Blockchain node risk information determination method and related apparatus

Abstract

Embodiments of the present application provide a method and related device for determining risk information of a blockchain node, the method comprising: obtaining n first behavior attribute information of a blockchain node to be evaluated; determining a relative importance matrix between the n first behavior attribute information; determining a first risk weight value corresponding to each of the n first behavior attribute information according to the relative importance matrix; determining a second risk weight value corresponding to each of the n first behavior attribute information, the second risk weight value being an entropy weight value; performing weight fusion processing on the first risk weight value corresponding to each of the n first behavior attribute information and the second risk weight value corresponding to each of the n first behavior attribute information to obtain a target risk weight value vector; and determining risk information of the blockchain node to be evaluated according to the target risk weight value vector, which can improve the accuracy of risk evaluation of the blockchain node.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain and data processing, and particularly relates to a risk information determination method for a blockchain node and a related device. BACKGROUND

[0002] The blockchain technology has the characteristics of decentralization, data security, and data tamper resistance, and its application value is increasingly prominent. It is widely used in the fields of medical care, e-commerce, intelligent manufacturing, power transaction, and energy management. With the development of the blockchain, the blockchain is divided into three parts: public chain, alliance chain, and private chain. The performance of a distributed system depends on the design of the consensus mechanism, especially for the blockchain, such as transaction throughput, delay, node scalability, and security level.

[0003] The blockchain is maintained and stored by network nodes. At this time, the system evaluated by the consensus node is crucial for determining the node that publishes a new proposal in a distributed system. The existing PBFT consensus algorithm in the blockchain uses an asynchronous consensus method. Therefore, the blockchain node can communicate without strict time limits and complete the entire consensus process within a limited number of steps. In improving the PBFT consensus mechanism, most research focuses on adjusting the logical process of the PBFT consensus. In addition, the physical structure, path length and transmission delay, network capacity and bandwidth, and the connection mode and network topology between nodes will affect the delay and reliability of message transmission, which often ignores the importance of the physical properties of the consensus node. When multiple nodes share a low-bandwidth connection, congestion is likely to occur, resulting in message transmission delay or loss. In a distributed system, if the nodes are distributed in different geographical locations, it will also bring higher network delay and uncertainty. This results in low accuracy when assessing the risk of the blockchain node. SUMMARY

[0004] The embodiments of the present application provide a risk information determination method for a blockchain node and a related device, which can improve the accuracy of risk assessment of the blockchain node.

[0005] A first aspect of the embodiments of the present application provides a risk information determination method for a blockchain node, which comprises:

[0006] Obtaining n first behavior attribute information of a blockchain node to be evaluated;

[0007] Determining a relative importance matrix between n first behavior attribute information;

[0008] Determining a first risk weight value corresponding to n first behavior attribute information according to the relative importance matrix;

[0009] determine a second risk weight value corresponding to each of the n first behavior attribute information, the second risk weight value being an entropy weight value;

[0010] perform weight fusion processing on the first risk weight value corresponding to each of the n first behavior attribute information and the second risk weight value corresponding thereto, to obtain a target risk weight value vector;

[0011] determine the risk information of the to-be-evaluated blockchain node according to the target risk weight value vector.

[0012] In this example, n first behavior attribute information of a to-be-evaluated blockchain node is obtained, a relative importance matrix among the n first behavior attribute information is determined, a first risk weight value corresponding to each of the n first behavior attribute information is determined according to the relative importance matrix, a second risk weight value corresponding to each of the n first behavior attribute information is determined, the second risk weight value being an entropy weight value, weight fusion processing is performed on the first risk weight value corresponding to each of the n first behavior attribute information and the second risk weight value corresponding thereto, to obtain a target risk weight value vector, and the risk information of the to-be-evaluated blockchain node is determined according to the target risk weight value vector. Therefore, the risk information of the to-be-evaluated blockchain node can be determined by the target risk weight value vector obtained through fusion processing, and the accuracy of risk information determination is improved.

