84 results about "Webhook" patented technology

The invention provides an instant messaging driving type automatic service processing method and system based on a large language model and a model context protocol (MCP), and relates to the technical field of computers, and the method comprises the following steps: a user sends a message in a group of an instant messaging platform and makes a group robot to form a Webhook event; the MCP client exposes an HTTP (Hyper Text Transport Protocol) interface to receive a Webhook event, completes authentication and analyzes a message; the MCP client sends the message to a large language model, ACL permission judgment is carried out, and an MCPServer method and parameters to be called are recognized; if the permission is judged to be'unauthorized ', stopping, and then submitting permission errors to a big language model organization and description and directly executing and returning'insufficient permission'; if the permission is judged to be'authorization ', the next step is carried out; and the MCP client calls the MCP server (through HTTP + SSE or stdio) to obtain a structured result. According to the method, the unstructured chat instruction is stably converted into the standardized MCP request, and coupling of dialect type APIs of all back-end systems is eliminated.

The method provides for securely harvesting and distributing trusted metadata from devices to artificial intelligence (Al) systems. It enables use of cryptographic hashes and signatures on data for supply chain provenance. It is an agentless method to enhance application security by design, and data protection with data authenticity and confidentiality in device to upstream services communications and data sharing. It helps securely harvest, filter, and forward device metadata to webhooks for AI / ML driven data analytics.

The invention discloses a method for realizing user authentication and nailing notification of a graph database document site, which belongs to the technical field of computer security, and comprises the following steps of: integrating an Oauth2 authentication module into a reverse proxy server, and docking with an identity authentication service; the external request for accessing the graph database document site is redirected to the identity authentication service for user authentication; after the user authentication is passed, the Oauth2 authentication module executes authentication operation; the identity authentication service captures a user registration event in real time, and when the registration event is monitored, user information corresponding to the registration event is extracted; filling a preset notification template according to the user information to obtain a notification message, and pushing the notification message to a management terminal through a configurable message notification interface; and the reverse proxy server dynamically controls the access authority of the user to the content of the graph database document site based on the authentication result. According to the application, user authentication can be carried out on the graph database document site, and meanwhile, an administrator can be notified of a user registration event through the nailing robot Webhook.

Intelligent container management is provided. An issue that caused a failed state of a container is identified based on an analysis of exit code information, container log information, host node agent log information, and resource utilization data corresponding to the container using a group of solution agents. It is determined whether the issue that caused the failed state of the container is a resolvable issue with a fix. The fix is applied to the container using a runtime mutating webhook controller prior to restart of the container in response to determining that the issue that caused the failed state of the container is the resolvable issue with the fix. The restart of the container is performed on a host node.

The invention discloses a distributed AI training task processing method and system, and relates to the technical field of cloud computing. The distributed AI training task processing method comprises the steps that labels are set for hardware resources of nodes of each cluster of each cloud platform, and resource information of each hardware resource is acquired; and intercepting a CRD configuration file containing a hardware resource quantity request through a pre-deployed webhook service in the cluster, and creating an AI training task carrying the hardware resource quantity request after modifying resource information in the CRD configuration file into a parameter format adaptive to the hardware environment of the cluster according to the tag. According to the method and the device, hardware resources in multi-cloud platform heterogeneous resources can be scheduled and used in a unified manner without manual intervention, dynamic parameter adaptation and automatic injection of hardware resource configuration can be completed through the webhook service deployed in the cluster, and seamless resource scheduling and adaptation across cloud platforms and across hardware architectures are realized.

The method provides for securely harvesting and distributing trusted metadata from devices to artificial intelligence (AI) systems. It enables use of cryptographic hashes and signatures on data for supply chain provenance. It is an agentless method to enhance application security by design, and data protection with data authenticity and confidentiality in device to upstream services communications and data sharing. It helps securely harvest, filter, and forward device metadata to webhooks for AI / ML driven data analytics.

