A terminal trust evaluation method and device based on consensus mechanism trust aggregation

Abstract

The present invention discloses a terminal trust evaluation method and device based on trust aggregation of a consensus mechanism, including: calculating objective historical trust evaluation values ​​of login nodes and historically visited nodes based on location trust factors, energy trust factors, and interaction trust factors; selecting nodes to form a related node list based on the reference value of the objective historical trust evaluation values; selecting nodes from the related node list to form a trusted consensus node list using a voting screening mechanism; performing trust consensus based on the trusted consensus node list to obtain a consensus trust value; and performing weighted averaging based on the consensus trust value and the objective historical trust evaluation value to obtain a total trust value of the terminal where the login node is located. When determining the consensus node, the node is evaluated and selected from the related node list determined based on the objective historical trust evaluation value, thereby improving the security of consensus node selection; and determining the total trust value based on the weighted averaging of the consensus trust value and the objective historical trust evaluation value, thereby improving the accuracy of trust value determination.

Description

Technical Field

[0001] The present invention relates to the fields of trust evaluation and blockchain technology, and in particular to a terminal trust evaluation method and device based on trust aggregation of a consensus mechanism. Background Art

[0002] In recent years, with the increasing adoption of 5G devices, network computing capabilities have been evolving towards greater accuracy and efficiency. Traditional cloud computing relies on cloud servers for data computation, a centralized processing model that results in long network latency. In distributed edge computing, data computation is performed directly by edge nodes, enabling faster task response. However, among the numerous edge nodes executing tasks, there may be malicious nodes that provide false feedback. Therefore, it is necessary to address the issue of trusted access to edge nodes and conduct trust assessments based on terminal access behavior. Due to the ambiguity and uncertainty of trust, accurately and rationally assessing the trustworthiness of nodes has been a major research topic for scholars both domestically and internationally. Currently, trust models can be broadly categorized as those based on data signatures, local recommendations, and global trust. Signature-based models are often used in data sharing applications and cannot prevent node team fraud. Trust models based on local recommendations rely on nodes simply querying a limited number of neighboring nodes to determine a node's trustworthiness. Global trust models involve a fusion of direct and indirect trust. Direct trust represents a node's immediate satisfaction with a target node, while feedback trust represents the trust value provided by indirect nodes not directly connected to the target node through appropriate trust links. This calculation requires consideration of the selection and search of trust links, requiring significant network computing power and varying understandings of indirect nodes. To strengthen the security management and control of network terminals, a more fine-grained trust assessment model is still needed.

[0003] Blockchain is a decentralized, distributed database that stores data in blocks and transmits it in a chain-like data structure. It also uses a consensus mechanism to provide decentralized services. As a core component of a blockchain system, the consensus mechanism is the mechanism by which nodes in the blockchain reach consensus on the transmitted block information. It primarily addresses the issues of who generates and packages blocks and how to maintain block consistency. It is commonly used in government agencies and the financial sector. Consensus methods include Proof of Work (PoW), Delegated Proof of Stake (DPOS), and Practical Byzantine Fault Tolerance (PBFT). Proof-of-Work consensus mechanisms, such as PoW, can easily lead to centralized computing power and waste resources. Probabilistic consensus methods, such as the longest chain rule and inclusive agreement, reach consensus on block data with a certain probability, which increases over time, making it difficult to ensure the security of block data transactions. PBFT consensus methods, designed to address Byzantine fault tolerance, can effectively address the issues associated with these consensus methods, but they cannot effectively filter out malicious nodes and are subject to issues such as malicious forgery and consensus interference. Based on the above, many scholars have improved the existing consensus methods. However, the current consensus evaluation methods have the problem of poor security of consensus nodes. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a terminal trust evaluation method and device based on trust aggregation of a consensus mechanism to solve the technical problem of poor security of consensus nodes in the prior art.

[0005] The technical solutions proposed by the present invention are as follows:

[0006] A first aspect of an embodiment of the present invention provides a terminal trust evaluation method based on trust aggregation of a consensus mechanism, comprising: calculating an objective historical trust evaluation value of a login node and a historically visited node based on a location trust factor, an energy trust factor, and an interaction trust factor; selecting nodes from the historically visited nodes based on a reference value of the objective historical trust evaluation value to form a related node list; adopting a voting screening mechanism to select nodes from the related node list to form a trusted consensus node list; performing trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value; and performing weighted averaging based on the consensus trust value and the objective historical trust evaluation value to obtain a total trust value of the terminal where the login node is located.

[0007] Optionally, the objective historical trust evaluation value of the login node and the historical access node is calculated based on the location trust factor, energy trust factor and interaction trust factor, including: determining the location trust factor based on the geographical location relationship between the login node and the historical access node; determining the energy trust factor based on the energy loss when the login node visits any historical access node; determining the interaction trust factor based on the successful interaction between the login node and any historical access node; and using the location trust factor, energy trust factor and interaction trust factor for weighted calculation to determine the objective historical trust evaluation value of the login node and any historical access node.

[0008] Optionally, before selecting nodes from historically visited nodes to form a list of relevant nodes based on the reference value of the objective historical trust evaluation value, it also includes: using a sliding time window to select location trust factors, energy trust factors and interaction trust factors during multiple visits to obtain a predicted trust factor of any historically visited node visited by the login node; performing weighted averaging on the predicted trust factors of all historically visited nodes to obtain a standard quantitative factor triplet; performing weighted calculation on the standard quantitative factor triplet to obtain a standard quantitative trust limit; and judging whether the standard quantitative factor triplet of the current visit of the login node exceeds the standard quantitative trust limit to determine whether to perform a subsequent trust evaluation.

[0009] Optionally, a voting screening mechanism is used to select nodes from the relevant node list to form a trusted consensus node list, including: selecting nodes from the relevant node list as organization nodes based on the most recent access, and the remaining nodes as ordinary nodes; calculating the initial credibility value of each ordinary node based on the voting results of each ordinary node to the organization node; and selecting nodes from the ordinary nodes based on the initial credibility value to form a trusted consensus node list.

[0010] Optionally, trust consensus is performed based on the nodes in the trusted consensus node list to obtain a consensus trust value, including: determining a master node and a consensus node group based on the initial credibility value of each node in the trusted consensus node list; performing trust consensus based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node to obtain a trust consensus result; and determining whether the trust consensus result is a consensus trust value based on the voting result on the trust consensus result in the consensus node group.

[0011] Optionally, the terminal trust assessment method based on consensus mechanism trust aggregation also includes: determining whether the master node is trustworthy based on the query request about the master node received by the arbitration node and the data interaction between the arbitration node and the master node, and the arbitration node is selected from the relevant node list based on the access frequency; when the master node is not trustworthy, the arbitration node is used to broadcast the master node replacement message to the consensus node group, and when the arbitration node receives confirmation messages from a preset number of consensus nodes, a new master node is selected from the master node candidate set for replacement, and the organization node is used to reduce the master node reputation value, and the master node candidate set is determined according to the initial reputation value of each node; when the arbitration node receives a query request about the consensus node, the arbitration node is used to access the local log data of the questioned consensus node to determine whether to replace the consensus node, and the local log data stores data of voting screening and trust consensus process; and the organization node is used to remove the replaced consensus node from the trusted consensus node list.

