Trust label processing method and device for agent

Abstract

Embodiments of the present specification provide an agent trust label processing method and device, wherein the trust label processing method of an agent comprises: an agent processing platform determines a label processing node according to an agent identifier and processing parameters carried by a label processing request in the process of trust rating processing of an agent, further performs label rating processing on the trust rating label of the target agent based on the label rating strategy corresponding to the label processing node, and finally synchronizes the label rating result to each cloud service platform where the target agent is deployed, so as to synchronize the trust rating label of the agent to the cloud service platform where the agent is deployed, thereby realizing the trust rating management of the agent.

Description

Technical Field

[0001] This document relates to the field of intelligent agent technology, and in particular to a method and apparatus for processing trust tags of intelligent agents. Background Technology

[0002] With the continuous development and promotion of the Internet and artificial intelligence, more and more service providers are beginning to provide users with various AI-based services online. Users can conveniently access various AI-related services online. For example, users can access intelligent agents provided by various service providers through web pages or applications, and can also access a variety of intelligent agent tools integrated with the intelligent agents. As the use of intelligent agents and intelligent agent tools becomes more and more widespread, competition surrounding intelligent agents is becoming increasingly fierce. How to enhance users' trust in intelligent agents has become a key focus for all parties. Summary of the Invention

[0003] This specification provides one or more embodiments of a method for processing trust labels for intelligent agents, applied to an intelligent agent processing platform. The method includes: obtaining a label processing request for trust rating labels of intelligent agents deployed on a cloud service platform; determining the label processing node of the target intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and processing parameters carried in the label processing request; performing label rating processing on the trust rating labels of the target intelligent agent based on the label rating strategy corresponding to the label processing node; and returning the label rating results to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating labels of the target intelligent agent.

[0004] This specification provides one or more embodiments of a trust label processing device for an intelligent agent, operating on an intelligent agent processing platform. The device includes: a request acquisition module configured to acquire label processing requests for trust rating labels of intelligent agents deployed on a cloud service platform; a node determination module configured to determine the label processing node of the target intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and processing parameters carried in the label processing request; a label rating processing module configured to perform label rating processing on the trust rating labels of the target intelligent agent based on the label rating strategy corresponding to the label processing node; and a label synchronization module configured to return label rating results to each cloud service platform where the target intelligent agent is deployed for trust rating label synchronization of the target intelligent agent.

[0005] This specification provides one or more embodiments of a trust tag processing device for intelligent agents, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: obtain a tag processing request for trust rating tags of intelligent agents deployed on a cloud service platform; determine a tag processing node for a target intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and processing parameters carried in the tag processing request; perform tag rating processing on the trust rating tags of the target intelligent agent based on the tag rating strategy corresponding to the tag processing node; and return the tag rating results to each cloud service platform where the target intelligent agent is deployed for trust rating tag synchronization of the target intelligent agent.

[0006] This specification provides one or more embodiments of a computer-readable storage medium for storing computer-executable instructions. When executed, these instructions implement the following process: obtaining a tag processing request for trust rating tags of an intelligent agent deployed on a cloud service platform; determining the tag processing node for the target intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and processing parameters carried in the tag processing request; performing tag rating processing on the trust rating tags of the target intelligent agent based on the tag rating strategy corresponding to the tag processing node; and returning the tag rating results to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating tags of the target intelligent agent. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic diagram illustrating the implementation environment of a trust tag processing method for an intelligent agent provided in one or more embodiments of this specification;

[0009] Figure 2 A flowchart illustrating a trust tag processing method for an intelligent agent, provided in one or more embodiments of this specification;

[0010] Figure 3 A flowchart illustrating a method for processing trust tags of an agent in an agent trust rating scenario, provided in one or more embodiments of this specification.

[0011] Figure 4 A schematic diagram of an embodiment of a trust tag processing device for an intelligent agent provided in one or more embodiments of this specification;

[0012] Figure 5 This is a schematic diagram of the structure of a trust tag processing device for an intelligent agent provided in one or more embodiments of this specification. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0014] The agent trust tag processing method provided in one or more embodiments of this specification is applicable to the implementation environment of an agent trust rating system. (Refer to...) Figure 1 The implementation environment includes at least:

[0015] The intelligent agent processing platform 101, one or more cloud service platforms 102, and at least one intelligent agent 103;

[0016] Among them, the intelligent agent processing platform 101 is used to perform trust rating label processing on the intelligent agent 103, and cooperates with the cloud service platform 102 to synchronously display the trust rating labels of the intelligent agent 103.

[0017] The cloud service platform 102 is used to deploy the intelligent agent 103 and, together with the intelligent agent processing platform 101, to synchronously display the trust rating labels of the intelligent agent 103;

[0018] In this implementation environment, when the agent processing platform 101 receives a tag processing request submitted by the cloud service platform 102 for any deployed agent 103-n, it determines the tag processing node of the target agent 103-n corresponding to the agent identifier based on the agent identifier and processing parameters carried in the tag processing request. Then, based on the tag rating strategy corresponding to the tag processing node, it performs tag rating processing on the trust rating tag of the target agent 103-n and returns the tag rating result to each cloud service platform where the target agent 103-n is deployed. This synchronizes the trust rating tag of the target agent 103-n to each cloud service platform where the target agent 103-n is deployed, and the trust rating tag of the target agent 103-n is synchronously displayed on each cloud service platform. Thus, through the cooperation between the agent processing platform 101 and the cloud service platform 102, the tag rating processing and synchronous display of the trust rating tag of agent 103 are achieved.

