28 results about "Crew scheduling" patented technology

The invention discloses a high-speed rail business travel service personnel scheduling method based on data analysis, and relates to the technical field of personnel scheduling management. Comprising the following steps: data acquisition and integration: acquiring high-speed rail station operation data, passenger information, service personnel information and environment data; streaming data processing and space-time alignment are carried out on the collected data, and a dynamic main data matrix is constructed; service demand prediction: extracting passenger multi-dimensional features from the dynamic main data matrix, and generating passenger multi-dimensional feature tags; and performing bimodal demand prediction based on the passenger multi-dimensional feature tag in combination with the environmental data to obtain an initial passenger service demand. According to the invention, through establishing the bimodal service demand prediction method, extracting the multi-dimensional features of the passengers, combining environment data and predicting the demands of the passengers, passive response is converted into active prediction, the response speed is accelerated, the waiting time of the passengers is reduced, and the service quality and efficiency are improved.

The invention provides a scheduling modeling and intelligent scheduling method for multi-wall parallel construction, which belongs to the technical field of intelligent scheduling, and comprises the following steps: considering the construction period calculation heterogeneity, and establishing a scheduling optimization model taking the minimum construction period as a target and the number of each work type as a decision variable; based on a particle swarm algorithm, designing an intelligent scheduling algorithm for solving the optimization model, and formulating a special two-layer coding and decoding rule; on the basis of nonlinear time-varying inertial parameters, the particle speed is updated in a negative feedback mode by using a difference value with local historical optimum and global historical optimum, and position updating is further realized; and finally, through multiple iterations, outputting global optimal particles, and forming a personnel scheduling scheme of multi-wall parallel construction. The method has the advantages of high specificity, high global search capability, high search speed and the like, and a reasonable multi-wall construction scheduling scheme can be provided for construction progress management, so that the construction period is shortened to the greatest extent under the condition of ensuring the construction quality, and the construction cost is reduced from the perspective of human resource investment.

The invention relates to the technical field of intelligent manufacturing and dynamic personnel scheduling optimization, and discloses a dynamic personnel scheduling optimization method based on DDQN. According to the method, a DDQN intelligent decision framework oriented to the industrial scheduling problem is constructed, and the problem of Q value over-estimation in a traditional DQN algorithm is effectively solved by designing a multi-dimensional state space containing a process state, a team and a parallel mark and directly mapping an action space of an actual scheduling decision; and process states and worker behaviors are modeled through a deep reinforcement learning framework, and a task allocation and resource scheduling scheme is dynamically adjusted in combination with a real-time worker behavior prediction module, so that the total completion time is minimized and the resource utilization rate is maximized in a multi-process and multi-constraint scene, and the working efficiency is improved. The method is suitable for complex industrial environments such as chemical workshops and flexible manufacturing systems, scheduling challenges caused by process dependence, worker heterogeneity and dynamic task requirements can be effectively handled, and the production efficiency and the response capability of the intelligent manufacturing system are improved.

The invention relates to a production management system oriented to automatic control. The production management system comprises a production planning module, a production resource scheduling module, a production control module, a quality detection module and a logistics management module. The production planning module is used for generating a production plan according to production requirements; the production resource scheduling module is used for raw material scheduling, equipment scheduling and personnel scheduling; the production control module is used for controlling production process parameters according to collected real-time production data; the quality detection module is used for detecting the quality of the produced product; and the logistics module is used for managing raw material and product distribution.

The invention relates to the technical field of power personnel training, in particular to a power personnel scheduling method and system and a product. The method comprises the following steps: for each power worker, acquiring all power tasks participated by the power worker, evaluating the performance condition of each power task participated by the power worker, and determining whether the current task is an adaptive task of the power worker according to an evaluation result; constructing a task scheduling graph by taking the electric power personnel as a point and taking a relationship between the electric power personnel and the adaptive task as an edge, obtaining a task scheduling set to be distributed, and determining a scheduling scheme to be selected for distributing the task scheduling set to the electric power personnel according to the task scheduling graph; and inputting the to-be-selected scheduling scheme into a pre-trained classification model to predict a qualified category of the to-be-selected scheduling scheme so as to intervene in advance to reduce loss.