[0013] A second aspect of an embodiment of the present application provides a blockchain node risk information determination apparatus, which comprises:

[0014] an acquisition unit configured to acquire n first behavior attribute information of a to-be-evaluated blockchain node;

[0015] a first determination unit configured to determine a relative importance matrix among the n first behavior attribute information;

[0016] a second determination unit configured to determine a first risk weight value corresponding to each of the n first behavior attribute information according to the relative importance matrix;

[0017] a third determination unit configured to determine a second risk weight value corresponding to each of the n first behavior attribute information, the second risk weight value being an entropy weight value;

[0018] a fusion unit configured to perform weight fusion processing on the first risk weight value corresponding to each of the n first behavior attribute information and the second risk weight value corresponding thereto, to obtain a target risk weight value vector;

[0019] a fourth determination unit configured to determine the risk information of the to-be-evaluated blockchain node according to the target risk weight value vector.

[0020] A third aspect of the embodiments of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, which are connected with each other, wherein the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as in the first aspect of the embodiments of the present application.

[0021] A fourth aspect of the embodiments of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0022] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product can be a software installation package. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0024] Figure 1 A flowchart of a method for determining risk information of a blockchain node is provided for the embodiments of the present application;

[0025] Figure 2 A structural diagram of a terminal is provided for the embodiments of the present application;

[0026] Figure 3 A structural diagram of a device for determining risk information of a blockchain node is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0028] The terms "first", "second", and the like in the description and in the claims of the present application and above drawings are used for distinguishing between similar objects and not necessarily for describing a specific sequential or chronological order. The terms "comprises", "comprising", "includes", "including" and the like are to be construed open- ended, meaning that they allow for the possibility that there are additional steps or elements that are not recited, other steps or elements that are not listed, or that the listed steps or elements can occur in different orders or repetitions with other steps or elements. The terms "comprises", "comprising", "includes", "including" and the like are to be construed open- ended, meaning that they allow for the possibility that there are additional steps or elements that are not recited, other steps or elements that are not listed, or that the listed steps or elements can occur in different orders or repetitions with other steps or elements.

[0029] Reference to "an embodiment" or "the embodiment" in this application means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all directed to the same embodiment, or to a single alternative embodiment. It is expressly understood that any of the embodiments described in this application can be combined with any of the other embodiments that are described in the application without changing the scope or spirit of the application.

[0030] In order to better understand the method for determining risk information of a blockchain node provided by the embodiments of the present application, first, the first behavior attribute information of the blockchain node will be introduced. The first behavior attribute information includes: historical behavior, number of votes, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, number of sent, and number of received.

[0031] The historical behavior can be understood as: the historical behavior of the node for the overall health and safety of the network in the consensus process, which is a positive indicator;

[0032] The number of votes can be understood as: the number of times the consensus node needs to make a correct answer after receiving messages from other nodes, which is a positive indicator;

[0033] The pre-preparation delay can be understood as: the time the consensus node needs to make a pre-preparation response after receiving messages from other nodes, which is a negative indicator;

[0034] The preparation delay can be understood as: the time the consensus node needs to make a preparation response after receiving messages from other nodes, which is a negative indicator;

[0035] The submission delay can be understood as: the time the consensus node takes to complete the proposal after receiving messages from other nodes, which is a negative indicator;

[0036] The verification delay can be understood as: the time the consensus node takes to complete the verification after receiving messages from other nodes, which is a negative indicator;

[0037] The throughput can be understood as: the number of transactions completed by the node per unit of time, which is a positive indicator;

[0038] Connection quality can be understood as: a larger number of node connections means a larger network range and provides a wider propagation capability, which is a positive indicator;

[0039] The number of messages sent can be understood as follows: a larger number of messages sent means stronger node performance and provides wider propagation capabilities, which is a positive indicator.

[0040] The number of messages received can be understood as: a larger number of messages received means a more central network location and provides wider propagation capabilities, which is a positive indicator.

[0041] Please see Figure 1 , Figure 1 This application provides a flowchart illustrating a method for determining risk information of a blockchain node. Figure 1 As shown, this method is applied to a blockchain system, and the method includes:

[0042] 101. Obtain the first-row attribute information of the n blockchain nodes to be evaluated.

[0043] Where n can be 10, and the 10 first-row attribute information can be historical behavior, number of votes, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, number of sends, and number of receives, as described in the previous embodiments. These 10 first-row attribute information can be obtained from the data monitoring module of the blockchain node to be evaluated.

[0044] 102. Determine the relative importance matrix among the n first behavioral attribute information.