[0019] It should be noted that, considering that the tag records, access records, or access evaluation data involved in this manual may, to some extent, fall under the data privacy of the relevant parties, authorization from the relevant parties should be obtained before collecting such data to ensure that the data collection operation complies with relevant data management regulations. For example, a data authorization reminder can be sent to the accessing users, accessing organizations, or other accessing parties before collecting such data.

[0020] One or more embodiments of a trust tag processing method for intelligent agents provided in this specification are as follows:

[0021] Reference Figure 2 The trust tag processing method for intelligent agents provided in this embodiment can be applied to an intelligent agent processing platform. The method specifically includes steps S202 to S208.

[0022] Step S202: Obtain the tag processing request for the trust rating tag of the intelligent agent deployed on the cloud service platform.

[0023] The cloud service platform described in this embodiment can be a cloud computing platform for deploying intelligent agents, an intelligent agent publishing platform for publishing intelligent agents to the outside world, an intelligent agent access platform or intelligent agent access system for providing intelligent agent access to users, or an intelligent agent platform that has the capability to deploy intelligent agents, publish intelligent agents to the outside world, and provide intelligent agent access to users. The cloud service platform includes an intelligent agent publishing platform, an intelligent agent access platform, an intelligent agent access system, and / or an intelligent agent platform. Alternatively, the cloud service platform can be replaced by an intelligent agent publishing platform, an intelligent agent access platform, an intelligent agent access system, and / or an intelligent agent platform.

[0024] In addition, a cloud service platform can be a service platform or server that runs or hosts the operation of intelligent agents, or it can be a server or operating system for intelligent agents. That is, a cloud service platform can be replaced by a service platform, server, operating server, or operating system that runs or hosts the operation of intelligent agents.

[0025] The agent processing platform refers to a trust rating platform that performs trust rating-related processing on agents. Specifically, the agent processing platform can perform initial configuration of trust rating for agents, update trust rating for agents (trust rating upgrade or trust rating downgrade), cancel or archive trust rating for agents, and / or perform trust rating inheritance processing based on the relationship between agents.

[0026] Optionally, the intelligent agent that performs tag rating processing through the intelligent agent processing platform includes at least one of the following: an interactive intelligent agent, a large language model, a processing tool integrated with the interactive intelligent agent, and a proxy middleware for the interactive intelligent agent.

[0027] In this context, an intelligent agent refers to an entity or system that autonomously perceives its environment, makes decisions, and executes actions to achieve specific goals. Specifically, an intelligent agent can be an entity or system that uses artificial intelligence technology for corresponding processing, such as an interactive intelligent agent. An interactive intelligent agent refers to a multimodal intelligent interactive system built based on a large language model, such as various dialogue intelligent agents provided by various service providers. In addition, an intelligent agent can also include processing tools integrated into the interactive intelligent agent (intelligent agent tools), or an intelligent agent can also include proxy middleware for the interactive intelligent agent. The proxy middleware for the interactive intelligent agent encapsulates the underlying logic of communication, collaboration, resource management, etc., enabling the interactive intelligent agent to interact efficiently, collaborate flexibly, and perform corresponding perception and decision-making processing.

[0028] In practice, the cloud service platform interacts with the intelligent agent processing platform by submitting tag processing requests to the intelligent agent processing platform. In turn, after the intelligent agent processing platform receives the tag processing request submitted by the cloud service platform, it performs trust rating processing on the intelligent agent corresponding to the tag processing request, specifically performing tag rating processing on the trust rating tag of the intelligent agent.

[0029] Trust rating labels can be labels that represent the degree of trust that users or organizations have in an intelligent agent, or they can be labels that represent the degree of trust or evaluation of an intelligent agent by other relevant parties, such as trust rating labels that represent the degree of trust or evaluation of an intelligent agent by management agencies or industry organizations.

[0030] Trust rating labels can be categorized into several types, each corresponding to a different trust rating. For example, trust rating labels include trustworthy labels, trustworthy labels, and / or untrustworthy labels, with the trust rating decreasing sequentially. Trust labels are used to characterize that the current intelligent agent is trustworthy to a certain extent but not entirely trustworthy.

[0031] Optionally, the trust rating label corresponds to the trust rating, and the trust rating corresponds to the agent's permissions. Different trust rating labels correspond to different agent permissions. Among them, the agent permissions can be data permissions, security permissions, functional permissions, service user type permissions, and / or service user scope permissions.

[0032] Furthermore, the trust rating can be positively correlated with the permission level and / or permission content of the agent's permissions; wherein, the permission level can be the data permission level of data permissions, the security permission level of security permissions, the functional permission level of function permissions, and / or the user scope level of service user scope permissions; the permission content can be the permission open content of data permissions, the permission open content of security permissions, the permission open content of function permissions, and / or the permission open content of service user type permissions.

[0033] To enhance the intuitiveness of the trust rating labels on the intelligent agent processing platform and cloud service platform, visualization parameters can be configured for the trust rating labels. This allows users or organizations accessing the intelligent agent to intuitively understand the trust rating and credibility of the intelligent agent through the visualization of the trust rating labels. Optionally, the visualization parameters of trust rating labels for different trust ratings are different, while the visualization parameters of trust rating labels for the same trust rating are the same.

