CN112381539A

CN112381539A - Transaction information processing method based on block chain and big data and digital financial platform

Info

Publication number: CN112381539A
Application number: CN202011264484.2A
Authority: CN
Inventors: 陈素华
Original assignee: 陈素华
Current assignee: Big brother (Shanghai) Cloud Data Service Co.,Ltd.
Priority date: 2020-11-13
Filing date: 2020-11-13
Publication date: 2021-02-19
Anticipated expiration: 2040-11-13
Also published as: CN113435884A; CN112381539B; CN113435882A

Abstract

The invention discloses a transaction information processing method based on a block chain and big data and a digital financial platform, and relates to the technical field of block chain finance. The transaction information processing method based on the block chain and the big data comprises the following steps: the method comprises the steps of determining a target block chain of transaction information sent by terminal equipment, storing the transaction information into a data vector to which an undetermined target node in the target block chain belongs, determining transaction attributes of the undetermined target node based on transaction items of the undetermined target node, selecting a first target node with the highest priority processing authority from the undetermined target nodes according to the transaction attributes, determining target transaction information from the target transaction attributes, allocating a processing serial number to the target transaction information, submitting the target transaction information allocated with the processing serial number to the target block chain for processing, directly processing the transaction information without any classification and sorting operation on the transaction data in the prior art, and efficiently processing the transaction information through the steps.

Description

Transaction information processing method based on block chain and big data and digital financial platform

Technical Field

The invention relates to the technical field of block chain finance, in particular to a transaction information processing method based on a block chain and big data and a digital financial platform.

Background

With the development of the blockchain technology and the arrival of the big data era, due to the security of the blockchain, the processing of transaction information (such as digital currency) by using the blockchain is more reliable, and due to the gradual increase of the data volume, in the prior art, the data volume processed by some nodes of the blockchain is more, the calculation efficiency is low, and meanwhile, the data volume processed by other nodes is far from the saturation level and is more idle. Moreover, since the processed data is not sorted and sorted explicitly, each node needs to process all types of data, which also causes a huge amount of calculation for the whole blockchain, and the above problems all result in low efficiency of processing transaction information by the blockchain.

In view of the above, a need exists in the art for a block chain-based transaction information efficient processing scheme.

Disclosure of Invention

The invention provides a transaction information processing method based on a block chain and big data and a digital financial platform.

In a first aspect, an embodiment of the present invention provides a transaction information processing method based on blockchains and big data, which is applied to a computer device, where the computer device is communicatively connected to a plurality of blockchains and a terminal device, and the method includes:

after transaction information sent by the terminal equipment is received, determining a target block chain for the transaction information;

determining an undetermined target node to which the transaction information belongs from a node group managed by the target block chain, and adding the transaction information into a data vector of the undetermined target node to which the transaction information belongs, wherein different undetermined target nodes are used for processing the transaction information aiming at the target block chain under different transactions, and each undetermined target node is respectively configured with a knowledge graph;

determining a transaction attribute corresponding to the undetermined target node based on a transaction item to which the undetermined target node belongs, wherein the same transaction item corresponds to the undetermined target node under the same transaction attribute, the transaction attribute is used for representing a current transaction process of the undetermined target node aiming at the transaction information, and the transaction attribute is determined by signature information of a knowledge graph of the undetermined target node;

determining a first target node with priority processing authority in the pending target nodes based on the transaction attribute;

selecting target transaction information from the data vectors processed by the first target node;

distributing a processing serial number for the target transaction information from a knowledge graph corresponding to the target transaction information, and submitting the target transaction information distributed with the processing serial number to the target block chain so that the target block chain processes the target transaction information according to the processing serial number.

Optionally, the transaction attributes include a first transaction attribute, a second transaction attribute and a third transaction attribute, the first transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is lower than the lowest transaction progress, the second transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is not lower than the lowest transaction progress and is not higher than the highest transaction progress, and the third transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is higher than the highest transaction progress;

the determining a first target node with priority processing authority in the pending target nodes based on the transaction attributes comprises:

if the transaction attribute of the undetermined target node is the first transaction attribute or the second transaction attribute, determining that the undetermined target node is a first target node with a priority processing authority;

and if the transaction attribute of the undetermined target node is the third transaction attribute, determining that the undetermined target node is a second undetermined target node without priority processing authority.

Optionally, configuring a first knowledge graph and a second knowledge graph for each undetermined target node, wherein the signature information generation progress of the first knowledge graph is the lowest transaction progress of the undetermined target node corresponding to the first knowledge graph, and the signature information generation progress of the second knowledge graph is the highest transaction progress of the undetermined target node corresponding to the second knowledge graph;

the allocating a processing serial number to the target transaction information from the knowledge graph corresponding to the target transaction information includes:

respectively taking out a processing serial number from a first knowledge graph and a second knowledge graph corresponding to a first target node to which the target transaction information belongs, and distributing the processing serial number to the target transaction information;

the determining the transaction attribute corresponding to the undetermined target node based on the transaction item to which the undetermined target node belongs comprises:

determining the transaction attribute of the undetermined target node according to the number of the residual processing serial numbers of the first knowledge graph and the second knowledge graph of the undetermined target node, wherein if the number of the residual processing serial numbers in the first knowledge graph of the undetermined target node is larger than the preset number, the transaction attribute of the undetermined target node is determined to be the first transaction attribute;

if the number of the remaining processing serial numbers in the first knowledge graph of the undetermined target node is not more than the preset number, and the number of the remaining processing serial numbers in the second knowledge graph is more than the preset number, determining the transaction attribute of the undetermined target node as the second transaction attribute;

if the number of the remaining processing sequence numbers in the second knowledge graph of the undetermined target node is not more than the preset number, determining the transaction attribute of the undetermined target node as the third transaction attribute;

creating a first transaction item based on a pending target node under the first transaction attribute;

creating a second transaction item based on the pending target node under the second transaction attribute;

acquiring a first transaction item and a second transaction item of the undetermined target node, wherein the first transaction item comprises the undetermined target node of which the transaction attribute is a first transaction attribute, the second transaction item comprises the undetermined target node of which the transaction attribute is a second transaction attribute, the number of residual processing serial numbers in the first knowledge graph of the undetermined target node is greater than a preset number under the first transaction attribute, the number of the residual processing serial numbers in the first knowledge graph of the undetermined target node is not greater than the preset number under the second transaction attribute, the number of the residual processing serial numbers in the second knowledge graph is greater than the preset number, and the number of the residual processing serial numbers in the second knowledge graph is not greater than the preset number under the third transaction attribute;

and determining the transaction attribute of each undetermined target node based on the transaction item to which each undetermined target node belongs.