[0045] The first row of attribute information can be compared to obtain the relative importance between every two first row attribute information; then a matrix can be constructed to obtain the relative importance matrix A.

[0046] The relative importance matrix A can be shown in the table below:

[0047]

[0048] Relative importance can be calculated using the following formula:

[0049]

[0050] Among them, a ij The first row represents attribute information B. i With the first row attribute information B j The ratio of the degree of influence on the next higher level evaluation indicator (relative importance).

[0051] 103. Determine a first risk weight value corresponding to each of the n first behavior attribute information according to the relative importance matrix.

[0052] The method for determining the first risk weight value can be:

[0053] The first reference risk weight value corresponding to the first behavior attribute information is determined by the following formula:

[0054]

[0055] wherein W i is the first reference risk weight value corresponding to the i-th first behavior attribute information in the n first behavior attribute information, the i-th first behavior attribute information is any one of the n first behavior attribute information, a ij is the relative importance between the i-th first behavior attribute information and the j-th first behavior attribute information;

[0056] Repeat the method for determining the first reference risk weight value corresponding to the i-th first behavior attribute information until the first risk weight values corresponding to the n first behavior attribute information are obtained.

[0057] When the first risk weight values are obtained, the maximum eigenvalue λ max of the relative importance matrix C also needs to be obtained.

[0058]

[0059] wherein λ max is the maximum eigenvalue, W i is the i-th first risk weight value of the to-be-evaluated blockchain node, and (AW) i is the i-th element after the relative importance matrix A and the weight vector W are multiplied.

[0060] After the first risk weight values are determined, the relative importance matrix C needs to be checked for consistency, which is as follows:

[0061]

[0062]

[0063] Calculate the relative consistency index CR. When the random consistency ratio CR is less than 0.1, it is considered that the hierarchical ranking weight obtained by calculation is correct and reasonable. Otherwise, the judgment matrix needs to be adjusted until the consistency check is qualified.

[0064] 104. Determine a second risk weight value corresponding to each of the n first behavior attribute information, wherein the second risk weight value is an entropy weight value.

[0065] The n reference behavior attribute information of the m reference block chain nodes can be obtained, and a judgment matrix constructed according to the n reference behavior attribute information is used to determine second reference risk weight values corresponding to the n reference behavior attribute information, and finally the second reference risk weight values corresponding to the n reference behavior attribute information are determined as second risk weight values corresponding to the corresponding first behavior attribute information.

[0066] The n reference behavior attribute information and the n first behavior attribute information are one-to-one corresponding, and specifically, the n reference behavior attribute information can be: historical behavior, voting quantity, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, sending quantity and receiving quantity; the n first behavior attribute information is: historical behavior, voting quantity, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, sending quantity and receiving quantity. The historical behavior in the reference behavior attribute information corresponds to the historical behavior in the first behavior attribute information, and the other behavior attribute information is one-to-one corresponding through the same way.

[0067] 105、The first risk weight value corresponding to the n first behavior attribute information and the corresponding second risk weight value are subjected to weight fusion processing to obtain a target risk weight value vector.

[0068] The first linear combination coefficient corresponding to the first weight vector determined by the n first behavior attribute information can be determined, the second linear combination coefficient corresponding to the second weight vector determined by the n first behavior attribute information can be determined, and finally the first weight vector, the second weight vector, the first linear combination coefficient and the second linear combination coefficient are subjected to fusion processing to obtain the target risk weight vector.

[0069] 106、According to the target risk weight value vector, the risk information of the to-be-evaluated block chain node is determined.

[0070] The n first behavior attribute information can be fused to obtain k target behavior attribute information; the weight values corresponding to the k target behavior attribute information are determined according to the target risk weight value vector, and finally the risk information is determined according to the weight values.

[0071] In the example, by acquiring n first behavior attribute information of a to-be-evaluated blockchain node, determining a relative importance matrix between the n first behavior attribute information, determining a first risk weight value corresponding to each of the n first behavior attribute information according to the relative importance matrix, determining a second risk weight value corresponding to each of the n first behavior attribute information, the second risk weight value being an entropy weight value, performing weight fusion processing on the first risk weight value corresponding to each of the n first behavior attribute information and the second risk weight value corresponding thereto to obtain a target risk weight value vector, and determining risk information of the to-be-evaluated blockchain node according to the target risk weight value vector, the risk information of the to-be-evaluated blockchain node can be determined through the target risk weight value vector obtained after fusion processing, and the accuracy of risk information determination is improved.