[0034] Among them, the visualization parameters are used to render and display the trust rating labels on the cloud service platform where the intelligent agent is deployed. In addition, the visualization parameters are also used to render and display the trust rating labels on the intelligent agent processing platform, and / or, the visualization parameters are also used to render and display the trust rating labels on the access platform, access interface or access channel of the access party to the intelligent agent.

[0035] The visualization parameters can specifically be parameters that render and display the tag features of the trust rating tag, such as the identifier parameters of the tag icon for rendering and displaying the trust rating tag, and / or the marker parameters of the trust rating mark corresponding to the trust rating tag, or the element parameters of the trust rating element; specifically, the icon parameters of the tag identifier can be the tag icon color, tag icon shape, tag icon size and / or icon keywords; the marker parameters of the trust rating mark can be the marker color, marker shape, marker size and / or marker keywords; the element parameters of the trust rating element can be the element color, element shape, element size and / or element keywords.

[0036] To ensure that the visualization of trust rating labels for intelligent agents is synchronized, and to ensure that visitors can intuitively understand the trust rating of intelligent agents through the visualization of trust rating labels on different cloud service platforms, the label features of the same intelligent agent rendered and displayed on different cloud service platforms are the same. The label features can be the label icon of the trust rating label, the trust rating mark corresponding to the trust rating label, and / or the trust rating element corresponding to the trust rating label.

[0037] Step S204: Based on the agent identifier and processing parameters carried in the tag processing request, determine the tag processing node of the target agent corresponding to the agent identifier.

[0038] As mentioned above, trust rating tags can be categorized into various types, each corresponding to a different trust rating. For example, trust rating tags include trusted tags, trust tags, and / or untrustworthy tags. During the trust rating process performed on agents by the agent processing platform, the trust rating processing of the agent's trust rating tags might involve initializing the trust rating tags, updating the trust rating tags (trust rating upgrade or downgrade), deregistering or archiving the trust rating tags, or inheriting trust rating tags based on agent relationships. Here, the tag processing node for the target agent refers to the type of tag processing performed on the target agent. In this case, the tag processing node can also be replaced by the tag processing type or tag processing status. Optionally, the tag processing node may include an initialization node, a trust rating update node, a deregistration node, an archiving node, and / or a tag inheritance node.

[0039] In practice, based on the tag processing request for the trust rating tag of the intelligent agent, the tag processing node of the target intelligent agent corresponding to the intelligent agent identifier is determined according to the intelligent agent identifier and processing parameters carried in the tag processing request. Here, the determined tag processing node of the target intelligent agent can be an initialization node, a trust rating update node, a deregistration node, an archive node, or a tag inheritance node. Correspondingly, the processing parameters can be the processing type for tag rating processing of the trust rating tag of the target intelligent agent, such as initialization parameters, tag upgrade parameters, tag downgrade parameters, deregistration parameters, archive parameters, or tag inheritance parameters. In addition, the processing parameters can also be the current real-time trust rating tag of the target intelligent agent or the historically stored trust rating tag.

[0040] Specifically, during the initial configuration of trust rating labels for intelligent agents, the label processing node of the target intelligent agent corresponding to the intelligent agent identifier is determined based on the intelligent agent identifier and processing parameters carried in the label processing request. This includes determining the label processing node as the initialization node based on the intelligent agent identifier and initialization parameters carried in the label processing request.

[0041] In the process of updating the trust rating label of an agent, taking label upgrade as an example, in an optional implementation of this embodiment, the label processing node of the target agent corresponding to the agent identifier is determined according to the agent identifier and processing parameters carried in the label processing request. This includes: querying the label record of the target agent according to the agent identifier, and determining the label upgrade node (the label processing node is the label upgrade node) according to the label record and label upgrade parameters.

[0042] In the process of inheriting trust rating tags for intelligent agents, one optional implementation of this embodiment involves determining the tag processing node of the target intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and processing parameters carried in the tag processing request. This includes: determining the tag inheritance node of the migrating intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and migration parameters carried in the intelligent agent migration request; or, determining the tag inheritance node of the updating intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and version update parameters carried in the intelligent agent update request. Optionally, the intelligent agent migration request includes migration requests for device migration, cloud service platform migration, and / or deployment location migration for the intelligent agent.

[0043] During the process of canceling or archiving trust rating tags for intelligent agents, the tag processing node of the target intelligent agent corresponding to the intelligent agent identifier is determined based on the intelligent agent identifier and processing parameters carried in the tag processing request. This includes determining the tag processing node as a cancellation node or an archiving node based on the intelligent agent identifier and cancellation or archiving parameters carried in the tag processing request.

[0044] In addition, in practice, there may be cases where the tag processing request does not carry processing parameters. In this case, the current real-time trust rating tag or the historically stored trust rating tag of the target intelligent agent corresponding to the intelligent agent identifier can be queried based on the intelligent agent identifier carried in the tag processing request. The tag processing node of the target intelligent agent can be determined based on the real-time trust rating tag or the historical trust rating tag. In this case, the process of determining the tag processing node of the target intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and processing parameters carried in the tag processing request can be replaced by: determining the tag processing node of the target intelligent agent corresponding to the intelligent agent identifier carried in the tag processing request based on the real-time trust rating tag or the historical trust rating tag, or it can be replaced by: determining the tag processing node of the target intelligent agent corresponding to the intelligent agent identifier carried in the tag processing request.

