodes, then the corresponding block node is taken as the starting node of the chain. In this case, the natural person who is a shareholder is also a shareholder of the corresponding block nodes of other enterprises, and the chain will continue to extend. Therefore, in this case, the present invention will take the corresponding block node as the starting node of the chain.

[0068] In one possible implementation, the step of generating a corresponding permission relationship chain by fusing and connecting the corresponding block nodes based on shareholder tags and node types includes:

[0069] If one block node is determined to have the same enterprise tag as another block node, the two block nodes are connected, with the chain of the block node with the corresponding enterprise tag oriented towards the corresponding block node. In this case, the two different block nodes have the same shareholder, and the invention will connect the two corresponding block nodes, thus establishing a connection between them based on their shareholder relationship.

[0070] If the personal tag of one block node is found to be the same as that of another block node, a personal relay node is generated. This personal relay node is then connected to both block nodes, with the chains of the two block nodes pointing towards the corresponding personal relay node. At this point, the natural person, acting as a shareholder of two companies, will direct the chains of the two block nodes towards the corresponding personal relay node, thus establishing a connection between the two blocks based on the natural person's identity.

[0071] After determining that there are other chain starting nodes before any chain starting node, the corresponding chain starting node is designated as a chain relay node. It should be noted that this will result in multiple smaller chains. These smaller chains may be links between enterprise block nodes, or they may be links between block nodes of multiple different enterprises connected by individuals.

[0072] This invention integrates the starting node, relay nodes, ending nodes, and personal tags of multiple block nodes with different attributes to form a permission relationship chain. Based on the attributes of each node, it performs a full-dimensional fusion of the starting node, relay nodes, ending nodes, and personal tags of multiple block nodes with different attributes. This ensures that any direct or indirect permission relationship can be formed within the permission relationship chain provided by this invention, making subsequent enterprise data analysis and processing more accurate.

[0073] In one possible implementation, the process of fusing the chain start node, chain relay node, chain end node, and personal tag to obtain the permission relationship chain includes:

[0074] By performing secondary connections on pairs of connected nodes, a permission relationship chain is obtained, consisting of all directly or indirectly connected nodes. This invention then reorganizes and merges all the scattered chains to obtain a merged permission relationship chain. This method statistically analyzes all blocks with direct and indirect relationships.

[0075] This invention generates personal relationship nodes by identifying the nodes corresponding to individual tags within the permission relationship chain, and extracts the share information of each shareholder under the corresponding chain. The invention locks individual nodes within the permission relationship chain and obtains these personal relationship nodes, thereby effectively distinguishing all blocks and nodes.

[0076] After receiving a data collection request from the first block node, the block server determines the first relationship chain, the first decomposition method, and the first calculation method based on the data collection request. In the technical solution provided by this invention, each company or individual corresponds to one block node. Upon receiving a data collection request from the first block node, this invention determines the corresponding first relationship chain, first decomposition method, and first calculation method. Different data collection requests and different first block nodes have different first relationship chains, first decomposition methods, and first calculation methods.

[0077] In one possible implementation, after receiving a data collection request from the first block node, the block server determines a first relationship chain, a first decomposition method, and a first calculation method based on the data collection request, including:

[0078] When the block server receives a data collection request from the first block node, it retrieves the permission relationship chain corresponding to the first block node to obtain the first relationship chain. It should be noted that after receiving the data collection request, the block server determines the first relationship chain corresponding to the first block node. This first relationship chain is the chain of all nodes that have a direct or indirect relationship with the first block node.

[0079] The attributes of the data collection request are extracted to determine the corresponding first decomposition method. Different attributes have preset first decomposition methods. Different data collection requests will have different decomposition methods. For example, when the data collection request is for financial data collection, it may only be able to collect information corresponding to the blocks where the requester is a direct or indirect shareholder, and will be decomposed according to this method. For example, when the data collection request is for questionnaire collection, it can collect data from all non-natural person enterprises, and will correspond to a different decomposition method. The decomposition method corresponding to the attributes of each data collection request can be preset.

