109results about How to "Reduce idling" patented technology

The invention discloses a logistics park vehicle path planning method and system, and the method comprises the steps: obtaining the real-time position and task state information of a vehicle in a park, dynamically constructing a vehicle path planning network model, dividing a road into nodes and edges, and forming a directed weighted graph; according to the directed weighted graph, an improved multi-lane algorithm is adopted, the path length, the congestion degree and the vehicle characteristic factors are comprehensively considered, and the optimal path of each vehicle is calculated; for the optimal path, introducing a virtual lane and a time dimension, pre-judging occupation conditions of multiple vehicles on the virtual lane for each time slice, and constructing a conflict constraint matrix; obtaining park high-precision map data, extracting road network topology, lane lines and traffic sign marking elements, performing lane-level refinement on the optimal path, and generating a driving track; and through a multi-sensor fusion positioning technology, obtaining high-precision position and attitude information of the vehicle, matching the high-precision position and attitude information with the driving track, and calculating a track tracking error.

The invention discloses an AGV task allocation method and system, and relates to the technical field of automatic navigation trucks, and the method comprises the steps: carrying out the initialization of AGV state information; acquiring environment information in real time through ground sensor equipment; performing priority evaluation on the task instructions, and adjusting task priorities in real time according to emergencies; task merging and splitting are carried out; task allocation is carried out by using an ant colony algorithm, and a task allocation result is output to the corresponding AGV to guide the AGV to execute operation according to the allocated task; an optimal path is planned for each AGV, and path planning is dynamically adjusted in the task execution process of the AGVs; and optimizing a task allocation algorithm by adjusting a pheromone volatilization coefficient. Limited AGV resources are reasonably scheduled by arranging a task instruction obtaining module, a task information processing module and a task planning and distributing module, high environment perception and adaptability are achieved, and task priorities and path planning can be rapidly adjusted.

The invention discloses a game theory improved model-based cloud computing node storage calculation training resource competition scheduling method. The method comprises the following steps of S1, constructing a global resource scheduling system; s2, carrying out initial division on the cloud computing nodes in each logic partition; s3, executing a local resource competition game model; s4, the global resource scheduling system collects a local resource competition result of each logic partition and residual available resources in each logic partition; s5, executing the global collaborative game model; s6, dynamically updating the resource state information of the cloud computing node; s7, the global resource scheduling system performs adaptive adjustment on resource demand strategies in the local game model and the global cooperative game model; and S8, the global resource scheduling system stores and analyzes past resource competition results and task execution conditions, and each cloud computing node updates a resource demand strategy according to the data. According to the invention, the problems of resource bottleneck and overload in the prior art are solved, and the resource utilization efficiency and the scheduling flexibility are greatly improved.

The invention relates to the technical field of production scheduling of manufacturing workshops, in particular to a distributed intelligent production scheduling decision-making system based on cloud computing. The system is composed of a cloud computing platform, a data acquisition module, a data processing module, an intelligent production scheduling engine and a distributed execution control module. The problems of performance bottleneck, data acquisition limitation, lack of flexibility and adaptability of a production scheduling scheme and the like in a traditional production scheduling system are solved, intelligent distribution and scheduling of production tasks can be realized through fusion application of advanced technologies such as cloud computing, big data and artificial intelligence, the production efficiency and the product quality are improved, and the production cost is reduced. And greater economic benefits and social benefits are created for enterprises.

The invention relates to the technical field of freight scheduling, in particular to a freight scheduling information management method and system and a storage medium. The method comprises the following steps that uncompleted order collection is carried out on a freight platform to obtain an uncompleted order set, and the uncompleted order set comprises a plurality of uncompleted orders; each uncompleted order at least comprises an order ID, order release time, target cargo attribute data, publisher identity data, a departure place, a destination, an order state identifier, an order transportation task schedule, cargo stacking attributes, predicted delivery time and cargo expected arrival time; and performing order state classification and marking on the uncompleted order set to obtain a classified order set. By optimizing the loading space utilization rate of the vehicle, the unloaded and half-loaded conditions of the vehicle can be reduced, so that the fuel consumption and the transportation cost are reduced.