[0045] For example, determining the label processing node for the target intelligent agent corresponding to the intelligent agent identifier carried in the label processing request based on the real-time trust rating label or historical trust rating label includes: querying the real-time trust rating label or historical trust rating label of the target intelligent agent based on the intelligent agent identifier, and determining the label processing node based on the real-time trust rating label or historical trust rating label; or, for another example, determining the label processing node for the target intelligent agent corresponding to the intelligent agent identifier carried in the label processing request includes: querying the trust rating label of the target intelligent agent based on the intelligent agent identifier, and determining the label processing node based on the query result. Specifically, if the query result is empty, the label processing node is determined to be the initialization node.

[0046] Step S206: Perform tag rating processing on the trust rating tag of the target intelligent agent based on the tag rating strategy corresponding to the tag processing node.

[0047] In specific implementation, based on the determined label processing nodes of the target intelligent agent, the trust rating labels of the target intelligent agent are processed according to the label processing nodes. For example, the target intelligent agent is initialized and configured at the initialization node, the trust rating labels of the target intelligent agent are updated at the trust rating update node, the trust rating labels of the target intelligent agent are cancelled or archived at the cancellation or archiving node, or the trust rating labels of the target intelligent agent are inherited at the label inheritance node. Specifically, the trust rating labels of the target intelligent agent can be processed according to the label rating strategy corresponding to the label processing node.

[0048] The following sections provide a detailed explanation of the tag rating process for each tag processing node.

[0049] (1) During the initialization configuration of the initialization node for the target intelligent agent (intelligent agent), intelligent agent registration authentication can be performed according to the intelligent agent registration request, and a trust label can be configured for the intelligent agent after the registration authentication is passed; for example, intelligent agent registration authentication can be performed according to the intelligent agent registration request submitted by the intelligent agent provider accessing the intelligent agent platform, and a trust label can be configured for the intelligent agent after the registration authentication is passed; another example is that intelligent agent registration authentication can be performed according to the intelligent agent registration request submitted by the cloud service platform that deploys the intelligent agent, and a trust label can be configured for the intelligent agent after the registration authentication is passed; in this case, intelligent agent registration authentication can specifically be performed on the intelligent agent for risk trust detection, interaction trust detection and / or accuracy trust detection, and if the detection is passed, the registration authentication is determined to be passed;

[0050] Specifically, risk trust detection can be to obtain risk data of the intelligent agent and perform risk detection on the risk data according to the risk trust detection process, or to detect whether the risk data meets the risk trust conditions; the risk data can be risk assessment data obtained by risk assessment of the intelligent agent and / or risk assessment data extracted from the access records or access evaluation data of the access party to the intelligent agent.

[0051] Similarly, interaction trust detection can be to obtain the interaction data of the agent and perform interaction detection on the interaction data according to the interaction trust detection process, or to detect whether the interaction data meets the interaction trust conditions; the interaction data can be interaction evaluation data obtained by evaluating the interaction function of the agent and / or interaction evaluation data extracted from the access records or access evaluation data of the access party to the agent.

[0052] Accuracy trust detection can be to obtain the accuracy data of the agent and perform accuracy detection on the accuracy data according to the accuracy trust detection process, or to detect whether the accuracy data meets the accuracy trust conditions; the accuracy data can be accuracy test data obtained by performing accuracy tests on the agent and / or accuracy evaluation data extracted from the access records or access evaluation data of the access party to the agent.

[0053] (2) In the process of updating the trust rating label of the trust rating update node for the target intelligent agent, taking the label upgrade as an example, in an optional implementation of this embodiment, the trust rating label of the target intelligent agent is processed by the label rating strategy corresponding to the label processing node, including: verifying the label upgrade of the trust rating label based on the trust data of the publisher of the target intelligent agent on the intelligent agent processing platform and / or cloud service platform, and obtaining the verification result.

[0054] In the specific execution process, based on the trust data of the target intelligent agent's publisher on the intelligent agent processing platform and / or cloud service platform, the trust rating label upgrade verification is carried out, including:

[0055] The publisher is authenticated, and the target intelligent agent is then subjected to risk detection after authentication.

[0056] If the risk detection passes, the target intelligent agent is verified by a trust rating based on the evaluation data of the target intelligent agent. If the verification passes, the trust rating label is configured as a trust label.

[0057] In addition, during the process of updating the trust rating label of the target intelligent agent's trust rating update node, the trust rating data of the target intelligent agent can also be tested based on the detection rules corresponding to the label processing node. If the test fails, the trust rating label of the target intelligent agent is downgraded; if the test passes, the trust rating label of the target intelligent agent is upgraded, or the trust rating label of the target intelligent agent is maintained.

[0058] In the specific implementation process, after receiving a rating update request submitted by the provider of the intelligent agent through the intelligent agent platform, or after receiving a rating update request submitted by the cloud service platform deploying the intelligent agent, a trust evaluation test is performed on the trust evaluation data of the target intelligent agent based on the detection rules corresponding to the tag processing node. If the test fails, the trust rating tag of the target intelligent agent is downgraded; if the test passes, the trust rating tag of the target intelligent agent is upgraded, or the trust rating tag of the target intelligent agent is maintained.

[0059] It should be noted that in the process of trust evaluation detection of the trust evaluation data of the target intelligent agent, the trust evaluation detection can also adopt risk trust detection, interaction trust detection and / or accuracy trust detection. The specific detection process of risk trust detection, interaction trust detection and accuracy trust detection is similar to the specific implementation methods of risk trust detection, interaction trust detection and accuracy trust detection provided above. Please refer to the specific implementation methods provided above, and they will not be repeated here.