The present application relates to the technical field of energy security, and more particularly to a power distribution network elasticity improvement and joint optimization scheduling of repair personnel scheduling method and system, comprising: obtaining active power distribution network topology parameters, diesel generator and energy storage device parameters, renewable energy predicted output and load curve and numerical value; the post-disaster recovery model of active power distribution network is constructed in the guidance of elasticity, the adjustment strategy of distributed power generation resources is determined based on the topology reconstruction and island segmentation strategy method; the optimization scheduling model of repair personnel and network reconstruction coordination is constructed in the guidance of elasticity, and the second order cone relaxation method is used for relaxation processing to determine the fault repair strategy; through the active power distribution network topology parameters, the adjustable resource parameters and the renewable energy predicted output, the optimization problem is reconstructed based on the active power distribution network fault scene under the disaster; the optimization problem is solved with the minimum fault economic loss as the target, and the active power distribution network topology structure and the fault repair strategy are obtained.

This invention discloses a crew scheduling method, system, and equipment based on multi-dimensional fatigue prediction and TEM optimization, relating to the field of aviation operation control technology. It generates crew member capability parameters by acquiring multi-dimensional flight mission data and performing time alignment and correlation integration; obtains a demand summary result based on the TEM threat knowledge base mapping, calculates the coupling increment by combining the situation-coupled threat map, and forms a situation-enhanced demand summary result; synthesizes basic operational requirements and situation-enhanced requirements into comprehensive capability requirements, and dynamically adjusts them according to a phased dynamic amplification coefficient under unexpected events; generates vulnerability correction parameters based on crew operation adaptation data, and calculates situation-effective maneuverability and cognitive ability; obtains energy state parameters through a fatigue prediction model, defines resilience margin indicators based on situation-effective capability and comprehensive capability requirements, and constructs capability matching constraints to generate a scheduling plan; updates parameters and performs rescheduling and decision chain output when data changes are detected.

The application discloses a crew scheduling method, system and equipment based on multi-dimensional fatigue prediction and TEM optimization, and relates to the technical field of aviation operation control. Multi-dimensional data of a flight task is acquired, time alignment and associated integration are performed, and crew member capability parameters are generated. A demand summary result is obtained based on a TEM threat knowledge base mapping, a coupling increment is calculated in combination with a situation coupling threat graph, a situation enhanced demand summary result is formed, basic operation demand and situation enhanced demand are summarized to form comprehensive capability demand, and dynamic adjustment is performed according to a phased dynamic amplification coefficient under an unexpected event. A vulnerability correction parameter is generated based on crew operation adaptation data, situation effective operating capability and cognitive capability are calculated, an energy state parameter is obtained through a fatigue prediction model, a resilience margin index is defined based on situation effective capability and comprehensive capability demand, capability matching constraints are constructed, a scheduling scheme is generated, and parameters are updated when data changes are monitored, and rescheduling and decision chain output are performed.

The invention discloses a dynamically-partitioned canteen people flow density monitoring method and system, and belongs to the field of intelligent catering and people flow perception. According to the method, through multi-source sensor and video fusion acquisition, restaurant plane digitization, scoring modeling based on static / dynamic characteristics and constraint clustering dynamic partition; an improved space-time diagram neural network (including a dynamic adjacency matrix, diagram attention and context feature embedding) is used to predict table-level / district-level short-time passenger flow, and a full seat early warning and district priority scheduling instruction is triggered according to the table-level / district-level short-time passenger flow prediction method. According to the method, the short-term prediction precision and the space-time adaptability are improved, real-time partition adjustment is realized, cleaning and personnel scheduling are optimized, the manual intervention and operation cost is reduced, and the seat turnover rate and the service efficiency are improved.

