Block chain node risk information determination method and related device

Abstract

The embodiment of the invention provides a risk information determination method of a block chain node and a related device. The method comprises the steps of obtaining n pieces of first behavior attribute information of a to-be-evaluated block chain node; determining a relative importance matrix among the n pieces of first behavior attribute information; determining first risk weight values respectively corresponding to the n pieces of first behavior attribute information according to the relative importance degree matrix; determining second risk weight values respectively corresponding to the n pieces of first behavior attribute information, wherein the second risk weight values are entropy weight values; performing weight fusion processing on first risk weight values and second risk weight values corresponding to the n pieces of first behavior attribute information to obtain a target risk weight value vector; and determining the risk information of the to-be-assessed block chain node according to the target risk weight value vector. The risk assessment accuracy of the block chain node can be improved.

Description

Technical Field

[0001] The present application relates to the fields of blockchain and data processing technology, and specifically to a method for determining risk information of blockchain nodes and related devices. Background Art

[0002] Blockchain technology has the characteristics of decentralization, data security, and data tamper-proofing. Its application value is becoming increasingly prominent and is widely used in the fields of healthcare, e-commerce, smart manufacturing, power trading, energy management, etc. With the development of blockchain, blockchain is divided into three parts: public chain, alliance chain, and private chain. The performance of distributed systems depends on the design of consensus mechanisms, especially for blockchain, such as transaction throughput, latency, node scalability, security level, etc.

[0003] The blockchain is jointly maintained and stored by network nodes. At this time, the system of consensus node evaluation is crucial to determine the node that publishes new proposals in the distributed system. The PBFT consensus algorithm in the existing blockchain uses an asynchronous consensus method. Therefore, blockchain nodes can communicate without strict time constraints and must 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 PBFT consensus. In addition, the physical structure, path length and transmission delay, network capacity and bandwidth, the connection method between nodes and the network topology will affect the delay and reliability of message transmission, which often ignores the importance of the physical properties of consensus nodes. When multiple nodes share a low-bandwidth connection, congestion is prone to occur, resulting in delayed or lost message transmission. In a distributed system, if the nodes are distributed in different geographical locations, it will also bring higher network delays and uncertainties. This leads to low accuracy in risk assessment of blockchain nodes. Summary of the invention

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

[0005] A first aspect of an embodiment of the present application provides a method for determining risk information of a blockchain node, the method comprising:

[0006] Obtain the attribute information of the n first behaviors of the blockchain node to be evaluated;

[0007] Determine a relative importance matrix between the n pieces of attribute information of the first behaviors;

[0008] Determine first risk weight values ​​corresponding to n pieces of first behavior attribute information respectively according to the relative importance matrix;

[0009] Determine second risk weight values ​​corresponding to the n pieces of the first behavior attribute information respectively, where the second risk weight value is an entropy weight value;

[0010] Performing weight fusion processing on the first risk weight values ​​and the corresponding second risk weight values ​​respectively corresponding to the n pieces of the first behavior attribute information to obtain a target risk weight value vector;

[0011] The risk information of the blockchain node to be evaluated is determined according to the target risk weight value vector.

[0012] In this example, by obtaining n first behavior attribute information of the blockchain node to be evaluated; determining the relative importance matrix between the n first behavior attribute information, determining the first risk weight values ​​corresponding to the n first behavior attribute information respectively according to the relative importance matrix, determining the second risk weight values ​​corresponding to the n first behavior attribute information respectively, the second risk weight value is the entropy weight value, performing weight fusion processing on the first risk weight values ​​and the corresponding second risk weight values ​​corresponding to the n first behavior attribute information respectively, to obtain a target risk weight value vector, and determining the risk information of the blockchain node to be evaluated according to the target risk weight value vector. Therefore, the risk information of the blockchain node to be evaluated can be determined by the target risk weight value vector obtained after the fusion processing, thereby improving the accuracy of risk information determination.

