16 results about "Platform as a service" patented technology

The invention relates to the technical field of equipment operation and maintenance management, in particular to a light operation and maintenance method and system for enabling intelligent equipment management, and the method comprises a data collection and perception layer, an infrastructure and platform support layer, an intelligent center and capability layer, an application service layer and a presentation and interaction layer. The data acquisition unit is responsible for acquisition and preliminary access of equipment data, and the infrastructure, namely the service unit, is used for providing basic resources; the platform-as-a-service unit is used for providing an enabling service and comprises a big data processing platform, a stream computing engine, an AI model training and deployment platform and a database service; the intelligent center and capability layer comprises a digital twinning engine unit, an AI analysis engine unit, a knowledge graph engine unit and a rule engine unit, the knowledge graph engine unit is used for carrying out structured and semantic association on knowledge in the operation and maintenance field to form a knowledge graph, and the rule engine unit is used for processing a deterministic task based on predefined logic to form a knowledge graph; the process automation is realized.

The technologies described herein are generally directed to using virtualized clusters deployed in containers of a platform-as-a-service system. For instance, a system can, based on an application system that includes at least two nodes, generate a testing model applicable to test respective operation of the at least two nodes. The system can further, based on a container-based virtualization layer, instantiate virtualized environments that host respective testing clusters applicable to the respective operation of the at least two nodes. Further, the system can, based on a testing plan applicable to the application system, interface with the testing clusters via a container orchestration layer.

Provided in the present disclosure is an application configuration method. The method is applied to an application platform as a service (aPaaS) platform. The method comprises: presenting a configuration page of a first application, wherein the first application is an application in an aPaaS platform; in the configuration page of the first application, receiving an association operation for a second application, wherein the second application is an application in a language model application development platform, and the language model application development platform provides a natural language processing capability; and on the basis of the association operation, establishing an association relationship between the first application and the second application, wherein the association relationship is used by the first application to call the second application. In the application configuration method, the natural language processing capability of the second application can be introduced into the first application without the need to re-establish the first application, such that the usage efficiency of using the first application by a user can be improved.

A cloud computing system is provided for testing a Distributed Energy Resource (DER) Management System (DERMS) that manages operation of DERs connected to a power system. The cloud system implements a DERMS testing platform on virtualized cloud computing resources of the system. The platform tests the DERMS and is modularized. In some embodiments, the DERMS is modularized into at least a simulation module and a communication module, where the simulation module is configured to simulate DER(s) of power system and the communication module communicatively interfaces DERMS with DER(s) via communication network(s). The simulation module also simulates the power system under test conditions as the DERMS manages operation of DER(s) via the communication module. The cloud computing system exposes the platform as a service for testing the DERMS under test conditions. The cloud system scales the resources allocated to the platform with the number of DER(s) simulated under different test conditions.

An extensible platform-as-a-service (PaaS) system is used during the software development lifecycle (SDLC) processes of an application. The system facilitates the process of getting security approval for the application. The system allows developers to use software bundles including software modules that are pre-approved. The system allows a developer to customize one or more software modules of the software bundle if necessary. The system monitors the development of the application to check if the developer customizes any software module to use a configuration that is not pre-approved. The system automatically generates a report noting the customization to the software module and requests the security team to review the modified configuration for security approval.

In some implementations, a platform as a service (PaaS) orchestrator may receive a set of values for a set of parameters of a software component template for a software component. The PaaS orchestrator may generate the software component using the set of values for the set of parameters. The PaaS orchestrator may execute the software component in a sandbox environment. The PaaS orchestrator may validate the software component for deployment. The PaaS orchestrator may deploy the software component in a deployment environment of the PaaS orchestrator. The PaaS orchestrator may detect a network event relating to the deployment environment. The PaaS orchestrator may identify a network management action associated with the network event. The PaaS orchestrator may one or more instructions to cause the network management action to be performed.

This invention provides a self-management method, apparatus, device, and storage medium for a platform as a service (PAS), comprising: collecting key parameters of the PAS within a target period; performing an upgrade operation on the PAS when the CPU utilization or memory utilization is greater than a predefined alarm threshold; generating a comprehensive score of service profile parameters and an actual score corresponding to each type of service profile parameter within the target period according to preset rules when both CPU utilization and memory utilization are less than the predefined alarm threshold; performing an upgrade operation on the PAS when the comprehensive score is less than a predefined service profile threshold; and performing a downgrade operation on the PAS when the actual score of the throughput indicator is less than a predefined target threshold. This invention improves the performance of the PAS by modifying resource parameter configurations through upgrade or downgrade operations.

The invention relates to a PaaS service construction method and device, equipment and a medium. The PaaS service construction method comprises the following steps: obtaining service instance information of a PaaS service instance; determining a service template corresponding to the service instance information from at least one preset service template, and creating a configuration file corresponding to the PaaS service instance based on the service instance information and the service template; and calling the target creation component interface in the container arrangement platform k8s cluster based on the template type corresponding to the service template, and creating the instance resource corresponding to the PaaS service instance based on the target creation component interface and the configuration file, so that creation of different types of PaaS services can be met through the service template, independent development and customization are not needed every time, and the efficiency of creating the PaaS services is improved. And meanwhile, the instance resources are created through the k8s cluster, so that the development cost of the PaaS service is reduced, and the development efficiency is improved.

