9 results about "Job specification" patented technology

A computing system can facilitate adjudication of updates to one or more job specifications for executing one or more tasks associated with a data processing workflow. The system can be configured to input one or more job specifications, such as originating from a build orchestration system, and one or more rules to generate an upgrade specification and a safest specification. The system can be configured to cause execution of the upgrade specification. The system can determine whether execution of the upgrade specification is successful. If execution of the upgrade specification is successful, the system can be configured to record one or more datasets generated via execution of the upgrade specification. If execution of the upgrade specification is not successful, the system may be configured to cause execution of the safest specification. The system may record one or more datasets generated via execution of the safest specification.

The machine learning operation method in a private network environment comprises the steps of: verifying, by a machine learning operation platform, a request when a user requests a machine learning job from the machine learning operation platform, and generating a job specification according to the request; performing, by a cluster agent, polling on the machine learning operation platform at a preset first period to check whether a machine learning job specification has been allocated from the machine learning operation platform; converting, by the cluster agent, the machine learning job specification received from the machine learning operation platform into a Kubernetes resource form; and dynamically scheduling, by a Kubernetes API server, container resources in response to a container resource generation request received from the cluster agent.

The invention discloses an early warning control method and system for an operation safety helmet, and relates to the technical field of early warning control methods, and the method comprises the steps: determining an electric power operation early warning signal according to a plurality of operation risk factors, the overall form of an electric power operator, and a plurality of posture parameters of the operation safety helmet; determining a risk optimization measure of the power operator according to the real-time operation image of the power operator and the project content of the current operation project, and determining a corresponding action early warning instruction based on the risk optimization measure of the power operator, the plurality of operation risk factors and the operation specification database, determining a power operation risk event of the space where the power operator is located according to the plurality of environmental parameters, the electromagnetic field intensity parameter and the action early warning indication; the electric power operation early warning level is determined according to the plurality of electric power parameters, the electric power operation risk events and the protection measures of the electric power operation personnel, and the accuracy of the electric power operation early warning level is improved.

Example embodiments provide systems and methods for formulating and evaluating a construction composition (such as a mixture for concrete, asphalt, mortar, etc.). According to exemplary embodiments, a predictive model, artificial intelligence, machine learning algorithm, etc., may be trained using historical performance data and current deployment information. Based on a job specification that identifies various requirements for the construction composition and a set of available inputs (e.g., raw materials, mixing techniques, etc.), the model, AI, or algorithm, may output one or more formulations that meet or best approximate the requirements. The formulations may be provided to a simulation to estimate or predict their performance. The performance characteristics of the output formulation(s) may be displayed. Optionally, the system may control mixing machinery to produce the formulation. Some embodiments may use these capabilities to evaluate an existing or proposed construction composition, rather than proposing a new construction composition.

Disclosed is a method for machine learning operations in a private network environment, which is performed by a machine learning operations system. The method includes verifying, by the machine learning operations platform, when a user requests a machine learning job from a machine learning operations platform, the request and generating a job specification according to the request, performing, by a cluster agent, polling on the machine learning operations platform based on a preset first period to check whether a machine learning job specification is allocated from the machine learning operations platform, converting, by the cluster agent, the machine learning job specification received from the machine learning operations platform in a Kubernetes resource form, and dynamically scheduling, by a Kubernetes API server, a container resource in response to a container resource generation request received from the cluster agent.

Federated learning (FL) may be deployed as a service by defining FL job management functionality. The FL service may have the ability to receive FL job specifications from the FL user, process and manage these FL jobs on behalf of the FL user through interaction with the FL client, and update the state of the FL jobs to the FL user during the processing of the FL jobs.

Example embodiments provide systems and methods for performing quality control of a construction composition. According to exemplary embodiments, a predictive model, artificial intelligence, machine learning algorithm, etc., may be trained using historical performance data and current deployment information. Based on a job specification that identifies various requirements for the construction composition and a set of available inputs, the AI / ML / model may output one or more formulations that meet or best approximate the requirements, and an initial batch of the construction composition may be produced. During or after deployment of the construction composition, information about the composition's performance may be received and applied to the AI / ML / model. The system may make real-time updates to the construction composition to improve the consistency or performance of the construction composition, within predefined acceptable change parameters. Optionally, the system may control mixing machinery to produce the updated construction composition.

To provide an information processing system or the like allowing a job seeker to easily use an automatic generation function.SOLUTION: According to an aspect of the present invention, there is provided an information processing system including at least one processor, wherein the processor is configured to execute the following steps by reading a program, in the generating step, an appeal sentence for appealing to a job applicant is generated according to job applicant information related to a job seeking activity of the job applicant, in the transmitting step, appeal information including the appeal sentence is transmitted, and the appeal information includes link information to an automatic generation function of a sentence related to job specifications of the job applicant.SELECTED DRAWING: Figure 1

Example embodiments provide systems and methods for performing quality control of a construction composition. According to exemplary embodiments, a predictive model, artificial intelligence, machine learning algorithm, etc., may be trained using historical performance data and current deployment information. Based on a job specification that identifies various requirements for the construction composition and a set of available inputs, the AI / ML / model may output one or more formulations that meet or best approximate the requirements, and an initial batch of the construction composition may be produced. During or after deployment of the construction composition, information about the composition's performance may be received and applied to the AI / ML / model. The system may make real-time updates to the construction composition to improve the consistency or performance of the construction composition, within predefined acceptable change parameters. Optionally, the system may control mixing machinery to produce the updated construction composition.