Node risk evaluation method and financial transaction data processing method

Abstract

The invention provides a node risk evaluation method and a financial transaction data processing method, relates to the technical field of block chains, in particular to the field of financial transaction data block chains, and the node risk evaluation method is applied to a first node in multiple nodes of a financial transaction data processing system. The method comprises the following steps: a first node obtains risk assessment data of each node in a plurality of nodes; the risk assessment data comprises at least one of technical security capability data, stability data and reputation data; and the first node determines a risk score corresponding to each node according to the risk assessment data of each node. The method is suitable for a block chain system node consensus process. The method is used for evaluating node risks in a block chain system.

Description

Technical Field

[0001] The present application relates to the field of blockchain technology, specifically to the field of financial transaction data blockchain, and in particular to a node risk assessment method and a financial transaction data processing method. Background Art

[0002] Blockchain is essentially a decentralized database that uses cryptographic methods to combine a series of data blocks (i.e. blocks) into a chain structure in a sequentially connected manner. Each data block contains information about network transactions, which is used to verify the validity of its information and generate the next block.

[0003] Blockchain technology can be used to achieve fast, secure, and low-cost cross-border payments and settlements, improve transaction efficiency, and reduce transaction costs. At the same time, it can also be used in financial services such as securities trading, lending, and insurance.

[0004] However, the blockchain system is prone to various risks in the process of generating blockchains, such as forged transactions or stolen assets. Therefore, how to evaluate the node risks in the blockchain system has become our need. Summary of the invention

[0005] The present application provides a node risk assessment method and a financial transaction data processing method, which are used to provide a method for evaluating node risks in a blockchain system, reduce the probability of various risks generated by the blockchain system in the process of generating a blockchain, and improve the stability of the blockchain system.

[0006] In a first aspect, the present application provides a node risk assessment method, which is applied to a first node among multiple nodes of a financial transaction data processing system; the method includes: the first node obtains risk assessment data of each node among the multiple nodes; the risk assessment data includes at least one of the following: technical security capability data, stability data, and reputation data; the first node determines the risk score corresponding to each node based on the risk assessment data of each node.

[0007] Optionally, technical security capability data includes computing power and version number of node running software; stability data includes online time, failure rate, and failure recovery time; reputation data includes the number of malicious attacks and the number of times false information is released.

[0008] Optionally, the first node determines the risk score corresponding to each node based on the risk assessment data of each node, including: the first node determines the technical security capability score of each node based on the technical security capability data of each node; the first node determines the stability score of each node based on the stability data of each node; the first node determines the credibility score of each node based on the credibility data of each node; the first node determines the risk score corresponding to each node based on the technical security capability score of each node, a preset technical security capability weight, the stability score of each node, a preset stability weight, the credibility score of each node, and a preset credibility weight.

[0009] Optionally, the first node determines the risk score corresponding to each node based on the risk assessment data of each node, including: the first node determines the risk score corresponding to each node based on the risk assessment data and a risk assessment model of each node; the risk assessment model is obtained by training a preset neural network based on a training sample set; the training sample set includes multiple training samples, each training sample includes training risk assessment data, and the label corresponding to each training sample is a risk score.

[0010] Optionally, the method also includes: the first node obtains a training sample set; the training sample set includes multiple training samples, each training sample includes training risk assessment data, and the label corresponding to each training sample is a risk score; the first node trains a preset neural network based on the training sample set to obtain a risk assessment model.

[0011] Optionally, the first node determines the risk score corresponding to each node based on the risk assessment data and risk assessment model of each node, including: the first node obtains a test sample and broadcasts it to the remaining nodes; the test sample includes test risk assessment data; the first node predicts the test sample based on the trained risk assessment model to obtain a test score predicted by the first node; the first node receives the test score sent by each of the remaining nodes; the test score is obtained by the corresponding node predicting the test sample based on the risk assessment model after the risk assessment model is trained; when the maximum difference between the test scores of multiple nodes is less than a preset difference threshold, the first node determines the risk score corresponding to each node based on the risk assessment data and risk assessment model of each node.

[0012] In the node risk assessment method provided by the present application, the first node can obtain the risk assessment data of each node among the multiple nodes, and determine the risk score corresponding to each node according to the risk assessment data of each node, thereby providing a method for evaluating the node risk in the blockchain system. In this way, each node can evaluate the risk scores of the remaining nodes, and in the subsequent election of the master node for generating the block, the node with less risk can be voted as the master node according to the risk score, thereby reducing the probability of various risks generated by the blockchain system in the process of generating the blockchain and improving the stability of the blockchain system.