Optionally, the selecting target transaction information from the data vector processed by the first target node includes:

traversing the undetermined target nodes in the first transaction item based on a linked list structure of the first transaction item, and selecting target transaction information from data vectors of the undetermined target nodes according to a calculation upper limit of a target block chain when traversing to each undetermined target node, wherein in the first transaction item, the undetermined target nodes are organized in a linked list form, one linked list node represents one undetermined target node, and the undetermined target node in the first transaction item is the first target node;

after the selection corresponding to the first transaction item is finished, traversing the second transaction item of a balanced binary search tree structure, and acquiring the priority of the node to be determined in the second transaction item;

acquiring the data volume of the knowledge graph of the undetermined target node in the second transaction item;

selecting candidate undetermined target nodes from the second transaction items according to the data volume and the priority of the knowledge graph;

and selecting target transaction information from the data vectors of the candidate undetermined target nodes based on the remaining available calculation upper limit of the target block chain, wherein the undetermined target nodes in the second transaction item are organized in a balanced binary search tree form, one balanced binary search tree node represents one undetermined target node, the keywords of the balanced binary search tree node are the memory addresses of the corresponding undetermined target nodes, and the undetermined target node in the second transaction item is the first target node.

Optionally, after receiving the transaction information sent by the terminal device, the method further includes:

acquiring the transaction information;

determining a first association relationship between the security indicators of the transaction information and the security indicators of the historical transaction information, including:

respectively constructing vector representation of each index in the transaction information and the historical transaction information to obtain a plurality of first characteristic vectors and a plurality of second characteristic vectors, determining a first association value between each first characteristic vector and each second characteristic vector to obtain a first association relation, wherein the historical transaction information is determined based on sample transaction information in a preset time range;

extracting candidate indexes meeting preset conditions of the transaction information from the historical transaction information to obtain a first candidate index set;

determining a first key-value pair value of each index in the transaction information about each index in the historical transaction information based on the first correlation value, wherein the first key-value pair value is used for reflecting the attention degree of each index in the transaction information to each index in the historical transaction information;

determining corresponding candidate indexes from the first candidate index set according to the sequence of the first key-value pair value from high to low, wherein the corresponding candidate indexes serve as first target indexes;

generating corresponding indexes at corresponding positions in the transaction information according to the weight of the first target index in the historical transaction information and content information corresponding to the first target index to obtain reference transaction information, wherein the safety indexes of the transaction information comprise: the content information of each known index in the transaction information and the weight in the transaction information, the safety index of the historical transaction information comprises: the content information of each index in the historical transaction information and the weight in the historical transaction information;

determining a second association relationship between the security index of the reference transaction information and the security index of the transaction information, including:

respectively constructing vector representation of each index in the transaction information and the reference transaction information to obtain a plurality of third feature vectors and a plurality of fourth feature vectors, and determining a second association value between each third feature vector and each fourth feature vector to obtain a second association relation;

determining a second key-value pair value of each index in the transaction information relative to each index in the reference transaction information based on the second correlation value, wherein the second key-value pair value is used for reflecting the attention degree of each index in the transaction information to each index in the reference transaction information;

determining candidate indexes from the reference transaction information according to the second key-value pair value and the weight of the indexes in the reference transaction information to obtain a second candidate index set, wherein the safety indexes of the reference transaction information comprise: the content information of each index in the reference transaction information and the weight in the reference transaction information;

determining a second target metric from the second set of candidate metrics based on known metrics and a sequence of metrics in the transaction information;

and generating corresponding indexes at corresponding positions in the transaction information based on the index sequence and the content information corresponding to the second target index so as to perform index verification on the transaction information to obtain the verified transaction information.

Optionally, the obtaining the transaction information includes:

obtaining a known index;

determining original transaction information based at least on the known metrics;

constructing vector representation of known indexes in the original transaction information;

determining a correlation value between every two known indexes in the original transaction information according to the vector representation of the known indexes;

determining a third key value pair value of each known index relative to other known indexes in the original transaction information based on the correlation value between every two known indexes, wherein the third key value pair value is used for reflecting the attention degree of each index in the original transaction information to other known indexes in the information;

and adjusting the vector representation of the known index in the original transaction information according to the third key value to obtain the transaction information.

Optionally, determining the historical transaction information based on the sample transaction information within the preset time range includes:

collecting sample transaction information within a preset time range;

constructing a plurality of pieces of historical sample transaction information according to the designated time period and the sample transaction information;

aligning the plurality of historical sample transaction information according to time, determining an index with the highest frequency of occurrence in the same time slice from the aligned plurality of historical sample transaction information, and constructing and obtaining target historical sample transaction information according to the index with the highest frequency of occurrence in the same time slice;

constructing a vector representation of each index in the target historical sample transaction information;

determining a correlation value between every two indexes in the target historical sample transaction information according to the vector representation of each index;

determining a fourth key value pair value of each index in the target historical sample transaction information about other indexes based on the correlation value between every two indexes, wherein the fourth key value pair value is used for reflecting the attention degree of each index in the target historical sample transaction information to other indexes in the target historical sample transaction information;

and adjusting the vector representation of the index in the target historical sample transaction information according to the fourth key value to obtain historical transaction information.