The invention relates to the technical field of inter-city cargo splicing and loading, in particular to an inter-city cargo splicing and loading transportation path optimization algorithm based on large model optimization, and solves the problems that in the prior art, an optimization algorithm based on a large model generally relates to a complex calculation process and needs a large amount of calculation resources and time, and especially in a real-time path planning scene, the calculation process is complicated. And the calculation delay can influence the transportation efficiency. The invention discloses an inter-city cargo splicing transportation path optimization algorithm based on large model optimization. The algorithm comprises data collection and preprocessing, model construction and training, algorithm optimization and verification, path optimization calculation, result evaluation and feedback, and algorithm deployment and application. According to the method, various possible transportation paths are analyzed and compared through an algorithm, the optimal path is selected, unnecessary detour and waiting time is reduced, so that the transportation efficiency is improved, and the algorithm can perform intelligent scheduling and reasonable arrangement of transportation vehicles and routes according to real-time traffic conditions, cargo demands, vehicle states and other information.

The invention discloses an intelligent scheduling method based on port bulk cargo transfer. The method comprises the steps of S1, obtaining related information of bulk cargos to be transferred and related information of transport vehicles; s2, matching transport vehicles of corresponding types; s3, calculating a transfer train number; s4, calculating the number of transport vehicles; when the number of the transport vehicles is one, executing the step S5; when the number of the transport vehicles is two or more than two, executing the step S6; s5, generating transfer scheduling planning information according to the loading position of the bulk cargo to be transferred and the departure position of the corresponding transport vehicle; s6, determining the departure sequence of the transport vehicles: for the first departure transport vehicle, calculating corresponding transfer scheduling planning information based on the step S5; and for the second transport vehicle and the subsequent departure transport vehicle, corresponding transfer scheduling planning information is generated according to the transfer scheduling planning information of the previous transport vehicle. According to the invention, the accuracy, orderliness and collaboration of bulk cargo transfer scheduling can be improved.

The invention discloses a running vehicle number prediction method, system, device, medium and program based on an SARIMA-RF combined model, and belongs to the technical field of railway freight volume prediction. The method comprises the following steps: acquiring the monthly data of the running condition of a railway international combined transport train to form a train running condition monthly data set; utilizing an SARIMA model to fit a running vehicle number sequence in the monthly data set of the running condition of the class, and obtaining a predicted value sequence and a residual sequence; fitting the residual error sequence by using an RF model to obtain a prediction result; and combining the predicted value sequence of the SARIMA model with the prediction result of the RF model to obtain a final prediction result of the SARIMA-RF combined model. According to the method, the accuracy of the prediction result of the running number of the international railway intermodal trains is improved, and the running uncertainty of railway transportation and the running uncertainty of the international railway intermodal trains are reduced.

The invention discloses an unmanned aerial vehicle arrival and departure sorting method based on an FCFS algorithm and reinforcement learning, and the method comprises the steps: enabling an unmanned aerial vehicle to enter a waiting layer of a terminal region, creating an initial execution time table, and obtaining the initial data of the unmanned aerial vehicle; obtaining an initial state matrix; determining a to-be-executed action set of the unmanned aerial vehicle based on the airport operation limiting condition; calculating a reward value of each to-be-executed action in the to-be-executed action set; the unmanned aerial vehicle executes the to-be-executed action with the maximum reward value; repeating the above steps until the accumulated reward value is maximum, and obtaining the sequence of the to-be-executed actions corresponding to the accumulated reward value; optimizing the optimal time execution table based on the sorting of the actions to be executed and the sorting result of the FCFS algorithm; and the unmanned aerial vehicle executes corresponding actions according to the optimal time execution table. According to the optimized take-off and landing sequence, the utilization rate of the take-off and landing channels can be effectively improved, the situation that the take-off and landing channels are idle or congested is reduced, and the safety risk caused by collision of the unmanned aerial vehicles is reduced to a great extent.

The invention discloses a self-adaptive traffic prediction and dynamic network optimization system, and relates to the technical field of automatic management of cloud computing networks. Comprising the following steps: step 1: carrying out data collection and processing, step 2: carrying out model training and constructing a prediction engine by utilizing a traffic prediction model, step 3: carrying out real-time monitoring and prediction, and step 4: dynamically adjusting a routing table and optimizing a routing path by utilizing a routing optimization algorithm according to predicted traffic data. The method comprises the following steps of: (1) predicting traffic data, dynamically distributing network loads to different servers or nodes according to the predicted traffic data by utilizing a load balancing algorithm to balance resource use, (5) carrying out decision support and automation: providing decision support, recommending configuration change and automatically executing the configuration change when necessary, and (6) carrying out feedback and iteration: continuously collecting effect data after execution, and performing iterative optimization on the flow prediction model fed back to the prediction engine.