[0013] In addition, when multiple nodes in the financial transaction data processing system use their own trained risk assessment models to predict risk scores, the risk scores may differ greatly due to inconsistent model performance. The first node can also broadcast test samples to other nodes, and receive test scores predicted by the other nodes using their own trained risk assessment models for the test samples, and only use the risk assessment model for prediction when the maximum difference between the test scores of multiple nodes is less than a preset difference. At this time, the consistency of the risk assessment models of the multiple nodes is good, and the accuracy of the predicted risk scores is high, thereby improving the accuracy of the predicted risk scores.

[0014] In a second aspect, the present application provides a node evaluation device, which includes various functional modules used for the node risk evaluation method described in the first aspect above.

[0015] In a third aspect, the present application provides a financial transaction data processing method, which is applied to a first node among multiple nodes of a financial transaction data processing system; the method includes: the first node determines the risk score corresponding to each node among the multiple nodes according to the node risk assessment method described in the first aspect above; the first node determines the election score of each node among the multiple nodes based on the risk score; when the election score of the first node is the highest, the first node broadcasts the master node election information to the remaining nodes among the multiple nodes; the master node election information includes the identity of the first node; the first node receives votes from the remaining nodes; the vote is cast by the corresponding node when the first node has the highest election score among the election scores of each node calculated based on the node; when the number of votes is greater than a preset threshold, the first node generates a block based on the financial transaction data within a preset time period, and adds the block to the blockchain; the blockchain includes multiple historical blocks; each historical block is used to record historical financial transaction data.

[0016] In a fourth aspect, the present application provides a financial transaction data processing device, which includes various functional units used in the financial transaction data processing method described in the third aspect above.

[0017] In a fifth aspect, the present application provides an electronic device, comprising: a processor and a memory; the memory stores instructions executable by the processor; when the processor is configured to execute the instructions, the electronic device implements the method described in the first aspect or the third aspect above.

[0018] In a sixth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are executed on an electronic device, the electronic device implements the method described in the first aspect or the third aspect.

[0019] In a seventh aspect, the present application provides a computer-readable storage medium, which includes: computer software instructions; when the computer software instructions are executed in an electronic device, the electronic device implements the method described in the first aspect or the third aspect above.

[0020] The beneficial effects of the second to seventh aspects mentioned above can be referred to the first aspect and will not be elaborated on again. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of the composition of a financial transaction data processing system provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of a process flow of a node risk assessment method provided in an embodiment of the present application;

[0024] Figure 3 A flowchart of a method for processing financial transaction data provided by an embodiment of the present application;

[0025] Figure 4 A schematic diagram of the composition of a node risk assessment device provided in an embodiment of the present application;

[0026] Figure 5 A schematic diagram of the composition of a financial transaction data processing device provided in an embodiment of the present application;

[0027] Figure 6 A schematic diagram of the composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] It should be noted that, in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

[0030] In order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical items or similar items with basically the same functions and effects. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order.

[0031] Blockchain is essentially a decentralized database that uses cryptographic methods to combine a series of data blocks (i.e. blocks) into a chain structure in a sequentially connected manner. Each data block contains information about network transactions, which is used to verify the validity of its information and generate the next block.

[0032] Blockchain technology can be used to achieve fast, secure, and low-cost cross-border payments and settlements, improve transaction efficiency, and reduce transaction costs. At the same time, it can also be used in financial services such as securities trading, lending, and insurance.

[0033] However, the blockchain system is prone to various risks in the process of generating blockchains, such as forged transactions or stolen assets. Therefore, how to evaluate the node risks in the blockchain system has become our need.

[0034] Based on this, an embodiment of the present application provides a node risk assessment method and a financial transaction data processing method, which can evaluate the risk score of each node through risk assessment data.

[0035] The following is an introduction with reference to the accompanying drawings.

[0036] Figure 1 This is a schematic diagram of the composition of the financial transaction data processing system provided in the embodiment of the present application. Figure 1 As shown, the system may include multiple nodes ( Figure 1In the figure, a total of three nodes, namely, node 1, node 2, and node 3, are used as an example.) The multiple nodes are connected to each other through communication.

[0037] Each of the multiple nodes may be an electronic device having a computing and processing function, such as a computer or a server.

[0038] Among them, the server can be a single server, or it can be a server cluster composed of multiple servers. In some implementations, the server cluster can also be a distributed cluster. Optionally, the server can also be implemented on a cloud platform, for example, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, and a multi-cloud, etc., or any combination thereof. The embodiments of the present application are not limited to this.

[0039] Each of the plurality of nodes may be operable to acquire and record financial transaction data.