In a second aspect, an embodiment of the present invention provides a digital financial platform, which is applied to a computer device, where the computer device is communicatively connected to a plurality of block chains and a terminal device, and the digital financial platform includes:

the acquisition module is used for determining a target block chain for the transaction information after receiving the transaction information sent by the terminal equipment;

an adding module, configured to determine an undetermined target node to which the transaction information belongs from a node group managed by the target block chain, and add the transaction information to a data vector of the undetermined target node to which the transaction information belongs, where different undetermined target nodes are used to process the transaction information for the target block chain under different transactions, and each undetermined target node is configured with a knowledge graph;

the processing module is used for determining transaction attributes corresponding to the undetermined target node based on the transaction item to which the undetermined target node belongs, wherein the same transaction item corresponds to the undetermined target node under the same transaction attribute, the transaction attributes are used for representing the current transaction progress of the undetermined target node aiming at the transaction information, and the transaction attributes are determined by signature information of a knowledge graph of the undetermined target node;

the determining module is used for determining a first target node with a priority processing authority in the undetermined target nodes based on the transaction attribute;

a selection module for selecting target transaction information from the data vectors processed by the first target node;

the allocation module is configured to allocate a processing serial number to the target transaction information from a knowledge graph corresponding to the target transaction information, and submit the target transaction information allocated with the processing serial number to the target block chain, so that the target block chain processes the target transaction information according to the processing serial number.

the determining module is specifically configured to:

if the transaction attribute of the undetermined target node is the first transaction attribute or the second transaction attribute, determining that the undetermined target node is a first target node with a priority processing authority; and if the transaction attribute of the undetermined target node is the third transaction attribute, determining that the undetermined target node is a second undetermined target node without priority processing authority.

the allocation module is specifically configured to:

the processing module is specifically configured to:

determining the transaction attribute of the undetermined target node according to the number of the residual processing serial numbers of the first knowledge graph and the second knowledge graph of the undetermined target node, wherein if the number of the residual processing serial numbers in the first knowledge graph of the undetermined target node is larger than the preset number, the transaction attribute of the undetermined target node is determined to be the first transaction attribute; if the number of the remaining processing serial numbers in the first knowledge graph of the undetermined target node is not more than the preset number, and the number of the remaining processing serial numbers in the second knowledge graph is more than the preset number, determining the transaction attribute of the undetermined target node as the second transaction attribute; if the number of the remaining processing sequence numbers in the second knowledge graph of the undetermined target node is not more than the preset number, determining the transaction attribute of the undetermined target node as the third transaction attribute; creating a first transaction item based on a pending target node under the first transaction attribute; creating a second transaction item based on the pending target node under the second transaction attribute; acquiring a first transaction item and a second transaction item of the undetermined target node, wherein the first transaction item comprises the undetermined target node of which the transaction attribute is a first transaction attribute, the second transaction item comprises the undetermined target node of which the transaction attribute is a second transaction attribute, the number of residual processing serial numbers in the first knowledge graph of the undetermined target node is greater than a preset number under the first transaction attribute, the number of the residual processing serial numbers in the first knowledge graph of the undetermined target node is not greater than the preset number under the second transaction attribute, the number of the residual processing serial numbers in the second knowledge graph is greater than the preset number, and the number of the residual processing serial numbers in the second knowledge graph is not greater than the preset number under the third transaction attribute; and determining the transaction attribute of each undetermined target node based on the transaction item to which each undetermined target node belongs.

Compared with the prior art, the beneficial effects provided by the invention comprise: by adopting the transaction information processing method based on the block chain and the big data, which is provided by the embodiment of the invention, after the transaction information sent by the terminal equipment is received, the target block chain for the transaction information is determined; further determining an undetermined target node to which the transaction information belongs from the node group managed by the target block chain, and adding the transaction information into a data vector of the undetermined target node to which the transaction information belongs, wherein different undetermined target nodes are used for processing the transaction information aiming at the target block chain under different transactions, and each undetermined target node is respectively configured with a knowledge graph; determining a transaction attribute corresponding to the undetermined target node based on a transaction item to which the undetermined target node belongs, wherein the same transaction item corresponds to the undetermined target node under the same transaction attribute, the transaction attribute is used for representing a current transaction process of the undetermined target node aiming at the transaction information, and the transaction attribute is determined by signature information of a knowledge graph of the undetermined target node; then, based on the transaction attribute, determining a first target node with a priority processing authority in the undetermined target nodes; further selecting target transaction information from the data vectors processed by the first target node; and finally, distributing a processing serial number for the target transaction information from a knowledge graph corresponding to the target transaction information, submitting the target transaction information distributed with the processing serial number to the target block chain so that the target block chain processes the target transaction information according to the processing serial number, and classifying and sequencing the transaction information ingeniously through the steps, so that the aim of efficiently processing the transaction information is fulfilled.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly described below. It is appreciated that the following drawings depict only certain embodiments of the invention and are therefore not to be considered limiting of its scope. For a person skilled in the art, it is possible to derive other relevant figures from these figures without inventive effort.

FIG. 1 is an interaction diagram of a transaction information processing system based on blockchains and big data according to an embodiment of the invention;

fig. 2 is a schematic flowchart illustrating steps of a transaction information processing method based on a blockchain and big data according to an embodiment of the present invention;

FIG. 3 is a block diagram illustrating the structure of a digital financial platform according to an embodiment of the present invention;

fig. 4 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

Furthermore, the terms "first," "second," and the like are used merely to distinguish one description from another, and are not to be construed as indicating or implying relative importance.

In the description of the present invention, it is also to be noted that, unless otherwise explicitly stated or limited, the terms "disposed" and "connected" are to be interpreted broadly, and for example, "connected" may be a fixed connection, a detachable connection, or an integral connection; can be mechanically or electrically connected; the connection may be direct or indirect via an intermediate medium, and may be a communication between the two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

The following detailed description of embodiments of the invention refers to the accompanying drawings.

Fig. 1 is an interaction diagram of a transaction information processing system based on a blockchain and big data according to an embodiment of the disclosure. A blockchain and big data based transaction information processing system may include a computer device 100 and a plurality of blockchains 200 and terminal devices 300 communicatively coupled to the computer device 100. The blockchain and big data based transaction information processing system shown in fig. 1 is only one possible example, and in other possible embodiments, the blockchain and big data based transaction information processing system may also include only a portion of the components shown in fig. 1 or may also include other components.