The invention discloses an unmanned aerial vehicle equipment access management and scheduling method, and relates to the technical field of cloud computing and big data. Comprising the following steps: step 1, creating a standardized access interface of unmanned aerial vehicle equipment; 2, collecting data in real time in the task execution process of the unmanned aerial vehicle equipment, preprocessing the collected data, comprehensively processing the preprocessed data from different unmanned aerial vehicle equipment by adopting a data fusion algorithm, and generating a state model of the unmanned aerial vehicle equipment; the method comprises the steps of 1, analyzing current available unmanned aerial vehicle equipment based on a state model by using an optimization algorithm, and dynamically adjusting resource allocation of the unmanned aerial vehicle equipment to execute a corresponding task, 4, monitoring execution state information of the unmanned aerial vehicle equipment in real time and obtaining feedback information, and 5, after the task is completed, evaluating and analyzing the unmanned aerial vehicle equipment and generating a task report.

The invention provides a distributed computing system based on pre-scheduling resource collaborative adaptation, and relates to the technical field of resource collaborative allocation systems. The distributed computing system based on pre-scheduling resource collaborative adaptation aims at solving the problems that in the prior art, resource scheduling is not flexible, and application configuration and resource scheduling are lack of coordination. By introducing a dynamic scheduling mechanism of the resources, organic combination of resource pre-scheduling and flexible allocation is realized. Meanwhile, through cooperation of application configuration and resource scheduling, optimal distribution and execution of calculation tasks are achieved, the system is composed of a server side and a client side, the client side has pre-scheduling resources and is used for feeding back the resource state in real time and receiving and executing the calculation tasks, and the server side is responsible for monitoring and scheduling of global resources and task distribution. And through a designed heuristic collaborative adaptation algorithm, the server can dynamically schedule resources in the pre-scheduling resources, and high efficiency and stability of task execution are ensured.

The invention discloses a warehouse logistics scheduling system and method based on big data analysis, relates to the technical field of logistics scheduling, and solves the problems of lack of accuracy of personnel scheduling and serious resource waste of a traditional logistics scheduling system. The system can perform personnel allocation based on the emergency degree of the order, the order quantity and the arrival time of the vehicle; by calculating the arrival time, the order quantity and the emergency order proportion of each vehicle, the system can reasonably arrange workers to carry out unloading operation according to the priorities of different vehicles; the mode based on big data analysis not only can ensure that the emergency order is processed in time, but also can avoid excessive scheduling and reduce the waste of human resources; compared with a traditional mode of arranging workers through experience or simple pre-estimation, the automatic scheduling mode has the advantages that the accuracy of personnel scheduling is remarkably improved, and idle workers or overload work caused by improper scheduling is reduced.

The invention relates to the technical field of computers, and provides a capacity elastic scaling method and a related device, which are used for storing the resource utilization rate, and the method comprises the following steps that: a main node respectively matches the measurement information of each logic storage unit in a cluster with a scaling rule set associated with each storage logic unit, and when the matching succeeds, the scaling rule set of each storage logic unit is matched with the measurement information of each logic storage unit; and adjusting the storage capacity of the corresponding logic storage unit according to the target capacity contained in the matched target scaling rule. According to the measurement data of the logic storage unit, the capacity of the logic storage unit is elastically expanded and contracted.

The invention discloses an intelligent logistics park vehicle scheduling optimization system. The system comprises a vehicle positioning module, a data analysis module, a monitoring module, a vehicle scheduling optimization module and a communication module. The vehicle positioning module is used for acquiring position information of each vehicle in the intelligent logistics park in real time; the data analysis module is used for analyzing the position information of the vehicle in real time to obtain a vehicle scheduling plan; the monitoring module is used for monitoring the running state of vehicles in the intelligent logistics park in real time; the vehicle scheduling optimization module is used for carrying out scheduling optimization on the vehicles in the intelligent logistics park according to the vehicle scheduling plan and the running states of the vehicles; and the communication module is used for connecting communication equipment between the intelligent logistics park vehicle management system and the vehicle to realize real-time instruction transmission and vehicle information feedback. According to the invention, the efficiency and accuracy of logistics park vehicle management and scheduling can be improved, and a better traffic safety guarantee is provided.