[0040] In some embodiments, the multiple nodes may include a master node, which may be used to generate a block based on the financial transaction data and add the generated block to the blockchain. The specific process may refer to the financial transaction data processing method provided in the following embodiment, and will not be repeated here.

[0041] In some embodiments, any node among the multiple nodes may also obtain risk assessment data of each node, and determine the risk score of each node according to the risk assessment data. The specific process may refer to the node risk assessment method provided in the following embodiment, and will not be repeated here.

[0042] The execution subjects of the node risk assessment method and the financial transaction data processing method provided in the embodiments of the present application can be understood as any first node in the above-mentioned financial transaction data processing system. Optionally, the execution subject of the node risk assessment method and the financial transaction data processing method provided in the embodiments of the present application can also be a processor (such as a central processing unit (CPU)) in the aforementioned first node; or, it can also be an application (application) installed in the aforementioned first node for executing the node risk assessment method and the financial transaction data processing method; or, it can also be a software system or platform deployed in the aforementioned first node; or, it can also be a functional module in the aforementioned first node for executing the node risk assessment method and the financial transaction data processing method, etc. The embodiments of the present application are not limited to this.

[0043] For simplicity of description, the following description will be uniformly introduced with the first node as the execution subject.

[0044] Figure 2 This is a flow chart of the node risk assessment method provided in the embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0045] S101. A first node obtains risk assessment data of each of a plurality of nodes.

[0046] Among them, the risk assessment data includes at least one of the following: technical security capability data, stability data, and reputation data.

[0047] Optionally, the technical security capability data may include: computing power and version number of the node running software.

[0048] Among them, nodes with higher computing power have greater say in the consensus process and are more likely to be attacked. In addition, when a node controls more than 50% of the computing power or holds more than 50% of the tokens in a blockchain system (that is, the above-mentioned financial transaction data processing system), the node can launch a 51% total, thereby conducting double-spending attacks, blocking transactions and other malicious behaviors. This attack seriously threatens the decentralization and security of the blockchain system.

[0049] The newer the version number of the node running software, or the smaller the difference with the latest version number, the more timely the node updates the running software. The updated running software may update security patches for existing vulnerability risks. Therefore, the version number of the node running software is directly related to the node's ability to be below known vulnerabilities and attacks.

[0050] Optionally, the stability data may include: online time, failure rate, and failure recovery time.

[0051] Among them, the online time of a node reflects its stability and reliability. Nodes that are online for a long time are more likely to provide continuous services to the blockchain system. Nodes with a shorter online time may be attacked and go offline. Therefore, nodes with a longer online time have a lower risk, while nodes with a shorter online time have a higher risk.

[0052] For example, each of the multiple nodes in a blockchain system (financial transaction data processing system) can broadcast heartbeat packets to other nodes at preset intervals. If other nodes receive the heartbeat packets broadcast by a certain node, the node can be considered online. If other nodes do not receive the heartbeat packets broadcast by a certain node for more than a preset period of time, the node can be considered offline.

[0053] Failure rate and failure recovery time are important indicators for evaluating node reliability and emergency response capabilities. The lower the node failure rate and the shorter the failure recovery time, the better the node reliability and the stronger the emergency response capability, so the risk is lower. The higher the node failure rate and the longer the failure recovery time, the poorer the node reliability and the weaker the emergency response capability, so the risk is higher.

[0054] For example, the time difference between two consecutive online node times can be used as the fault recovery time.

[0055] Optionally, the reputation data may include: the number of malicious attacks and the number of times false information is published.

[0056] The more malicious attacks and false information released, the more bad records the node has, and the higher the risk of the node. The fewer malicious attacks and false information released, the fewer bad records the node has, and the lower the risk of the node.

[0057] In some embodiments, the risk assessment data may also include a third-party rating. For example, the third-party rating may come from a security assessment or rating by an authoritative organization, or may also come from the evaluation and feedback of the node by users in the blockchain community. This embodiment of the application is not limited to this.

[0058] S102: The first node determines a risk score corresponding to each node according to the risk assessment data of each node.

[0059] In a possible implementation, the first node may preset weights corresponding to several types of data, and the first node may comprehensively determine the risk score according to the weights corresponding to each type of data. In this case, the above S102 may specifically include the following steps:

[0060] Step 1a: The first node determines the technical security capability score of each node based on the technical security capability data of each node.

[0061] For example, the first node may determine the technical safety capability score according to the technical safety capability data and the first mapping relationship.

[0062] Among them, the first mapping relationship can be used to represent the mapping relationship between technical security capability data and technical security capability scores.