[0060] (3) During the process of canceling or archiving the trust rating label of the target agent's deregistration node or archive node, the agent's trust rating label can be canceled according to the agent's deregistration request and synchronized to each cloud service platform. This will synchronize the cancellation of the agent's trust rating label to all cloud service platforms that deploy the current agent, so that the trust rating label displayed on the cloud service platform can be kept synchronized in real time. Furthermore, the agent's trust rating label can also be archived so that the agent's trust rating label can be restored after the agent is restored to its deployment on the cloud service platform.

[0061] Similarly, before archiving the trust rating labels of the intelligent agent, a trust evaluation test can be performed on the trust evaluation data of the target intelligent agent based on the detection rules corresponding to the archive node. If the test passes, the trust rating labels of the intelligent agent are archived; if the test fails, no processing is required, or an archiving failure reminder is generated. In the process of performing trust evaluation tests on the trust evaluation data of the target intelligent agent, the trust evaluation test can also adopt risk trust test, interaction trust test, and / or accuracy trust test. The specific detection process of risk trust test, interaction trust test, and accuracy trust test is similar to the specific implementation methods of risk trust test, interaction trust test, and accuracy trust test provided above. Please refer to the specific implementation methods provided above, which will not be repeated here.

[0062] (4) In the process of inheriting the trust rating label of the label inheritance node for the target agent, the trust rating label of the target agent is processed by the label rating strategy corresponding to the label processing node, including: reading the trust rating label of the source agent corresponding to the migration agent and configuring it as the trust rating label of the migration agent, or configuring the trust rating label of the source agent corresponding to the update agent as the trust rating label of the update agent.

[0063] In the specific execution process, after reading the trust rating label of the source agent corresponding to the migrated agent and before configuring the read trust rating label as the trust rating label of the migrated agent, a trust evaluation detection can be performed on the trust evaluation data of the target agent based on the detection rules corresponding to the label inheritance node. If the detection passes, the read trust rating label is configured as the trust rating label of the migrated agent; if the detection fails, no processing is required, or a label inheritance failure reminder is generated.

[0064] In the process of trust evaluation and detection of the trust evaluation data of the target intelligent agent, the trust evaluation and detection can also adopt risk trust detection, interaction trust detection and / or accuracy trust detection. The specific detection process of risk trust detection, interaction trust detection and accuracy trust detection is similar to the specific implementation methods of risk trust detection, interaction trust detection and accuracy trust detection provided above. Please refer to the specific implementation methods provided above, which will not be repeated here.

[0065] Step S208: Return the tag rating results to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating tags of the target intelligent agent.

[0066] After performing tag rating processing on the trust rating tags of the target intelligent agent based on the tag rating strategy corresponding to the tag processing node, the tag rating result is obtained. Here, the tag rating result is returned to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating tags of the target intelligent agent.

[0067] Specifically, during the initialization configuration of the target intelligent agent (intelligent agent) initialization node, the obtained trust label of the target intelligent agent is synchronized to each cloud service platform where the current target intelligent agent is deployed, so as to visualize the trust label of the target intelligent agent on each cloud service platform. Specifically, the trust label of the target intelligent agent is rendered and displayed on each cloud service platform according to the visualization parameters of the trust label of the target intelligent agent.

[0068] Similarly, during the process of updating the trust rating label of the target intelligent agent at the trust rating update node, the obtained trust rating label of the target intelligent agent is synchronized to each cloud service platform where the target intelligent agent is deployed, so as to visualize the trust rating label of the target intelligent agent on each cloud service platform. Specifically, the trust rating label of the target intelligent agent is rendered and displayed on each cloud service platform according to the visualization parameters of the trust rating label of the target intelligent agent.

[0069] During the process of inheriting the trust rating labels of the target intelligent agent's label inheritance nodes, the obtained trust rating labels of the target intelligent agent are synchronized to each cloud service platform where the target intelligent agent is deployed, so as to visualize the trust rating labels of the target intelligent agent on each cloud service platform. Specifically, the trust rating labels of the target intelligent agent are rendered and displayed on each cloud service platform according to the visualization parameters of the trust rating labels of the target intelligent agent.

[0070] In addition, during the process of canceling or archiving the trust rating labels of the target intelligent agent at the node or the archive node, the cancellation or archiving results are synchronized to each cloud service platform where the target intelligent agent is deployed, so as to visualize the cancellation of the trust rating labels of the target intelligent agent on each cloud service platform.

[0071] In practical applications, to enhance the flexibility of the agent processing platform in processing trust rating labels for agents, and also to improve the flexibility of agent management, the trust rating labels for agents stored by the agent processing platform are determined based on a trusted or untrusted agent list synchronized by the management agency, and / or based on the trust rating votes of the rating member group for the agent. The rating member group can be a group of voting users who manage the trust rating of agents.

[0072] In addition, in order to enhance the recognition of trust rating labels for intelligent agents by users, organizations, and other visitors, and also to improve the accuracy and effectiveness of the intelligent agent processing platform in processing trust rating labels for intelligent agents, the label configuration rules and / or access permissions of intelligent agents stored on the intelligent agent processing platform are determined based on the access permissions of intelligent agents synchronized by industry organizations, and / or based on the voting on the permissions of intelligent agents by the rating member group.