In this embodiment, the terminal device 300 may include a mobile device, a tablet computer, a laptop computer, etc., or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include control devices of smart electrical devices, smart monitoring devices, smart televisions, smart cameras, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistant, a gaming device, and the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like.

In this embodiment, the computer device 100, the blockchain 200, and the terminal device 300 in the transaction information processing system based on the blockchain and the big data may cooperatively perform the transaction information processing method based on the blockchain and the big data described in the following method embodiments, and the detailed description of the method embodiments may be referred to in the execution step portions of the computer device 100, the blockchain 200, and the terminal device 300.

In order to solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of a transaction information processing method based on a blockchain and big data according to an embodiment of the present disclosure, and the transaction information processing method based on a blockchain and big data according to this embodiment may be executed by the computer device 100 shown in fig. 1, and the detailed description is provided below.

In step 201, after receiving the transaction information sent by the terminal device 300, the target block chain 200 for the transaction information is determined.

In the embodiment of the present invention, as described above, the terminal device 300 may be a mobile device held by a user, and the transaction information sent by the terminal device may refer to information including transaction-related data, such as transaction items, transaction amount, amount types, transaction time, and the like. In an embodiment of the invention, each transaction message is processed by a blockchain node in the blockchain.

Step 202, determining an undetermined target node to which the transaction information belongs from the node group managed by the target block chain 200, and adding the transaction information into the data vector of the undetermined target node to which the transaction information belongs.

Different undetermined target nodes are used for processing transaction information aiming at the target block chain 200 under different transactions, and a knowledge graph is configured for each undetermined target node.

In the embodiment of the present invention, in order to improve the efficiency of the transaction, different nodes may perform special processing on different transaction information, that is, different nodes may only need to process corresponding transactions. And the knowledge graph configured by each node can be used for storing and recording transaction information distribution processing serial numbers, and the transaction information distribution processing serial numbers can be used for representing and processing corresponding transaction sequences.

Step 203, determining a transaction attribute corresponding to the undetermined target node based on the transaction item to which the undetermined target node belongs.

The same transaction item corresponds to the undetermined target node under the same transaction attribute, the transaction attribute is used for representing the current transaction process of the undetermined target node aiming at the transaction information, and the transaction attribute is determined by signature information of a knowledge graph of the undetermined target node.

As described above, in order to improve the processing efficiency of the blockchain, each node may process one transaction item correspondingly, and thus the transaction attribute of the node may be determined according to the transaction item to which the node belongs. For example, the transaction item to which the pending target node belongs may be a deposited digital property, and the transaction attributes associated with the deposited digital property may be determined to include data such as the type, quantity, time of deposition, and operator of the deposited digital property.

And step 204, determining a first target node with priority processing authority in the pending target nodes based on the transaction attribute.

In the embodiment of the present invention, a plurality of pending target nodes meeting the condition may all be related to processing a related transaction stored in the digital property, and in order to further improve the processing efficiency, a first target node having a priority processing authority may be selected according to the transaction attribute, for example, in the transaction attribute, the priority processing authority of each pending target node may be specifically sorted according to the related digital property type, and the sorting may be based on an adaptation degree of the corresponding node for processing the corresponding digital property type.

Step 205, selecting target transaction information from the data vectors processed by the first target node.

In the embodiment of the present invention, all data processed in the first target node may be vectorized to improve the calculation speed, and before the receiving of the transaction information, a plurality of transaction information already exist in the first target node, and the target transaction information may be selected from the already vectorized transaction information, and it should be understood that the target transaction information may be any one of the plurality of transaction information already existing in the first target node.

Step 206, allocating a processing serial number to the target transaction information from the knowledge graph corresponding to the target transaction information, and submitting the target transaction information allocated with the processing serial number to the target block chain 200, so that the target block chain 200 processes the target transaction information according to the processing serial number.

After the target transaction information is determined, the processing serial number of the target transaction information may be obtained from a knowledge graph corresponding to the target transaction information, that is, a knowledge graph configured by an undetermined target node that processes the target transaction information, and the target transaction information to which the processing serial number has been assigned may be processed to the target block chain 200, so that the target block chain 200 may process the target transaction information based on the processing serial number. Through the steps, the transaction information can be accurately pushed, the node of the block chain which is most suitable for processing the target transaction information processes the target transaction information, and meanwhile, the processing sequence of each type of transaction information based on the processing serial number can be ensured, so that the transaction data can be accurately and efficiently processed.

It should be understood that, during the process executed in the foregoing step 201, i.e., after the transaction information is acquired and before the target block chain 200 to which the transaction information is directed is determined, the following implementation manner is further provided in the embodiments of the present invention.

And receiving the read-write operation sent by the terminal device 300 and aiming at the nodes of the block chain network.

And converting the read-write operation into transaction information in a preset format based on the data of the read-write operation.

On this basis, the transaction attributes include a first transaction attribute, a second transaction attribute and a third transaction attribute, the first transaction attribute is used for characterizing that the current transaction process of the pending target node for the transaction information is lower than the lowest transaction process, the second transaction attribute is used for characterizing that the current transaction process of the pending target node for the transaction information is not lower than the lowest transaction process and is not higher than the highest transaction process, and the third transaction attribute is used for characterizing that the current transaction process of the pending target node for the transaction information is higher than the highest transaction process.

And a substep 204-1, if the transaction attribute of the pending target node is the first transaction attribute or the second transaction attribute, determining that the pending target node is the first target node with the priority processing authority.

And a substep 204-2, if the transaction attribute of the undetermined target node is a third transaction attribute, determining that the undetermined target node is a second undetermined target node without the priority processing authority.

On this basis, for each undetermined target node, a first knowledge graph and a second knowledge graph are configured, the signature information generation progress of the first knowledge graph is the lowest transaction progress of the undetermined target node corresponding to the first knowledge graph, and the signature information generation progress of the second knowledge graph is the highest transaction progress of the undetermined target node corresponding to the second knowledge graph.

In sub-step 206-1, a processing serial number is respectively extracted from the first and second knowledge maps corresponding to the first and second target nodes to which the target transaction information belongs and is allocated to the target transaction information.

Accordingly, the foregoing step 203 can be implemented by the following specific steps.