[0012] Optionally, the terminal trust evaluation method based on consensus mechanism trust aggregation also includes: rewarding or punishing the organization node and arbitration node after each round of consensus; and updating the credibility value of each consensus node based on the number of times the consensus node feeds back the subjective trust evaluation value using the organization node.

[0013] Optionally, trust consensus is performed based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node to obtain a trust consensus result, including: calculating a correlation coefficient based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node and the number of feedbacks, feedback duration and feedback timestamp; performing fine-grained fusion based on the calculated correlation coefficient and the subjective trust evaluation value to obtain a consensus node reputation value; normalizing the consensus node trust value to obtain a reputation feature vector; and performing an inner product calculation on the subjective trust evaluation value and the reputation feature vector to obtain a trust consensus result.

[0014] An embodiment of the present invention also provides a terminal trust evaluation device based on consensus mechanism trust aggregation, including: an evaluation value determination module, which is used to calculate the objective historical trust evaluation value of the login node and the historical access node based on the location trust factor, the energy trust factor and the interaction trust factor; a first list determination module, which is used to select nodes from the historical access nodes based on the reference value of the objective historical trust evaluation value to form a related node list; a second list determination module, which is used to select nodes from the related node list using a voting screening mechanism to form a trusted consensus node list; a trust value determination module, which is used to perform trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value; a total trust value determination module, which is used to perform weighted averaging based on the consensus trust value and the objective historical trust evaluation value to obtain the total trust value of the terminal where the login node is located.

[0015] Optionally, the evaluation value determination module is specifically used to: determine a location trust factor based on the geographical location relationship between the login node and the historically visited node; determine an energy trust factor based on the energy loss when the login node visits any historically visited node; determine an interaction trust factor based on the successful interaction between the login node and any historically visited node; and use the location trust factor, energy trust factor, and interaction trust factor to perform weighted calculations to determine the objective historical trust evaluation value of the login node and any historically visited node.

[0016] Optionally, it also includes: a login node evaluation module, which is specifically used to use a sliding time window to select the location trust factor, energy trust factor and interaction trust factor during multiple visits to obtain the predicted trust factor of any historical visit node visited by the login node; perform weighted average of the predicted trust factors of all historical visit nodes to obtain a standard quantitative factor triplet; perform weighted calculation on the standard quantitative factor triplet to obtain a standard quantitative trust limit; determine whether the standard quantitative factor triplet of the current visit of the login node exceeds the standard quantitative trust limit to determine whether to perform a subsequent trust evaluation.

[0017] Optionally, the second list determination module is specifically used to: select a node from the relevant node list as an organization node based on the most recent visit, and the remaining nodes as ordinary nodes; calculate the initial credibility value of each ordinary node based on the voting results of each ordinary node to the organization node; and select nodes from the ordinary nodes based on the initial credibility value to form a trusted consensus node list.

[0018] Optionally, the trust value determination module includes: a first determination module, used to determine the master node and the consensus node group based on the initial credibility value of each node in the trusted consensus node list; a trust consensus module, used to perform trust consensus based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node, and obtain a trust consensus result; a voting module, used to determine whether the trust consensus result is a consensus trust value based on the voting result of the trust consensus result in the consensus node group.

[0019] Optionally, the terminal trust evaluation device based on consensus mechanism trust aggregation also includes: a node evaluation module, which is specifically used to determine whether the master node is trustworthy based on the query request about the master node received by the arbitration node and the data interaction between the arbitration node and the master node, and the arbitration node is selected from the relevant node list based on the access frequency; when the master node is not trustworthy, the arbitration node is used to broadcast the master node replacement message to the consensus node group, and when the arbitration node receives confirmation messages from a preset number of consensus nodes, a new master node is selected from the master node candidate set for replacement, and the organization node is used to reduce the master node reputation value, and the master node candidate set is determined according to the initial reputation value of each node; when the arbitration node receives a query request about the consensus node, the arbitration node is used to access the local log data of the questioned consensus node to determine whether to replace the consensus node, and the local log data stores data of voting screening and trust consensus process; the organization node is used to remove the replaced consensus node from the trusted consensus node list.

[0020] Optionally, the terminal trust evaluation device based on consensus mechanism trust aggregation also includes: a reward and punishment module, which is used to reward and punish the organization node and the arbitration node after each round of consensus; and an update module, which is used to update the credibility value of each consensus node based on the number of times the consensus node feeds back the subjective trust evaluation value using the organization node.

[0021] Optionally, the trust consensus module is specifically used to: calculate a correlation coefficient based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node and the number of feedbacks, feedback duration and feedback timestamp; perform fine-grained fusion based on the calculated correlation coefficient and the subjective trust evaluation value to obtain a consensus node reputation value; normalize the consensus node trust value to obtain a reputation feature vector; perform inner product calculation on the subjective trust evaluation value and the reputation feature vector to obtain a trust consensus result.

[0022] A third aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the terminal trust evaluation method based on consensus mechanism trust aggregation as described in the first aspect of the embodiment of the present invention and any one of the first aspects.

[0023] A fourth aspect of an embodiment of the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the terminal trust assessment method based on consensus mechanism trust aggregation as described in the first aspect of the embodiment of the present invention and any one of the first aspects.

[0024] The technical solution provided by the present invention has the following effects:

[0025] The embodiments of the present invention provide a terminal trust assessment method and device based on trust aggregation of a consensus mechanism. The method calculates the objective historical trust assessment value of the login node and the historically visited node based on the location trust factor, the energy trust factor, and the interaction trust factor; selects nodes from the historically visited nodes based on the reference value of the objective historical trust assessment value to form a related node list; adopts a voting screening mechanism to select nodes from the related node list to form a trusted consensus node list; conducts trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value; and performs a weighted average based on the consensus trust value and the objective historical trust assessment value to obtain the total trust value of the terminal where the login node is located. Therefore, when determining the consensus node, the node is evaluated and selected from the related node list determined based on the objective historical trust assessment value, thereby improving the security of the consensus node selection; in addition, when determining the total trust value, the node is determined based on the weighted average of the consensus trust value and the objective historical trust assessment value, thereby improving the accuracy of the trust value determination.

[0026] To address the resource waste caused by multiple transmissions between cloud servers and terminal nodes during edge computing, the trust assessment method provided by the present invention is implemented collaboratively by multiple consensus nodes. The master node aggregates the feedback trust, alleviating the computational costs of server-multi-terminal interactions and meeting consensus security requirements. Furthermore, voting and verification of the trust consensus results determined by the master node can constrain the master node responsible for consensus, improve the security and initiative of the master node's feedback information, and prevent the master node from tampering with broadcast information.

[0027] In the trust evaluation method provided by the embodiment of the present invention, untrusted nodes determined during the consensus process are replaced to avoid their influence on the consensus results, and the subsequent consensus work of the nodes is constrained by reducing their credibility values, thereby ensuring the security of the consensus process.