[0013] A second aspect of an embodiment of the present application provides a risk information determination device for a blockchain node, the device comprising:

[0014] An acquisition unit, used to acquire n first behavior attribute information of the blockchain node to be evaluated;

[0015] A first determining unit, used to determine a relative importance matrix between n pieces of the first behavior attribute information;

[0016] A second determining unit, configured to determine first risk weight values ​​respectively corresponding to n pieces of the first behavior attribute information according to the relative importance matrix;

[0017] A third determining unit is used to determine second risk weight values ​​corresponding to the n pieces of the first behavior attribute information respectively, where the second risk weight value is an entropy weight value;

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

[0019] The fourth determination unit is used to determine the risk information of the blockchain node to be evaluated according to the target risk weight value vector.

[0020] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiment of the present application.

[0021] The 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, wherein the computer program enables a computer to execute part 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 may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

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

[0025] Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the structure of a risk information determination device for a blockchain node is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0027] 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.

[0028] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0029] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0030] In order to better understand the risk information determination method of a blockchain node provided by an embodiment of the present application, the first behavior attribute information of the blockchain node is first introduced below. 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 sends, and number of receives.

[0031] Historical behavior can be understood as the historical behavior of nodes in the consensus process for the overall health and security of the network, which is a positive indicator;

[0032] The number of votes can be understood as the number of times a 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 it takes for a consensus node to make a pre-preparation response after receiving a message from another node. It is a negative indicator.

[0034] Preparation delay can be understood as the time it takes for a consensus node to prepare a response after receiving a message from another node. It is a negative indicator.

[0035] The submission delay can be understood as the time it takes for a consensus node to complete a proposal after receiving a message from other nodes. It is a negative indicator.

[0036] Verification delay can be understood as the time it takes for a consensus node to complete verification after receiving a message from another node. It is a negative indicator.

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

[0038] The connection quality can be understood as follows: larger node connections mean a larger network range and provide a wider dissemination capability, which is a positive indicator;

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

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

[0041] See also Figure 1 , Figure 1 A flowchart of a method for determining risk information of a blockchain node is provided for an embodiment of the present application. Figure 1 As shown, the method is applied to a blockchain system, and the method includes:

[0042] 101. Obtain n first behavior attribute information of the blockchain node to be evaluated.

[0043] Wherein, n can be 10, and the 10 first behavior attribute information can be the historical behavior, voting quantity, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, sending quantity and receiving quantity in the aforementioned embodiment. The above 10 first behavior attribute information can be obtained from the data monitoring module of the blockchain node to be evaluated.

[0044] 102. Determine a relative importance matrix between the n pieces of attribute information of the first behaviors.

[0045] The first behavior attribute information may be compared attentively to obtain the relative importance between every two first behavior attribute information; and then a matrix is ​​constructed to obtain a relative importance matrix A.

[0046] The relative importance matrix A can be shown as follows:

[0047]

[0048] The relative importance can be calculated by the following formula:

[0049]

[0050] Among them, a ij Indicates the first line attribute information B i With the first line attribute information B j The ratio of the degree of influence on the evaluation indicators of the previous level (relative importance).

[0051] 103. Determine first risk weight values ​​corresponding to the n pieces of first behavior attribute information respectively according to the relative importance matrix.

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

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

[0054]

[0055] Among them, W i is the first reference risk weight value corresponding to the ith first behavior attribute information among the n first behavior attribute information, the ith 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 above 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 n first behavior attribute information are obtained.

[0057] When the first risk weight value is obtained, the maximum characteristic root λ of the relative importance matrix C needs to be obtained. max :

[0058]

[0059] Among them, λ max is the largest characteristic root, W i is the first risk weight value of the i-th blockchain node to be evaluated, (AW) i It is the i-th element after the relative importance matrix A is multiplied by the weight vector W.