And a substep 203-1 of determining the transaction attribute of the node to be targeted according to the residual processing serial number of the first knowledge graph and the second knowledge graph of the node to be targeted.

If the number of the residual processing serial numbers is larger than the preset number in the first knowledge graph of the undetermined target node, determining the transaction attribute of the undetermined target node as a first transaction attribute.

And a substep 203-2, if the number of the remaining processing sequence numbers in the first knowledge graph of the undetermined target node is not more than the preset number and the number of the remaining processing sequence numbers in the second knowledge graph is more than the preset number, determining the transaction attribute of the undetermined target node as a second transaction attribute.

And a substep 203-3, if the number of the remaining processing sequence numbers in the second knowledge graph of the undetermined target node is not more than the preset number, determining the transaction attribute of the undetermined target node as a third transaction attribute.

Sub-step 203-4, creating a first transaction item based on the pending target node under the first transaction attribute.

Sub-step 203-5, creating a second transaction item based on the pending target node under the second transaction attribute.

And a substep 203-6 of obtaining a first transaction item and a second transaction item of the pending target node.

The method comprises the steps that a first transaction item comprises an undetermined target node with a first transaction attribute, a second transaction item comprises an undetermined target node with a second transaction attribute, the number of residual processing serial numbers is larger than a preset number in a first knowledge graph of the undetermined target node under the first transaction attribute, the number of the residual processing serial numbers is not larger than the preset number in the first knowledge graph of the undetermined target node under the second transaction attribute, the number of the residual processing serial numbers is larger than the preset number in the second knowledge graph, and the number of the residual processing serial numbers is not larger than the preset number in a second knowledge graph of the undetermined target node under the third transaction attribute.

And a substep 203-7 of determining the transaction attribute of each pending target node based on the transaction item to which each pending target node belongs.

On the basis of the above, as an alternative embodiment, the foregoing step 205 may be implemented by the following steps.

In sub-step 205-1, based on the linked list structure of the first transaction item, the nodes to be targeted in the first transaction item are traversed, and when traversing to each node to be targeted, the target transaction information is selected from the data vectors of the nodes to be targeted according to the upper limit of the calculation of the target block chain 200.

In the first transaction item, the nodes to be determined are organized in a linked list mode, one linked list node represents one node to be determined, and the node to be determined in the first transaction item is the first target node.

In sub-step 205-2, after the selection corresponding to the first transaction item is finished, traversing the second transaction item of the balanced binary search tree structure, and acquiring the priority of the node to be targeted in the second transaction item.

And a substep 205-3 of obtaining a data volume of a knowledge graph of a pending target node in the second transaction item.

And a substep 205-4 of selecting a candidate pending target node from the second transaction item based on the knowledge-graph data volume and the priority.

Sub-step 205-5, selects target transaction information from the data vectors of candidate pending target nodes based on the remaining available computational ceiling of target blockchain 200.

The nodes to be determined in the second transaction item are organized in a balanced binary search tree mode, one balanced binary search tree node represents one node to be determined, the keywords of the balanced binary search tree node are the memory addresses of the corresponding nodes to be determined, and the nodes to be determined in the second transaction item are the first target nodes.

As described above, in order to more clearly describe the scheme provided by the present invention, the transaction information has at least two types, and the transaction information of different types corresponds to different submission priorities, the foregoing step 205 further provides the following alternative embodiments.

And a substep 205-6 of selecting transaction information with a submission priority meeting preset requirements from the data vectors as target transaction information based on the submission priorities corresponding to the transaction information in the data vectors processed by the first target node.

In addition, in the embodiment of the present invention, the following specific embodiments are provided.

Step 301, transaction information is obtained.

Step 302, determining a first association relationship between a security index of transaction information and a security index of historical transaction information, including:

(1) vector representation of each index in the transaction information and the historical transaction information is respectively constructed to obtain a plurality of first characteristic vectors and a plurality of second characteristic vectors, a first association value between each first characteristic vector and each second characteristic vector is determined, and a first association relation is obtained.

Wherein the historical transaction information is determined based on the sample transaction information within a preset time range.

Step 303, extracting candidate indexes meeting preset conditions of the transaction information from the historical transaction information to obtain a first candidate index set.

Based on the first correlation value, a first key-value pair value of each index in the transaction information with respect to each index in the historical transaction information is determined, step 304.

The first key-value pair value is used for reflecting the attention degree of each index in the transaction information to each index in the historical transaction information.

And 305, determining corresponding candidate indexes from the first candidate index set as first target indexes according to the sequence of the first key value pair value from high to low.

Step 306, generating a corresponding index at a corresponding position in the transaction information according to the weight of the first target index in the historical transaction information and the content information corresponding to the first target point, so as to obtain the reference transaction information.

Wherein, the safety index of the transaction information comprises: the content information of each known index in the transaction information and the weight in the transaction information, the safety index of the historical transaction information comprises: content information of each index in the historical transaction information, and a weight in the historical transaction information.

Step 307, determining a second association relationship between the security index of the reference transaction information and the security index of the transaction information, including:

(1) and respectively constructing vector representation of each index in the transaction information and the reference transaction information to obtain a plurality of third feature vectors and a plurality of fourth feature vectors, and determining a second association value between each third feature vector and each fourth feature vector to obtain a second association relation.

And 308, determining a second key-value pair value of each index in the transaction information relative to each index in the reference transaction information based on the second correlation value.

And the second key-value pair value is used for reflecting the attention degree of each index in the transaction information to each index in the reference transaction information.

Step 309, determining candidate indexes from the reference transaction information according to the second key value pair value and the weight of the indexes in the reference transaction information, and obtaining a second candidate index set.

Wherein, the safety index of the reference transaction information comprises: the content information of each index in the reference transaction information and the weight in the reference transaction information.

A second target metric is determined from the second set of candidate metrics based on the known metrics and the sequence of metrics in the transaction information, step 310.

And 311, generating a corresponding index at a corresponding position in the transaction information based on the index sequence and the content information corresponding to the second target index, so as to perform index verification on the transaction information, and obtain verified transaction information.