[0063] Exemplarily, the first mapping relationship may be specifically as shown in the following Table 1:

[0064] Table 1

[0065]

[0066] As shown in Table 1, if the computing power of a node accounts for 40% to 50% of the (financial transaction data processing) system, or the version number of the running software differs from the latest version by 5 versions, the technical security capability score of the node is 1 point. If the computing power of a node accounts for 30% to 40% of the (financial transaction data processing) system, or the version number of the running software differs from the latest version by 4 versions, the technical security capability score of the node is 2 points. If the computing power of a node accounts for 20% to 30% of the (financial transaction data processing) system, or the version number of the running software differs from the latest version by 3 versions, the technical security capability score of the node is 3 points. If the computing power of a node accounts for 10% to 20% of the (financial transaction data processing) system, or the version number of the running software differs from the latest version by 2 versions, the technical security capability score of the node is 4 points. If the computing power of a node accounts for 0% to 10% of the (financial transaction data processing) system, or the version number of the running software differs from the latest version by 1 version, the technical security capability score of the node is 5 points.

[0067] Step 2a: The first node determines the stability score of each node according to the stability data of each node.

[0068] For example, the first node may determine the stability score according to the stability data and the second mapping relationship.

[0069] The second mapping relationship may be used to represent the mapping relationship between the stability data and the stability score.

[0070] Exemplarily, the second mapping relationship may be specifically as shown in the following Table 2:

[0071] Table 2

[0072]

[0073]

[0074] As shown in Table 2, if a node is online for less than one month, or the failure rate is greater than 25%, or the failure recovery time is greater than 5 days, the stability score of the node is 1. If a node is online for one to two months, or the failure rate is 20% to 25%, or the failure recovery time is 4 to 5 days, the stability score of the node is 2. If a node is online for two to three months, or the failure rate is 15% to 20%, or the failure recovery time is 3 to 4 days, the stability score of the node is 3. If a node is online for three to four months, or the failure rate is 10% to 15%, or the failure recovery time is 2 to 3 days, the stability score of the node is 4. If a node is online for more than four months, or the failure rate is less than 10%, or the failure recovery time is less than 2 days, the stability score of the node is 5.

[0075] Step 3a: The first node determines the reputation score of each node according to the reputation data of each node.

[0076] For example, the first node may determine the reputation score according to the reputation data and the third mapping relationship.

[0077] The third mapping relationship may be used to represent the mapping relationship between the reputation data and the reputation score.

[0078] Exemplarily, the third mapping relationship may be specifically as shown in the following Table 3:

[0079] Table 3

[0080]

[0081] As shown in Table 3, if a node maliciously attacks 4 times or publishes false information 4 times, the reputation score of the node is 1. If a node maliciously attacks 3 times or publishes false information 3 times, the reputation score of the node is 2. If a node maliciously attacks 2 times or publishes false information 2 times, the reputation score of the node is 3. If a node maliciously attacks 1 time or publishes false information 1 time, the reputation score of the node is 4. If a node maliciously attacks 0 times or publishes false information 0 times, the reputation score of the node is 5.

[0082] Step 4a, the first node determines the risk score corresponding to each node based on the technical security capability score of each node, the preset technical security capability weight, the stability score of each node, the preset stability weight, the reputation score of each node, and the preset reputation weight.

[0083] For example, the first node may perform weighted summation of the technical security capability score, the stability score, and the reputation score according to the technical security capability weight, the stability weight, and the reputation weight to obtain a risk score.

[0084] In another possible implementation, the first node may use a neural network model to predict the risk score corresponding to each node. In this case, the above S102 may specifically include the following steps:

[0085] Step 1b: The first node determines the risk score corresponding to each node according to the risk assessment data and risk assessment model of each node.

[0086] Among them, the risk assessment model is obtained by training a preset neural network based on a training sample set; the training sample set includes multiple training samples, each training sample includes training risk assessment data, and the label corresponding to each training sample is a risk score.

[0087] In some embodiments, before the above step 1b, the first node may also obtain a trained risk assessment model.

[0088] Optionally, the first node may directly obtain a trained risk assessment model from other devices.

[0089] For example, the multiple nodes may include a training node, the training node may be used to train a risk assessment model, and the first node may obtain the trained risk assessment model from the training node.

[0090] Optionally, the first node can also train itself to obtain a risk assessment model. In this case, before the above step 1b, the method can also include the following steps:

[0091] Step 1c: The first node obtains a training sample set.

[0092] The training sample set includes multiple training samples, each training sample includes training risk assessment data, and the label corresponding to each training sample is a risk score.

[0093] Step 2c: The first node trains a preset neural network based on a training sample set to obtain a risk assessment model.