[0072] In one possible implementation, a method for determining a second risk weight value corresponding to each of the n first behavior attribute information includes:

[0073] A1, acquiring n reference behavior attribute information of m reference blockchain nodes, the n reference behavior attribute information corresponding one by one to the n first behavior attribute information;

[0074] A2, constructing a first judgment matrix according to the n reference behavior attribute information of the m reference blockchain nodes;

[0075] A3, performing normalization processing on the first judgment matrix to obtain a second judgment matrix;

[0076] A4, determining a second reference risk weight value corresponding to the n reference behavior attribute information according to the second judgment matrix;

[0077] A5, determining the second reference risk weight value corresponding to the n reference behavior attribute information as a risk weight value of the corresponding first behavior attribute information to obtain a second risk weight value corresponding to each of the n first behavior attribute information.

[0078] Wherein, the n reference behavior attribute information and the n first behavior attribute information correspond one by one, which can be specifically understood as: the n reference behavior attribute information can be: historical behavior, voting quantity, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, sending quantity, and receiving quantity; the n first behavior attribute information is: historical behavior, voting quantity, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, sending quantity, and receiving quantity. The historical behavior in the reference behavior attribute information corresponds to the historical behavior in the first behavior attribute information, and the other behavior attribute information corresponds one by one in the same way.

[0079] The first judgment matrix can be constructed according to the n reference behavior attribute information of the m reference blockchain nodes, and the construction method is as follows:

[0080] R=(r ij ) mn (i=1,2,…,m;j=1,2,…,n),

[0081] Wherein, r ij is the value of the jth reference behavior attribute information of the ith reference blockchain node.

[0082] The second judgment matrix D obtained after the first judgment matrix is normalized can be represented as: ij mn

[0083]

[0084] Wherein: r max is the optimal index value of the same reference behavior attribute information under different reference blockchain nodes; r min is the worst index value of the same reference behavior attribute information under different reference blockchain nodes.

[0085] The second reference risk weight value can be determined by the following formula, and the specific method is as follows:

[0086] The entropy S j of the jth reference behavior attribute information is determined as follows:

[0087]

[0088] Wherein:

[0089]

[0090] The second reference risk weight value vector W of the n reference behavior attribute information is:

[0091] W=(ω j ) 1×n ,

[0092] Wherein, ω j is the second reference risk weight value of the jth reference behavior attribute information, and:

[0093]

[0094] The second reference risk weight value can be accurately obtained by the above method.

[0095] ​​In a possible implementation, a method for performing weight fusion processing on first risk weight values corresponding to n first behavior attribute information and second risk weight values corresponding to the n first behavior attribute information respectively to obtain a target risk weight value vector includes the following steps:

[0096] B1, vector construction is performed on the first risk weight values corresponding to the n first behavior attribute information respectively to obtain a first weight vector;

[0097] B2, vector construction is performed on the n second risk weight values to obtain a second weight vector;

[0098] B3, a first linear combination coefficient corresponding to the first weight vector is determined, and a second linear combination coefficient corresponding to the second weight vector is determined;

[0099] B4, fusion processing is performed according to the first weight vector, the second weight vector, the first linear combination coefficient, and the second linear combination coefficient to obtain the target risk weight vector.

[0100] Any linear combination of the first weight vector and the second weight vector is:

[0101]

[0102] wherein β m is a linear combination coefficient, and β m > 0. Specifically, β1 is the first linear combination coefficient, and β2 is the first linear combination coefficient. is a transpose of the first weight vector, is a transpose of the second weight vector.

[0103] The linear combination coefficient is optimized, so that the dispersion of w and each w m is minimized, that is:

[0104]

[0105] According to the properties of matrix differentiation, the first derivative condition of formula (13) is obtained as:

[0106]

[0107] The linear equation is:

[0108]

[0109] The β m is normalized:

[0110]

[0111] wherein, is a normalization coefficient for the i-th linear combination coefficient.