On the basis of the above, in order to more clearly describe the solution provided by the present invention, the aforementioned description 301 can be obtained by the following embodiments.

Substep 301-1, obtaining a known indicator.

Sub-step 301-2, determining the original transaction information based at least on the known metrics.

Substep 301-3, a vector representation of known indicators in the original transaction information is constructed.

And a substep 301-4 of determining a correlation value between each two known indexes in the original transaction information according to the vector representation of the known indexes.

Sub-step 301-5, determining a third key value pair value of each known index relative to other known indexes in the original transaction information based on the correlation value between every two known indexes.

Wherein, the third key value pair value is used for reflecting the attention degree of each index in the original transaction information to other known indexes in the information.

And a substep 301-6 of adjusting the vector representation of the known index in the original transaction information according to the third key value to obtain the transaction information.

In order to clearly explain the scheme provided by the present invention, the embodiments of the present invention further provide the following detailed description, for example, on the basis of the above description.

(1) And acquiring a first set of to-be-determined indexes.

The first to-be-determined target set comprises undetermined target nodes with transaction attributes being third transaction attributes, the undetermined target nodes in the first to-be-determined target set are organized in a balanced binary search tree mode, one balanced binary search tree node represents one undetermined target node, keywords of the balanced binary search tree node represent first designated time of the undetermined target nodes, the first designated time is future time when the transaction attributes of the undetermined target nodes are converted from the third transaction attributes into the second transaction attributes, and the first designated time of a root node of the balanced binary search tree is closest to the current time.

(2) And when the first appointed time of the root node of the first to-be-appointed index set is reached, deleting the root node in the first to-be-appointed index set, and adding the to-be-appointed target node corresponding to the root node into the second transaction item.

Accordingly, the following embodiments are also provided.

(1) And acquiring a second undetermined index set, wherein the second undetermined index set comprises undetermined target nodes with transaction attributes of a second transaction attribute and a third transaction attribute, the undetermined target nodes in the second undetermined index set are organized in a balanced binary search tree mode, one balanced binary search tree node represents one undetermined target node, a keyword of the balanced binary search tree node represents a second appointed time of the undetermined target node, the second appointed time is a future time when the transaction attributes of the undetermined target nodes are converted from the current transaction attributes into the first transaction attributes, and the second appointed time of a root node of the balanced binary search tree is closest to the current time.

(2) And when the second appointed time of the root node of the second undetermined index set is reached, deleting the root node in the second undetermined index set, and adding the undetermined target node corresponding to the root node into the first transaction item.

On the basis of the foregoing, as an alternative embodiment, the embodiment of the present invention provides an example of determining historical transaction information based on sample transaction information within a preset time range, which may be implemented by the following steps.

Step 401, collecting sample transaction information within a preset time range.

Step 402, constructing a plurality of historical sample transaction information according to the designated time period and the sample transaction information.

Step 403, aligning the plurality of historical sample transaction information according to time, determining the index with the highest frequency of occurrence in the same time slice from the aligned plurality of historical sample transaction information, and constructing and obtaining the target historical sample transaction information according to the index with the highest frequency of occurrence in the same time slice.

Step 404, constructing a vector representation of each index in the target historical sample transaction information.

Step 405, determining a correlation value between every two indexes in the target historical sample transaction information according to the vector representation of each index.

And 406, determining a fourth key value pair value of each index relative to other indexes in the target historical sample transaction information based on the correlation value between every two indexes.

And the fourth key value pair value is used for reflecting the attention degree of each index in the target historical sample transaction information to other indexes in the target historical sample transaction information.

Step 407, adjusting the vector representation of the index in the target historical sample transaction information according to the fourth key value to obtain historical transaction information.

In addition, the embodiment of the present invention further provides another way, such as the following embodiment, to determine the historical transaction information based on the sample transaction information within the preset time range:

step 408, collecting sample transaction information within a preset time range; constructing a plurality of pieces of historical sample transaction information according to the appointed time period and the sample transaction information; and comparing the plurality of historical sample transaction information through an attention mechanism to obtain historical transaction information.

The embodiment of the present invention provides a digital financial platform 110, which is applied to a computer device 100, wherein the computer device 100 is in communication connection with a plurality of block chains 200 and a terminal device 300, and the digital financial platform 110 includes:

an obtaining module 1101, configured to determine a target block chain 200 to which transaction information is directed after receiving the transaction information sent by the terminal device 300.

An adding module 1102, configured to determine an undetermined target node to which the transaction information belongs from a node group managed by the target block chain 200, and add the transaction information to a data vector of the undetermined target node to which the transaction information belongs, where different undetermined target nodes are used to process the transaction information for the target block chain 200 under different transactions, and a knowledge graph is configured for each undetermined target node.

The processing module 1103 is configured to determine, based on a transaction item to which the undetermined target node belongs, a transaction attribute corresponding to the undetermined target node, where the same transaction item corresponds to the undetermined target node under the same transaction attribute, the transaction attribute is used to characterize a current transaction process of the undetermined target node for transaction information, and the transaction attribute is determined by signature information of a knowledge graph of the undetermined target node.

And a determining module 1104, configured to determine, based on the transaction attribute, a first target node having a priority processing right in the pending target nodes.

A selecting module 1105 configured to select target transaction information from the data vectors processed by the first target node.

The allocating module 1106 is configured to allocate a processing serial number to the target transaction information from the knowledge graph corresponding to the target transaction information, and submit the target transaction information allocated with the processing serial number to the target block chain 200, so that the target block chain 200 processes the target transaction information according to the processing serial number.

Further, the transaction attributes comprise a first transaction attribute, a second transaction attribute and a third transaction attribute, the first transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is lower than the lowest transaction progress, the second transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is not lower than the lowest transaction progress and is not higher than the highest transaction progress, and the third transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is higher than the highest transaction progress;

the determining module 1104 is specifically configured to:

if the transaction attribute of the undetermined target node is the first transaction attribute or the second transaction attribute, determining that the undetermined target node is the first target node with the priority processing authority; and if the transaction attribute of the undetermined target node is the third transaction attribute, determining that the undetermined target node is a second undetermined target node without the priority processing authority.