[0094] The preset neural network may be, for example, a back propagation neural network (BP), a convolutional neural network (CNN), or a long short-term memory network (LSTM). The embodiment of the present application does not limit the specific type of the preset neural network.

[0095] Optionally, as described above, the training sample set may include multiple training samples, and the first node may input one or more training samples into a preset neural network each time to obtain a predicted value (predicted risk score) output by the preset neural network, and calculate a loss function (loss) based on the predicted value (predicted risk score) and the risk score in the training sample label, and adjust the parameters in the preset neural network based on the loss function until the preset neural network converges.

[0096] Optionally, the condition for the preset neural network to converge may include: the number of times the first node inputs the training sample into the preset neural network reaches a number threshold, and / or the error of the preset neural network is less than an error threshold.

[0097] Among them, the number threshold can be preset in the first node by the administrator. For example, the number threshold can be set to 1000 times, 2000 times, or 5000 times, etc. The implementation example in this community does not limit the specific value of the number threshold. The error threshold can also be preset in the first node by the administrator. For example, the error threshold can be set to 5%, 8%, or 10%, etc. The implementation example of this application does not limit the specific value of the error threshold.

[0098] In some possible embodiments, as described above, the first node can train its own risk assessment model, and the other models can also train their own risk assessment models. In order to avoid large differences in the performance of risk assessment models trained by different nodes, the first node can also perform consistency assessment and then use the risk assessment model for prediction. In this case, the above step 1b can specifically include the following steps:

[0099] Step 1.1b: The first node obtains the test sample and broadcasts it to the remaining nodes.

[0100] Among them, the test samples include test risk assessment data.

[0101] Step 1.2b: The first node predicts the test sample according to the trained risk assessment model to obtain the test score predicted by the first node.

[0102] Step 1.3b: The first node receives the test scores sent by each of the remaining nodes.

[0103] Among them, the test scores sent by the remaining nodes are obtained by the corresponding nodes after training the risk assessment model and predicting the test samples based on the risk assessment model.

[0104] Step 1.4b: When the maximum difference between the test scores of multiple nodes is less than a preset difference threshold, the first node determines the risk score corresponding to each node according to the risk assessment data and risk assessment model of each node.

[0105] The preset difference threshold may be preset in the first node by the administrator. For example, the preset difference threshold may be set to 1 point or 2 points, etc. The embodiment of the present application does not limit the specific value of the preset difference threshold.

[0106] For example, when the maximum difference between the test scores of multiple nodes is less than a preset difference threshold, the first node can broadcast the model consistency test results to the remaining nodes, so that the multiple nodes agree to use the currently trained risk assessment model for prediction.

[0107] In the node risk assessment method provided in the embodiment of the present application, the first node can obtain the risk assessment data of each node in the multiple nodes, and determine the risk score corresponding to each node according to the risk assessment data of each node, thereby providing a method for evaluating the node risk in the blockchain system. In this way, each node can evaluate the risk scores of the remaining nodes, and in the subsequent election of the master node for generating the block, the node with lower risk can be voted as the master node according to the risk score, thereby reducing the probability of various risks generated by the blockchain system in the process of generating the blockchain and improving the stability of the blockchain system.

[0108] In addition, when multiple nodes in the financial transaction data processing system use their own trained risk assessment models to predict risk scores, the risk scores may differ greatly due to inconsistent model performance. The first node can also broadcast test samples to other nodes, and receive test scores predicted by the other nodes using their own trained risk assessment models for the test samples, and only use the risk assessment model for prediction when the maximum difference between the test scores of multiple nodes is less than a preset difference. At this time, the consistency of the risk assessment models of the multiple nodes is good, and the accuracy of the predicted risk scores is high, thereby improving the accuracy of the predicted risk scores.

[0109] In some possible embodiments, after determining the risk score corresponding to each node, the first node may also determine the campaign score of each node according to the risk score corresponding to each node.

[0110] For example, the first node may perform a weighted summation of scores such as the risk score and the computing power score to obtain a campaign score.

[0111] In some possible embodiments, when the first node has the highest election score, the first node may broadcast master node election information to the remaining nodes among the multiple nodes, and the master node election information may include the identity of the first node; the first node receives votes from the remaining nodes, and the votes are cast by the corresponding node when the first node has the highest election score among the election scores of each node calculated based on the node; when the number of votes is greater than a preset threshold, the first node generates a block based on the financial transaction data within a preset time period, and adds the block to the blockchain; the blockchain includes multiple historical blocks; each historical block is used to record historical financial transaction data.