The invention relates to the technical field of medical data analysis, in particular to an orthopedic treatment data analysis method and system, and the method comprises the following steps: analyzing demand data based on operation data, evaluating heterogeneous computing resources, enabling the processing capability and the response time to correspond to the analysis demands of operation time and rehabilitation progress data, and obtaining an analysis result; and determining the dependency degree of multiple tasks on the computing resources, and generating a resource demand matching table. According to the method, the heterogeneous computing resources are evaluated to match operation time and rehabilitation progress data requirements, the resources are accurately configured, each task can be matched with the optimal computing resource, the resource utilization rate and the data processing efficiency are improved, the task execution sequence and the resource utilization are dynamically optimized, resource vacancy and task delay are reduced, and the operation efficiency is improved. The response speed and accuracy of data processing are improved, the performance of differentiated resources is monitored and analyzed in real time, key resources are effectively identified and reconfigured, the flexibility and expandability of data processing are enhanced, and the high efficiency of orthopedic data analysis is ensured.

The invention provides an intelligent building method for a parameterized three-dimensional power grid frame, and relates to the technical field of electric power, and the method comprises the steps of first collection, second collection, third collection, model construction, demand prediction, power grid division, first setting, architecture construction and the like. The charging demand fluctuation condition of the electric vehicle can be dynamically monitored, the power grid is divided by adopting the clustering algorithm according to the charging demands and regional characteristics of different regions, the load center is set based on the power grid division result, and the power grid architecture is constructed. The constructed power grid architecture can better cope with the conditions of power load fluctuation and power load regional difference caused by popularization of electric vehicles, the dynamic response capability of the power grid is enhanced, and the stability of the power grid is improved.

The present invention discloses a method for processing matrix data, including: obtaining matrix data to be processed and weight matrix data of a recursive neural network; matching element parallel parameters and vector parallel parameters corresponding to the scale parameters according to the scale parameters of the recursive neural network; dividing the weight matrix data into blocks according to the element parallel parameters and the vector parallel parameters to obtain weight vector blocks; multiplying the weight vector blocks with the matrix elements to be processed to obtain a first processing result; configuring an addition tree tail according to the element parallel parameters, accumulating the first processing result through an addition tree including the addition tree tail to obtain a second processing result, and outputting the second processing result as the processing result of the matrix data to be processed. The utilization rate of hardware resources is improved, the slicing strategy can be made more flexible, and it can be applied to recursive neural networks of various sizes.

The invention relates to the technical field of garbage treatment, and discloses an intelligent garbage recycling and garbage can cleaning, disinfecting and drying system which comprises a vehicle body for garbage transportation, a mechanical overturning module for controlling the position of a garbage can is arranged on the vehicle body, and a cleaning bin and a garbage bin are arranged on the vehicle body. The mechanical overturning module controls garbage in the garbage can to be poured into the garbage bin, the mechanical overturning module controls the garbage can to be located in the cleaning bin to be cleaned, and a filtering module used for filtering and recycling sewage is arranged in the cleaning bin. The cleaning and disinfection strategy is adjusted, the working efficiency and quality are improved, intelligent management, efficient cleaning and disinfection, environmental protection, energy conservation and data monitoring are achieved, convenience is brought to urban environmental sanitation work, urban environmental sanitation management is promoted to advance towards the intelligent, green and sustainable development direction, and the intelligent, green and sustainable development of urban environmental sanitation management is promoted through path planning. The vehicle can complete the cleaning task of the designated garbage can in the shortest route and the least time, and unnecessary round trip and empty driving are reduced.

This invention provides a robust resource scheduling method, apparatus, and device for satellite communication systems, relating to the field of wireless communication technology. The method includes: acquiring real-time environmental data and the channel coefficients at the current moment; determining the channel parameters corresponding to the real-time environmental data using an environment-parameter correspondence table, the channel parameters including the channel time correlation coefficient and the innovation process variance; calculating the statistical characteristics of the channel coefficients at the time to be transmitted based on the channel coefficients and channel parameters at the current moment; constructing a probabilistic constraint relationship using a one-sided Chebyshev inequality based on the statistical characteristics and a preset confidence parameter, calculating the minimum channel carrying power gain at the time to be transmitted that satisfies the preset probability guarantee; and performing resource scheduling based on the minimum channel carrying power gain at the time to be transmitted using a robust proportional fair scheduling algorithm. This invention can improve resource scheduling efficiency.