This application discloses a generalized invocation method, apparatus, device, and storage medium applicable to gateways, relating to the field of gateway technology. The method includes: loading and registering generalized invocation metadata to construct remote procedure call parameters based on the metadata; obtaining a generalized invocation request initiated by a client; initiating a generalized invocation based on the request and obtaining the corresponding result; encapsulating the result and feeding it back to the client in a preset output format. By implementing the dynamic generalized invocation method for gateways disclosed in this application, a code-free, interface-free remote invocation capability for remote procedure call services is achieved; the gateway is decoupled from downstream services, and invocation rules can be added or modified through the application platform as a service system configuration without restarting the gateway; configuration consistency is ensured through full and incremental loading; configuration is completed through a visual interface, reducing the operational threshold.

The invention provides a data query method, device and equipment based on an APaaS platform and a medium, and relates to the technical field of data query, the method comprises the following steps: obtaining a target query session initiated by a user; based on the target query session, determining a target entity list of an application platform as a service ApaaS platform through a vector database; based on the target entity list, determining an entity association relationship corresponding to the target entity list through an ApaaS platform; and determining a data query result corresponding to the target query session based on the entity association relationship, the target entity list and the conversational business intelligence model. According to the method, an underlying data table is not needed, management and maintenance of technicians are not needed, common users can easily use and maintain ChatBI, seamless integration of user permission and an APaaS platform is achieved, differentiated data display based on the users is supported, the ChatBI available for everyone is achieved, the operation and maintenance cost is reduced, and meanwhile the flexibility of the user permission is ensured.

Systems and methods for integrating machine learning models are provided, and more specifically, platforms as a service (PaaS) cloud are used to predict the probability of an operator's success in an environment. An embodiment includes a system comprising: a processor executing computer-executable components stored in memory; a trained machine learning model predicting the probability of successful deployment of an operator in an environment having a namespace of a platform as a service (PaaS) cloud; and a deployment component receiving a first operator and a first namespace, and employing the trained machine learning model to predict the success of deployment of the first operator in the first environment.

The present invention relates to an editing method of a dynamic spectrum program, which includes the following steps. Downloading the spectral recipe is to download the spectral recipe with multi light scene with specific color temperature or at least one specific color temperature to the editing device through the internet, and connect the editing device with the software as a service system or platform as a service system configured in the cloud. Perform the correlation editing between the specific function and the spectral recipe. The editing device divides the specific function to be achieved into multiple blocks through the software as a service system or the platform as a service system, and the blocks correspond to the spectral recipe of the multi light scene respectively. And forming a functional dynamic spectrum program, which is to configure multiple blocks for corresponding time to form a dynamic spectrum program.

A system for secure processing and storing of sensitive data and non-sensitive data for a tenant in an execution environment of a Software as a Service provider or a Platform as a Service provider is disclosed. In the present disclosure, the sensitive data includes a plaintext sensitive data element. The non-sensitive data does not include a plaintext sensitive data element. The execution environment of the system comprises a general execution area. The general execution area allows full access by the Software or the Platform as a Service provider, and a general application service running in the general execution area, wherein the general application service does not have access to the sensitive data, and wherein the general application service only processes the non-sensitive data. Furthermore, the execution environment of the system comprises a trusted execution area, and a trusted application service running in the trusted execution area.

The disclosure discloses a computing device supporting privacy computing, comprising: a platform as a service layer, the platform as a service layer comprising a privacy computing platform, the privacy computing platform being configured to perform privacy computing; an infrastructure as a service layer, comprising a hardware resource layer, a secure operating system layer and a TEE running on the secure operating system layer; wherein the hardware resource layer comprises a CPU, at least one trusted application running in the TEE, the at least one trusted application accessing the CPU through an SDK interface, and the SDK interface being compatible with SDKs of multiple TEE technologies.

State-of-the-art Integration Platform as a Service (iPaaS) platforms do not enable users to utilize their own artificial intelligence (AI) models (e.g., large language models or other generative model). Disclosed embodiments enable users to bring your own model (BYOM) within an integration platform. In particular, users may build or import their own AI model, and integrate that AI model into their integration processes in the same manner as any other integration step. This may involve dragging a shape, representing the AI model, from a virtual palette onto a virtual canvas, and configuring the corresponding AI step. During execution, the resulting integration process may automatically and dynamically, in real time, receive input data and retrieve contextual metadata to enhance the input data with context. The combination of input data and contextual metadata may be provided to the AI step to perform the desired task with improved accuracy.

A system for provisioning a plurality of resources in connection with a service to be provided in an autonomous vehicle (AV) infrastructure environment is described and includes a cloud platform executing a platform as a service (PaaS) cluster; a service-specific file specifying the plurality of resources; a values file specifying configuration information for each of the plurality of resource specified in the service-specific file; and a service for deploying the plurality of resources comprising the service on the cloud services platform using the service-specific file and the values file.