[0028] To solve the problem of assigning node identities without considering the initial reputation of the nodes in some trust models, the trust assessment method provided by the embodiment of the present invention determines the initial reputation of the node based on the voting scoring mechanism before consensus. The initial reputation depends on two points: the node's voting enthusiasm and the number of votes. In the later consensus process, the feedback number indicator is considered to update the node reputation to prevent malicious nodes from sending interference information frequently. The voting ability of the node is not determined by its computing power, but is determined by its reputation in the long-term participation in the consensus process, further improving the security of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 is a flow chart of a terminal trust evaluation method based on consensus mechanism trust aggregation according to an embodiment of the present invention;

[0031] Figure 2 is a flowchart of determining a list of trusted consensus nodes according to an embodiment of the present invention;

[0032] Figure 3 This is a block diagram of the consensus node selection structure according to an embodiment of the present invention;

[0033] Figure 4 is a flowchart of a consensus process according to an embodiment of the present invention;

[0034] Figure 5 is a schematic diagram of a node role architecture according to an embodiment of the present invention;

[0035] Figure 6 is a flow chart of a terminal trust evaluation method based on consensus mechanism trust aggregation according to another embodiment of the present invention;

[0036] Figure 7 This is a general framework diagram of the consensus process according to an embodiment of the present invention;

[0037] Figure 8 2 is a block diagram of a terminal trust evaluation device based on consensus mechanism trust aggregation according to an embodiment of the present invention;

[0038] Figure 9 is a schematic diagram of the structure of a computer-readable storage medium provided according to an embodiment of the present invention;

[0039] Figure 10 is a schematic structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0041] The terms "first," "second," "third," "fourth," and the like in the specification and claims of the present invention and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. 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 apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0042] According to an embodiment of the present invention, a terminal trust assessment method based on trust aggregation of a consensus mechanism is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] In this embodiment, a terminal trust evaluation method based on trust aggregation of a consensus mechanism is provided, which can be used for electronic devices such as computers, mobile phones, tablet computers, etc. Figure 1 : is a flow chart of a terminal trust evaluation method based on trust aggregation via a consensus mechanism according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0044] Step S101: Calculate the objective historical trust evaluation value of the login node and the historical access node based on the location trust factor, energy trust factor and interaction trust factor. Specifically, before calculating the objective historical trust evaluation value, the historical access node is determined by collecting historical access data and constructing a historical access behavior database. Among them, the relational model of the constructed user historical access behavior database includes: user attributes, login node attributes, access target node attributes, and historical access links; the user attributes describe the basic attributes of the user, including user ID, contact information, address, etc.; the login node attributes describe the basic characteristics of the login node, such as its IP address, model, version number, power, etc.; the access target node attributes are the same as the login node attributes, and also include basic characteristics that can describe the target node; the historical access link includes the primary keys of other relational models and attribute characteristics of the access behavior, including user ID, login node IP, target node IP, access content, access duration, access time, number of visits, etc.

[0045] When the server receives an access request from login node A, it traverses and searches the access records of login node A in the local historical access behavior database according to the login node IP address, counts the access data of the traversed login node A, and evaluates the objective historical trust evaluation value between login node A and each historical access node in the network based on the determined location trust factor, energy trust factor, and interaction trust factor.

[0046] Step S102: Select nodes from the historically visited nodes based on the reference value of the objective historical trust evaluation value to form a list of related nodes. When determining the list of related nodes, the objective historical trust evaluation value is used as an indicator to evaluate the reference value of the objective historical trust evaluation value of each historically visited node at the current moment. Specifically, Figure 2 and Figure 3 As shown, any historical access node r i The historical trust value of the login node A is time-sensitive, and its reference value decays dynamically over time. At the current moment, the reference value of the objective historical trust evaluation value is specifically defined by the following formula:

[0047]

[0048] in, Represents the historical access node r i In [t1,t n ] the historical trust evaluation vector of the login node A within the time range, namely [t1,t n ] is a vector of objective historical trust evaluation values ​​at each moment in time, Indicates t n The time decay coefficient of the moment determines the value decay rate of the historical trust value. t1 represents the time between login node A and node r. i The time when the historical trust value is first stored in the database, t n Indicates the current time. It should be noted that the current time refers to the time when the current login node applies for access.

[0049] When determining the list of related nodes, randomly select [t1,t n ] Select m time nodes within the time period, and calculate the different historical access nodes r at the corresponding time nodes according to the definition formula of the reference value above i The reference value of the objective historical trust evaluation value of the login node A is selected, and the K historical access nodes with the largest average reference value are selected to form the relevant node list Q K ={q1,...,q i ,...,q K}.

[0050] Step S103: A voting screening mechanism is used to select nodes from the relevant node list to form a trusted consensus node list. Specifically, a voting screening mechanism is used to evaluate the initial reputation values ​​of the nodes in the relevant node list, and the initial reputation values ​​are used as the consensus node selection index to select nodes from the relevant node list to form the trusted consensus node list.

[0051] Step S104: A trust consensus is performed based on the nodes in the trusted consensus node list to obtain a consensus trust value. Specifically, after the trusted consensus node list is determined, a consensus network is formed by several consensus nodes in the list. The consensus nodes participate in the trust consensus and determine the subjective trust evaluation value of the login node. The master node then determines the final consensus trust value based on the subjective trust evaluation value of the consensus node.

[0052] Step S105: Perform a weighted average based on the consensus trust value and the objective historical trust evaluation value to obtain the total trust value of the terminal where the login node is located. Specifically, the server can collect the consensus trust value and the objective historical trust evaluation value and perform a weighted calculation to obtain the total trust value of the terminal where the login node is located.

[0053] The terminal trust evaluation method based on consensus mechanism trust aggregation provided by the embodiment of the present invention calculates the objective historical trust evaluation value of the login node and the historical access node based on the location trust factor, energy trust factor and interaction trust factor; selects nodes from the historical access nodes based on the reference value of the objective historical trust evaluation value to form a related node list; adopts a voting screening mechanism to select nodes from the related node list to form a trusted consensus node list; conducts trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value; and performs a weighted average based on the consensus trust value and the objective historical trust evaluation value to obtain the total trust value of the terminal where the login node is located. Therefore, when determining the consensus node, it is evaluated and selected from the related node list determined based on the objective historical trust evaluation value, thereby improving the security of the consensus node selection; in addition, when determining the total trust value, it is determined based on the weighted average of the consensus trust value and the objective historical trust evaluation value, thereby improving the accuracy of the trust value determination.

[0054] In one embodiment, calculating the objective historical trust evaluation value of the login node and the historically visited node based on the location trust factor, the energy trust factor, and the interaction trust factor includes the following steps:

[0055] Step 201: Determine the location trust factor based on the geographical location relationship between the login node and the historically visited nodes. Specifically, the location trust factor reflects the geographical location relationship between the login node A and the historically visited node r. i The trust in geographical location is inversely proportional to the distance between the two points and directly proportional to the communication service radius of the login node. The larger the location trust factor of login node A, the higher the credibility. The location trust factor is specifically defined as:

[0056]

[0057] Among them, dis A,i Represents the login node A and the historical access node r i The distance between them is obtained through the GPS positioning of the device, R i Indicates the communication service radius of login node A. When the distance between two points exceeds the service radius of the login node, it means that the access is a remote thread access. is negative.