The present invention discloses a cloth dyeing machine with a double-circulation operation structure, comprising a head, a tail, a body and a cloth guide pipe, wherein a cloth storage tank is arranged in the body, the cloth guide pipe is located at the lower side of the body, and one end is connected to the head, and the other end is connected to the tail; a cloth guide structure is arranged in the head, and the cloth guide structure comprises a cloth guide wheel and a detachable arc-shaped guide pipe, and when the arc-shaped guide pipe is installed in the head, one end opening of the arc-shaped guide pipe is connected to the cloth guide pipe; and a first nozzle is also included, and the first nozzle is used to spray dye liquid toward the cloth guide wheel, or to spray dye liquid toward the arc-shaped guide pipe. The present invention provides a detachable arc-shaped guide pipe, which is practically used in conjunction with the cloth guide wheel, so that the cloth dyeing machine of the present invention can cover all applicable cloth types in existing cloth guide wheel models and cloth guide wheel-free models, that is, one machine achieves the functions of two machines, greatly reducing idleness.

The present invention provides a method and device for allocating manufacturing tasks for multimodal transport under multi-factor constraints, which solves the problem that it is difficult to consider the optimization of resource allocation from a comprehensive perspective, so there are problems such as low efficiency and unreasonable service resource allocation in task allocation. The method for allocating manufacturing tasks for multimodal transport under multi-factor constraints includes: obtaining order requirements, decomposing the order requirements into multiple task requirements; obtaining the time cost and economic cost of completing each task based on the task requirements; obtaining the total time cost and total economic cost of completing the order based on the time cost and the economic cost of all tasks; optimizing the time cost and the economic cost of each task and the total time cost and the total economic cost of the order to perform resource allocation.

The application discloses a kind of urban sanitation vehicle collaborative perception management systems based on Internet of Things, and the application relates to the technical field of sanitation cleaning, lack of system analysis and mining of historical cleaning and transporting data, and the problem that the characteristics of garbage generation interval of each single route cannot be accurately mastered is solved, the application is based on the dynamic programming logic of the total amount of operation by combining analysis processing center, the route combination satisfying the requirement of transport capacity is filtered, and the optimal cleaning and transporting route is determined with the shortest travel distance as the core sorting principle, which effectively reduces the invalid driving mileage of sanitation vehicle, such as empty driving and detour, improves the cleaning and transporting efficiency of single sanitation vehicle per unit time;At the same time, through the scientific combination of multiple groups of to-be-planned routes, it is ensured that the transport capacity of sanitation vehicle is fully utilized, the resource waste phenomenon of part of vehicles overloading and part of vehicles idling is avoided, and the balanced allocation of regional sanitation vehicle transport capacity is realized.

The invention discloses a cloud edge hierarchical collaborative information system integrated resource scheduling system. The system comprises an acquisition module. Data preliminary cleaning and feature extraction are locally executed through the edge end, only key feature data are uploaded, network transmission quantity is reduced, cloud computing pressure is relieved, real-time tasks can respond quickly, task timeout caused by transmission delay is avoided, feature vectors of the edge end and the cloud end are dynamically analyzed based on a scheduling decision model of reinforcement learning, and the real-time performance of the edge end is improved. Task requirements and resource states are intelligently matched, the scheduling accuracy is improved, model parameters are dynamically updated through task execution state data (such as duration and resource utilization rate deviation), the model is adaptively optimized along with environment changes (such as edge end hardware aging and cloud load fluctuation), the efficient scheduling capacity is kept for a long time, and the scheduling efficiency is improved. Layered processing and intelligent scheduling avoid edge end overload or cloud end resource idling, resource allocation of a CPU, a memory and the like is optimized, resource waste is reduced, and the system collaboration efficiency is integrally improved.