Further, configuring a first knowledge graph and a second knowledge graph for each undetermined target node, wherein the generation progress of the signature information of the first knowledge graph is the lowest transaction progress of the undetermined target node corresponding to the first knowledge graph, and the generation progress of the signature information of the second knowledge graph is the highest transaction progress of the undetermined target node corresponding to the second knowledge graph;

the assignment module 1106 is specifically configured to:

respectively taking out a processing serial number from a first knowledge graph and a second knowledge graph corresponding to a first target node to which the target transaction information belongs and distributing the processing serial number to the target transaction information;

the processing module 1103 is specifically configured to:

determining the transaction attribute of the undetermined target node according to the number of the residual processing serial numbers of the first knowledge graph and the second knowledge graph of the undetermined target node, wherein if the number of the residual processing serial numbers in the first knowledge graph of the undetermined target node is larger than the preset number, the transaction attribute of the undetermined target node is determined to be the first transaction attribute; if the number of the residual processing serial numbers in the first knowledge graph of the undetermined target node is not more than the preset number, and the number of the residual processing serial numbers in the second knowledge graph is more than the preset number, determining the transaction attribute of the undetermined target node as a second transaction attribute; if the number of the residual processing serial numbers is not greater than the preset number in the second knowledge graph of the target node to be determined, determining the transaction attribute of the target node to be determined as a third transaction attribute; creating a first transaction item based on a pending target node under the first transaction attribute; creating a second transaction item based on the pending target node under the second transaction attribute; acquiring a first transaction item and a second transaction item of an undetermined target node, wherein the first transaction item comprises the undetermined target node with a transaction attribute as a first transaction attribute, the second transaction item comprises the undetermined target node with a transaction attribute as a second transaction attribute, under the first transaction attribute, the number of residual processing serial numbers is larger than a preset number in a first knowledge graph of the undetermined target node, under the second transaction attribute, the number of the residual processing serial numbers is not larger than the preset number in the first knowledge graph of the undetermined target node, the number of the residual processing serial numbers is larger than the preset number in the second knowledge graph, and under the third transaction attribute, the number of the residual processing serial numbers is not larger than the preset number in a second knowledge graph of the undetermined target node; and determining the transaction attribute of each undetermined target node based on the transaction item to which each undetermined target node belongs.

Further, the selecting module 1105 is specifically configured to:

traversing undetermined target nodes in the first transaction item based on a linked list structure of the first transaction item, and selecting target transaction information from data vectors of the undetermined target nodes according to a calculation upper limit of a target block chain 200 when traversing to each undetermined target node, wherein in the first transaction item, the undetermined target nodes are organized in a linked list mode, one linked list node represents one undetermined target node, and the undetermined target node in the first transaction item is the first target node; after the selection corresponding to the first transaction item is finished, traversing a second transaction item of the balanced binary search tree structure, and acquiring the priority of a node to be determined in the second transaction item; acquiring the data volume of a knowledge graph of a node to be determined in a second transaction item; selecting candidate undetermined target nodes from the second transaction items according to the data volume and the priority of the knowledge graph; and selecting target transaction information from the data vectors of the candidate undetermined target nodes based on the remaining available calculation upper limit of the target block chain 200, wherein the undetermined target nodes in the second transaction item are organized in a balanced binary search tree form, one balanced binary search tree node represents one undetermined target node, the keywords of the balanced binary search tree node are the memory addresses of the corresponding undetermined target nodes, and the undetermined target node in the second transaction item is the first target node.

Further, the obtaining module 1101 is further configured to:

acquiring transaction information; determining a first association relationship between the security index of the transaction information and the security index of the historical transaction information, including: respectively constructing vector representation of each index in the transaction information and the historical transaction information to obtain a plurality of first characteristic vectors and a plurality of second characteristic vectors, determining a first association value between each first characteristic vector and each second characteristic vector to obtain a first association relation, wherein the historical transaction information is determined based on sample transaction information in a preset time range; extracting candidate indexes meeting preset conditions of the transaction information from the historical transaction information to obtain a first candidate index set; determining a first key-value pair value of each index in the transaction information relative to each index in the historical transaction information based on the first correlation value, wherein the first key-value pair value is used for reflecting the attention degree of each index in the transaction information to each index in the historical transaction information; determining corresponding candidate indexes from the first candidate index set according to the sequence of the first key-value pair values from high to low, wherein the corresponding candidate indexes serve as first target indexes; generating corresponding indexes at corresponding positions in the transaction information according to the weight of the first target index in the historical transaction information and the content information corresponding to the first target index to obtain reference transaction information, wherein the safety indexes of the transaction information comprise: the content information of each known index in the transaction information and the weight in the transaction information, the safety index of the historical transaction information comprises: the content information of each index in the historical transaction information and the weight in the historical transaction information; determining a second association relationship between the security index of the reference transaction information and the security index of the transaction information, including: respectively constructing vector representation of each index in the transaction information and the reference transaction information to obtain a plurality of third feature vectors and a plurality of fourth feature vectors, and determining a second association value between each third feature vector and each fourth feature vector to obtain a second association relation; determining a second key-value pair value of each index in the transaction information relative to each index in the reference transaction information based on the second correlation value, wherein the second key-value pair value is used for reflecting the attention degree of each index in the transaction information to each index in the reference transaction information; determining candidate indexes from the reference transaction information according to the second key value pair value and the weight of the indexes in the reference transaction information to obtain a second candidate index set, wherein the safety indexes of the reference transaction information comprise: the content information of each index in the reference transaction information and the weight in the reference transaction information; determining a second target index from the second set of candidate indexes based on the known indexes and the index sequence in the transaction information; and generating corresponding indexes at corresponding positions in the transaction information based on the index sequence and the content information corresponding to the second target index so as to perform index verification on the transaction information to obtain verified transaction information.

Further, the obtaining module 1101 is specifically configured to:

obtaining a known index; determining original transaction information based at least on the known metrics; constructing vector representation of known indexes in original transaction information; determining a correlation value between every two known indexes in the original transaction information according to the vector representation of the known indexes; determining a third key value pair value of each known index relative to other known indexes in the original transaction information based on the correlation value between every two known indexes, wherein the third key value pair value is used for reflecting the attention degree of each index in the original transaction information to other known indexes in the information; and adjusting the vector representation of the known index in the original transaction information according to the third key value to obtain the transaction information.