[0058] Step 202: Determine an energy trust factor based on the energy loss of the login node when accessing any historically visited node. Specifically, the energy trust factor reflects the energy loss of the login node A when accessing the historically visited node r. i The energy loss when With initial energy Ratio The energy here can be understood as the battery energy of the node. The smaller the energy loss, the greater the energy trust factor of the login node A.

[0059] Step 203: Determine the interaction trust factor based on the successful interaction between the login node and any historically visited node. Specifically, the interaction trust factor represents the interaction trust between the login node A and the historically visited node r. i Number of successful interactions in the past period INT suc Total number of interactions INT total Ratio It reflects the enthusiasm of login node A to participate in the interaction. The more frequent the interaction and the higher the success rate, the greater the interaction trust factor.

[0060] Step 204: Perform weighted calculation using the location trust factor, energy trust factor, and interaction trust factor to determine the objective historical trust evaluation value between the login node and any historically visited node. Specifically, the objective historical trust evaluation value is calculated using the following formula:

[0061] T A.i old =w1×T A.i loc +w2×T A.i ene +w3×T A.i int

[0062] Among them, T A.i old Represents the login node A and the historical access node r i The objective historical trust evaluation value of A.i locrepresents the location trust factor, T A.i ene represents the energy trust factor, T A.i int Represents the interactive trust factor, w1, w2, w3 represent the weight coefficients of the factors, and the weight coefficients are equally distributed here.

[0063] In one embodiment, before selecting nodes from historically visited nodes to form a list of related nodes based on the reference value of the objective historical trust evaluation value, the following steps are further included:

[0064] Step S301: Use a sliding time window to select the location trust factor, energy trust factor, and interaction trust factor during multiple visits to obtain the predicted trust factor of any historical visit node visited by the login node. Specifically, the location trust factor, energy trust factor, and interaction trust factor determined by the above steps can also be saved in the historical visit behavior database. When receiving the login node's current visit, the login node A and the node r in the historical visit node set R are obtained from the historical visit behavior database. i The trust factor T = [T A.i loc ,T A.i ene ,T A.i int ]. According to the login node A and the historical access node r i The access time sequence of multiple access records is used to arrange each trust factor in chronological order. A sliding time window with a window size of T is used to obtain the T-dimensional trust factor sequence with the closest time interval, and the predicted trust factor of the login node A is obtained. Taking the location trust factor as an example, its corresponding prediction trust factor can be expressed as follows:

[0065]

[0066] Energy trust factor and predicted trust factor corresponding to interaction trust factor and The same method is used to determine it, which will not be repeated here.

[0067] Step S302: weighted average the predicted trust factors of all historically visited nodes to obtain a standard quantized factor triplet; specifically, for all historically visited nodes r visited by login node A, i The weighted average of the predicted trust factors of ∈R,i=1,2,...,N is used to obtain the standard quantization factor triple of the login node A The specific weighted calculation method is: The two quantization factors in the triplet are determined in the same way.

[0068] Step S303: weighted calculation is performed on the standard quantization factor triplet to obtain the standard quantization trust limit. Specifically, a weighted calculation method of equally distributing weight coefficients can be adopted to perform weighted calculation on the standard quantization factor triplet to obtain the standard quantization trust limit φ of the standard quantization factor. A old and update it to the database.

[0069] Step S304: Determine whether the standard quantization factor triplet of the login node's current visit exceeds the standard quantization trust limit to determine whether to perform a subsequent trust evaluation. Specifically, the standard quantization trust limit can be pre-calculated and stored. When receiving the login node's current visit, each factor in the corresponding standard quantization factor triplet is compared with the standard quantization trust limit. If the standard quantization factor triplet exceeds the standard quantization trust limit, the trust value is calculated and stored in advance. If two factors in the BP_CONF_D exceed the historical upper limit, the current node is considered to be at risk and the node is marked with a risk tag ψ A , and stop the subsequent trust evaluation process. Among them, the risk marker ψ A The specific formula is as follows:

[0070]

[0071] In this embodiment, a voting screening mechanism is used to select nodes from the relevant node list to form a trusted consensus node list, including the following steps:

[0072] Step S401: Based on the most recent access, select a node from the relevant node list as the organization node, and the remaining nodes as ordinary nodes. Specifically, select the node that the login node visited most recently as the organization node q organize vote . Assign numbers to all related nodes in the related node list and the login node in descending order of the most recent visit, and start from the related node list Q K Select the node with the highest frequency of interaction with the login node as the arbitration node q check vote , the rest default to ordinary nodes q common vote .

[0073] Step S402: Calculate the initial reputation value of each common node based on the voting results of each common node to the organization node; specifically, Figure 2 and Figure 3As shown in the figure, during the voting cycle, the elected organization node broadcasts a voting preparation message to ordinary nodes; all ordinary nodes receive the message, verify the identity of the organization node, participate in the voting in turn according to the assigned number, sign and timestamp the voting content, store one copy in the local log, and provide one copy to the organization node; it is stipulated that each node must participate in the voting process, and only one vote is valid.

[0074] The organization node sorts out the voting results and counts the number of votes participated in by each ordinary node V i , effective votes i and the number of votes n i , calculate the initial reputation value of ordinary nodes. The reputation value calculation mechanism is triggered only when the organizing node receives more than half of the valid votes of ordinary nodes. Otherwise, the vote is re-voted. When the organizing node confirms that it has received more than half of the valid votes, it broadcasts the voting phase completion message to ordinary nodes.

[0075] The calculation of the initial reputation value of an ordinary node includes the node's participation in voting and the number of votes received. The specific calculation formula is defined as:

[0076]

[0077] Among them, fame i vote represents a common node q i The initial reputation value is in the range of [0, 1], n i Represents node q i The number of votes, vote i Indicates the common node q in the voting cycle i The number of valid votes, V i Indicates the common node q in the voting cycle i The number of participating votes. Nodes that successfully complete the voting will be awarded G points. Nodes that have invalid votes or frequent voting during the voting cycle will have G points deducted.

[0078] Step S403: Select nodes from common nodes based on the initial reputation value to form a trusted consensus node list. Figure 2 and Figure 3 As shown, the organization nodes are arranged in descending order according to the size of the initial credibility value, and the top l nodes with the largest initial credibility value are counted to form the trusted consensus node list Q l And broadcast the initial reputation value list and initial reputation ranking of the top l nodes to each ordinary node.

[0079] In one embodiment, performing trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value includes the following steps:

[0080] Step S501: Determine the master node and consensus node group based on the initial reputation value of each node in the trusted consensus node list; specifically, Figure 2 and Figure 3 As shown, the initial reputation value in the trusted consensus node list is The nodes are assigned to the master node candidate set Key, and the node with the highest credibility is selected from the master node candidate set as the master node q participating in the first consensus key ; Add the nodes in the trusted consensus node list except the master node candidate set to the consensus node candidate set Sub, and then select the top M nodes in the initial credibility ranking from the consensus node candidate set as the consensus node group participating in the first consensus. The number of consensus nodes is fixed at M for each consensus; Represents the average reputation value of the consensus node group.