The invention relates to the technical field of intelligent building and parking lot management, and discloses a parking lot operation and maintenance event automatic disposal and personnel scheduling method, which comprises the following steps: firstly, obtaining operation and maintenance event information in real time through various sensing devices; secondly, a dynamic priority calculation model is constructed, and the model integrates the type severity of the events, the potential influence range based on the equipment dependency graph and the time urgency, and gives quantitative priorities to the events; secondly, vectorizing task requirements of events and multi-dimensional state information such as capabilities, workloads and positions of operation and maintenance personnel, determining an optimal matching personnel by calculating a matching score, and performing a final scheduling decision in combination with a task preemption mechanism; and finally, the system performs closed-loop parameter self-optimization on the priority calculation model and the personnel ability model according to feedback data of task completion. According to the invention, quick response and intelligent accurate scheduling of the operation and maintenance event can be realized, and the resource allocation efficiency is remarkably improved.

This invention provides an intelligent scheduling and configuration method and system for shared vehicle maintenance personnel, relating to the field of shared vehicle technology. Based on a preset time period, it retrieves historical scheduling data of shared vehicles, constructs a scheduling task matching module and a scheduling path generation module, generates a vehicle maintenance scheduling model, obtains user vehicle demand applications and integrates them as batch target application information, generates a single set of scheduling tasks and multiple scheduling paths, wherein each single set of scheduling tasks corresponds one-to-one with the multiple scheduling paths, and achieves intelligent vehicle scheduling and maintenance based on the single set of scheduling tasks and multiple scheduling paths. This invention solves the technical problem that existing shared vehicle maintenance personnel scheduling and configuration methods cannot establish reasonable indicators based on the demand for shared vehicle resources in different times and spaces, leading to unreasonable allocation of maintenance personnel. It achieves multi-model coordination and optimization, performs optimal allocation of maintenance tasks, and achieves the technical effect of improving vehicle utilization and reducing the workload of maintenance personnel.

The invention provides a unit scheduling method, device and equipment and a storage medium, relates to the technical field of data processing, and is used for improving the flight scheduling efficiency. The method comprises the following steps: acquiring a function interface array of a flight sub-rule; the function interface array comprises a plurality of function interfaces, and one function interface corresponds to one flight sub-rule. Under the condition that a first function interface in the function interface array is called, obtaining a calling value corresponding to the first function interface; the first function interface is any function interface in the plurality of function interfaces. Under the condition that the calling value is the first numerical value, obtaining a first adaptation result corresponding to the first function interface; the first numerical value is a numerical value obtained after the first function interface is called, and the adaptation result is a first adaptation result obtained by performing adaptation on the employee information of the flight crew by the first function interface based on a flight sub-rule corresponding to the first function interface. And arranging flights for the flight crew based on the first adaptation result.

The present application relates to a kind of track traffic fast driver scheduling system and method, track traffic fast driver scheduling system includes space-time trajectory data analysis module, crew segment synthesis module and crew operation package distribution module.Space-time trajectory data analysis module is used to analyze space-time trajectory data, to obtain the running state information of train, with crew transfer station as boundary, and the running state of train is combined to divide diagram, obtain several crew segments.Crew segment synthesis module is used to synthesize several crew segments obtained as crew operation section.Crew operation package distribution module is used to synthesize several crew operation sections as anonymous crew operation package, each anonymous crew operation package corresponds to the all tasks of a driver in a day.It is through crew segment division, crew operation section generation, anonymous crew operation package generation, driver and operation package matching etc.Steps, realize the purpose of quickly preparing crew scheduling plan according to train operation plan.