[0080] The first calculation method includes a combination of different machine learning algorithms, with different machine learning algorithms having different preset fusion weights. The technical method provided by this invention may also include different combination methods. For example, in predicting corporate credit risk, this invention pre-trains multiple sets of machine learning algorithms and then performs credit risk prediction based on the corresponding machine learning algorithms.

[0081] After decomposing the first relationship chain using the first decomposition method, a second relationship chain is obtained. Different second relationship chains are then assembled to generate a fused data chain. It should be noted that this invention decomposes the first relationship chain to obtain the second relationship chain. For example, if the first relationship chain is D→C→B→A→Zhang San, and we need to count companies with B as a direct or indirect shareholder to obtain the second relationship chain, then D→C→B→A→Zhang San will be split into D→C→B.

[0082] The system generates request instructions, distributes them to fusion nodes within the fusion data chain, and then sends them back to the block server based on hash encryption. The block server calculates the data processing indicators using a first calculation method. The technical solution provided by this invention distributes request instructions to fusion nodes within the fusion data chain, and each fusion node then sends the results back to the block server for comprehensive calculation, such as credit risk prediction. In this case, the fusion nodes may provide relevant financial data, operational data, etc., related to credit risk assessment.

[0083] In one possible implementation, the process of decomposing the first relation chain based on the first decomposition method to obtain a second relation chain, and then assembling different second relation chains to generate a fused data chain, includes:

[0084] If there are multiple first relationship chains, the first relationship chain with the most nodes is selected as the master chain, and the other first relationship chains are designated as slave chains. The technical solution provided by this invention decomposes the chains into master-slave relationships based on the number of nodes, thereby reducing the amount of data processing during subsequent chain reorganization.

[0085] The chain node corresponding to the first block node in the chain is removed as the decomposition point to obtain the decomposition chain. The other chain nodes in the decomposition chain connected to the decomposition point are connected to the chain node corresponding to the first block node in the main chain to obtain the fused data chain.

[0086] In one possible implementation, the generation request instruction is distributed to the fusion nodes within the fusion data chain and fed back to the block server based on hash encryption. The block server calculates the data processing indicators based on a first calculation method, including:

[0087] The invention identifies enterprise information across different chain dimensions corresponding to the data collection request. Based on this information, a fused data chain is obtained, decomposed, and hashed to generate multi-level hash keys for different fused nodes. The technical solution provided by this invention first generates corresponding chain slots based on the number of nodes within the chain. Multi-dimensional codes are generated at each slot according to their order. These codes include node level and node number dimensions. For example, if a chain has three levels (A, B, C), and each level has 1, 1, and 2 nodes, the node number dimensions are 1, 1, and 2 respectively. The corresponding multi-dimensional codes are then extracted for each A1, B1, and C2. The invention further extracts characters corresponding to the shareholder tags of the chain slots and fills these characters into each slot. For example, if the three shareholders are A, B, and C, the underlying characters used to generate the hash key are A1AB1BC2C. The invention then performs a first-step hash calculation on these underlying characters to obtain the first sub-key. At this point, the first sub-key of all fused nodes within the fused data chain is the same.

[0088] To ensure data security and confidentiality, this invention will extract the on-chain time and current time of the block node corresponding to each chain slot, and split and recombine the on-chain time and current time to obtain other underlying characters. The splitting method is to cross-set the on-chain time first and the current time last.

[0089] During the cross-setting process, this invention generates a first time slot group corresponding to the on-chain time and a second time slot group for the current time, and sorts them separately. Slots with the same sequence number from the first and second time slot groups are selected and recombined according to the on-chain time first, followed by the current time, to obtain combined slots. A second hash calculation is performed on the combined slots to obtain the second subkey. The final hash key is generated by placing the first subkey first and the second subkey last.

[0090] This invention encrypts enterprise information at different fusion nodes using a multi-level hash key, with each fusion node possessing different dimensions within the fusion data chain. The invention encrypts the enterprise information of fusion nodes at different locations based on the generated hash key, which is sent by the server to the corresponding fusion node via a plugin, thereby ensuring data encryption.