[0081] Step S502: Based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node, a trust consensus result is obtained. Specifically, Figure 4 As shown, after determining the master node, the server sends a trust feedback instruction to the master node, and the master node broadcasts a feedback preparation message to the consensus node group based on the trust feedback instruction. <prepare,timestamp,id key >,σ key >, where id key Indicates the identity certification number of the master node, timestamp is the timestamp of sending feedback, σ key Indicates the master node's signature on the message.

[0082] After receiving the feedback preparation message sent by the master node, the consensus node determines the subjective trust evaluation value of the login node, and sends it independently to the master node and the organization node, and broadcasts the trust evaluation success message to other nodes in the consensus node group. After receiving the subjective trust evaluation value, the master node determines the subjective trust evaluation value G of the consensus node. A.i Objective historical trust evaluation value of node local log The coupling of If the value exceeds 0.7, the subjective trust evaluation value is adopted. Then, a trust consensus is performed based on the received subjective trust evaluation values ​​to obtain a trust consensus result.

[0083] The trust consensus process is specifically implemented as follows: when the master node receives at least 2f+1 trust evaluation values ​​from different consensus nodes, the trust consensus mechanism is triggered and trust consensus is performed; f represents the maximum number of Byzantine nodes that the system can tolerate. Trust consensus is performed based on the subjective trust evaluation values ​​fed back to the master node by each consensus node in the consensus node group, resulting in a trust consensus result. This includes: calculating a correlation coefficient based on the subjective trust evaluation values ​​fed back to the master node by each consensus node in the consensus node group, the number of feedbacks, the duration of the feedback, and the timestamp of the feedback; performing a fine-grained fusion based on the calculated correlation coefficient and the subjective trust evaluation values ​​to obtain a consensus node reputation value; normalizing the consensus node trust values ​​to obtain a reputation feature vector; and performing an inner product calculation between the subjective trust evaluation value and the reputation feature vector to obtain a trust consensus result.

[0084] Specifically, the trust consensus process is implemented using the following process: the master node analyzes the data features generated by the feedback behavior of the consensus nodes when multiple consensus nodes participate in the feedback, including the number of consensus node feedbacks within the cycle Q1, the feedback duration of the consensus node within the cycle Q2, the feedback timestamp Q3, and the subjective trust evaluation value of the feedback Q4. The data feature values ​​of all consensus nodes within the consensus cycle T are counted to construct the feedback behavior matrix Q of the consensus nodes. m×4 , m represents the number of consensus nodes, and the correlation coefficient matrix R between the trust evaluation value Q4 of the feedback behavior matrix and the remaining attribute features is calculated m×3 =[R 14 ,R 24 ,R 34 ], the trust evaluation value Q4 is finely fused with the other attribute features to obtain the consensus node reputation value O; the correlation coefficient matrix column vector R i4 Medium data feature Q i The correlation coefficient calculation formula with Q4 is:

[0085]

[0086] For a certain consensus node, the fine-grained fusion is determined by the correlation coefficient value and the fusion formula is:

[0087]

[0088] The reputation value of all consensus nodes after a round of consensus is O k ,k=1,2,...,l is normalized to obtain the consensus node group Q l The reputation feature vector O'={O'1,O'2,...,O' l}, the final subjective trust evaluation value Q4 and the node's reputation feature vector O' = {O'1, O'2, ..., O' l}Do the inner product to get the final consensus trust value Trust = O'·Q4, and the master node broadcasts the final consensus trust value; otherwise, it broadcasts a consensus failure message and requires the consensus node to resend the subjective trust evaluation value.

[0089] Step S503: Determine whether the trust consensus result is a consensus trust value based on the voting results of the consensus node group on the trust consensus result. Figure 4 As shown, after the consensus node receives the trust consensus result broadcast by the master node, it votes to verify the trust consensus result; when the consensus node receives the upload instruction sent by the master node, it sends the voting content to the organization node; the organization node counts the voting results and calculates the approval and disapproval votes sent by the consensus node. When the approval vote is greater than the disapproval vote, it is judged that the trust consensus result reaches a consistent consensus, and a consensus success message is sent to the master node and the arbitration node. Otherwise, the master node trust consensus fails; when the master node receives the consensus success message, it determines the trust consensus result as the final consensus trust value.

[0090] To address the resource waste caused by multiple transmissions between cloud servers and terminal nodes during edge computing, the trust assessment method provided by the present invention is implemented collaboratively by multiple consensus nodes. The master node aggregates the feedback trust, alleviating the computational costs of server-multi-terminal interactions and meeting consensus security requirements. Furthermore, voting and verification of the trust consensus results determined by the master node can constrain the master node responsible for consensus, improve the security and initiative of the master node's feedback information, and prevent the master node from tampering with broadcast information.

[0091] In one embodiment, the terminal trust assessment method based on consensus mechanism trust aggregation further includes the following steps:

[0092] Step S601: Determine whether the master node is trustworthy based on the query request received by the arbitration node regarding the master node and the data interaction between the arbitration node and the master node. The arbitration node is selected from the list of related nodes based on the access frequency.

[0093] Specifically, if Figure 4 As shown in the figure, during the consensus process, after receiving a consensus success message from the organizing node, the arbitration node queries the log information of the nodes involved in this round of consensus and initiates arbitration against malicious nodes, such as those experiencing node downtime, malicious voting, or invalid voting. At the same time, the arbitration node can also receive challenge requests from other nodes regarding the master node, initiate arbitration with the master node, and access the master node's local log data. Upon receiving the arbitration message, the master node presents evidence to the arbitration node, demonstrating the amount of tasks it completed within the consensus time and the correctness of the broadcast results. The arbitration node then determines whether the node is trustworthy based on the local log data it accesses.

[0094] Step S602: When the master node is untrustworthy, the arbitration node broadcasts a master node replacement message to the consensus node group. When the arbitration node receives confirmation messages from a preset number of consensus nodes, it selects a new master node from the master node candidate set to replace it, and uses the organization node to reduce the master node's reputation value. The master node candidate set is determined based on the initial reputation value of each node. Specifically, if the master node is considered untrustworthy, the arbitration node broadcasts a master node replacement message to the consensus node group nodes. The consensus node receives the replacement message and replies with confirmation. When the arbitration node receives confirmation messages from different f+1 consensus nodes, it randomly selects a new master node from the master node candidate set Key to replace it, broadcasts a master node replacement success message, and starts a new round of consensus. After receiving the master node replacement message, the organization node reduces the master node's reputation value. When the master node is arbitrated more than a certain number of times or its reputation value is in the bottom 20% of all candidate sets, the node is removed from the master node candidate set and cannot participate in consensus, but can only passively receive consensus data.