This application relates to the field of intelligent adjustment and compilation technology for train crew routes, and provides a method and system for optimizing high-speed train crew scheduling plans based on heuristic algorithms. The method includes: generating virtual routes with consistent origin and destination stations by pairing routes in the route set where the origin and destination stations are inconsistent; solving the problem using a heuristic algorithm to obtain a single-cycle route sequence covering all routes once; and generating a crew scheduling plan based on the single-cycle route sequence. This method, through the design of a specialized route preprocessing and matching mechanism, intelligently pairs outbound and return routes to form virtual routes, and combines feasible connection rules and connection time cost matrices to transform the scheduling problem into a single-cycle route sequence optimization problem for heuristic solution. This not only ensures the feasibility of the scheduling plan, but also improves overall operational efficiency by optimizing pairing and compressing non-duty time such as outbound stops.

The invention relates to the technical field of airport analog simulation, in particular to an adaptive optimization method for airport dynamic simulation parameters based on SAC reinforcement learning, and the method comprises the steps: combining an SAC algorithm with an improved Transform model for multi-modal spatial-temporal feature extraction; static configuration and multi-time-frame real-time states of various business objects such as airport flights, airport positions, runways, guarantee vehicles and personnel are used as input, and parameters such as release intervals, sliding path priorities and resource allocation modes required by simulation are output through a strategy network; a strategy is continuously optimized in a simulation-real error closed loop by using a double-Q network and an entropy regularization mechanism, and key control parameters such as runway distribution, sliding path priority, release interval and guarantee vehicle and personnel scheduling are learned and adjusted online in real time from the simulation-real error closed loop; therefore, the simulation precision and the system robustness are ensured in a complex dynamic environment.

The invention relates to a unit scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a unit scheduling model; the crew scheduling model is constructed according to flight task data, crew personnel data and scheduling rule data; inputting the flight crew data and the flight task data into a prediction model to obtain the selection probability of the flight crew for the to-be-scheduled flight; according to the selection probability, adjusting a solution search space of the unit scheduling model to obtain an adjusted solution search space; and solving the unit scheduling model based on the adjusted solution search space to obtain a target unit scheduling scheme. By adopting the method, the scheduling efficiency and the scheduling result quality can be improved.

The application provides a multi-wall parallel construction scheduling modeling and intelligent scheduling method, and belongs to the technical field of intelligent scheduling, which considers the heterogeneity of construction period calculation, establishes a scheduling optimization model with the minimum construction period as the target and the number of each type of work as the decision variable, designs an intelligent scheduling algorithm for solving the optimization model based on a particle swarm algorithm, formulates special two-layer coding and decoding rules, uses the difference between the local historical optimum and the global historical optimum in a negative feedback mode to update the particle speed based on nonlinear time-varying inertia parameters, and further realizes position updating, and finally outputs the global optimal particle through multiple iterations to form a personnel scheduling scheme for multi-wall parallel construction. The application has the advantages of strong speciality, strong global search capability, fast search speed and the like, can provide a reasonable multi-wall construction scheduling scheme for construction progress management, maximally shortens the construction period under the condition of ensuring the construction quality, and reduces the construction cost from the perspective of human resource investment.

The application discloses a power distribution network recovery method and device considering switch uncertainty, a three-layer robust post-disaster recovery model based on a linearized Distflow model and a traveling salesman model, considers the failure of elements and remote switches in the process of post-disaster recovery, considers distributed generator rescheduling and network reconstruction technology, solves the repair personnel scheduling problem under the condition of uncertain fault type, and obtains a power distribution network recovery scheme applicable to all possible fault scenarios, reduces the maximum load loss that the power distribution network may suffer under the condition of incomplete fault information, in addition, the model only needs the power distribution network operator to predict the maximum number of various types of faults according to historical data, reduces the required information, effectively reduces the added scene and lightens the calculation burden, can find a global optimal solution in a short time, improves the efficiency of post-disaster recovery of the power distribution network, and provides an efficient tool for power distribution network recovery under the condition of incomplete fault information.