[0060] After determining the first risk weight value, it is also necessary to perform a consistency check on the relative importance matrix C, as follows:

[0061]

[0062]

[0063] Calculate the relative consistency index CR. When the random consistency ratio CR < 0.1, the calculated hierarchical ranking weight is considered correct and reasonable. Otherwise, the judgment matrix needs to be readjusted until the consistency test is qualified.

[0064] 104. Determine second risk weight values ​​corresponding to the n pieces of the first behavior attribute information respectively, where the second risk weight value is an entropy weight value.

[0065] Among them, n reference behavior attribute information of m reference blockchain nodes can be obtained, and then the second reference risk weight values ​​corresponding to the n reference behavior attribute information can be determined according to the judgment matrix constructed according to the n reference behavior attribute information, and finally the second reference risk weight values ​​corresponding to the n reference behavior attribute information can be determined as the second risk weight values ​​corresponding to the corresponding first behavior attribute information.

[0066] The n reference behavior attribute information corresponds to the n first behavior attribute information one by one, which can be specifically understood as follows: the n reference behavior 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; the n first behavior attribute information is: historical behavior, number of votes, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, number of sends and number of receives. 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 to each other in the same way.

[0067] 105. Perform weight fusion processing on the first risk weight values ​​and the corresponding second risk weight values ​​corresponding to the n pieces of the first behavior attribute information to obtain a target risk weight value vector.

[0068] Among them, the first linear combination coefficient corresponding to the first weight vector determined by the n first behavior attribute information can be determined, and the second linear combination coefficient corresponding to the second weight vector determined by the n first behavior attribute information can be determined. Finally, the first weight vector, the second weight vector, the first linear combination coefficient and the second linear combination coefficient are fused to obtain the target risk weight vector.

[0069] 106. Determine the risk information of the blockchain node to be evaluated according to the target risk weight value vector.

[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 value.

[0071] In this example, by obtaining n first behavior attribute information of the blockchain node to be evaluated; determining the relative importance matrix between the n first behavior attribute information, determining the first risk weight values ​​corresponding to the n first behavior attribute information respectively according to the relative importance matrix, determining the second risk weight values ​​corresponding to the n first behavior attribute information respectively, the second risk weight value is the entropy weight value, performing weight fusion processing on the first risk weight values ​​and the corresponding second risk weight values ​​corresponding to the n first behavior attribute information respectively, to obtain a target risk weight value vector, and determining the risk information of the blockchain node to be evaluated according to the target risk weight value vector. Therefore, the risk information of the blockchain node to be evaluated can be determined by the target risk weight value vector obtained after the fusion processing, thereby improving the accuracy of risk information determination.

[0072] In a possible implementation, a method for determining second risk weight values ​​corresponding to the n pieces of the first behavior attribute information respectively includes:

[0073] A1. Obtain n reference behavior attribute information of m reference blockchain nodes, where the n reference behavior attribute information correspond one-to-one to the n first behavior attribute information;

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

[0075] A3, normalizing the first judgment matrix to obtain a second judgment matrix;

[0076] A4. Determine the second reference risk weight values ​​corresponding to the n reference behavior attribute information according to the second judgment matrix;

[0077] A5. Determine the second reference risk weight values ​​corresponding to the n reference behavior attribute information as the risk weight values ​​of the corresponding first behavior attribute information, so as to obtain the second risk weight values ​​corresponding to the n first behavior attribute information respectively.

[0078] Among them, the n reference behavior attribute information corresponds to the n first behavior attribute information one by one, which can be specifically understood as: the n reference behavior 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; the n first behavior attribute information is: historical behavior, number of votes, pre-preparation delay, preparation delay, submission delay, verification delay, throughput, connection quality, number of sends and number of receives. The historical behavior in the reference behavior attribute information corresponds to the historical behavior in the first behavior attribute information, and other behavior attribute information corresponds one by one in the same way.

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

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

[0081] Among them, r ij is the value of the j-th reference behavior attribute information of the ith reference blockchain node.

