16 results about "Task level" patented technology

The invention relates to a task scheduling method and system based on a zero-work system, and relates to the technical field of data processing, and the method comprises the steps: receiving a task issuing instruction, and calling a task level and a task type based on the task issuing instruction; determining a capability level based on the task type, and querying whether preset capability information of the order receiving user is smaller than the capability level or not; if yes, filtering the task for the order receiving user; if not, a task access permission is opened for the order receiving user; receiving a receiving instruction of an order receiving user based on the open task receiving permission, generating a task package based on the receiving instruction, and taking off the task; and acquiring identity information based on the task package, and after the identity information is consistent with preset order receiving user information, matching a supervision method and performing supervision. The method has the effect of improving the task completion rate.

The invention relates to the technical field of mold processing, and discloses an intelligent scheduling method and device for a production line, equipment and a medium. Obtaining task levels of all unexecuted scheduling tasks in the task pool, device priorities of used devices and workpiece priorities of workpieces needing to be machined; determining a task priority corresponding to each non-executed scheduling task according to the task level, the equipment priority and the workpiece priority corresponding to the same non-executed scheduling task; and determining a target task queue according to all the task priorities, and issuing and executing a scheduling task according to the target task queue. The task priority is calculated according to the task level, the equipment priority and the workpiece priority, so that the emergency task is preferentially processed, the production sequence is adjusted according to the production requirement, and the production efficiency is improved.

The application relates to a task scheduling method and system based on a zero-work system, and relates to the technical field of data processing. The method comprises the following steps: receiving a task publishing instruction, calling a task level and a task type based on the task publishing instruction; determining a capability level based on the task type, and inquiring whether the capability level is less than preset capability information of an order-receiving user; if the capability level is less than the preset capability information of the order-receiving user, filtering the task for the order-receiving user; if the capability level is not less than the preset capability information of the order-receiving user, opening a task receiving permission for the order-receiving user; receiving a receiving instruction of the order-receiving user based on the opened task receiving permission, generating a task package based on the receiving instruction, and taking down the task; collecting identity information based on the task package, and after the identity information is consistent with preset order-receiving user information, a supervision method is matched and supervision is performed. The application has the effect of improving the task completion rate.

This application provides a method and related equipment for constructing a skill tree profile of maintenance personnel based on work order task topology mapping, which can improve overall work efficiency. The method includes: acquiring historical work order data from the maintenance work order system, which includes equipment information, task information, and personnel information; performing structured processing on the historical work order data to construct a full lifecycle map of equipment and a personnel skill tree, establishing a bidirectional mapping relationship between task nodes in the equipment lifecycle map and skill nodes in the personnel skill tree through personnel and equipment information; when receiving new work order data, extracting feature information from the new work order data to generate a composite identifier, and verifying the composite identifier against historical composite identifiers in a pre-stored topology cache library, where the composite identifier includes equipment type and task level; if the verification result is invalid, calculating the mapping relationship in real time based on the equipment lifecycle map and the new work order data to obtain the target mapping relationship.

The present application relates to the technical field of industrial control system, specifically to a kind of forest fire pump group's collaborative operation control method and system, comprising the following steps: decomposition forest fire pump group collaborative operation process, aggregate all local pump controller's discretization control task list, establish whole system real-time task set.The present application is by forest fire pump group collaborative operation process discretization into task level control unit, and constructs complete whole system real-time task set, enhances the structural clarity and operation accuracy of task timing scheduling.With quantitative extraction and contrast execution to task resource allocation parameters, a clear priority order is assigned to the tasks, and accurate scheduling boundaries are provided by the deterministic execution time window table, allowing each pump controller to respond collaboratively within a strict time limit.

The invention relates to the technical field of resource allocation, in particular to an office resource allocation method based on the Internet of Things, which comprises the following steps: acquiring a task flow and a resource calling record, identifying a task dependence flow, extracting a resource type, a use time period and calling frequency, constructing a resource demand structure list, calculating resource occupation, and marking a high occupation flow. Generating a conflict area list, screening high-conflict requests, executing resource rearrangement, correcting calling duration, and generating allocation information. According to the method, the resource demand distribution structure is constructed by extracting the resource type, the use time period and the calling frequency in the task process, the resource allocation conflict area is identified, the combined screening is executed, the resource calling urgency degree is calculated in combination with the task priority and the resource utilization rate, and the high-conflict request starting point is effectively positioned; by eliminating the calling records with long resource idle period and low task level, the reasonability of task scheduling and the stability of office resource supply are enhanced.

The invention discloses a task planning method and device, electronic equipment and a storage medium, and relates to the technical field of task simulation deduction, and the method comprises the steps: constructing a mind map ontology model according to a task level and an initial node contained in the task level; and performing logic verification on the mind map editing information of the user, if the logic verification is passed, adjusting the mind map ontology model according to the mind map editing information, and generating a target task file according to the obtained target mind map. The problems that in the task planning process, the logic relation and the overall conception among multiple tasks are difficult to visually reflect, and task errors are likely to occur can be solved. According to the method, the mind map is used as a front-end carrier, and the logic relationship is displayed through the mind map, so that a user can quickly view the whole task planning process from macroscopic to microscopic, and the planning efficiency is greatly improved. In addition, the method verifies editing information in real time, information missing or information conflict is avoided, and completeness and executable performance of the task are ensured.

This invention provides a method for autonomous mobile navigation path planning in an ICU, relating to the field of path planning technology. The method includes: acquiring patient tags, task identifiers, and intervention records in an ICU disaster scenario; extracting task time series and analyzing frequency characteristics; filtering passage segments and matching time windows; identifying equipment overlap and operational conflicts; extracting remaining tasks to generate a scheduling sequence; rearranging path nodes and integrating task levels; and generating a navigation path segment sequence. In this invention, by extracting task time features and spatial states, priority task points are generated and passable paths are filtered. This adapts to the high density of ICU beds and path compression in disaster scenarios. Resource conflicts are identified by combining equipment usage records and node spatial relationships to avoid task overlap. A scheduling sequence is constructed based on task stagnation status, improving path adjustment efficiency and enhancing the robot's ability to respond to unexpected tasks and the continuity of path execution.

The invention discloses an application interaction method, system and device based on a cloud edge collaborative edge cluster system and a medium, and belongs to the technical field of system application. The level of the system application task is determined from two dimensions of time required for processing the system application task and the emergency degree of the system application task, so that the level determination of the system application task is more scientific; meanwhile, weight calculation is carried out on the time required for processing the system application task and the emergency degree of the system application task through a subjective and objective combination method, so that the accuracy of weight determination is improved, and better cloud edge interaction can be realized.

The invention provides a robot ICU autonomous movement cruise path planning method, and relates to the technical field of path planning. The method comprises the steps of obtaining a critical patient label, a task identifier and an intervention record in an ICU disaster scene, extracting a task time sequence and analyzing frequency characteristics, screening a passing section and matching a time window, identifying equipment overlapping and job cross conflicts, extracting residual tasks to generate a scheduling sequence, and rearranging path node fusion task levels. A cruise path segment sequence is generated. According to the method, the scheduling sequence is constructed based on the task retention state by extracting the task time feature and the space state, generating the priority task point, screening the passable path, adapting to the ICU bed density and path compression state in the disaster scene, identifying the resource conflict in combination with the equipment use record and the node space relationship, and avoiding the task overlapping; the path adjustment efficiency is improved, and the coping ability of the robot to the sudden task and the path execution continuity are enhanced.

Embodiments of the present application provide a task exception early warning method and device, electronic equipment and readable storage medium, the task exception early warning method includes: for any scheduling stage Stage in each Job included in the required monitoring Spark task, obtain the execution duration of multiple Task tasks in the scheduling stage Stage, and the first number of Task tasks included in the scheduling stage Stage;When the first number is less than the Task task number threshold, compare the execution duration of each Task task with the size of the first execution duration threshold;When there is an abnormal Task task with an execution duration greater than the first execution duration threshold in the multiple Task tasks, generate an alarm information, and the alarm information includes the task identifier of the abnormal Task task. The present application realizes the abnormal task early warning of the Task task level of the Spark task.

The invention discloses an integral-system emolument performance assessment management platform, and the platform comprises a task and level management module which is used for decomposing a work task into a plurality of tasks which can be completed, and setting different levels; the integral and reward system module is used for setting an integral rule according to the difficulty, importance and completion quality of each task level after decomposition; the salary docking module is used for decomposing the salary into a plurality of parts according to the performance target and the task difficulty and associating the point data with the individual salary; the digital management module collects and analyzes performance data of each employee in real time by using a digital tool and monitors the performance of the employees in real time; and the segment visual management module is used for dynamically displaying a three-dimensional growth map of each employee according to the corresponding points obtained by the employees and the introduced game elements. According to the method, the salary can be adjusted according to the integral condition of task completion of the employees, so that the task completion efficiency and enthusiasm are improved.

The invention discloses a task scheduling method, system and device based on a multi-domain computing power network and a storage medium. The method comprises the following steps: constructing a task feature vector based on structured attribute data of a to-be-scheduled power task; determining a task level label corresponding to the electric power task based on the task feature vector and a task classification model; obtaining a computing power resource state matrix corresponding to the current multi-domain computing power network; inputting the task level label, the computing power resource state matrix and the task feature vector into a multi-level strategy collaborative scheduling model to obtain a node selection action; and according to the node selection action, adding the electric power task into a task queue of a corresponding target computing power node so as to execute the electric power task based on the target computing power node. According to the method, the to-be-scheduled electric power tasks are classified through the task classification model, how to schedule the electric power tasks is determined through the multi-level strategy collaborative scheduling model, and intelligent scheduling of the electric power tasks can be achieved.

The invention relates to the technical field of industrial control systems, in particular to a cooperative operation control method and system for a forest fire pump group, and the method comprises the following steps: decomposing the cooperative operation process of the forest fire pump group, aggregating discretization control task lists of all local pump controllers, and building a whole-system real-time task set. According to the method, the collaborative operation process of the forest fire pump group is discretized into the task-level control units, and the complete whole-system real-time task set is constructed, so that the structural definition and the operation accuracy of task time sequence scheduling are enhanced. By means of quantitative extraction and comparative execution of task resource allocation parameters, a clear priority order is allocated for tasks, and all pump controllers cooperatively respond within a strict time limit by means of an accurate scheduling boundary provided by a deterministic execution time window table.

The invention discloses a dynamic task allocation and optimization method for aviation robots, relates to the technical field of task allocation, is used for solving the problems of equipment overload and frequent airspace conflicts, obtains a current task list of each aviation robot through a task database, calls a task level of a task target, collects residual electric quantity and calculates endurance time, and provides a dynamic task allocation and optimization method for the aviation robots. Screening and marking task targets in combination with task levels; importing position coordinates of the marked task targets and available time into a clustering analysis algorithm to generate candidate areas; marking overlapped parts of the candidate areas of different robots; obtaining a robot list of the marked candidate areas; according to the method, optimal scores are evaluated and executed in combination with track density characteristics, sorting is carried out in marked candidate areas according to the scores, a sorting result is imported into an analytic hierarchy process algorithm to generate a candidate robot list, and the safety and execution efficiency of a multi-aviation-robot collaborative task are improved.

The invention relates to the technical field of project management, in particular to a field service scheduling system and method based on artificial intelligence. On-site service information is preprocessed to obtain multiple pieces of preprocessed information, then a scheduling model is created, and on-site task levels and capability levels of workers are calculated based on on-site task information and on-site personnel information through the scheduling model; and finally, collecting real-time field information and obtaining a real-time task level, calculating an energy efficiency value of surrounding personnel based on the real-time field information, and carrying out field scheduling by combining the energy efficiency value and the real-time task level. According to the method and the device, the coupling relationship between the field task level and the capability level of the working personnel is constructed, and the energy efficiency value of the surrounding personnel of the real-time field is calculated based on the capability level, so that the field scheduling is performed by combining the energy efficiency value and the real-time task level, and the personnel cost is reduced as much as possible on the premise of improving the scheduling efficiency.