[0112] The final target risk weight vector is:

[0113]

[0114] In one possible implementation, a method for determining risk information of a blockchain node to be evaluated according to the target risk weight value vector includes:

[0115] C1, attribute fusion is performed according to n first behavior attribute information to obtain k target behavior attribute information;

[0116] C2, according to the target risk weight value vector, determining a first target weight value corresponding to each of the k target behavior attribute information;

[0117] C3, risk score processing is performed according to the k first target weight values to obtain a target risk score value;

[0118] C4, the target risk score value is determined as the risk information of the blockchain node to be evaluated.

[0119] The n first behavior attribute information is classified to obtain k type information, and the behavior attribute information corresponding to the k type information is determined as the target behavior attribute information. Specifically:

[0120] The target behavior attribute information includes credit level, response speed, and communication efficiency. The types indicated by the type information corresponding to the credit level include historical behavior and voting quantity. The types indicated by the type information corresponding to the response speed include pre-preparation delay, preparation delay, submission delay, and verification delay. The types indicated by the type information corresponding to the communication efficiency include throughput, connection quality, sending quantity, and receiving quantity.

[0121] The method for determining the first target weight value corresponding to the target behavior attribute information can be that the sum of the weight values of the first behavior attribute information corresponding to the target behavior attribute information is determined as the first target weight value. Specifically, for example, the target behavior attribute information is credit level, and the first target weight value corresponding thereto is the sum of the target risk weight value corresponding to historical behavior and the target risk weight value corresponding to voting quantity.

[0122] The target risk score value can be determined by the following formula:

[0123]

[0124] wherein CW-Scores is the target risk score value, W TW is a first target weight value corresponding to the credit degree of the target behavior attribute information C W is a first target weight value corresponding to the response speed of the target behavior attribute information R B is a first target weight value corresponding to the communication efficiency of the target behavior attribute information T B is a credit degree C B is a response speed R B is a communication efficiency. And the communication efficiency is a negative index, when calculating or evaluating, we need to take the absolute value of its numerical value to perform related calculations to ensure the correctness of the weight and the consistency of the actual application.

[0125] After determining the target risk score value, the risk information of the to-be-evaluated blockchain node is determined according to the numerical value of the target risk score value, for example, the target risk score value is a negative number, then the to-be-evaluated blockchain node is an abnormal node; the target risk score value is a positive number, then the to-be-evaluated blockchain node is a normal node.

[0126] Consistent with the above embodiments, please refer to Figure 2 , Figure 2 A structure schematic diagram of a terminal provided by an embodiment of the present application is shown in the figure, which includes a processor, an input device, an output device and a memory, and the processor, the input device, the output device and the memory are connected with each other, wherein the memory is used for storing a computer program, the computer program includes program instructions, and the processor is configured to invoke the program instructions, and the above program includes instructions for executing the following steps;

[0127] Obtain n first behavior attribute information of a to-be-evaluated blockchain node;

[0128] Determine a relative importance matrix between n first behavior attribute information;

[0129] Determine a first risk weight value corresponding to n first behavior attribute information according to the relative importance matrix;

[0130] Determine a second risk weight value corresponding to n first behavior attribute information, and the second risk weight value is an entropy weight value;

[0131] Perform weight fusion processing on the first risk weight value and the second risk weight value corresponding to n first behavior attribute information respectively to obtain a target risk weight value vector;

[0132] Determine the risk information of the to-be-evaluated blockchain node according to the target risk weight value vector.

[0133] The above describes the scheme of the embodiments of the present application mainly from the perspective of the process of executing the method. It can be understood that, in order to implement the above functions, the terminal comprises a hardware structure and / or a software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the unit and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0134] The embodiments of the present application can divide the functional units of the terminal according to the above method examples. For example, each functional unit can be divided according to each function, or two or more functions can be integrated in one processing unit. The integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical functional division. Actual implementation can have another division manner.

[0135] Consistent with the above, please refer to Figure 3 , Figure 3 A structural schematic diagram of a risk information determination apparatus of a blockchain node is provided for the embodiments of the present application. As shown in Figure 3 , the apparatus comprises:

[0136] The acquisition unit 301 is configured to acquire n first behavior attribute information of a to-be-evaluated blockchain node.

[0137] The first determination unit 302 is configured to determine a relative importance matrix between the n first behavior attribute information.

[0138] The second determination unit 303 is configured to determine first risk weight values corresponding to the n first behavior attribute information respectively according to the relative importance matrix.