The invention discloses an NVMeoF far-end storage system and method based on GPU direct command control, an I / O processing task in an NVMeoF environment is clearly divided into two modules, namely a GPU processing NVMe command module and a CPU processing network protocol module, cooperation of the two modules is realized through a doorbell notification mechanism, a CPU is enabled to concentrate on the skilled network protocol processing, and the NVMeoF remote storage system and method based on GPU direct command control are realized. The GPU is enabled to concentrate on the skilled NVMe command generation and data processing, so that the load of the CPU is remarkably reduced, an end-to-end zero-copy data path from remote NVMe storage to a GPU video memory is constructed, unnecessary intermediate data copy is eliminated, and the data throughput is maximized. According to the invention, in an NVMeoF environment, approximate localization low-delay and high-bandwidth access of the GPU to the remote NVMe storage device can be realized.

The application discloses a multi-layer strip-shaped low-altitude unmanned aerial vehicle continuous coordination release traffic control system and method. The application relates to the technical field of low-altitude traffic control, and aims at the problems of coarse strip-shaped low-altitude airspace resource description, lack of coordination in continuous node release, and window mismatch and air route conflict in existing low-altitude traffic control. The application provides the following scheme: obtaining strip-shaped airspace operation state and unmanned aerial vehicle traffic demand, generating low-altitude airspace resource units and traffic permission windows, coordinating adjacent node release phase difference based on predicted traffic time, forming a low-altitude continuous traffic flight wave, and generating unmanned aerial vehicle low-altitude traffic control instructions. The application is suitable for low-altitude traffic management platforms, unmanned aerial vehicle operation control systems and take-off and landing field control units.

The invention relates to a charging demand prediction method based on regional distribution of user portraits, and belongs to the technical field of electric power. The method comprises the following steps: collecting geographical location information and charging behavior data of a user, and preprocessing the collected data; analyzing the preprocessed data, and creating a portrait of the user by using the charging place, the charging frequency, the charging time and the charging electric quantity of the user; carrying out regional distribution analysis on the user portraits, and researching user charging demands and behavior modes of different regions; and predicting the charging demand of the specific user group based on the created user portrait and the regional distribution analysis result. According to the invention, through the analysis of the user portrait and the regional distribution, the future charging demand can be predicted more accurately, and the situation of resource waste or short supply caused by inaccurate estimation is effectively avoided.

The utility model provides a kind of adjustable filter outer frame, including outer frame, outer frame includes a pair of left and right sides oppositely arranged frame body, connecting piece is arranged between two side frame bodies relative upper and lower sides, connecting piece is detachably connected with frame body and encloses outer frame;Each frame body includes the bottom plate extending along length direction, the two sides of bottom plate length direction are bent to form fixed plate respectively, fixed plate is equipped with first connecting hole, air hole and scale, connecting piece is used to connect the upper and lower sides of air filter medium relative;Through scale and the design of cuttable frame body, the size of air filter can be adjusted according to installation environment, improve versatility, avoid the installation difficulty or resource waste caused by size inconsistency, can be applicable to a variety of installation environments, reduce the idle and inventory of multi-size customization, and improve the efficiency of air filtration and outer frame practicality.

The invention discloses a bandwidth management method and device based on a multi-wave WDM-PON system, equipment and a medium, and relates to the technical field of optical communication. The method is applied to an optical line terminal OLT in a multi-wave WDM-PON system, the single-port MAC bandwidth of the OLT is divided into a plurality of bandwidth time slots SLOTs, and the method comprises the following steps: distributing each SLOT to a plurality of supported wavelength channels; and when valid data are written into the SLOT, performing time slot allocation on the SLOT based on the valid data. By implementing the technical scheme provided by the invention, the overall bandwidth utilization rate of the multi-wave WDM-PON system can be improved, and the system can utilize available bandwidth resources more efficiently, so that the problem of low utilization rate caused by unbalanced bandwidth resource allocation in the prior art is solved.

This application relates to a logistics sorting method, electronic device, computer-readable storage medium, and computer program product, belonging to the field of logistics sorting technology. The method includes: acquiring the delivery time information of each package in a set of packages to be sorted; the delivery time information of each package is used to characterize the sorting start time of each package; based on the delivery time information of each package, determining a target sorting method corresponding to the set of packages from preset sorting methods; the preset sorting methods include at least static sorting and dynamic sorting; and sorting each package based on the target sorting method. This method can improve the utilization rate of sorting compartments.