[0095] Step S603: When the arbitration node receives a questioning request about a consensus node, the arbitration node accesses the local log data of the questioned consensus node to determine whether to replace the consensus node. The local log data stores data of voting screening and trust consensus process. Specifically, in addition to the arbitration node accessing the local log data of the consensus node, it can also receive questioning requests about the consensus node from other nodes and directly access the local log data of the node. If the node is untrustworthy after verification, the message of the node being replaced is directly broadcast, and a new consensus node is randomly selected from the consensus node candidate set outside the current consensus node group to replace it, and the consensus node number after replacement is broadcast to start a new round of consensus.

[0096] Step S604: The organization node removes the replaced consensus node from the trusted consensus node list. After receiving the consensus node replacement message, the organization node directly clears the reputation value of the original consensus node and removes the node from the trusted consensus node list. The node is marked as a Byzantine node.

[0097] It should be noted that if the arbitration node does not receive a challenge request or finds no suspicious node in the node log information, it will broadcast the node security information to all nodes. At the same time, the master node receives the consensus success message sent by the organization node and the security information broadcast by the arbitration node, uploads the final consensus trust value to the server, and broadcasts the upload message to all nodes.

[0098] In the trust evaluation method provided by the embodiment of the present invention, untrusted nodes determined during the consensus process are replaced to avoid their influence on the consensus results, and the subsequent consensus work of the nodes is constrained by reducing their credibility values, thereby ensuring the security of the consensus process.

[0099] In one embodiment, the terminal trust evaluation method based on consensus mechanism trust aggregation also includes: after each round of consensus, rewarding or punishing the organization node and arbitration node; using the organization node to update the reputation value of each consensus node based on the number of times the consensus node feeds back the subjective trust evaluation value.

[0100] Specifically, the rewards and penalties for organizing and arbitrating nodes include: After each round of consensus, organizing and arbitrating nodes are rewarded and punished. If the arbitrating node does not initiate arbitration against other nodes during the consensus round, that is, no node is replaced, it indicates that the nodes participating in the consensus round behaved normally, and the node's reputation value is rewarded by 10%. Otherwise, the node's reputation value is reduced by 30%. The organizing and arbitrating nodes must reach the top 50% of all node reputations to participate in the next round of consensus.

[0101] Different node roles result in different rewards after each consensus. Organizational nodes, arbitration nodes, and master nodes are all selected through a single vote, leaving them vulnerable to malicious tampering and the broadcast of erroneous information during consensus. To constrain these nodes, a reward indicator is set. Assuming the master node successfully uploads its final trust rating, the total system reward is 1. The organization node receives 20% of the reward, the arbitration node 20%, the master node 20%, and the consensus node group splits the remaining 40% equally. These rewards can be understood as a cumulative measure to encourage node participation in consensus. If a node's reward remains zero for an extended period, it is considered inactive and no feedback instructions will be sent to it.

[0102] Among them, the selection of consensus nodes in the trust consensus is determined by the reputation value. Therefore, in order to prevent the nodes from maliciously sending interference information frequently and affecting the consensus process, the reputation value is updated according to the number of feedbacks from the consensus nodes. Specifically, Figure 4 As shown in Figure 2, the reputation value of a consensus node is updated iteratively with the number of feedbacks during each consensus process. The more feedbacks a node receives during a consensus process, the faster the reputation value decreases. The specific update method is defined as:

[0103]

[0104] in, It represents the reputation value of the consensus node when it feeds back the subjective trust evaluation value for the tth time, It represents the reputation value of the consensus node when it feeds back the subjective trust evaluation value for the t-1th time. Represents the initial reputation value of the node, equal to

[0105] Specifically, if Figure 5As shown in the figure, in the trust evaluation process, the node roles participating in the trust consensus include master node, consensus node, organization node, arbitration node, and candidate node.

[0106] The master node is the only node that communicates with the server. It is responsible for receiving feedback instructions from the server and forwarding these instructions to other nodes in the system to perform trust consensus operations and upload trust consensus results. The master node is required to regularly broadcast its identity proof of each consensus to other nodes to prevent the master node from downtime or attack.

[0107] The consensus node group represents the M consensus nodes outside the master node candidate set in the consensus node list. It is responsible for receiving feedback instructions forwarded by the master node, feeding back the subjective trust evaluation of the login node to the master node, broadcasting the successful message to other consensus nodes, voting to verify the master node trust consensus result, and then sending the voting content to the organization node.

[0108] Organization nodes represent nodes that participate in the voting process described above, but do not participate in consensus. They are responsible for calculating and updating the reputation of master nodes and consensus nodes during the consensus process, dividing nodes based on reputation, and then analyzing the voting results of consensus nodes, integrating reputation values ​​to determine the voting results of consensus nodes.

[0109] Arbitration nodes represent a group of nodes that arbitrate the broadcast content during the node consensus process. They are responsible for receiving arbitration requests from other nodes, verifying the node's broadcast content, and broadcasting a request to replace the master node and consensus node. Arbitration nodes have access to the local logs of all nodes and oversee the entire consensus process.

[0110] A candidate node means that when a master node or consensus node is found to have malicious behavior, the candidate node can be given an opportunity to change its identity as an alternative and participate in the consensus as a master node or consensus node.

[0111] To solve the problem of assigning node identities without considering the initial reputation of the nodes in some trust models, the trust assessment method provided by the embodiment of the present invention determines the initial reputation of the node based on the voting scoring mechanism before consensus. The initial reputation depends on two points: the node's voting enthusiasm and the number of votes. In the later consensus process, the feedback number indicator is considered to update the node reputation to prevent malicious nodes from sending interference information frequently. The voting ability of the node is not determined by its computing power, but is determined by its reputation in the long-term participation in the consensus process, further improving the security of the system.

[0112] In one embodiment, if Figure 6As shown in FIG, the terminal trust evaluation method based on trust aggregation of the consensus mechanism is implemented by the following process: the proxy server searches the historical access behavior database for nodes that have interacted with the login node A within a certain period of time to form a historical access node set, calculates the trust evaluation factors of all access nodes and login node A in the historical access node set, and obtains an objective historical trust evaluation value; for each historical access node, constructs a historical trust time series, and predicts its predicted trust factor with the login node; the predicted trust factor of all historical access nodes; the predicted trust factors of all historical access nodes are weighted averaged to obtain the final standard quantitative trust limit of the login node; the standard quantitative factor of the current login node is compared with the limit. If two factors in the standard quantitative factor triplet exceed the historical upper limit, the current node is considered to be at risk, the node is marked as risky, and the trust evaluation is stopped. Otherwise, find the recently active related nodes and construct a list of related nodes; determine the list of trusted consensus nodes based on the votes of the related nodes; elect the master node and consensus node group based on the initial reputation value of the node; the consensus node determines the subjective trust evaluation value; the master node performs trust aggregation based on the subjective trust evaluation value and determines the consensus trust value based on the feedback of the consensus node and uploads it; the server performs weighted calculation based on the consensus trust value and the objective historical trust evaluation value to obtain the total trust value.

[0113] Specifically, if Figure 7 As shown in the figure, the trust consensus process of the master node specifically includes the following three steps: the initial node, determining the list of trusted consensus nodes and defining node roles; the consensus phase, broadcasting feedback consensus results, performing consensus verification and updating reputation values; the end of consensus, including reward and punishment mechanisms and replacement of malicious or suspicious nodes.