[0082] The second judgment matrix D obtained by normalizing the first judgment matrix is ​​(d ij ) mn It can be characterized as:

[0083]

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

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

[0086] Determine the entropy S of the jth reference behavior attribute information j for:

[0087]

[0088] in:

[0089]

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

[0091] W=(ω j ) 1×n ,

[0092] Among them, ω j is the second reference risk weight value of the j-th reference behavior attribute information, and:

[0093]

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

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

[0096] B1. constructing a vector according to the first risk weight values ​​respectively corresponding to the n first behavior attribute information to obtain a first weight vector;

[0097] B2. construct a vector of n second risk weight values ​​to obtain a second weight vector;

[0098] B3. 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;

[0099] B4. Perform fusion processing on 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] Among them, β m is the linear combination coefficient, And β m > 0. Specifically, β1 is the first linear combination coefficient, and β2 is the second linear combination coefficient. is the transpose of the first weight vector, is the transpose of the second weight vector.

[0103] Optimize the linear combination coefficients, that is, w and each w m The deviation of is minimized, that is:

[0104]

[0105] According to the properties of matrix differentials, the first-order derivative condition of equation (13) can be obtained as follows:

[0106]

[0107] Its linear equation is:

[0108]

[0109] β m Perform normalization:

[0110]

[0111] in, is the normalized coefficient of the i-th linear combination coefficient.

[0112] The final target risk weight vector is:

[0113]

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

[0115] C1. merging attributes according to n first behavior attribute information to obtain k target behavior attribute information;

[0116] C2. Determine first target weight values ​​corresponding to k pieces of target behavior attribute information respectively according to the target risk weight value vector;

[0117] C3. Perform risk scoring processing according to the k first target weight values ​​to obtain a target risk score value;

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

[0119] The n first behavior attribute information are classified and processed 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 number of votes; 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, number of sends, and number of receives.

[0121] The method for determining the first target weight value corresponding to the target behavior attribute information may be: determining the sum of the weight values ​​of the first behavior attribute information corresponding to the target behavior attribute information as the first target weight value. Specifically, for example, if the target behavior attribute information is credit level, the first target weight value corresponding to it is: the sum of the target risk weight value corresponding to the historical behavior and the target risk weight value corresponding to the number of votes.

[0122] The target risk score value can be determined using the method shown in the following formula:

[0123]

[0124] Among them, CW-Scores is the target risk score, W Tis the first target weight value corresponding to the credit level of the target behavior attribute information, W C is the first target weight value corresponding to the target behavior attribute information response speed, W R is the first target weight value corresponding to the target behavior attribute information communication efficiency, B T is the credit level, B C is the response speed, B R is the communication efficiency. And the communication efficiency is a negative indicator. When calculating or evaluating, we need to use its absolute value to perform relevant 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 blockchain node to be evaluated is determined according to the value of the target risk score value. For example, if the target risk score value is a negative number, the blockchain node to be evaluated is an abnormal node; if the target risk score value is a positive number, the blockchain node to be evaluated is a normal node.

[0126] For the above embodiments, please refer to Figure 2 , Figure 2 A schematic diagram of the structure of a terminal provided in an embodiment of the present application, as shown in the figure, includes a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for executing the following steps;

[0127] Obtain the attribute information of the n first behaviors of the blockchain node to be evaluated;

[0128] Determine a relative importance matrix between the n pieces of attribute information of the first behaviors;

[0129] Determine first risk weight values ​​corresponding to n pieces of first behavior attribute information respectively according to the relative importance matrix;

[0130] Determine second risk weight values ​​corresponding to the n pieces of the first behavior attribute information respectively, where the second risk weight value is an entropy weight value;

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

[0132] The risk information of the blockchain node to be evaluated is determined according to the target risk weight value vector.

[0133] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that in order to realize the above functions, the terminal includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units 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 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 personnel 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.