[0112] Based on the understanding of the above embodiments, in an exemplary embodiment, the present application also provides a financial transaction data processing method, which can also be applied to the above first node. Figure 3 The flowchart of the financial transaction data processing method provided in the embodiment of the present application is as follows. Figure 3As shown, the method includes the following steps:

[0113] S201. A first node determines a risk score corresponding to each of a plurality of nodes.

[0114] The specific steps of S201 can refer to those described in S101 to S102 above, and will not be repeated here.

[0115] S202: The first node determines a campaign score for each of the multiple nodes according to the risk score.

[0116] For example, the first node may perform a weighted summation of scores such as the risk score and the computing power score to obtain a campaign score.

[0117] S203: When the first node has the highest election score, the first node broadcasts master node election information to the remaining nodes among the multiple nodes.

[0118] The master node election information includes the identity of the first node.

[0119] S204: The first node receives votes from other nodes.

[0120] The vote is cast by the corresponding node when the election score of the first node is the highest among the election scores of each node calculated based on the node.

[0121] For example, the remaining nodes may include a second node, and the second node may also calculate the election score of each node according to the above-mentioned process from S201 to S202. When the second node receives the master node election information broadcast by the first node, and the election score calculated by the second node is the highest among the election scores of the first node, the second node may vote for the first node.

[0122] S205. When the number of votes is greater than a preset threshold, the first node generates a block according to the financial transaction data within a preset period of time and adds the block to the blockchain.

[0123] The blockchain includes a plurality of historical blocks; each historical block is used to record historical financial transaction data. The preset threshold value can be preset in the first node by the administrator. For example, taking the above-mentioned financial transaction data processing system including n nodes as an example, n is a positive integer, then the preset threshold value can be set to (n / 2)+1, (n / 2)+2, or (n / 2)+3, etc. The embodiment of the present application does not limit the specific value of the preset threshold value.

[0124] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy to realize that the technical goals in this field are combined with the units and algorithm steps of each example described in the embodiments disclosed in this article, and the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical goals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] In an exemplary embodiment, the present application also provides a node risk assessment device, which can be applied to the above-mentioned first node. Figure 4 This is a schematic diagram of the composition of the node risk assessment device provided in the embodiment of the present application. Figure 4 As shown, the device includes: an acquisition module 401 and a processing module 402.

[0126] The acquisition module 401 is used to acquire risk assessment data of each node among the multiple nodes; the risk assessment data includes at least one of the following: technical security capability data, stability data, and reputation data.

[0127] The processing module 402 is used to determine the risk score corresponding to each node according to the risk assessment data of each node.

[0128] In some possible embodiments, technical security capability data includes computing power and version number of node running software; stability data includes online time, failure rate, and failure recovery time; reputation data includes the number of malicious attacks and the number of times false information is released.

[0129] In other possible embodiments, processing module 402 is specifically used to determine the technical security capability score of each node based on the technical security capability data of each node; determine the stability score of each node based on the stability data of each node; determine the credibility score of each node based on the credibility data of each node; and determine the risk score corresponding to each node based on the technical security capability score of each node, a preset technical security capability weight, the stability score of each node, a preset stability weight, the credibility score of each node, and a preset credibility weight.

[0130] In some other possible embodiments, the processing module 402 is specifically used to determine the risk score corresponding to each node based on the risk assessment data and risk assessment model of each node; the risk assessment model is obtained by training a preset neural network based on a training sample set; the training sample set includes multiple training samples, each training sample includes training risk assessment data, and the label corresponding to each training sample is a risk score.

[0131] In some other possible embodiments, the acquisition module 401 is also used to obtain a training sample set; the training sample set includes multiple training samples, each training sample includes training risk assessment data, and the label corresponding to each training sample is a risk score; the processing module 402 is also used to train a preset neural network based on the training sample set to obtain a risk assessment model.

[0132] In some other possible embodiments, the processing module 402 is specifically used to obtain a test sample and broadcast it to the remaining nodes; the test sample includes test risk assessment data; the test sample is predicted according to the trained risk assessment model to obtain a test score predicted by the processing module 402; the test score sent by each of the remaining nodes is received; the test score is obtained by the corresponding node after the risk assessment model is trained and the test sample is predicted based on the risk assessment model; when the maximum difference between the test scores of multiple nodes is less than a preset difference threshold, the risk score corresponding to each node is determined according to the risk assessment data and risk assessment model of each node.

[0133] In an exemplary embodiment, the present application also provides a financial transaction data processing device, which can be applied to the above-mentioned first node. Figure 5 This is a schematic diagram of the composition of the financial transaction data processing device provided in the embodiment of the present application. Figure 5 As shown, the device includes: a risk assessment unit 501, a campaign unit 502, and a generation unit 503.