[0114] The embodiment of the present invention also provides a terminal trust evaluation device based on trust aggregation of consensus mechanism, such as Figure 8 Shown, including:

[0115] The evaluation value determination module is used to calculate the objective historical trust evaluation value of the login node and the historical access node based on the location trust factor, energy trust factor and interaction trust factor; the specific content can be found in the corresponding part of the above method embodiment, which will not be repeated here.

[0116] The first list determination module is used to select nodes from historically visited nodes based on the reference value of the objective historical trust evaluation value to form a related node list; the specific content can be found in the corresponding part of the above method embodiment, which will not be repeated here.

[0117] The second list determination module is used to select nodes from the relevant node list using a voting screening mechanism to form a trusted consensus node list; the specific content can be found in the corresponding part of the above method embodiment, which will not be repeated here.

[0118] The trust value determination module is used to perform trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value; the specific content can be found in the corresponding part of the above method embodiment, which will not be repeated here.

[0119] The total trust value determination module is used to perform a weighted average based on the consensus trust value and the objective historical trust evaluation value to obtain the total trust value of the terminal where the login node is located. For details, please refer to the corresponding part of the above method embodiment and will not be repeated here.

[0120] The terminal trust evaluation device based on consensus mechanism trust aggregation provided by an embodiment of the present invention calculates the objective historical trust evaluation value of the login node and the historically visited node based on the location trust factor, energy trust factor, and interaction trust factor; selects nodes from the historically visited nodes based on the reference value of the objective historical trust evaluation value to form a related node list; adopts a voting screening mechanism to select nodes from the related node list to form a trusted consensus node list; conducts trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value; and performs a weighted average based on the consensus trust value and the objective historical trust evaluation value to obtain the total trust value of the terminal where the login node is located. Therefore, when determining the consensus node, the node is evaluated and selected from the related node list determined based on the objective historical trust evaluation value, thereby improving the security of the consensus node selection; in addition, when determining the total trust value, the node is determined based on the weighted average of the consensus trust value and the objective historical trust evaluation value, thereby improving the accuracy of the trust value determination.

[0121] For a detailed description of the functions of the terminal trust evaluation device based on consensus mechanism trust aggregation provided by an embodiment of the present invention, please refer to the description of the terminal trust evaluation method based on consensus mechanism trust aggregation in the above embodiment.

[0122] Optionally, the evaluation value determination module is specifically used to: determine a location trust factor based on the geographical location relationship between the login node and the historically visited node; determine an energy trust factor based on the energy loss when the login node visits any historically visited node; determine an interaction trust factor based on the successful interaction between the login node and any historically visited node; and use the location trust factor, energy trust factor, and interaction trust factor to perform weighted calculations to determine the objective historical trust evaluation value of the login node and any historically visited node.

[0123] Optionally, it also includes: a login node evaluation module, which is specifically used to use a sliding time window to select the location trust factor, energy trust factor and interaction trust factor during multiple visits to obtain the predicted trust factor of any historical visit node visited by the login node; perform weighted average of the predicted trust factors of all historical visit nodes to obtain a standard quantitative factor triplet; perform weighted calculation on the standard quantitative factor triplet to obtain a standard quantitative trust limit; determine whether the standard quantitative factor triplet of the current visit of the login node exceeds the standard quantitative trust limit to determine whether to perform a subsequent trust evaluation.

[0124] Optionally, the second list determination module is specifically used to: select a node from the relevant node list as an organization node based on the most recent visit, and the remaining nodes as ordinary nodes; calculate the initial credibility value of each ordinary node based on the voting results of each ordinary node to the organization node; and select nodes from the ordinary nodes based on the initial credibility value to form a trusted consensus node list.

[0125] Optionally, the trust value determination module includes: a first determination module, used to determine the master node and the consensus node group based on the initial credibility value of each node in the trusted consensus node list; a trust consensus module, used to perform trust consensus based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node, and obtain a trust consensus result; a voting module, used to determine whether the trust consensus result is a consensus trust value based on the voting result of the trust consensus result in the consensus node group.

[0126] Optionally, the terminal trust evaluation device based on consensus mechanism trust aggregation also includes: a node evaluation module, which is specifically used to determine whether the master node is trustworthy based on the query request about the master node received by the arbitration node and the data interaction between the arbitration node and the master node, and the arbitration node is selected from the relevant node list based on the access frequency; when the master node is not trustworthy, the arbitration node is used to broadcast the master node replacement message to the consensus node group, and when the arbitration node receives confirmation messages from a preset number of consensus nodes, a new master node is selected from the master node candidate set for replacement, and the organization node is used to reduce the master node reputation value, and the master node candidate set is determined according to the initial reputation value of each node; when the arbitration node receives a query request about the consensus node, the arbitration node is used to access the local log data of the questioned consensus node to determine whether to replace the consensus node, and the local log data stores data of voting screening and trust consensus process; the organization node is used to remove the replaced consensus node from the trusted consensus node list.

[0127] Optionally, the terminal trust evaluation device based on consensus mechanism trust aggregation also includes: a reward and punishment module, which is used to reward and punish the organization node and the arbitration node after each round of consensus; and an update module, which is used to update the credibility value of each consensus node based on the number of times the consensus node feeds back the subjective trust evaluation value using the organization node.

[0128] Optionally, the trust consensus module is specifically used to: calculate a correlation coefficient based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node and the number of feedbacks, feedback duration and feedback timestamp; perform fine-grained fusion based on the calculated correlation coefficient and the subjective trust evaluation value to obtain a consensus node reputation value; normalize the consensus node trust value to obtain a reputation feature vector; perform inner product calculation on the subjective trust evaluation value and the reputation feature vector to obtain a trust consensus result.

[0129] The embodiment of the present invention also provides a storage medium, such as Figure 9 As shown, a computer program 601 is stored thereon, and when the instructions are executed by the processor, the steps of the terminal trust assessment method based on the consensus mechanism trust aggregation in the above embodiment are implemented. The storage medium also stores audio and video stream data, feature frame data, interaction request signaling, encrypted data, and preset data size, etc. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0130] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). The storage medium can also include a combination of the above-mentioned types of memory.

[0131] The embodiment of the present invention further provides an electronic device, such as Figure 10As shown, the electronic device may include a processor 51 and a memory 52, wherein the processor 51 and the memory 52 may be connected via a bus or other means. Figure 10 The bus connection is taken as an example.

[0132] The processor 51 may be a central processing unit (CPU). The processor 51 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0133] Memory 52, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as the corresponding program instructions / modules in the embodiments of the present invention. Processor 51 executes the non-transitory software programs, instructions, and modules stored in memory 52 to perform various processor functions and data processing, thereby implementing the terminal trust assessment method based on consensus mechanism trust aggregation in the above-mentioned method embodiment.

[0134] The memory 52 may include a program storage area and a data storage area, wherein the program storage area may store applications required for operating the device and at least one function; the data storage area may store data created by the processor 51, etc. In addition, the memory 52 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 52 may optionally include a memory remotely located relative to the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0135] The one or more modules are stored in the memory 52 and when executed by the processor 51, perform the following steps: Figure 1 -2 shows a terminal trust assessment method based on consensus mechanism trust aggregation in the embodiment.