Techniques for webhook registration are disclosed, including: receiving, by an integration platform, a metadata document associated with a producer, wherein the metadata document describes (a) a set of conditions for identifying webhook validation requests and (b) a set of one or more operations for validating webhook validation requests; instantiating, by the integration platform, an adapter based on the metadata document; determining, by the integration platform using the adapter, that a request from a producer satisfies the set of conditions for identifying webhook validation requests; responsive to determining that the validation request satisfies the set of conditions for identifying webhook validation requests: executing the set of one or more operations for validating webhook validation requests, to validate a registration of a consumer with the producer.

The invention discloses a code risk identification method and system based on a large language model. The method comprises the following steps: submitting a code of a current version on a code management platform, and triggering a submission event; a submission event is captured through Webhook in the code management platform, and an HTTP request is sent to the difference analysis system; analyzing the HTTP request according to a difference analysis system and obtaining a code of a previous version and a code of a current version; comparing the obtained code of the previous version with the code of the current version according to a difference analysis module of the difference analysis system to obtain a difference code of the current version and the previous version and a specific position of the difference code; according to the difference analysis system, the difference codes of the current version and the previous version and the specific positions of the difference codes can be quickly identified, the analysis efficiency is improved, and the accuracy of an analysis result is ensured; and the risk category and the risk level of the difference code can be accurately identified through multiple classifiers of a large language model and a context sensing module.

The invention provides an incremental code optimization method based on the combination of DevOps and AI, belongs to the technical field of software development and optimization, and provides an incremental code optimization scheme based on GitLab and AI large models aiming at the requirement of code quality improvement in modern software development. According to the scheme, code submission operation is monitored through the Webhook technology of GitLab, incremental code changes are captured in real time, and contrastive analysis is conducted on the incremental code changes and original codes. By means of the strong analysis capability of the AI large model, the system can quickly identify potential problems in codes and generate targeted optimization suggestions. The optimization suggestions are notified to a submitter through mails, the submitter can evaluate the suggestions, and meanwhile, the codes before and after optimization are comprehensively evaluated regularly. Based on real evaluation feedback, the system further adjusts parameters and a knowledge base of the AI large model, and continuous optimization and improvement of the model are achieved.

A method for using a fraud detection application running on a mobile device to monitor, in real-time, a telephonic communication on the mobile device. Data may be continuously extracted from the telephonic communication by the fraud detection application. The method may include determining from the extracted data, that the data includes a fraud indicator. In response to the determining, a webhook may be generated that includes a payload storing metadata of the telephonic communication. The method may include identifying a group of mobile devices that may be associated with the data and further transmitting the webhook to each of the group of mobile devices and an entity server supporting the fraud detection application. The method may include receiving a rating of the telephonic communication from at least some of the mobile devices and from an entity server and using the ratings to determine a threshold level of fraud.

Systems, methods, and computer-readable media for intelligent webhook are described herein. The intelligent webhook can insert code into one or containers associated with an application being deployed by an orchestration service. The code enables the intelligent webhook to monitor operations, including startup, of a container mutated to include the code. The intelligent webhook has knowledge of whether a mutated container failed to startup in a prior instance, and if it fails, the intelligent webhook can insert a modified version of the code and / or adjust resource limit constraints to facilitate mutation of a container scheduled to be deployed with an application.

The invention discloses an automatic system for customizing and distributing and deploying system software of an embedded system. The automatic system comprises a configuration management module, a configuration management module and a configuration management module, wherein the configuration management module is used for storing a declarative configuration file in a Git version control system; the automatic execution module is realized by a CI / CD server, the CI / CD server is closely integrated with a Git warehouse of the configuration management module through a Webhook mechanism, and a CI / CD assembly line can be automatically triggered once the change of a configuration file is detected to be pushed to a specified branch; the product management module is used for archiving, storing and distributing the constructed product after the automatic execution module successfully completes the construction; and the end-side deployment module is used for deploying the system software on development equipment according to requirements for rapid deployment, verification and rollback use.