[0073] In summary, the agent trust label processing method provided in this embodiment involves the agent processing platform, during the label rating process of agent trust rating labels, starting from the label processing request of the agent trust rating label deployed on the cloud service platform, determining the label processing node of the target agent corresponding to the agent identifier based on the agent identifier and processing parameters carried in the label processing request, or determining the label processing node of the target agent corresponding to the agent identifier. Further, based on the label rating strategy corresponding to the label processing node, the agent trust rating label of the target agent is processed for label rating. Finally, the label rating result is returned to each cloud service platform where the target agent is deployed for synchronization of the target agent trust rating label. This improves the real-time and comprehensiveness of agent trust rating, and by visually displaying the agent trust rating label, users or organizations accessing the agent can intuitively understand the agent's trust rating and whether it is trustworthy, thus enhancing the credibility of agent trust management performed by the agent processing platform.

[0074] The following example uses the trust label processing method for intelligent agents provided in this embodiment as an example in the scenario of intelligent agent trust rating. Figure 3 The trust label processing method for intelligent agents provided in this embodiment will be further explained below. Figure 3 The method for processing trust labels of intelligent agents, which is applied to the trust rating scenario of intelligent agents, specifically includes the following steps.

[0075] Step S302: Perform agent registration authentication based on the agent registration request, and configure a trust label for the agent after successful registration authentication.

[0076] Step S304: Synchronize the trust label with each cloud service platform where the agent is deployed, so as to visualize the trust label of the agent on each cloud service platform.

[0077] Step S306: Based on the agent identifier and tag upgrade parameters carried in the agent update request of the agent, determine the tag processing node as the tag upgrade node.

[0078] Step S308: Based on the trust data of the agent's publisher on the agent processing platform and / or cloud service platform, perform tag upgrade verification of the trust tag.

[0079] Step S310: If the verification is successful, the trust label of the intelligent agent is upgraded to a trusted label and synchronized to various cloud service platforms to update and visualize the trusted label of the intelligent agent.

[0080] Step S312: Based on the agent identifier and migration parameters carried in the agent migration request, determine the agent's label processing node as the label inheritance node.

[0081] Step S314: Read the trust rating label of the agent and configure it as the trusted label of the migration agent and synchronize it with each cloud service platform to synchronize and display the trusted label of the migration agent.

[0082] It should be noted that any one or more steps in steps S302 to S314 can be combined with any one or more steps in steps S202 to S208 to form a new implementation method according to the needs of implementation and deployment. In addition, any one or more technical features in steps S302 to S314 can be selected and combined with any one or more technical features provided in steps S202 to S208 to form a new implementation method according to the actual deployment needs. Alternatively, any one or more technical features in steps S302 to S314 can be replaced with any one or more technical features provided in steps S202 to S208 to form a new implementation method according to the actual deployment needs. These will not be elaborated on here.

[0083] This specification provides an embodiment of a trust tag processing device for intelligent agents as follows:

[0084] In the above embodiments, a method for processing trust tags of an intelligent agent is provided, and correspondingly, a device for processing trust tags of an intelligent agent is also provided, which will be described below with reference to the accompanying drawings.

[0085] Reference Figure 4 This illustration shows a schematic diagram of an embodiment of a trust tag processing device for an intelligent agent provided in this embodiment.

[0086] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0087] This embodiment provides a trust tag processing device for intelligent agents, which runs on an intelligent agent processing platform. The device includes:

[0088] The request acquisition module 402 is configured to acquire a tag processing request for the trust rating tags of the intelligent agents deployed on the cloud service platform;

[0089] The node determination module 404 is configured to determine the tag processing node of the target intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and processing parameters carried in the tag processing request.

[0090] The tag rating processing module 406 is configured to perform tag rating processing on the trust rating tag of the target intelligent agent based on the tag rating strategy corresponding to the tag processing node.

[0091] The tag synchronization module 408 is configured to return tag rating results to each cloud service platform where the target intelligent agent is deployed in order to synchronize the trust rating tags of the target intelligent agent.

[0092] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0093] This specification provides an embodiment of a trust tag processing device for intelligent agents as follows:

[0094] Corresponding to the trust tag processing method for an intelligent agent described above, based on the same technical concept, one or more embodiments of this specification also provide a trust tag processing device for an intelligent agent, which is used to execute the trust tag processing method for an intelligent agent provided above. Figure 5 This is a schematic diagram of the structure of a trust tag processing device for an intelligent agent provided in one or more embodiments of this specification.

[0095] This embodiment provides a trust tag processing device for an intelligent agent, comprising:

[0096] like Figure 5As shown, device 500 mainly consists of a communication interface 502, a user interface 504, a processor 506, and a data storage 508. These components are interconnected and communicate with each other via a system bus, network, or other connection mechanism 510. The communication interface 502 enables device 500 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 502 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 502 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 502 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 502 may also include multiple physical communication interfaces, such as Wi-Fi, Bluetooth, and wide-area wireless interfaces. The user interface 504 includes receiving operator input and providing output to operators. Therefore, user interface 504 may include input components such as a keypad, keyboard, touch-sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, and output components such as a display screen (which may be combined with a touch-sensitive panel), CRT, LCD, LED, display using DLP technology, printer, and other similar devices known or developed in the future. User interface 504 may also generate auditory output via speakers, speaker jacks, audio output ports, audio output devices, headphones, and other similar devices known or developed in the future. In some embodiments, user interface 504 may include software, circuitry, or other forms of logic capable of transmitting and receiving data to and from external operator input / output devices. Additionally or alternatively, device 500 may support remote access from other devices via communication interface 502 or another physical interface (not shown). User interface 504 may be configured to receive operator input, the position and movement of which may be indicated by indicators or cursors described herein. User interface 504 may also be configured as a display device for rendering or displaying text fragments.