[0139] The third determination unit 304 is configured to determine second risk weight values corresponding to the n first behavior attribute information respectively, the second risk weight values being entropy weight values.

[0140] The fusion unit 305 is configured to perform weight fusion processing on the first risk weight values and the corresponding second risk weight values of the n first behavior attribute information respectively to obtain a target risk weight value vector.

[0141] The fourth determination unit 306 is configured to determine risk information of the to-be-evaluated blockchain node according to the target risk weight value vector.

[0142] In a possible implementation, the second determining unit 303 is configured to:

[0143] determine the first reference risk weight value corresponding to the first behavior attribute information by a method shown in the following formula:

[0144]

[0145] wherein W i is the first reference risk weight value corresponding to the i-th first behavior attribute information in the n first behavior attribute information, the i-th first behavior attribute information is any one of the n first behavior attribute information, a ij is the relative importance between the i-th first behavior attribute information and the j-th first behavior attribute information;

[0146] repeat the method of determining the first reference risk weight value corresponding to the i-th first behavior attribute information until the n first risk weight values corresponding to the n first behavior attribute information are obtained.

[0147] In a possible implementation, the third determining unit 304 is configured to:

[0148] obtain n reference behavior attribute information of m reference blockchain nodes, the n reference behavior attribute information corresponds to the n first behavior attribute information one by one;

[0149] construct a first judgment matrix according to the n reference behavior attribute information of the m reference blockchain nodes;

[0150] normalize the first judgment matrix to obtain a second judgment matrix;

[0151] determine the second reference risk weight value corresponding to the n reference behavior attribute information according to the second judgment matrix;

[0152] determine the second reference risk weight value corresponding to the n reference behavior attribute information as the risk weight value of the corresponding first behavior attribute information, to obtain the second risk weight value corresponding to the n first behavior attribute information.

[0153] In a possible implementation, the fusion unit 305 is configured to:

[0154] construct a first weight vector according to the first risk weight value corresponding to the n first behavior attribute information;

[0155] construct a second weight vector according to the n second risk weight value;

[0156] determine a first linear combination coefficient corresponding to the first weight vector, and determine a second linear combination coefficient corresponding to the second weight vector;

[0157] perform fusion processing according to the first weight vector, the second weight vector, the first linear combination coefficient, and the second linear combination coefficient, to obtain the target risk weight vector.

[0158] In one possible implementation, the fourth determination unit 306 is configured to:

[0159] perform attribute fusion according to the n first behavior attribute information, to obtain k target behavior attribute information;

[0160] determine, according to the target risk weight value vector, first target weight values corresponding to the k target behavior attribute information respectively;

[0161] perform risk score processing according to the k first target weight values, to obtain a target risk score value;

[0162] determine the target risk score value as the risk information of the to-be-evaluated blockchain node.

[0163] Embodiments of the present application further provide a computer storage medium, which stores a computer program for electronic data exchange, and the computer program causes a computer to perform part or all of steps of any one of the risk information determination methods of the blockchain node described in the above method embodiments.

[0164] Embodiments of the present application further provide a computer program product, which comprises a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to perform part or all of steps of any one of the risk information determination methods of the blockchain node described in the above method embodiments.

[0165] It should be noted that, for each of the above method embodiments, in order to simply describe, each is described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the action order described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0166] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0167] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments described above is merely an example, and the division can be other forms. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0168] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0169] In addition, the functional units in each embodiment of the application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software program module.

[0170] The integrated unit, if realized in the form of a software program module and sold or used as an independent product, can be stored in a computer readable memory. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0171] A person of ordinary skill in the art can understand that all or part of the steps of the various methods in the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer readable memory, which can include a flash disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0172] The above has carried out the detailed introduction to the embodiment of the application, the principle and implementation mode of the application have been described by applying specific examples in this paper, the above embodiment explanation is only for helping understanding the method of the application and its core idea; at the same time, for the general technical personnel in the art, according to the idea of the application, there will be changes in specific implementation mode and application range, and the above-mentioned, the content of the specification should not be understood as the limitation of the application.