The invention discloses an automatic scheduling method and device for a unit, and belongs to a task scheduling method.The method comprises the steps that task ring information and unit resource information are read, the task ring information comprises flight tasks and on-duty days, and the unit resource information comprises a unit member set and personnel positions corresponding to the unit member set; expanding posts for the task ring information according to personnel configuration standards in the unit resource information; the restriction rule and the constraint condition are converted into corresponding data structures, and a set coverage model is established. Manual operation is replaced with an automatic unit scheduling algorithm, cost loss caused by lack of manual service experience is reduced, scheduling efficiency and unit operation quality can be improved, better attendance experience is provided for crew personnel, and improvement of overall benefits of airline companies is assisted.

The application relates to a substation inspection scheme planning method for an extreme load scene, which comprises the following steps: constructing a substation inspection model fusing a value-keeping scene; proposing a dynamic updating mechanism of an inspection path adapting to the change of the number of stations; establishing a time-cost collaborative planning model considering resource constraints to generate an optimal inspection scheme considering the inspection time length and fuel cost; defining work length, station inspection proportion, overtime intensity, work frequency and fatigue degree as work quantity evaluation indexes, constructing an inspection task allocation model, and optimizing and solving the substation inspection scheme with the most balanced work quantity allocation. The application constructs a multi-dimensional work quantity evaluation index system and an inspection task allocation model, realizes the balanced allocation of work quantities among various inspection teams, and provides a scientific decision basis for personnel scheduling in operation and maintenance management.

The invention discloses a temporary employment scheduling and community autonomy method and device based on a block chain, and a storage medium. The method comprises the steps of performing data mining on historical service data when service personnel serve a community based on a data mining and machine learning algorithm to obtain a service knowledge rule, and constructing a knowledge base according to the service knowledge rule; setting an optimization problem and constructing an intelligent optimization scheduling model based on knowledge driving based on the optimization problem; obtaining a to-be-scheduled service order task, and obtaining a service personnel order receiving allocation scheme of the to-be-scheduled service order task according to the intelligent optimization scheduling model based on knowledge driving and all service personnel registered in the system, so as to realize order receiving of the to-be-scheduled service order personnel; and meanwhile, the order receiving record of the service order task to be scheduled is uploaded to the block chain. According to the invention, the problems of difficulty in personnel identity verification, poor personnel scheduling efficiency and the like in the existing resident autonomous cross-community process can be solved.

The application discloses a high-speed rail business travel service personnel scheduling method based on data analysis, and relates to the technical field of personnel scheduling management. The method comprises the following steps: data collection and integration: collecting high-speed rail station operation data, passenger information, service personnel information and environment data; performing stream data processing and space-time alignment on the collected data, and constructing a dynamic main data matrix; service demand prediction: extracting passenger multi-dimensional features from the dynamic main data matrix, and generating passenger multi-dimensional feature labels; based on the passenger multi-dimensional feature labels, combining environment data, performing bimodal demand prediction, and obtaining initial passenger service demand. Through the establishment of the bimodal service demand prediction method, the extraction of passenger multi-dimensional features, the combination of environment data and the prediction of passenger demand, the application realizes the transition from passive response to active prediction, accelerates the response speed, reduces the waiting time of passengers and improves the service quality and efficiency.

The purpose of the present invention is to propose a subway crew scheduling optimization based on a two-stage generation-selection algorithm, which relates to the technical field of urban rail transit operation planning. This application divides the preparation of subway crew scheduling into two stages: "generation-selection". For the generation stage, a spatiotemporal state network is constructed, the crew task generation is converted into a path search problem, and an improved Floyd algorithm is designed to generate a large number of alternative crew tasks; for the selection stage, an improved set covering model is constructed, and a greedy algorithm and a genetic algorithm are designed for solving the problem. Finally, the Chengdu Metro Line 5 is used as an example for verification. The results of the example analysis show that compared with the manual preparation scheme, the preparation method proposed in this paper has a significant optimization effect, with the total number of crew tasks reduced by 14.48%, the total on-duty time of crew tasks reduced by 9.39%, and the total connection time reduced by 15.79%.