The invention discloses a data transmission method and device based on SQL query and a storage medium, and relates to the technical field of data processing.The method comprises the steps that configuration parameters input by a user in a visual configuration page are received; a configuration file in a YAML format is generated based on the configuration parameters, the configuration file comprises data source connection parameters, structured query language (SQL) query statements, a target transmission mode, target transmission mode configuration corresponding to the target transmission mode and a triggering mode, and the target transmission mode comprises mail transmission, RESTful transmission or Webhook transmission; establishing a connection with a target database according to the data source connection parameter, executing the SQL query statement, and obtaining query data from the target database; according to the target transmission mode and the target transmission mode configuration, the query data is sent to the target receiving end through the target transmission mode, the technical problem that in existing data transmission, different transmission targets need to be independently configured, so that operation steps are tedious is solved, and the configuration efficiency of data transmission is improved.

Intelligent container management is provided. An issue that caused a failed state of a container is identified based on an analysis of exit code information, container log information, host node agent log information, and resource utilization data corresponding to the container using a group of solution agents. It is determined whether the issue that caused the failed state of the container is a resolvable issue with a fix. The fix is applied to the container using a runtime mutating webhook controller prior to restart of the container in response to determining that the issue that caused the failed state of the container is the resolvable issue with the fix. The restart of the container is performed on a host node.

Techniques for a container orchestration system are disclosed. A container orchestration API server receives a request from a virtual agent in a container orchestration cluster that includes a cloud provider authentication token. The request is generated by the virtual agent using a resource principal that corresponds to the virtual agent. The container orchestration API server attempts authentication of the virtual agent using a container orchestration native authentication. Upon failure of the container orchestration native authentication, the container orchestration API server authenticates the virtual agent using a cloud provider authentication unit via an authentication webhook using the cloud provider authentication token.

The application discloses a kind of collaborative instruction analysis and multi-actuator task processing method and system, the method includes: configuration Webhook and solidification server configuration, skill resource root directory is physically separated from business warehouse;With original byte stream reception request and sign, it is parsed into structured event by post;Extract text and match valid instruction, capture provider keyword and instruction text;Build basic context;Carry out intention recognition, route to corresponding branch and read skill description file, assemble complete prompt;Distribute independent working directory and clone code;According to the unique actuator execution task of provider keyword from mapping table;With single comment iterative update mode, present progress;Result is written back to topic or merged request.The application realizes multiple entry collaborative closed loop, skill and business decoupling, multi-actuator unified scheduling and progress visible execution.

Disclosed is a system for AI-based auction bidding and a method thereof. The method includes receiving a sign-up request for an auction. The method includes initiating, by an AI bidding bot, a phone call to a mobile device of the client before the initiating the auction process. The method also includes presenting a disclaimer to the client specifying bid finality, bidding currency, urgency of bidding, and liability limitations. The method further includes presenting bid amounts to the client and requesting confirmation of the bid amounts. The method also includes receiving the bid confirmation from a client as a voice prompts. The method includes sending the voice prompt to an NLP engine and converting voice prompt to text data. The method further includes updating, by the AI bidding bot, the database with the confirmed bid. The method also includes automatically transmitting the confirmed bid to a central site via Webhook.

The invention discloses an AI-based code quality intelligent review and automatic distribution system and method, and relates to the technical field of software development and quality assurance, the AI-based code quality intelligent review and automatic distribution system comprises an event triggering and capturing unit, a code change extraction and analysis unit, an intelligent review engine starting unit, an AI analysis and decision unit and a feedback and closed loop unit; the event triggering and capturing unit is used for monitoring a code pushing event for submitting a code to a remote GitLab code library after a developer completes function development locally through a GitLab platform, generating an HTTP request and sending the HTTP request to the code change extraction and analysis unit; through the Webhook mechanism provided by the Gitlab, 'triggering upon code submission 'is realized, manual intervention is not needed, the feedback period is greatly shortened, and the development efficiency is improved.