[0134] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0135] In line with the above, see Figure 3 , Figure 3 The present invention provides a schematic diagram of a device for determining risk information of a blockchain node. Figure 3 As shown, the device comprises:

[0136] An acquisition unit 301 is used to acquire n first behavior attribute information of a blockchain node to be evaluated;

[0137] A first determining unit 302, configured to determine a relative importance matrix between n pieces of first behavior attribute information;

[0138] A second determining unit 303 is used to determine first risk weight values ​​corresponding to n pieces of first behavior attribute information respectively according to the relative importance matrix;

[0139] A third determining unit 304 is used to determine second risk weight values ​​corresponding to the n pieces of the first behavior attribute information, wherein the second risk weight value is an entropy weight value;

[0140] A fusion unit 305 is used to perform weight fusion processing on the first risk weight values ​​and the corresponding second risk weight values ​​respectively corresponding to the n first behavior attribute information to obtain a target risk weight value vector;

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

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

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

[0144]

[0145] Among them, W i is the first reference risk weight value corresponding to the ith first behavior attribute information among the n first behavior attribute information, the ith 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 above 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 n first behavior attribute information are obtained.

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

[0148] Obtaining n reference behavior attribute information of m reference blockchain nodes, where the n reference behavior attribute information correspond one-to-one to the n first behavior attribute information;

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

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

[0151] Determine the second reference risk weight values ​​corresponding to the n reference behavior attribute information according to the second judgment matrix;

[0152] The second reference risk weight values ​​corresponding to the n reference behavior attribute information are determined as the risk weight values ​​of the corresponding first behavior attribute information to obtain the second risk weight values ​​corresponding to the n first behavior attribute information respectively.

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

[0154] A vector is constructed according to the first risk weight values ​​respectively corresponding to the n first behavior attribute information to obtain a first weight vector;

[0155] Constructing a vector of n second risk weight values ​​to obtain a second weight vector;

[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] The target risk weight vector is obtained by performing fusion processing on the first weight vector, the second weight vector, the first linear combination coefficient and the second linear combination coefficient.

[0158] In a possible implementation, the fourth determining 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] According to the target risk weight value vector, determining first target weight values ​​corresponding to k target behavior attribute information respectively;

[0161] Perform risk scoring processing according to k first target weight values ​​to obtain a target risk score value;

[0162] The target risk score value is determined as the risk information of the blockchain node to be evaluated.

[0163] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method for determining risk information of a blockchain node as recorded in the above method embodiments.

[0164] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any method for determining risk information of a blockchain node as recorded in the above method embodiments.

[0165] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0166] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0167] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0168] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, the functional units in the various embodiments of the application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software program modules.

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

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

[0172] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for determining risk information of a blockchain node, characterized in that: The method comprises: Obtain the attribute information of the n first behaviors of the blockchain node to be evaluated; Determine a relative importance matrix between the n pieces of attribute information of the first behaviors; Determine first risk weight values ​​corresponding to n pieces of first behavior attribute information respectively according to the relative importance matrix; Determine second risk weight values ​​corresponding to the n pieces of the first behavior attribute information respectively, where the second risk weight value is an entropy weight value; Performing weight fusion processing on the first risk weight values ​​and the corresponding second risk weight values ​​respectively corresponding to the n pieces of the first behavior attribute information to obtain a target risk weight value vector; The risk information of the blockchain node to be evaluated is determined according to the target risk weight value vector.

2. The method for determining risk information of a blockchain node according to claim 1, characterized in that: The determining, according to the relative importance matrix, first risk weight values ​​respectively corresponding to the n first behavior attribute information includes: The first reference risk weight value corresponding to the first behavior attribute information is determined by the method shown in the following formula: Among them, W i is the first reference risk weight value corresponding to the ith first behavior attribute information among the n first behavior attribute information, the ith 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; Repeat the above 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 n first behavior attribute information are obtained.