[0134] The risk assessment unit 501 is used to determine a risk score corresponding to each of the multiple nodes;

[0135] The election unit 502 is used for the first node to determine the election score of each node among the multiple nodes according to the risk score; when the election score of the first node is the highest, the first node broadcasts the master node election information to the remaining nodes among the multiple nodes; the master node election information includes the identity of the first node; the first node receives votes from the remaining nodes; the vote is cast by the corresponding node when the election score of the first node is the highest among the election scores of each node calculated based on the node;

[0136] The generation unit 503 is used to generate a block according to the financial transaction data within a preset time period at the first node when the number of votes is greater than a preset threshold, and add the block to the blockchain; the blockchain includes multiple historical blocks; each historical block is used to record historical financial transaction data.

[0137] As described above, the nodes of the financial transaction data processing system can be electronic devices with computing and processing functions, such as computers or servers. In an exemplary embodiment, the present application also provides an electronic device. Figure 6 The following is a schematic diagram of the composition of the electronic device provided in the embodiment of the present application. Figure 6 As shown, the electronic device may include: a processor 10 , a memory 20 , a communication line 30 , a communication interface 40 , and an input / output interface 50 .

[0138] The processor 10 , the memory 20 , the communication interface 40 and the input / output interface 50 may be connected via a communication line 30 .

[0139] The processor 10 is used to execute the instructions stored in the memory 20 to implement the node risk assessment method and the financial transaction data processing method provided in the above-mentioned embodiments of the present application. The processor 10 can be a CPU, a general-purpose processor network processor (network processor, NP), a digital signal processor (digital signal processing, DSP), a microprocessor, a microcontroller (micro control unit, MCU) / single-chip microcomputer / single-chip microcomputer, a programmable logic device (programmable logic device, PLD) or any combination thereof. The processor 10 can also be any other device with processing functions, such as a circuit, a device or a software module, which is not limited in the embodiments of the present application. In one example, the processor 10 may include one or more CPUs, such as Figure 6 As an optional implementation, the electronic device may include multiple processors, for example, in addition to the processor 10, it may also include a processor 60 ( Figure 6 The dashed line is used as an example.

[0140] The memory 20 is used to store instructions. For example, the instructions may be computer programs. Optionally, the memory 20 may be a read-only memory (ROM) or other types of static storage devices that can store static information and / or instructions, or a random access memory (RAM) or other types of dynamic storage devices that can store information and / or instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage devices, etc., and the embodiments of the present application are not limited to this.

[0141] It should be noted that the memory 20 may exist independently of the processor 10, or may be integrated with the processor 10. The memory 20 may be located inside the electronic device, or may be located outside the electronic device, which is not limited in the embodiment of the present application.

[0142] The communication line 30 is used to transmit information between various components included in the electronic device.

[0143] The communication interface 40 is used to communicate with other devices or other communication networks. The other communication networks may be Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. The communication interface 40 may be a module, a circuit, a transceiver or any device capable of achieving communication.

[0144] The input / output interface 50 is used to implement human-computer interaction between a user and an electronic device, for example, to implement action interaction or information interaction between a user and an electronic device.

[0145] Exemplarily, the input / output interface 50 may be a mouse, a keyboard, a display screen, or a touch display screen, etc. Action interaction or information interaction between a user and an electronic device may be achieved through a mouse, a keyboard, a display screen, or a touch display screen, etc.

[0146] It should be noted that Figure 6 The structure shown in the figure does not constitute a limitation on the electronic device, except Figure 6 In addition to the components shown, the electronic device may include more or fewer components than shown, or a combination of certain components, or a different arrangement of components.

[0147] In an exemplary embodiment, the embodiment of the present application further provides a computer program product, which includes computer instructions. When the computer instructions are executed on an electronic device, the computer implements the method in the aforementioned method embodiment.

[0148] In an exemplary embodiment, the present application also provides a computer-readable storage medium on which computer program instructions are stored; when the computer program instructions are executed by an electronic device, the electronic device implements the method described in the above embodiment. The computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0149] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer-executable instructions. When loading and executing computer-executable instructions on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-executable instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer-executable instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0150] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other changes to the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit can implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0151] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0152] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A node risk assessment method, characterized in that: The method is applied to a first node among a plurality of nodes of a financial transaction data processing system; the method comprises: The first node acquires risk assessment data of each of the multiple nodes; the risk assessment data includes at least one of the following: technical security capability data, stability data, and reputation data; The first node determines a risk score corresponding to each node according to the risk assessment data of each node.