Further, the obtaining module 1101 is specifically configured to:

collecting sample transaction information within a preset time range; constructing a plurality of pieces of historical sample transaction information according to the appointed time period and the sample transaction information; aligning a plurality of historical sample transaction information according to time, determining an index with the highest frequency of occurrence in the same time slice from the aligned plurality of historical sample transaction information, and constructing to obtain target historical sample transaction information according to the index with the highest frequency of occurrence in the same time slice; constructing vector representation of each index in the target historical sample transaction information; determining a correlation value between every two indexes in the target historical sample transaction information according to the vector representation of each index; determining a fourth key value pair value of each index in the target historical sample transaction information about other indexes based on the correlation value between every two indexes, wherein the fourth key value pair value is used for reflecting the attention degree of each index in the target historical sample transaction information to other indexes in the target historical sample transaction information; and adjusting the vector representation of the index in the target historical sample transaction information according to the fourth key value to obtain the historical transaction information.

It should be noted that, for the implementation principle of the foregoing digital financial platform 110, reference may be made to the implementation principle of the foregoing transaction information processing method based on a blockchain and big data, and details are not described herein again. It should be understood that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the obtaining module 1101 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the obtaining module 1101. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

The embodiment of the present invention provides a computer device 100, where the computer device 100 includes a processor and a non-volatile memory storing computer instructions, and when the computer instructions are executed by the processor, the computer device 100 executes the aforementioned transaction information processing method based on the blockchain and the big data. As shown in fig. 4, fig. 4 is a block diagram of a computer device 100 according to an embodiment of the present invention. The computer device 100 includes a digital financial platform 110, a memory 111, a processor 112, and a communication unit 113.

To facilitate the transfer or interaction of data, the elements of the memory 111, the processor 112 and the communication unit 113 are electrically connected to each other, directly or indirectly. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The digital financial platform 110 includes at least one software function module which may be stored in the memory 111 in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the computer device 100. The processor 112 is used for executing the digital financial platform 110 stored in the memory 111, such as software functional modules and computer programs included in the digital financial platform 110.

The readable storage medium comprises a computer program, and when the computer program runs, the computer device where the readable storage medium is located is controlled to execute the above transaction information processing method based on the blockchain and the big data.

In summary, with the transaction information processing method based on the blockchain and the big data provided by the embodiment of the present invention, after receiving the transaction information sent by the terminal device, a target blockchain for the transaction information is determined; determining an undetermined target node to which the transaction information belongs from a node group managed by the target block chain, and adding the transaction information into a data vector of the undetermined target node to which the transaction information belongs, wherein different undetermined target nodes are used for processing the transaction information aiming at the target block chain under different transactions, and each undetermined target node is respectively configured with a knowledge graph; determining a transaction attribute corresponding to the undetermined target node based on a transaction item to which the undetermined target node belongs, wherein the same transaction item corresponds to the undetermined target node under the same transaction attribute, the transaction attribute is used for representing a current transaction process of the undetermined target node aiming at transaction information, and the transaction attribute is determined by signature information of a knowledge graph of the undetermined target node; then, determining a first target node with a priority processing authority in the undetermined target nodes based on the transaction attribute; further selecting target transaction information from the data vector processed by the first target node; and finally, distributing a processing serial number to the target transaction information from a knowledge graph corresponding to the target transaction information, submitting the target transaction information distributed with the processing serial number to the target block chain so that the target block chain processes the target transaction information according to the processing serial number, and classifying and sequencing the transaction information ingeniously through the steps to achieve the aim of efficiently processing the transaction information.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated. The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A transaction information processing method based on blockchains and big data is applied to computer equipment, the computer equipment is in communication connection with a plurality of blockchains and terminal equipment, and the method comprises the following steps:

2. The method of claim 1, wherein the transaction attributes comprise a first transaction attribute, a second transaction attribute and a third transaction attribute, the first transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is lower than the lowest transaction progress, the second transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is not lower than the lowest transaction progress and is not higher than the highest transaction progress, and the third transaction attribute is used for representing that the current transaction progress of the pending target node for the transaction information is higher than the highest transaction progress;

3. The method according to claim 2, wherein for each node to be targeted, a first knowledge graph and a second knowledge graph are configured, the signature information generation progress of the first knowledge graph is the lowest transaction progress of the node to be targeted corresponding to the first knowledge graph, and the signature information generation progress of the second knowledge graph is the highest transaction progress of the node to be targeted corresponding to the second knowledge graph;

4. The method of claim 3, wherein selecting target transaction information from the data vectors processed by the first target node comprises:

5. The method according to claim 1, further comprising, after receiving the transaction information sent by the terminal device:

acquiring the transaction information;

6. The method of claim 5, wherein the obtaining the transaction information comprises:

obtaining a known index;

7. The method of claim 5, wherein determining historical transaction information based on sample transaction information within a preset time range comprises:

collecting sample transaction information within a preset time range;

8. A digital financial platform for use with a computer device, the computer device communicatively coupled to a plurality of blockchains and a terminal device, the digital financial platform comprising:

9. The digital financial platform of claim 8, wherein the trade attributes include a first trade attribute for characterizing that a current trade progress of the pending target node for the trade information is below a lowest trade progress, a second trade attribute for characterizing that a current trade progress of the pending target node for the trade information is not below the lowest trade progress and is not above a highest trade progress, and a third trade attribute for characterizing that a current trade progress of the pending target node for the trade information is above the highest trade progress;

the determining module is specifically configured to:

10. The digital financial platform of claim 9, wherein for each pending target node, a first knowledge graph and a second knowledge graph are configured, a signature information generation progress of the first knowledge graph is a lowest transaction progress of the pending target node corresponding to the first knowledge graph, and a signature information generation progress of the second knowledge graph is a highest transaction progress of the pending target node corresponding to the second knowledge graph;

the allocation module is specifically configured to:

the processing module is specifically configured to: