62 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 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.

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.

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.

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 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.

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.

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.

The invention relates to the technical field of container management, in particular to a method and system for automatically triggering K8s Pod updating. The method for automatically triggering K8s Pod update comprises the following steps: receiving a Webhook event from a mirror image warehouse, performing security verification, extracting a mirror image content abstract from the Webhook event passing the verification, and comparing the mirror image content abstract with a historical mirror image content abstract stored in a persistent database in real time; when it is found that the mirror image content abstract changes, a Kubernetes API is called to modify a Pod template annotation of the target Deployment, so that an annotation representing a new mirror image content abstract is injected; and a rolling updating process is automatically triggered to gradually replace the old Pod instance, so that zero-shutdown deployment is realized. According to the method and system for automatically triggering the updating of the K8s Pod, a content awareness updating mechanism is utilized, the problem of updating failure caused by unchanged mirror image labels is thoroughly solved, a Kubernetes updating mechanism is triggered by utilizing annotation injection, the operation and maintenance complexity is greatly reduced, and repeated operations generated by frequently changing packets in a development stage are reduced.

The invention relates to the technical field of software analysis, in particular to a WebHook-based software component automatic analysis method and system and a storage medium, and the method comprises the steps: configuring WebHook in a code library, responding to triggering of a monitoring event in the code library, sending the triggered event to an analysis server for condition judgment, and if the condition is not met, directly ending the process; if the conditions are met, a double-branch scanning and differentiation comparison method is adopted to identify newly-added components and version-upgraded components, and dependency analysis and root cause positioning are carried out to obtain associated root cause components; and performing vulnerability query to identify the components with security vulnerabilities, and establishing association between the components with the security vulnerabilities and the corresponding root cause components based on the root cause components to generate an analysis result. Configurable trigger judgment logic is introduced, fine filtering of code change events is achieved, the resource utilization efficiency is improved, and meanwhile, the root cause component causing the security problem is positioned to provide repair guidance for developers.

The invention discloses an automatic test platform and a test method based on resource scheduling, and relates to an automatic test technology. Comprising a resource scheduling module deployed in a server and an AutoTester for testing a host; the resource scheduling module responds to a code merging request through a GitLab WebHook monitor, the equipment state monitoring unit maintains an equipment state machine model in real time, and the equipment scheduling unit adopts a dual-mode mechanism: a binding mode specifies an equipment ID to execute tasks, and a weighted automatic allocation mode dynamically allocates tasks according to idle time of equipment. According to the method, GitLab WebHook is deeply integrated, a task triggering mechanism driven by a GitLab code merging event is adopted, and the defect that a traditional testing system is disjointed with a development process is overcome; meanwhile, a dual-mode scheduling mechanism is adopted, the equipment utilization rate and the test efficiency are remarkably improved, and the problems of resource competition and resource idleness caused by traditional static binding are solved.

A container orchestration method and system applied to Kubernetes, the method comprising: a Webhook component intercepts and scans a Pod creation request to determine whether a first environment variable is included in a target container, the first environment variable being used to indicate that the target container is a sidecar container; in the case where the target container includes the first environment variable, the target container is Downward API formalized; after the Downward API formalization is completed, the API server writes the Pod configuration information of the target Pod to a distributed database, and the sidecar container is Downward API formalized, so as to facilitate subsequent modification of the startup command of the container through the Downward API, implement the exit mechanism of the sidecar container, and realize the universality of the container exit scheme.

The invention relates to an alarm notification method, a monitoring system, electronic equipment and a readable medium. The method is applied to a monitoring system comprising a server side and a client side, the server side comprises a webhook service component and a websocket service component, and the client side comprises a Grafana service monitoring panel and a voice broadcast panel self-defined in Grafana in advance. The method comprises the following steps: a webhook service component receives alarm information pushed by Grafana; the alarm information is generated when the Grafana monitors that the service index in the service monitoring panel meets the alarm triggering condition; the webhook service component converts the alarm information into a voice file; the websocket service component generates a voice broadcast instruction by adopting the voice file, and sends the voice broadcast instruction to the voice broadcast panel; and the voice broadcast panel plays the voice file based on the voice broadcast instruction. According to the scheme provided by the invention, the alarm sensing capability of Grafana can be enhanced, and the interactivity and real-time performance of a monitoring system are improved.