3. The method for determining risk information of a blockchain node according to claim 1 or 2, characterized in that: The determining the second risk weight values ​​respectively corresponding to the n pieces of the first behavior attribute information includes: Obtaining n reference behavior attribute information of m reference blockchain nodes, where the n reference behavior attribute information correspond one-to-one to the n first behavior attribute information; Constructing a first judgment matrix according to the n reference behavior attribute information of the m reference blockchain nodes; Normalizing the first judgment matrix to obtain a second judgment matrix; Determine the second reference risk weight values ​​corresponding to the n reference behavior attribute information according to the second judgment matrix; The second reference risk weight values ​​corresponding to the n reference behavior attribute information are determined as the risk weight values ​​of the corresponding first behavior attribute information to obtain the second risk weight values ​​corresponding to the n first behavior attribute information respectively.

4. The method for determining risk information of a blockchain node according to claim 3, characterized in that: The weight fusion processing is performed on the first risk weight values ​​and the second risk weight values ​​corresponding to the n first behavior attribute information to obtain a target risk weight value vector, including: A vector is constructed according to the first risk weight values ​​respectively corresponding to the n first behavior attribute information to obtain a first weight vector; Constructing a vector of n second risk weight values ​​to obtain a second weight vector; 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; The target risk weight vector is obtained by performing fusion processing on the first weight vector, the second weight vector, the first linear combination coefficient and the second linear combination coefficient.

5. The method for determining risk information of a blockchain node according to claim 4, characterized in that: The step of determining the risk information of the blockchain node to be evaluated according to the target risk weight value vector includes: Perform attribute fusion according to the n first behavior attribute information to obtain k target behavior attribute information; According to the target risk weight value vector, determining first target weight values ​​corresponding to k target behavior attribute information respectively; Perform risk scoring processing according to k first target weight values ​​to obtain a target risk score value; The target risk score value is determined as the risk information of the blockchain node to be evaluated.

6. A risk information determination device for a blockchain node, characterized in that: The device comprises: An acquisition unit, used to acquire n first behavior attribute information of the blockchain node to be evaluated; A first determining unit, used to determine a relative importance matrix between n pieces of the first behavior attribute information; A second determining unit, configured to determine first risk weight values ​​respectively corresponding to n pieces of the first behavior attribute information according to the relative importance matrix; A third determining unit is used to determine second risk weight values ​​corresponding to the n pieces of the first behavior attribute information respectively, where the second risk weight value is an entropy weight value; a fusion unit, configured to perform weight fusion processing on the first risk weight values ​​and the corresponding second risk weight values ​​respectively corresponding to the n pieces of the first behavior attribute information to obtain a target risk weight value vector; The fourth determination unit is used to determine the risk information of the blockchain node to be evaluated according to the target risk weight value vector.

7. The risk information determination device for a blockchain node according to claim 6, characterized in that: The second determining unit is used for: The first reference risk weight value corresponding to the first behavior attribute information is determined by the method shown in the following formula: Among them, W i is the first reference risk weight value corresponding to the ith first behavior attribute information among the n first behavior attribute information, the ith 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; Repeat the above 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 n first behavior attribute information are obtained.

8. The risk information determination device for a blockchain node according to claim 6 or 7, characterized in that: The third determining unit is used for: Obtaining n reference behavior attribute information of m reference blockchain nodes, where the n reference behavior attribute information correspond one-to-one to the n first behavior attribute information; Constructing a first judgment matrix according to the n reference behavior attribute information of the m reference blockchain nodes; Normalizing the first judgment matrix to obtain a second judgment matrix; Determine the second reference risk weight values ​​corresponding to the n reference behavior attribute information according to the second judgment matrix; The second reference risk weight values ​​corresponding to the n reference behavior attribute information are determined as the risk weight values ​​of the corresponding first behavior attribute information to obtain the second risk weight values ​​corresponding to the n first behavior attribute information respectively.

9. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5.

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