16 results about "Work scheduling" patented technology

The invention relates to a charging pile control method and system, a terminal and a storage medium, and relates to the technical field of charging piles, and the method comprises the steps: collecting the working state information of a charging pile; acquiring position information, destination information and charging demand parameters of a target vehicle; predicting the arrival time of the target vehicle according to the position information and the destination information; based on the charging demand parameters, the arrival time and the working state information, whether a target charging pile meeting the charging demand parameters exists or not is judged; if not, determining a candidate charging pile combination according to the charging demand parameter; determining a target charging pile combination according to the candidate charging pile combination and generating a work scheduling instruction and a vehicle scheduling instruction corresponding to the target charging pile combination; sending a vehicle scheduling instruction to the target vehicle; and in response to the charging starting operation, controlling the target charging pile combination to cooperatively supply power according to the work scheduling instruction. The method has the effects of improving the charging efficiency, guaranteeing the power supply reliability and realizing efficient cooperative distribution of power resources.

The invention discloses a base station power distribution system which comprises at least one of a PV / DC photovoltaic power module and a DC / DC bidirectional stacked storage power module, a stacked storage battery, a system centralized monitoring module, a load primary power-off port, a load secondary power-off port and a load power-off and data acquisition control module. The system can be flexibly configured as a pure light system, a stack storage system or a light storage integrated system, facilitates the subsequent increase, decrease and change of the configuration according to the needs, is high in expansibility, and enables the resource utilization rate to be maximized. The integrated design is integrated to complete system operation and work scheduling required by functions, high reliability and stability are achieved, meanwhile, system operation state information is integrated to be in butt joint with a data platform, and remote inspection operation and maintenance are facilitated.

Technologies for enhancing work scheduling of a big data framework. The technologies can include generating, in a database, configurable namespaces to be used by a work scheduling enhancement application to group together tasks of one or more big data clusters. The namespaces can be hierarchical. The technologies can also include linking in the database, by the application, related tasks with respective namespaces to categorize and group together the related tasks. The technologies can also include configuring, by the application, a display scheme for displaying error handling and root cause analysis of tasks of the one or more big data clusters. The technologies can also include generating or rendering, by the application, a GUI having a navigable hierarchal view for displaying the namespaces. The generation or rendering of the GUI can be based partially on the display scheme.

A computer system and computer implemented method. The method includes identifying a plurality of operational readiness data and a plurality of policy data for a workforce; filtering the plurality of operational readiness data and the plurality of policy data to define a ready workforce; providing a first user interface to a manager; providing a second user interface to a worker; and detecting a selection of a date range for scheduling the ready workforce. Further, the method optionally includes receiving at least one priority constraint from the manager; receiving at least one scheduling preferences from a worker. The method includes calculating, by applying an iterative computer model, a work schedule for the ready workforce during the date range and publishing the work schedule.

This application discloses a conflict detection method, system, and storage medium for planned work scheduling of nuclear power equipment. The conflict detection method includes: acquiring configuration files pre-established for each master device, the configuration files including availability dependency information and redundancy information. The availability dependency information includes: the identifiers of upstream devices on which the availability of the master device depends, and the dependency logic relationship between the availability of the master device and the upstream devices. The upstream devices include at least one other master device and / or at least one end device; acquiring an isolation instruction table from the SAP system; determining the status of each master device according to the isolation instruction table and the availability dependency information corresponding to each master device; and identifying master devices that do not meet the corresponding redundancy requirements according to the status of each master device and the redundancy information corresponding to each master device. Implementing the technical solution of this application improves the efficiency of conflict detection in planned work scheduling and reduces the probability of human error.