[0091] By obtaining the above multi-dimensional data, the randomness of the key is guaranteed, and the traceability of the data is achieved, which greatly improves the efficiency of key generation and effective feedback traceability, and ensures the authenticity of the data.

[0092] After receiving enterprise information, the blockchain server inputs it into a combination of different machine learning algorithms, and calculates data processing indicators based on different preset fusion weights. During machine learning algorithm calculations, different algorithms can be used, such as calculating the enterprise's debt, revenue, etc. Calculating the enterprise's debt and revenue using a single model can be done in existing technologies, and will not be elaborated upon in this invention. After each algorithm performs its calculation, corresponding weights can be manually configured for each algorithm to obtain the fused prediction result.

[0093] Linear Regression: Linear regression is a supervised learning algorithm used to establish linear relationships between variables. It predicts the values ​​of one or more continuous variables by fitting an optimal line. Linear regression is widely used in fields such as sales trends, market forecasting, and economic analysis.

[0094] Logistic Regression: Logistic regression is a binary classification algorithm used to predict the probability of discrete output variables. It is commonly used in fields such as risk assessment, marketing, and medical disease prediction.

[0095] Decision Trees: Decision trees are a type of supervised learning algorithm based on a tree structure used for classification and regression problems. They divide a dataset into different categories or values ​​through a series of decision nodes. Decision trees are easy to understand and interpret, and are commonly used for tasks such as customer segmentation, fraud detection, and recommender systems.

[0096] Random Forests: Random forests are an ensemble learning algorithm that combines the predictions of multiple decision trees to improve accuracy and stability. They are suitable for applications such as large-scale datasets, feature selection, and anomaly detection.

[0097] Support Vector Machines (SVMs) are supervised learning algorithms used for classification and regression problems. They separate samples of different classes by constructing a hyperplane in the feature space. SVMs have wide applications in text classification, image recognition, and bioinformatics.

[0098] K-Nearest Neighbors (KNN) is an instance-based supervised learning algorithm used for classification and regression problems. It makes predictions by calculating the distances between a sample and its K nearest neighbors. KNN is commonly used in tasks such as recommender systems, image recognition, and pattern recognition.

[0099] Principal Component Analysis (PCA): PCA is an unsupervised learning algorithm used to reduce data dimensionality and extract key features. It projects the original data onto a new set of orthogonal variables through a linear transformation. PCA is widely used in data visualization, feature extraction, and anomaly detection.

[0100] Clustering algorithms are unsupervised learning algorithms used to group objects in a dataset into categories with similar characteristics. Common clustering algorithms include K-means clustering and hierarchical clustering. Clustering has wide applications in market segmentation, social network analysis, and image segmentation.

[0101] In practice, the weight of a particular machine learning algorithm in the overall algorithm is also influenced by the weights of other machine learning algorithms. Therefore, considering the differences in the overall weight of each machine learning algorithm and the relationships between them, a multi-machine learning combination strategy for weight optimization is proposed.

[0102] The technical solution provided by this invention first combines various machine learning algorithms into an algorithm matrix. Based on the characteristics of each algorithm in the matrix, all algorithm vectors in the matrix are traversed. When each algorithm vector is encountered, the classification accuracy of the algorithm matrix excluding that vector is calculated. The overall classification effect of the algorithm matrix can be considered as 1, and each algorithm vector in the matrix can be considered as an individual. After excluding each individual machine learning algorithm, the weight of that machine learning algorithm is obtained by subtracting the classification effect of the remaining algorithms from the overall classification effect. Therefore, this strategy uses 1 - accuracy as the weight of each machine learning algorithm.

[0103] To implement the blockchain-based enterprise data processing method of the present invention, the present invention also provides a blockchain-based enterprise data acquisition and processing system, such as... Figure 2 As shown, it includes:

[0104] The generation module is used to enable the block server to analyze the enterprise attributes within the block node and generate a permission relationship chain corresponding to the block node. The permission relationship chain is a unidirectional attribute and the permissions are set from low to high.