The application provides a rail transit crew scheduling optimization method based on a space-time connection network. The method comprises the following steps: dividing a rail transit crew segment, generating a crew work shift according to the crew segment, constructing a scheduling plan space-time connection network based on the crew work shift, establishing a target function of a rail transit crew scheduling plan optimization model based on the scheduling plan space-time connection network, and solving the target function of the rail transit crew scheduling plan optimization model through a Lagrange relaxation algorithm to obtain a rail transit crew scheduling plan. The method divides a rail transit diagram according to a vehicle depot and a value station, combines the generated crew segments with each other to obtain corresponding crew work shifts, constructs a scheduling plan space-time connection network, establishes a rail transit crew scheduling plan optimization model, and finally designs a Lagrange relaxation algorithm to solve the model, so that the rail transit crew scheduling plan can be effectively optimized.

The invention provides a robot cargo scheduling method and system based on artificial intelligence, relates to a cargo scheduling technology, and can collect dynamic point locations of cargoes and temporary point locations of personnel through an unmanned aerial vehicle and monitoring equipment, so that the cargoes can be accurately positioned, and personnel scheduling requirements can be timely obtained. In addition, various devices such as transportation devices, monitoring devices and unmanned aerial vehicles in the cargo scheduling process can be linked, the distribution efficiency of scheduling tasks is improved, automation and intelligentization of the whole cargo scheduling process are achieved, and the cargo scheduling efficiency is improved.

This invention discloses a low-disturbance-impact method for railway passenger service scheduling, comprising: generating several initial scheduling plans sequentially using a greedy algorithm constrained by scheduling rules; using these initial scheduling plans as the initial population for a particle swarm optimization algorithm; globally optimizing the initial scheduling plans with scheduling rule satisfaction and disturbance impact indicators as optimization objectives to obtain a baseline passenger service scheduling scheme; acquiring sudden impact events; performing semantic extraction and credibility calculation on the sudden impact events to obtain structured event objects, and converting them into constraint objects; extracting the set of affected resources and the set of affected task links from the constraint objects; determining the repair area based on the set of affected resources and the set of affected task links; using the baseline passenger service scheduling scheme as the initial value, performing a local update search based on task relationships within the repair area to generate candidate solutions in the domain; selecting the optimal feasible local solution with minimal disturbance to update the baseline passenger service scheduling scheme.

The invention provides an aircraft landing taxiing time prediction method, a computer program product and a system, positioning data of a third-party flight tracking system are accessed in real time through a cloud message queue priority mechanism, and intelligent matching optimization positioning is performed in combination with flight information; automatically identifying and correcting geographical drift in the positioning data through a drift correction algorithm, namely a composite model of Kalman filtering and moving average, and obtaining accurate trajectory data; then, a high-frequency path priority model is adopted to perform clustering analysis on a landing sliding path under historical similar conditions, and a high-frequency path is dynamically weighted and selected in combination with a real-time multi-machine conflict state; and finally, predicting the taxiing time based on track matching of a high-frequency path, specifically, comparing an actual path with a predicted path through a dynamic time warping algorithm so as to adjust the predicted path in real time and accurately calculate the corresponding residual taxiing time, thereby improving the reliability and real-time response capability of overall prediction, and improving the prediction accuracy. And the flight guarantee intelligence and the personnel scheduling efficiency are improved.

The embodiments of the present disclosure provide methods and systems for scheduling operation and maintenance personnel based on an Internet of Things (IoT) system for smart gas installation management. The method may include: obtaining user installation information of at least one operation and maintenance area; determining target installation information of the at least one operation and maintenance area; determining a door-to-door service plan for the at least one operation and maintenance area; obtaining collection data of a gas device configured in the operation and maintenance area; determining a count of on-call personnel of the at least one operation and maintenance area; determining a scheduling capability value of the at least one operation and maintenance area; determining a real-time scheduling instruction; and in response to compensation time off information, updating the scheduling capability value, and adjusting the real-time scheduling instruction.