The invention is suitable for the technical field of intelligent monitoring, and provides a fault prediction method and system based on elevator parts, and the method comprises the steps: obtaining the real-time operation data set information of a plurality of elevator parts based on a plurality of sensors, and then generating operation evaluation information based on the real-time operation data set information, and finally, sending the operation evaluation information to a specified display terminal. According to the method and the device, an owner party can carry out work scheduling of shutdown maintenance on the elevator to be maintained in advance, the occurrence probability of sudden failures is avoided, the use safety of the elevator is improved, an elevator maintenance company can purchase and prepare targeted key parts, the inventory number of the key parts is effectively reduced, and the maintenance cost is reduced. According to the method and the system, key parts of corresponding brands and models can be more accurately provided for a user for maintenance, meanwhile, an owner can follow real-time data of a mechanical vibration analysis curve for comparison, the operation state of the key parts can be clearly mastered in real time, and maintenance work is effectively supervised.

This invention discloses an intelligent scheduling method and system for medical waste robots based on the Internet of Things (IoT), relating to the field of IoT technology. The method includes: installing an IoT sensing device group on the medical waste robot; starting the robot and processing data to obtain a multi-dimensional data stream of standard medical waste; obtaining medical waste production prediction information and simultaneously collecting the robot's working status information; determining a robot work scheduling scheme; controlling the robot to perform medical waste processing and full-process recording, obtaining a processing data chain, and tracing and managing the process through the data chain. This invention solves the technical problems of low efficiency, exposure risks, and difficulty in rationally scheduling waste processing tasks based on actual conditions in existing medical waste processing processes. It achieves intelligent and automated medical waste processing, improves processing efficiency, reduces occupational exposure risks for processing personnel, and enables rational scheduling based on actual waste conditions, achieving the technical effect of refined management throughout the entire process.

To provide a system for supporting a sub-business optimal to an individual user.SOLUTION: A system, comprising: inputting means for a user to create an account and input information on skills, hobbies, work experience, and lifestyle; receiving and storing the information inputted by the user in a database; generating a sub-business idea using a AI engine based on the information stored in the database; presenting the sub-business idea to the user; generating a schedule of sub-business work based on schedule information of the user; evaluating marketability of the sub-business idea and the schedule and providing feedback; and providing specific procedures and resources for starting the sub-business.SELECTED DRAWING: Figure 1

We provide the system. [Solution] A device and means for acquiring and recording construction condition information, A device and means for evaluating information on multiple contractors based on acquired construction condition information and selecting an appropriate contractor, A device that automatically places construction orders with selected contractors, A device that recognizes the worker's operational status in real time and presents the optimal work schedule, A device and means for collecting construction progress data and updating the contractor's capability database, A device and means that provides an interface for displaying the construction progress status on a smartphone and remotely monitoring the project's progress, A system that includes this.

The invention discloses an urban elevator cluster power-saving monitoring system based on AI learning, relates to the field of elevator control and energy saving, and aims to solve the problems that in a traditional system, a single elevator runs disorderly, a power-saving strategy is not iterated and is very fixed, AI application is not wide, and monitoring is not completely covered. The system comprises a centralized control management unit, a transmission layer and a field control unit. The centralized control management unit comprises a monitoring platform and a sensing layer AI learning module, the monitoring platform can realize data visualization, abnormity alarm and remote intervention, and the sensing layer AI learning module is responsible for data preprocessing, strategy model training and iterative optimization. The field control unit can collect elevator energy consumption, people flow and running state data and can also execute start-stop control, speed adjustment, standby dormancy and other power-saving actions. According to the invention, elevator cluster co-work scheduling and continuous electricity-saving efficiency are realized, the operation and maintenance cost of the elevator is reduced, and the elevator waiting time of the user is shortened.

The application provides a work order allocation method and related device, electronic equipment, medium and program product. The method comprises the following steps: obtaining a production task, determining a production performance priority according to the production task; determining a target production process from the multiple production processes according to the production performance priority; obtaining historical production data of each production personnel in a production personnel to which a to-be-allocated work order belongs; determining a target production personnel according to the target production process and the historical production data of each production personnel; determining a target work order associated with the target production process; and allocating the target work order to the target production personnel. By implementing the application, suitable production personnel can be arranged at suitable work posts to realize reasonable work arrangement and facilitate reasonable allocation of production resources under the condition of meeting the demand of the production task.

The invention relates to the technical field of swarm intelligence, in particular to an infrastructure site dynamic job scheduling method based on multi-agent collaboration, and the method comprises the steps: constructing a task state matrix; in optimization algorithm iteration, the positions of population individuals are decoded into task priorities, and the predicted execution time of each task node is obtained in combination with a task state matrix; calculating a process coupling degree used for representing a process connection tightness degree; counting the process coupling degree of the population individuals in the current iteration, calculating the evolutionary stagnation risk in combination with the standard deviation of the process coupling degree in the initial iteration, and selecting a target strategy from the Cauchy variation strategy and the Gaussian variation strategy to update the population individuals; and converting the global optimal position at the end of iteration into a scheduling instruction set. According to the technical scheme, the invalid waiting time between procedures can be shortened, and the comprehensive operation efficiency of an infrastructure site is improved.

The invention discloses a work scheduling method for a deep neural network reasoning task. The work scheduling method is applied to a vehicle edge computing system comprising a plurality of roadside units. The method comprises the following steps: firstly, constructing a system model, and modeling an inference task into a directed acyclic graph to distinguish executed and unexecuted layer nodes; establishing a communication time delay and energy consumption model, and defining a system total cost function as a weighted sum of task completion total time delay and intermediate result transmission energy consumption; modeling a scheduling decision process into a multi-agent Markov decision process, and taking each roadside unit as an independent agent; solving by adopting a multi-agent depth deterministic strategy gradient algorithm to generate an optimal scheduling strategy; and finally, determining a target roadside unit according to a strategy, and transmitting an intermediate result for relay calculation. According to the invention, the problems of service interruption caused by high mobility of the vehicle and lack of cooperation of multiple roadside units are effectively solved.

The application discloses a kind of multi-micro manipulator device parallel collaborative work scheduling and path planning method and system, comprising: according to system state space model, the task information of the task to be processed is obtained;According to the spatial distribution characteristics of target task in task information and task demand, the target task is grouped, and a plurality of task subgroups are obtained;Several task subgroups are assigned to corresponding micro manipulator device, and initial task allocation scheme is generated;In the process that micro manipulator device executes task, its execution state is monitored in real time, and according to the deviation of actual execution progress and expected planning, the remaining tasks are dynamically rescheduled, and updated task scheduling sequence is generated;According to the target position of each micro manipulator device, kinematic constraint and collision constraint, the optimal path without collision is planned for each micro manipulator device, and position calibration is carried out in the process of path execution.The method proposed in the application significantly improves the efficiency of multi-device parallel collaborative work.

In one embodiment, a system includes a peripheral data connection bus configured to connect to devices and transfer data between the devices, a scheduling machine configured to connect to the peripheral data connection bus and send a read request message to a first processing device, and the first processing device configured to be connected to the peripheral data connection bus, and responsively to the read request message add a time value to a read response message, and provide the read response message to the scheduling machine, and wherein the scheduling machine is configured to read the time value from the provided read response message and schedule processing of an operation by a second processing device responsively to the read time value.

A method for performing automatic maintenance of an autonomous vehicle is disclosed. The method includes receiving a maintenance request from the autonomous vehicle, wherein the maintenance request includes diagnostic data. Further, the method includes analyzing the diagnostic data to identify at least one recommended car service for the autonomous vehicle and comparing a vehicle location against a plurality of facility locations to identify a closest facility from a plurality of maintenance facilities, wherein the plurality of facility locations is associated with the locations of the plurality of maintenance facilities. Further, the method includes receiving a work schedule of the closest facility and generating an appointment reservation with the closest facility in the one or more facility locations based on the received one or more work schedules. Moreover, the method includes sending the appointment reservation to the autonomous vehicle.