[0097] Processor 506 may include one or more general-purpose processors and / or special-purpose processors. Data storage 508 may include one or more volatile and / or non-volatile storage components and may be integrated wholly or partially with processor 506. Data storage 508 may include removable and non-removable components.

[0098] Processor 506 is capable of executing program instructions 518 (e.g., compiled or uncompiled program logic and / or machine code) stored in data storage 508 to perform the various functions described herein. Data storage 508 may contain a non-transitory computer-readable medium on which program instructions are stored, which, when executed by device 500, enable device 500 to perform any methods, processes, or functions disclosed in this specification and / or the accompanying drawings. Execution of program instructions 518 by processor 506 may result in processor 506 using data 512. For example, program instructions 518 may include an operating system 522 (e.g., an operating system kernel, device drivers, and / or other modules) installed on device 500 and one or more application programs 520 (e.g., a browser, social application, or game application). Similarly, data 512 may include operating system data 516 and application data 514. Operating system data 516 is primarily accessible to operating system 522, while application data 514 is primarily accessible to one or more application programs 520. Application data 514 may reside in a file system visible or hidden by operators of device 500. Application 520 can communicate with operating system 512 through one or more application programming interfaces (APIs). These APIs facilitate application 520 in reading and / or writing application data 514, transmitting or receiving information via communication interface 502, and receiving or displaying information on user interface 504. In some terms, application 520 may be simply referred to as "app". Furthermore, application 520 can be downloaded to device 500 through one or more online app stores or app markets. However, applications can also be installed on device 500 in other ways, such as through a web browser or a physical interface on device 500 (e.g., a USB port).

[0099] In one specific embodiment, the trust tag processing device for an intelligent agent includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the trust tag processing device for the intelligent agent, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0100] Obtain the tag processing request for the trust rating tags of the intelligent agents deployed on the cloud service platform;

[0101] Based on the agent identifier and processing parameters carried in the tag processing request, determine the tag processing node of the target agent corresponding to the agent identifier;

[0102] The trust rating labels of the target intelligent agent are processed by the tag rating strategy corresponding to the tag processing node.

[0103] The label rating results are returned to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating labels of the target intelligent agent.

[0104] This specification provides an embodiment of a computer-readable storage medium as follows:

[0105] Corresponding to the trust tag processing method for an intelligent agent described above, based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0106] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, implement the following process:

[0107] Obtain the tag processing request for the trust rating tags of the intelligent agents deployed on the cloud service platform;

[0108] Based on the agent identifier and processing parameters carried in the tag processing request, determine the tag processing node of the target agent corresponding to the agent identifier;

[0109] The trust rating labels of the target intelligent agent are processed by the tag rating strategy corresponding to the tag processing node.

[0110] The label rating results are returned to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating labels of the target intelligent agent.

[0111] It should be noted that the embodiments of a computer-readable storage medium described in this specification and the embodiments of a trust tag processing method for an intelligent agent described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0112] This specification provides an example of a computer program product as follows:

[0113] Corresponding to the trust tag processing method for an intelligent agent described above, based on the same technical concept, one or more embodiments of this specification also provide a computer program product.

[0114] A computer program product includes a computer program / instructions that, when executed by a processor, perform the following steps:

[0115] Obtain the tag processing request for the trust rating tags of the intelligent agents deployed on the cloud service platform;

[0116] Based on the agent identifier and processing parameters carried in the tag processing request, determine the tag processing node of the target agent corresponding to the agent identifier;

[0117] The trust rating labels of the target intelligent agent are processed by the tag rating strategy corresponding to the tag processing node.

[0118] The label rating results are returned to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating labels of the target intelligent agent.

[0119] It should be noted that the embodiments of a computer program product described in this specification and the embodiments of a trust tag processing method for an intelligent agent described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0120] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments. For example, the device embodiment, equipment embodiment and computer-readable storage medium embodiment are all similar to the method embodiment, so the description is relatively simple. When reading the relevant content of the device embodiment, equipment embodiment and computer-readable storage medium embodiment, please refer to the description of the method embodiment.

[0121] While one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is merely one possible execution order among many steps, and does not represent the only execution order. Therefore, when the claims involve method steps, any changes or adjustments to the order of such steps, or the parallelism between steps, are also within the scope of protection of the claims. This specification uses specific terms to describe embodiments of this specification. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0122] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0123] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0124] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 525D, Atmel AT91SAM, Microchip PIC18F25K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0125] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0126] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0127] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0132] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0133] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0134] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising at least one…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0135] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0136] The above description is merely an embodiment of this document and is not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A method for processing trust tags of intelligent agents, applied to an intelligent agent processing platform, the method comprising: Obtain the tag processing request for the trust rating tags of the intelligent agents deployed on the cloud service platform; Based on the agent identifier and processing parameters carried in the tag processing request, determine the tag processing node of the target agent corresponding to the agent identifier; The trust rating labels of the target intelligent agent are processed by the tag rating strategy corresponding to the tag processing node. The label rating results are returned to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating labels of the target intelligent agent.

