82 results about "Packing algorithm" patented technology

The invention discloses a method for scheduling virtual machines, which belongs to the field of computer networks. The method comprises the following steps of: 1) running a physical machine monitor on each physical server for regularly collecting loads of all virtual machines and sending the loads to a virtual machine scheduler, and receiving and executing instructions sent by the virtual machine scheduler; 2) regularly judging the virtual machines with load data changes and the physical server where the virtual machines are positioned by the virtual machine scheduler; 3) adjusting the virtual machines with load data changes by the virtual machine scheduler by using bin packing algorithms to obtain the target corresponding relationship of the virtual machines and the physical servers; 4) comparing the current corresponding relationship with the target corresponding relationship of the virtual machines and the physical servers by the virtual machine scheduler to generate a virtual machine scheduling plan; and 5) scheduling the virtual machines by the physical machine monitor according to the virtual machine scheduling plan. Compared with the prior art, the invention has the effect of load balance and can also make the physical servers in an idle state dormant and further reduce the energy consumption.

The invention provides a server integration method oriented to minimum energy consumption. The server integration method oriented to minimum energy consumption includes that resource states and performance data of servers and virtual machines on the servers are periodically obtained, and meanwhile, energy consumption of the servers is periodically measured by an external-connected wattmeter on a physical server to be stored; resource state data of the servers, resource state data of the virtual machines, performance data of the servers and the performance data of the virtual machines are periodically collected, and data pre-processing is performed; a server energy consumption model is established; a virtual machine transfer cost prediction model is established; transfer cost prediction value of each virtual machine is obtained; virtual machine comprehensive assessment is performed by means of an improved analytic hierarchy process; service stability index of the servers is calculated; a server integration scheme is determined; server integration is performed. According to the server integration method oriented to minimum energy consumption, the virtual machines are transferred to proper servers by means of a dynamic packing algorithm according to virtual machine resource required quantity and server resource surplus, and the number of starting servers is the minimum under the condition of stable service operation.

Methods and systems for allocating address space resources to resource requesting peripheral devices in an efficient manner. Resource request are gathered for enumerated peripheral devices host by a computer platform. A map containing resource alignment requirements is built, and a virtual resource allocation map is computed based on aggregated resource requests and the alignment requirements. The resource aggregations are, in turn, based on a hierarchy of the peripheral devices. A bin-packing algorithm is employed to determine allocation of the resource requests so as to minimize resource address space allocations. The virtual resource map is then used to perform actual resource allocations. The resources include peripheral device I / O address allocation and peripheral device memory address allocations.

The invention provides a lithium ion battery remaining life prediction method based on a wolf pack optimization LSTM network, and relates to the technical field of lithium ion batteries. The method comprises the following steps: firstly, acquiring monitoring data of a lithium ion battery, and extracting lithium ion battery capacity data from the monitoring data; determining a long short-term memory network structure, and constructing an LSTM-based lithium ion battery remaining life prediction model; secondly, optimizing key parameters in the lithium ion battery remaining life direct predictionmodel by utilizing a wolf pack algorithm to obtain a direct prediction model based on a wolf pack optimization LSTM network; determining an optimal lithium ion battery remaining life direct prediction model by using the optimization data; and finally predicting later-stage lithium ion battery capacity data by using the optimal lithium ion battery residual life direct prediction model. According to the lithium ion battery remaining life prediction method based on the wolf pack optimization LSTM network provided by the invention, the remaining life of the lithium ion battery can be accurately predicted.

The invention discloses an optimization method of workshop material distribution considering packing constraints, which comprises the following steps: according to load, volume, directivity and stability constraint, a three-dimensional box-packing model in the process of workshop material transportation and loading is constructed with the objective function of maximizing the space utilization ratio of the carriage; Utilizing the maximum vehicle loading capacity of the packing algorithm, the number of carriages needed to be loaded is generated, and the number of distribution routes is constituted. To optimize the routing of each vehicle, a workshop vehicle routing planning model is built to adapt to the actual production and distribution, taking the lowest cost of distribution as the objective function. The invention solves the problem that the traditional manual experience distribution can not meet the production demand, effectively avoids the phenomenon that the materials on the distribution path can not be loaded, and can be applied to the workshop material distribution to improve the loading rate of vehicles and reduce the internal logistics distribution cost of an enterprise.

The embodiment of the invention discloses a traffic-flow forecasting method, device and system based on the wolf-pack algorithm. The traffic-flow forecasting method includes the steps that traffic-flow data is obtained; the traffic-flow data is processed through a pre-established wavelet-neural-network traffic-flow forecasting model to obtain the traffic-flow forecasting result, wherein the wavelet-neural-network traffic-flow forecasting model is trained based on the wolf-pack algorithm, and the training process of the pre-established wavelet-neural-network traffic-flow forecasting model is that an initialized wavelet-neural-network parameter is calculated according to historical data and the wolf-pack algorithm; the initialized wavelet-neural-network parameter is trained through a wavelet neural network and the historical data to obtain the wavelet-neural-network traffic-flow forecasting model. According to the traffic-flow forecasting method, device and system based on the wolf-pack algorithm in the embodiment, when traffic flow is forecasted through the wavelet-neural-network traffic-flow forecasting model trained through the initialized wavelet-neural-network parameter obtained based on the wolf-pack algorithm, the forecasting speed and the forecasting accuracy are increased to a certain degree.