The fastjson deserialization vulnerability call chain mining method and system comprise the following steps: configuring a Webhook of a code repository; receiving updated project information, identifying the URL address of the project code, and storing the Java source code of the project; parsing the Java source code into an abstract syntax tree and then converting it into a Python dictionary tree; detecting whether the Java source code references or uses the Fastjson library and whether a fastjson vulnerability version exists; detecting whether a JSON string exists; detecting whether a JSON string is deserialized into an object and whether the object is operated, and verifying whether an injection vulnerability exists; and determining whether a fastjson deserialization vulnerability exists, and if so, outputting the call chain result of the fastjson deserialization vulnerability. The present application greatly reduces the burden of manual mining and improves the mining efficiency and accuracy.

In response to detecting that a voice call has been placed by a first user to an interaction entity, a voice spectrogram is generated of a voice interaction between the first user and a second user associated with the interaction entity and a plurality of signal attributes are extracted from the voice spectrogram. Additionally, a content of the voice interaction is extracted from the voice call. Based on the signal attributes and the content of the voice call, an issue is determined. A resolution entity that is configured to resolve the issue is identified and a webhook payload including an indication of the identified issue is transmitted to the resolution entity for resolution of the issue.

The invention discloses an interface adaptive accurate analysis AI intelligent question system and method, and belongs to the technical field of artificial intelligence. The method is a comprehensive solution based on user Token dynamic authentication, a Webhook-driven API metadata adaptive mechanism, a large language model and execution environment cooperative computing. The system automatically synchronizes and semantically enhances interface description by monitoring a change event of the API management platform, and stores the interface description into a vector database; when the user asks questions, semantic retrieval and API selection are carried out in combination with the identity Token of the user, and permission verification is carried out based on the Token; and when the returned data volume is relatively large, the Python sandbox is automatically triggered to execute the statistical code generated by the LLM, so that the accuracy of numerical calculation is ensured. According to the invention, the integrated intelligent number asking capability with controllable authority, accurate result and self-adaptive interface is realized, and the safety, reliability, maintainability and the like of an enterprise-level information system can be remarkably improved.

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 application relates to the technical field of traffic processing, in particular to a Spark job traffic control method, which comprises the following steps: in the case that a new service container group is created, a webhook injects an agent container and a wasm plug-in container in the new service container group, and an adapter collects and manages Pod information of the new service container group to build a service container group topology relationship; in the running process, all network traffics of the service container are hijacked by the wasm plug-in container, original traffic connection information carried in the network traffics is extracted and sent to the agent container; the agent container reconstructs the original traffic connection information to obtain target traffic connection information, and sends the target traffic connection information to the adapter; the adapter generates a traffic control strategy based on the target traffic connection information, processes the target traffic connection information in combination with a current processing stage, generates a traffic processing instruction, and transmits the traffic processing instruction to the wasm plug-in container through the agent; the wasm plug-in container executes the traffic processing decision to determine whether to allow the traffic to pass.

The invention provides a method, a system and equipment for continuous integration and delivery of DevSecOps private cloud Ansible fusion, and a storage medium, and relates to the technical field of DevSecOps. According to the method, an Ansible Automation technical architecture is planned, and task execution weights are evaluated and priorities are divided based on a resource, role and security script three-index normalization algorithm; constructing an Ansible script library and combining the Ansible script library with an AI fine tuning model to realize baseline compliance automatic closed loop; a random forest model is adopted to predict the fusion degree, and seamless integration of Ansible and a CI / CD tool is realized through Webhook; and generating an enterprise diagnosis report based on a multi-dimensional maturity scoring algorithm. According to the method, the problems of scattered security baselines, insufficient compliance verification and low efficiency of dynamic operation and maintenance response are solved, the security automation execution efficiency and the process response capability are improved, and an innovative solution is provided for DevSecOps landing in a private cloud environment.

The application discloses an intelligent code analysis system and method based on a graph database, and the system realizes accurate modeling and efficient query of code complex dependence by constructing a multi-dimensional code relation graph and utilizing a graph database. The system supports incremental analysis triggered based on a GitLab Webhook event and full-scan mode triggered manually or at a fixed time, and simultaneously integrates multiple AI models, dynamically calls lightweight or deep analysis models, and optimizes AI calling cost through intelligent context filtering and token rate control mechanism. The application significantly improves the accuracy, efficiency and real-time performance of code analysis, and the AI analysis accuracy is improved by more than 30%, the analysis time of large projects is shortened by 60%, and the AI calling cost is reduced by 50%, which is suitable for enterprise-level code quality management and control and DevOps process integration.

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.