[0105] The determination module is used to enable the block server to determine the first relationship chain, the first decomposition method, and the first calculation method based on the data collection request after receiving the data collection request from the first block node.

[0106] The fusion module is used to decompose the first relation chain based on the first decomposition method to obtain the second relation chain, and to assemble different second relation chains to generate a fused data chain;

[0107] The calculation module is used to distribute the generation request instruction to the fusion node in the fusion data chain and feed it back to the block server based on hash encryption. The block server calculates the data processing indicators based on the first calculation method.

[0108] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, is used to implement the methods provided in the various embodiments described above.

[0109] The storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, the storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be a component of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). This ASIC can also be located within a user device. Alternatively, the processor and storage medium can exist as discrete components in a communication device. Storage media can be read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage devices, etc.

[0110] The present invention also provides a program product including execution instructions stored in a storage medium. At least one processor of the device can read the execution instructions from the storage medium, and the execution instructions by the at least one processor cause the device to implement the methods provided in the various embodiments described above.

[0111] In the above-described terminal or server embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blockchain-based enterprise data processing method, characterized in that, include: The block server analyzes the enterprise attributes within the block node to generate a permission relationship chain corresponding to the block node. The permission relationship chain is a unidirectional attribute and the permissions are set from low to high. After receiving the data collection request from the first block node, the block server determines the first relationship chain, the first decomposition method, and the first calculation method based on the data collection request; The first relation chain is decomposed based on the first decomposition method to obtain the second relation chain. Different second relation chains are assembled to generate a fused data chain. The generated request instruction is distributed to the fusion nodes within the fusion data chain and fed back to the block server based on hash encryption. The block server calculates the data processing indicators based on the first calculation method. The block server analyzes the enterprise attributes within the block nodes to generate a permission relationship chain corresponding to the block nodes. This permission relationship chain is unidirectional and permissions are set from low to high, including: Extract shareholder information for each block node's enterprise attributes, and generate shareholder tags and node types for each block node based on the shareholder information; Based on shareholder tags and node types, corresponding block nodes are merged and connected to generate corresponding permission relationship chains; After receiving a data collection request from the first block node, the block server determines the first relationship chain, the first decomposition method, and the first calculation method based on the data collection request, including: When the block server receives a data collection request from the first block node, it retrieves the permission relationship chain corresponding to the first block node to obtain the first relationship chain. Extract the attributes of the data collection request to determine the corresponding first decomposition method. Different attributes have preset first decomposition methods. The first calculation method includes a combination of different machine learning algorithms, and the different machine learning algorithms have different preset fusion weights.

2. The blockchain-based enterprise data processing method according to claim 1, characterized in that, The step of extracting shareholder information for each block node's enterprise attributes and generating shareholder tags for each block node based on this information includes: Shareholder tags are categorized to obtain individual tags and corporate tags, with each block node corresponding to at least one individual tag and / or corporate tag; If a block node is determined to have only a personal tag and does not correspond to the personal tags of other block nodes, then the corresponding block node will be taken as the chain termination node. If it is determined that the personal label of a block node corresponds to the personal labels of other block nodes, then the corresponding block node is taken as the starting node of the chain; If it is determined that the enterprise label of a block node corresponds to other block nodes or other block node enterprise labels, then the corresponding block node is taken as the starting node of the chain.

3. The blockchain-based enterprise data processing method according to claim 2, characterized in that, If a block node is determined to have only an enterprise label and does not correspond to the enterprise labels of other block nodes, then the corresponding block node will be taken as the chain termination node. If it is determined that the enterprise label of a block node corresponds to the enterprise label of other block nodes, then the corresponding block node is taken as the starting node of the chain. If it is determined that the personal label of a block node corresponds to the personal labels of other block nodes, then the corresponding block node is taken as the starting node of the chain.