2. The trust tag processing method for an agent according to claim 1, wherein determining the tag processing node of the target agent corresponding to the agent identifier based on the agent identifier and processing parameters carried in the tag processing request includes: The tag record of the target intelligent agent is queried according to the intelligent agent identifier, and the tag upgrade node is determined according to the tag record and the tag upgrade parameters; Accordingly, the tag rating processing of the trust rating tag of the target intelligent agent based on the tag rating strategy corresponding to the tag processing node includes: Based on the trust data of the publisher of the target intelligent agent on the intelligent agent processing platform and / or the cloud service platform, the tag upgrade verification of the trust rating label is performed to obtain the verification result.

3. The trust label processing method for intelligent agents according to claim 2, wherein the step of performing tag upgrade verification of the trust rating label based on the trust data of the publisher of the target intelligent agent on the intelligent agent processing platform and / or the cloud service platform includes: The publisher is authenticated, and the target intelligent agent is then subjected to risk detection after authentication. If the risk detection passes, the target intelligent agent is verified by a trust rating based on the evaluation data of the target intelligent agent. If the verification passes, the trust rating label is configured as a trust label.

4. The trust tag processing method for an intelligent agent according to claim 1 further includes: Perform agent registration and authentication based on the agent registration request, and configure a trust label for the agent after successful registration and authentication; or, Based on the detection rules corresponding to the tag processing node, a trust evaluation detection is performed on the trust evaluation data of the target intelligent agent. If the detection fails, the trust rating tag of the target intelligent agent is downgraded.

5. The trust tag processing method for an intelligent agent according to claim 4, wherein the trust rating tag corresponds to the trust rating, and the trust rating corresponds to the intelligent agent's permissions, and the intelligent agent permissions corresponding to the trust rating tags of different trust ratings are different. in, The trust rating is positively correlated with the permission level and / or permission content of the agent's permissions.

6. The trust tag processing method for an intelligent agent according to claim 5, wherein the visualization parameters of trust rating tags with different trust ratings are different, and the visualization parameters of trust rating tags with the same trust rating are the same. The visualization parameters are used to render and display trust rating tags on the cloud service platform where the agent is deployed. The tag features of the same agent are the same when rendered and displayed on different cloud service platforms.

7. The trust tag processing method for an agent according to claim 1, wherein determining the tag processing node of the target agent corresponding to the agent identifier based on the agent identifier and processing parameters carried in the tag processing request includes: Based on the agent identifier and migration parameters carried in the agent migration request, determine the tag inheritance node of the migrating agent corresponding to the agent identifier; Alternatively, based on the agent identifier and version update parameters carried in the agent update request, the tag inheritance node of the updating agent corresponding to the agent identifier can be determined.

8. The trust label processing method for an intelligent agent according to claim 7, wherein the step of performing label rating processing on the trust rating label of the target intelligent agent based on the label rating strategy corresponding to the label processing node includes: Read the trust rating label of the source agent corresponding to the migrating agent and configure it as the trust rating label of the migrating agent; or, configure the trust rating label of the source agent corresponding to the updating agent as the trust rating label of the updating agent. The agent migration request includes migration requests for agents to migrate devices, cloud service platforms, and / or deployment locations.

9. The trust tag processing method for an intelligent agent according to claim 1, further comprising: Based on the agent's deregistration request, the trust rating label of the agent is deregistered and synchronized to various cloud service platforms; Perform archiving of agent trust rating labels so that the agent's trust rating labels can be restored after the agent is re-deployed on the cloud service platform.

10. The trust label processing method for intelligent agents according to claim 1, wherein the trust rating label of the intelligent agent stored in the intelligent agent processing platform is determined according to the trusted list or untrusted list of intelligent agents synchronized by the management agency, and / or, according to the trust rating vote of the rating member group for the intelligent agent; Alternatively, the tag configuration rules and / or access permissions of the intelligent agents stored in the intelligent agent processing platform may be determined based on the access permissions of the intelligent agents synchronized by industry organizations, and / or based on the voting on the permissions of the intelligent agents by the rating member group.

11. The trust label processing method for intelligent agents according to claim 1, wherein the intelligent agent performing label rating processing through the intelligent agent processing platform includes at least one of the following: an interactive intelligent agent, a large language model, a processing tool integrated with the interactive intelligent agent, and a proxy middleware for the interactive intelligent agent.

12. A trust tag processing device for an intelligent agent, operating on an intelligent agent processing platform, the device comprising: The request acquisition module is configured to acquire tag processing requests for trust rating tags of intelligent agents deployed on the cloud service platform; The node determination module is configured to determine the tag processing node of the target intelligent agent corresponding to the intelligent agent identifier based on the intelligent agent identifier and processing parameters carried in the tag processing request. The tag rating processing module is configured to perform tag rating processing on the trust rating tags of the target intelligent agent based on the tag rating strategy corresponding to the tag processing node. The tag synchronization module is configured to return tag rating results to each cloud service platform where the target intelligent agent is deployed in order to synchronize the trust rating tags of the target intelligent agent.

13. A trust tag processing device for an intelligent agent, comprising: processor; And, a memory configured to store computer-executable instructions, which, when executed, cause the processor to: Obtain the tag processing request for the trust rating tags of the intelligent agents deployed on the cloud service platform; Based on the agent identifier and processing parameters carried in the tag processing request, determine the tag processing node of the target agent corresponding to the agent identifier; The trust rating labels of the target intelligent agent are processed by the tag rating strategy corresponding to the tag processing node. The label rating results are returned to each cloud service platform where the target intelligent agent is deployed to synchronize the trust rating labels of the target intelligent agent.

14. A computer-readable storage medium for storing computer-executable instructions that, when executed, implement the steps of the method of claim 1.

Images