The present invention discloses a Kubernetes optimization scheduling method and system based on running a virtual machine in a container, relating to the technical field of container orchestration. The method intercepts a request for applying to create a virtual machine object through an Admission Webhook in Kubernetes, analyzes the KubeVirt resource allocation situation according to the resource usage of an existing virtual machine pod through the Admission Webhook, modifies the overselling ratio of the kubevirt configuration through the Admission Webhook under the condition that the virtual machine resources remain unchanged, calculates the resource request quantity of the virtual machine object according to the modified overselling ratio, modifies the configuration of the virtual object, resets the resource request quantity of the virtual machine pod, releases the request for creating the virtual machine object and creates the virtual machine object, creates a corresponding virtual machine pod according to the virtual machine object, and uses a cluster scheduler to sequentially select cluster nodes using a pre-selection algorithm and an optimization algorithm, and schedules the virtual machine pod to run on the selected cluster nodes.

Techniques for a container orchestration system are disclosed. A container orchestration API server receives a request from a virtual agent in a container orchestration cluster that includes a cloud provider authentication token. The request is generated by the virtual agent using a resource principal that corresponds to the virtual agent. The container orchestration API server attempts authentication of the virtual agent using a container orchestration native authentication. Upon failure of the container orchestration native authentication, the container orchestration API server authenticates the virtual agent using a cloud provider authentication unit via an authentication webhook using the cloud provider authentication token.

The present invention provides a method for deploying distributed database containers in multiple availability zones, belonging to the fields of distributed databases and container clouds. The present invention uses built-in CRD resources to accurately describe the deployment of distributed database containers in multiple availability zones, deploys a webhook Server inside kubernetes to capture the startup events of distributed database containers, and modifies the node affinity of the containers according to the CRD resource information, so as to achieve precise control of the deployment of distributed database containers in multiple availability zones.

A method or a system accesses an envelope stored in a first database. The envelope includes a set of secure electronic documents, a workflow of tasks, and one or more events associated with the tasks. The system also accesses a second database that stores a credential of an entity associated with the envelope. In response to authenticating the credential, the system receives and caches an access token in a third database. Further, the system monitors the events associated with the tasks in the workflow. In response to detecting an occurrence of at least one event, the system generates and sends a message with the cached access token to a webhook listener. After authentication of the entity using the access token, the webhook listener is caused to generate and send a notification to an administrator of the entity.

The invention discloses an integrated multi-dimensional monitoring and unified alarm system and method. The system comprises a service availability monitoring module, a log monitoring module, a middleware monitoring module, a business monitoring module and a unified alarm aggregation and intelligent distribution module. The system carries out automatic discovery and availability detection on the service instances in the Kubernetes through HertzBeat; the method comprises the following steps of: acquiring, analyzing and alarming a log through Logstash, Elasticsearch and Grafana; the index monitoring and the alarm of the middleware are realized through Prometheus and Grafana; and real-time analysis and alarm of service data are realized through Dores and Grafana. And all alarms are uniformly aggregated by HertzBeat, forwarded to Sentry for intelligent distribution, and finally pushed to an enterprise communication tool in real time through Webhook. According to the invention, comprehensive and integrated monitoring of service availability, logs, middleware and businesses is realized, an automatic closed-loop process from alarm generation to accurate reaching is constructed, and the operation and maintenance efficiency and the system reliability in a cloud native environment are significantly improved.

Some embodiments of the invention provide a method of modifying and validating API requests received at an API server. At a mutating admission controller of the API server, the method intercepts an API request received at the API server. The method invokes a mutating webhook to query a policy agent that includes a set of policies for modifying API requests. When the policy agent determines that the API request requires modifications based on an identified policy, the method performs the modifications and forwards the modified API request for validation by the API server. After validating the API request, the method intercepts the API request at a validating admission controller and invokes a validating webhook to query the policy agent to determine whether the API request is valid. When the policy agent determines that the API request is valid, the method forwards the API request to be admitted to a database.