Claims

1. A method for determining risk information of a blockchain node, characterized in that, The method comprises: obtaining n first behavior attribute information of a to-be-evaluated blockchain node; determining a relative importance matrix between n first behavior attribute information; determining a first risk weight value corresponding to n first behavior attribute information according to the relative importance matrix; determining a second risk weight value corresponding to n first behavior attribute information, the second risk weight value being an entropy weight value; performing weight fusion processing on the first risk weight value corresponding to n first behavior attribute information and the second risk weight value corresponding thereto to obtain a target risk weight value vector; determining risk information of the to-be-evaluated blockchain node according to the target risk weight value vector; determining a relative importance matrix between n first behavior attribute information comprises: performing attention comparison on the first behavior attribute information to obtain the relative importance between each two first behavior attribute information; and then performing matrix construction to obtain the relative importance matrix; determining the second risk weight value corresponding to n first behavior attribute information comprises: obtaining n reference behavior attribute information of m reference blockchain nodes, the n reference behavior attribute information corresponding one-to-one to n first behavior attribute information; constructing a first judgment matrix according to n reference behavior attribute information of m reference blockchain nodes; performing normalization processing on the first judgment matrix to obtain a second judgment matrix; determining a second reference risk weight value corresponding to n reference behavior attribute information according to the second judgment matrix; determining the second reference risk weight value corresponding to n reference behavior attribute information as a risk weight value of the corresponding first behavior attribute information to obtain a second risk weight value corresponding to n first behavior attribute information. 2.The method of claim 1, wherein, performing weight fusion processing on the first risk weight value corresponding to n first behavior attribute information and the second risk weight value corresponding thereto to obtain a target risk weight value vector comprises: performing vector construction on the first risk weight value corresponding to n first behavior attribute information to obtain a first weight vector; performing vector construction on n second risk weight values to obtain a second weight vector; determining a first linear combination coefficient corresponding to the first weight vector and a second linear combination coefficient corresponding to the second weight vector; performing fusion processing according to the first weight vector, the second weight vector, the first linear combination coefficient and the second linear combination coefficient to obtain the target risk weight vector. 3.The method of claim 2, wherein, determining risk information of the to-be-evaluated blockchain node according to the target risk weight value vector comprises: performing attribute fusion according to n first behavior attribute information to obtain k target behavior attribute information; determining a first target weight value corresponding to k target behavior attribute information according to the target risk weight value vector; performing risk score processing according to k first target weight values to obtain a target risk score value; determining the target risk score value as the risk information of the to-be-evaluated blockchain node. 4.A risk information determination apparatus of a blockchain node, characterized in that, The device comprises: An acquisition unit is configured to acquire n first behavior attribute information of a blockchain node to be evaluated; A first determination unit is configured to determine a relative importance matrix among the n first behavior attribute information; A second determination unit is configured to determine a first risk weight value corresponding to each of the n first behavior attribute information according to the relative importance matrix; A third determination unit is configured to determine a second risk weight value corresponding to each of the n first behavior attribute information, the second risk weight value being an entropy weight value; A fusion unit is configured to perform weight fusion processing on the first risk weight value and the second risk weight value corresponding to each of the n first behavior attribute information to obtain a target risk weight value vector; A fourth determination unit is configured to determine risk information of the blockchain node to be evaluated according to the target risk weight value vector; The first determination unit is specifically configured to perform attention comparison on the first behavior attribute information to obtain the relative importance between each two first behavior attribute information, and then perform matrix construction to obtain the relative importance matrix; The third determination unit is configured to: Acquire n reference behavior attribute information of m reference blockchain nodes, the n reference behavior attribute information corresponding to the n first behavior attribute information one by one; Construct a first judgment matrix according to the n reference behavior attribute information of the m reference blockchain nodes; Perform normalization processing on the first judgment matrix to obtain a second judgment matrix; Determine a second reference risk weight value corresponding to the n reference behavior attribute information according to the second judgment matrix; Determine the second reference risk weight value corresponding to the n reference behavior attribute information as a risk weight value of the corresponding first behavior attribute information to obtain the second risk weight value corresponding to each of the n first behavior attribute information.

5. A terminal, characterized by comprising: A processor, an input device, an output device and a memory are connected to each other, wherein the memory is configured to store a computer program, the computer program includes program instructions, and the processor is configured to invoke the program instructions to execute the method according to any one of claims 1-3.

6. A computer readable storage medium characterized by The computer readable storage medium stores a computer program, the computer program includes program instructions, and the program instructions, when executed by a processor, cause the processor to execute the method according to any one of claims 1-3.

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