[0136] For details of the above electronic equipment, please refer to Figures 1 to 2 The corresponding descriptions and effects in the embodiments shown can be understood and will not be repeated here.

[0137] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A terminal trust evaluation method based on trust aggregation of consensus mechanism, characterized in that: include: Calculate the objective historical trust evaluation value of the login node and the historically visited nodes based on the location trust factor, energy trust factor, and interaction trust factor; Selecting nodes from historically visited nodes based on the reference value of the objective historical trust evaluation value to form a list of related nodes; A voting screening mechanism is used to select nodes from the relevant node list to form a trusted consensus node list; Conduct trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value; A total trust value of the terminal where the login node is located is obtained by performing a weighted average based on the consensus trust value and the objective historical trust evaluation value; The objective historical trust evaluation value of the login node and the historically visited nodes is calculated based on the location trust factor, energy trust factor, and interaction trust factor, including: Determine the location trust factor based on the geographical location relationship between the login node and the historically visited nodes; Determine the energy trust factor based on the energy loss of the login node when accessing any historically visited node; Determine the interaction trust factor based on the successful interaction between the login node and any historically visited node; The location trust factor, energy trust factor, and interaction trust factor are used to perform weighted calculation to determine an objective historical trust evaluation value of the login node and any historically visited node; Before selecting nodes from historically visited nodes to form a list of related nodes based on the reference value of the objective historical trust evaluation value, the method further includes: A sliding time window is used to select the location trust factor, energy trust factor, and interaction trust factor during multiple visits to obtain the predicted trust factor of any historical visit node visited by the login node. The predicted trust factors of all historically visited nodes are weighted averaged to obtain the standard quantized factor triplet; Performing weighted calculation on the standard quantization factor triplet to obtain a standard quantization trust limit; It is determined whether the standard quantization factor triplet of the current access of the login node exceeds the standard quantization trust limit to determine whether to perform subsequent trust evaluation.

2. The terminal trust evaluation method based on consensus mechanism trust aggregation according to claim 1 is characterized in that: A voting screening mechanism is used to select nodes from the relevant node list to form a trusted consensus node list, including: Selecting a node from the relevant node list based on the most recent access as an organization node and the remaining nodes as ordinary nodes; Calculate the initial reputation value of each ordinary node based on the voting results of each ordinary node to the organization node; Based on the initial reputation value, nodes are selected from ordinary nodes to form a trusted consensus node list.

3. The terminal trust evaluation method based on consensus mechanism trust aggregation according to claim 2 is characterized in that: A trust consensus is performed based on the nodes in the trusted consensus node list to obtain a consensus trust value, including: Determine the master node and consensus node group based on the initial reputation value of each node in the trusted consensus node list; Based on the subjective trust evaluation value fed back by each consensus node in the consensus node group to the master node, trust consensus is performed to obtain a trust consensus result; Determine whether the trust consensus result is a consensus trust value based on a voting result on the trust consensus result in the consensus node group.

4. The terminal trust evaluation method based on consensus mechanism trust aggregation according to claim 3 is characterized in that: Also includes: determining whether the master node is trustworthy based on a query request received by the arbitration node regarding the master node and based on data interaction between the arbitration node and the master node, the arbitration node being selected from the list of relevant nodes based on access frequency; When the master node is untrustworthy, the arbitration node broadcasts a master node replacement message to the consensus node group. When the arbitration node receives confirmation messages from a preset number of consensus nodes, it selects a new master node from the master node candidate set and uses the organization node to reduce the master node's reputation value. The master node candidate set is determined based on the initial reputation value of each node. When an arbitration node receives a questioning request about a consensus node, it uses the arbitration node to access the local log data of the questioned consensus node to determine whether to replace the consensus node. The local log data stores data on voting screening and trust consensus process; The adopted organization node will be removed from the list of trusted consensus nodes by the replaced consensus node.

5. The terminal trust evaluation method based on consensus mechanism trust aggregation according to claim 4 is characterized in that: Also includes: After each round of consensus, rewards and punishments are given to the organization nodes and arbitration nodes; The organizational node updates the reputation value of each consensus node based on the number of times the consensus node feeds back the subjective trust evaluation value.

6. The terminal trust evaluation method based on consensus mechanism trust aggregation according to claim 3 is characterized in that: Trust consensus is performed based on the subjective trust evaluation values ​​fed back to the master node by each consensus node in the consensus node group, and the trust consensus result is obtained, including: Calculate the correlation coefficient based on the subjective trust evaluation value fed back to the master node by each consensus node in the consensus node group and the number of feedbacks, feedback duration, and feedback timestamp; Based on the calculated correlation coefficient and subjective trust evaluation value, a fine-grained fusion is performed to obtain the consensus node reputation value; Normalizing the trust value of the consensus node to obtain a reputation feature vector; An inner product calculation is performed on the subjective trust evaluation value and the reputation feature vector to obtain a trust consensus result.

7. A terminal trust evaluation device based on consensus mechanism trust aggregation, characterized in that: include: An evaluation value determination module, configured to calculate an objective historical trust evaluation value of a login node and a historically visited node based on a location trust factor, an energy trust factor, and an interaction trust factor; A first list determination module, configured to select nodes from historically visited nodes based on a reference value of the objective historical trust evaluation value to form a related node list; A second list determination module is used to select nodes from the relevant node list using a voting screening mechanism to form a trusted consensus node list; A trust value determination module, configured to perform trust consensus based on the nodes in the trusted consensus node list to obtain a consensus trust value; A total trust value determination module, configured to obtain a total trust value of the terminal where the login node is located by performing a weighted average based on the consensus trust value and the objective historical trust evaluation value; The objective historical trust evaluation value of the login node and the historically visited nodes is calculated based on the location trust factor, energy trust factor, and interaction trust factor, including: Determine the location trust factor based on the geographical location relationship between the login node and the historically visited nodes; Determine the energy trust factor based on the energy loss of the login node when accessing any historically visited node; Determine the interaction trust factor based on the successful interaction between the login node and any historically visited node; The location trust factor, energy trust factor, and interaction trust factor are used to perform weighted calculation to determine an objective historical trust evaluation value of the login node and any historically visited node; Before selecting nodes from historically visited nodes to form a list of related nodes based on the reference value of the objective historical trust evaluation value, the method further includes: A sliding time window is used to select the location trust factor, energy trust factor, and interaction trust factor during multiple visits to obtain the predicted trust factor of any historical visit node visited by the login node. The predicted trust factors of all historically visited nodes are weighted averaged to obtain the standard quantized factor triplet; Performing weighted calculation on the standard quantization factor triples to obtain a standard quantization trust limit; It is determined whether the standard quantization factor triplet of the current access of the login node exceeds the standard quantization trust limit to determine whether to perform subsequent trust evaluation.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the terminal trust evaluation method based on consensus mechanism trust aggregation as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the terminal trust assessment method based on consensus mechanism trust aggregation as described in any one of claims 1 to 6 by executing the computer instructions.

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