2. The method according to claim 1, characterized in that The technical security capability data includes computing power and the version number of the node running software; The stability data includes online time, failure rate, and failure recovery time; The reputation data includes the number of malicious attacks and the number of times false information is published.

3. The method according to claim 1 or 2, characterized in that: The first node determines the risk score corresponding to each node according to the risk assessment data of each node, including: The first node determines the technical security capability score of each node according to the technical security capability data of each node; The first node determines the stability score of each node according to the stability data of each node; The first node determines the reputation score of each node according to the reputation data of each node; The first node determines the risk score corresponding to each node based on the technical security capability score of each node, the preset technical security capability weight, the stability score of each node, the preset stability weight, the reputation score of each node, and the preset reputation weight.

4. The method according to claim 1 or 2, characterized in that: The first node determines the risk score corresponding to each node according to the risk assessment data of each node, including: The first node determines the risk score corresponding to each node according to the risk assessment data and the risk assessment model of each node; the risk assessment model is obtained by training a preset neural network based on a training sample set; the training sample set includes multiple training samples, each training sample includes training risk assessment data, and the label corresponding to each training sample is a risk score.

5. The method according to claim 4, characterized in that The method further comprises: The first node obtains a training sample set; the training sample set includes multiple training samples, each training sample includes training risk assessment data, and the label corresponding to each training sample is a risk score; The first node trains the preset neural network based on the training sample set to obtain the risk assessment model.

6. The method according to claim 5, characterized in that The first node determines the risk score corresponding to each node according to the risk assessment data and the risk assessment model of each node, including: The first node obtains a test sample and broadcasts it to the remaining nodes; the test sample includes test risk assessment data; The first node predicts the test sample according to the risk assessment model obtained through training to obtain a test score predicted by the first node; The first node receives a test score sent by each of the remaining nodes; the test score is obtained by the corresponding node after training the risk assessment model and predicting the test sample based on the risk assessment model; When the maximum difference between the test scores of the multiple nodes is less than a preset difference threshold, the first node determines the risk score corresponding to each node according to the risk assessment data and the risk assessment model of each node.

7. A method for processing financial transaction data, characterized in that: The method is applied to a first node among a plurality of nodes of a financial transaction data processing system; the method comprises: The first node determines a risk score corresponding to each of the multiple nodes according to the method described in any one of claims 1 to 6; The first node determines a respective campaign score of each of the plurality of nodes according to the risk score; In the case where the election score of the first node is the highest, the first node broadcasts master node election information to the remaining nodes among the multiple nodes; the master node election information includes the identity of the first node; The first node receives votes from the remaining nodes; the votes are cast by the corresponding node when the first node has the highest election score among the election scores of each node calculated based on the node; When the number of votes in the vote is greater than a preset threshold, the first node generates a block based on the financial transaction data within a preset time period and adds the block to the blockchain; the blockchain includes multiple historical blocks; each historical block is used to record historical financial transaction data.

8. A node risk assessment device, characterized in that: The device is applied to a first node among multiple nodes of a financial transaction data processing system; the device comprises: an acquisition module and a processing module; The acquisition module is used to acquire risk assessment data of each of the multiple nodes; the risk assessment data includes at least one of the following: technical security capability data, stability data, and reputation data; The processing module is used to determine the risk score corresponding to each node according to the risk assessment data of each node.

9. A financial transaction data processing device, characterized in that: The device is applied to a first node among multiple nodes of a financial transaction data processing system; the device comprises: a risk assessment unit, a campaign unit, and a generation unit; The risk assessment unit is used to determine the risk score corresponding to each of the multiple nodes according to the method described in any one of claims 1 to 6; The election unit is used for the first node to determine the election score of each node among the multiple nodes according to the risk score; when the election score of the first node is the highest, the first node broadcasts the master node election information to the remaining nodes among the multiple nodes; the master node election information includes the identity of the first node; the first node receives votes from the remaining nodes; the votes are cast by the corresponding node when the election score of the first node is the highest among the election scores of each node calculated based on the node; The generation unit is used for generating a block according to the financial transaction data within a preset time period by the first node when the number of votes in the vote is greater than a preset threshold, and adding the block to the blockchain; the blockchain includes multiple historical blocks; each historical block is used to record historical financial transaction data.

10. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; The memory stores instructions executable by the processor; When the processor is configured to execute the instructions, the electronic device implements the method according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes: computer software instructions; When the computer software instructions are executed in an electronic device, the electronic device implements the method according to any one of claims 1 to 7.

12. A computer program product, characterized in that The computer program product comprises computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the method according to any one of claims 1 to 7.

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