4. The blockchain-based enterprise data processing method according to any one of claims 2 or 3, characterized in that, The process of generating the corresponding permission relationship chain by fusing and connecting the corresponding block nodes based on shareholder tags and node types includes: If it is determined that one block node has the same enterprise tag as another block node, then the two block nodes are connected, and the chain of the block node with the corresponding enterprise tag is set to face the corresponding block node. If it is determined that the personal label of one block node is the same as the personal label of another block node, a personal relay node is generated, and the personal relay node is connected to the two block nodes respectively, with the chains of the two block nodes pointing towards the corresponding personal relay node. If it is determined that there are other chain start nodes before any chain start node, then the corresponding chain start node is taken as a chain relay node; The permission relationship chain is obtained by merging the chain start node, chain relay node, chain end node and personal tag.

5. The blockchain-based enterprise data processing method according to claim 4, characterized in that, The permission relationship chain obtained by fusing the chain's starting node, relay nodes, ending node, and personal tags includes: By performing secondary connections on pairs of connected nodes, a chain of permission relationships is obtained, consisting of all directly or indirectly connected nodes. The system determines the nodes corresponding to individual tags within the permission relationship chain, generates individual relationship nodes, and extracts the share information of each shareholder in the corresponding chain.

6. The blockchain-based enterprise data processing method according to claim 1, characterized in that, The process of decomposing the first relation chain based on the first decomposition method to obtain the second relation chain, and then assembling different second relation chains to generate a fused data chain, includes: If it is determined that there are multiple first relationship chains, then the first relationship chain with the most nodes is selected as the main chain and the other first relationship chains are selected as slave chains; The chain node corresponding to the first block node in the chain is removed as the decomposition point to obtain the decomposition chain. The other chain nodes in the decomposition chain connected to the decomposition point are connected to the chain node corresponding to the first block node in the main chain to obtain the fused data chain.

7. The blockchain-based enterprise data processing method according to claim 6, characterized in that, The generation request instruction is distributed to the fusion nodes within the fusion data chain and fed back to the block server based on hash encryption. The block server calculates the data processing indicators based on the first calculation method, including: Identify enterprise information at different chain dimensions corresponding to the data collection request, decompose the fused data chain based on the enterprise information at different chain dimensions, perform hash calculation, and obtain multi-level hash keys for different fused nodes; Enterprise information at different fusion nodes is encrypted using multi-level hash keys, with each fusion node having different dimensions within the fusion data chain; After receiving enterprise information, the block server inputs it into a combination of different machine learning algorithms, and calculates data processing indicators based on different preset fusion weights.

8. A blockchain-based enterprise data acquisition and processing system, characterized in that, include: The generation module is used to enable the block server to analyze the enterprise attributes within the block node and generate a permission relationship chain corresponding to the block node. The permission relationship chain is a unidirectional attribute and the permissions are set from low to high. The determination module is used to enable the block server to determine the first relationship chain, the first decomposition method, and the first calculation method based on the data collection request after receiving the data collection request from the first block node. The fusion module is used to decompose the first relation chain based on the first decomposition method to obtain the second relation chain, and to assemble different second relation chains to generate a fused data chain; The calculation module is used to distribute the generation request instruction to the fusion node in the fusion data chain and feed it back to the block server based on hash encryption. The block server calculates the data processing indicators based on the first calculation method. The block server analyzes the enterprise attributes within the block nodes to generate a permission relationship chain corresponding to the block nodes. This permission relationship chain is unidirectional and permissions are set from low to high, including: Extract shareholder information for each block node's enterprise attributes, and generate shareholder tags and node types for each block node based on the shareholder information; Based on shareholder tags and node types, corresponding block nodes are merged and connected to generate corresponding permission relationship chains; After receiving a data collection request from the first block node, the block server determines the first relationship chain, the first decomposition method, and the first calculation method based on the data collection request, including: When the block server receives a data collection request from the first block node, it retrieves the permission relationship chain corresponding to the first block node to obtain the first relationship chain. Extract the attributes of the data collection request to determine the corresponding first decomposition method. Different attributes have preset first decomposition methods. The first calculation method includes a combination of different machine learning algorithms, and the different machine learning algorithms have different preset fusion weights.

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