This invention discloses a system for implementing blue-green deployment in Kubernetes, belonging to the field of computer technology. The system includes: a custom resource for blue-green deployment; a workload webhook module for monitoring new version releases and performing pre-processing for blue-green deployment of workloads; and a custom resource controller for executing deployment control logic on the corresponding workloads based on the custom resource for blue-green deployment, to achieve the desired blue-green deployment behavior. Specifically, when the webhook module detects a new version release, it checks whether the release should be allowed. If allowed, the release is temporarily paused so that the custom resource controller can manage the deployment later. This invention performs blue-green deployment of the target workload in a bypass manner, without changing the workload's metadata before and after deployment; it does not require the creation of additional workloads, simplifying the management of the deployment process, and supports deployment of existing workloads.

The present disclosure provides a Webhook-based data sending method and device, electronic equipment and medium, wherein the method comprises detecting a data sending task of an upstream system to a downstream system; after detecting the data sending task, obtaining data configuration information matched with the downstream system; wherein the data configuration information is used to indicate a data structure matched with a system interface of the downstream system and data content corresponding to the data structure; generating target data for the downstream system based on the data configuration information; and sending the target data to the downstream system by using Webhook in a data sending mode matched with the downstream system.

The invention discloses an assembly line construction method and system for version automatic marking, and the method comprises the steps: completing information collection and writing by an automatic deployment tool according to a self-defined metadata structure, monitoring code submission through Webhook, and transmitting a trigger signal; the continuous integration server pulls the latest submitted code from the code warehouse and executes construction operation; after the construction is passed, constructing an application mirror image, and pushing the application mirror image to a specified mirror image warehouse; and the automatic deployment tool pulls the application mirror image of the corresponding version from the mirror image warehouse, and starts the application in the target environment to complete the automatic deployment of the project. According to the method, the correlation tracing efficiency of the code version information and the construction product is improved, the manual recording and maintenance cost is reduced, and the reliability and maintainability of automatic deployment are improved.

The invention relates to the technical field of data processing, in particular to a message pushing method and system for the Internet of Things, and the method comprises the steps: obtaining a to-be-pushed device message; carrying out batch aggregation processing on the equipment message to obtain a processed message; based on a double-layer thread pool architecture, performing Webhook pushing on the processed message; pushing a corresponding URL (Uniform Resource Locator) on the basis of the Webhook, and determining the number of pushing failures; if the number of times of pushing failures reaches a preset number-of-times threshold value, fusing operation corresponding to Webhook pushing, and generating and outputting fusing information; and under the condition that the fusing information meets a preset time condition, continuing to carry out Webhook pushing on the processed message. Therefore, the failure times are monitored in real time based on the URL dimension, the downstream service health state is intelligently judged, the fusing strategy is automatically triggered, the overall availability is guaranteed, and the message pushing safety is improved.

The invention provides a data interaction system and method for directly triggering a Jira workflow based on a flying book card message, and the method comprises the steps: monitoring a specific event through a webhook configured on a Jira platform, and transmitting event data to a local server when the specific event is monitored, and carrying out the analysis and conversion of the event data to generate flying book card information; and the local server sends the flying book card information to the created flying book application, and the flying book application forwards the received flying book card information to a specified user in a configuration permission range, so that the corresponding user enters a Jira platform through the flying book card information to perform corresponding operation. According to the method, the Jira workflow is directly triggered by the flying book card message, the information island problem existing in an existing integration scheme is solved, and the automation level and the response speed of the whole workflow are improved.

A computer can connect with a middleware computing system. The middleware computing system may use application programming interfaces (APIs), webhooks, file-based integration, database replication, message queues, websockets, or direct integration to establish connections with different computing devices. The computer may request a data structure classification for an external data structure stored in a remote computing device. The middleware computing system can receive the request, identify the connection that the middleware computing device has with the remote computing device, and retrieve records for transactions performed by the external data structure from the remote computing device. The middleware computing system can use metadata in the records to automatically determine a data structure type of the data structure. The middleware computing system can generate instructions that cause the computer to link the external data structure with the profile.