The invention discloses a three-dimensional packing overall optimization method and system for putting multiple goods and materials into multi-specification packets and belongs to a method for intelligentized processing of goods and material packing. A packing overall optimization calculation module is started, the packing overall optimization calculation of one task is finished by combining a quantum genetic algorithm with a heuristic three-dimensional packing algorithm, and packing schemes are output. Reasonable schemes to be presented are stored in a database after judgment. Through organic combination of the heuristic three-dimensional packing algorithm and the quantum genetic algorithm, the overall packing optimization calculation can be performed for the multiple goods and materials which are to be placed into multi-specification packet containers. Compared with existing optimization calculation for processing a single packet container or one type of packet containers, the three-dimensional packing overall optimization method and system has the obvious overall advantages.

The invention discloses a driverless smart car automatic collision avoidance method based on a quantum wolf pack algorithm. The method is based on global path planning and monitors the surrounding environment of the driverless car in real time during the safe driving process of the smart car, and in the case of a dynamic or static obstacle, with the shortest target path as an objective function, the quantum wolf pack algorithm is optimized to obtain a destination arrival path with the shortest local collision avoidance; and the optimal steering angle of the local driverless smart car and the angle of restoring the original path are determined to obtain a local path planning result of the driverless smart car. Global path planning and local path planning are combined to be applied to driverless driving of the smart car, driving of the smart car can be planned on the whole globally, the road condition information can be judged in real time during the driving process through local path planning, the real-time path is changed timely, the driverless smart car is thus ensured to arrive at the destination quickly and steadily, and the safety and the reliability of driverless driving can be improved.

The invention provides an internet application dispatching method based on cloud computing, and the method comprises the following steps: 1) a dispatcher which is installed at the front end of application servers monitors the configuration information of the application servers, the application requirements on each server, the user request number of the previous moment and the current moment and the operation information of all examples in each application server; 2) when the change of the application is monitored, the server load with the changed application is adjusted by utilizing the load descending of the application, an application logging-out system, an application adding system and the load ascending of the application through a packing algorithm, and a transponder modifies the load distribution among the examples of each application, thereby reducing started new application examples on a new server; and 3) the dispatcher outputs the application example which needs to be closed, the application example which needs to be started again and the server on which the application examples are started. In the method provided by the invention, the service number of resources is dynamically adjusted for users according to the dynamic requirements of the user application; the new application examples are avoided from being started to the greatest extent; and the cost is low.

The invention discloses a wireless sensor network node optimal deployment method based on an improved wolf pack algorithm, which is applied to node optimal deployment of a wireless sensor network, improves the effective coverage rate of wireless sensor nodes, and uses a nonlinear convergence factor balance algorithm to perform earlier global search and later local search capability. An elitist strategy is added to accelerate the convergence speed of the algorithm. A dynamic weight strategy is provided, so that position updating of individuals with poor positions is more reasonable. Meanwhile,a dynamic position crossing processing strategy is provided, and the possibility of searching a global optimal solution in an area is increased. A dynamic variation strategy is introduced to increasethe diversity of wolf groups and effectively expand the search range of the algorithm. The method has the advantages that the problem that the GWO algorithm is prone to falling into local optimum later is solved. The IGWO algorithm improves the coverage performance of the nodes of the wireless sensor network, the higher coverage rate can be achieved with fewer nodes, coverage holes are reduced, and the deployment cost of the network is reduced.

Methods and systems for allocating address space resources to resource requesting peripheral devices in an efficient manner. Resource request are gathered for enumerated peripheral devices host by a computer platform. A map containing resource alignment requirements is built, and a virtual resource allocation map is computed based on aggregated resource requests and the alignment requirements. The resource aggregations are, in turn, based on a hierarchy of the peripheral devices. A bin-packing algorithm is employed to determine allocation of the resource requests so as to minimize resource address space allocations. The virtual resource map is then used to perform actual resource allocations. The resources include peripheral device I / O address allocation and peripheral device memory address allocations.

The invention provides an article packing method, which can determine a packing algorithm corresponding to the number of articles to be packed according to the number of articles to be packed. If thenumber of articles is large, the time complexity of the determined packing algorithm is low, thereby improving the execution efficiency of the packing algorithm. In addition, the specification information of the preserved articles can be obtained, and the specification information can be inputted into the packing algorithm to obtain a package box capable of packing all the articles and the articles packed in each package box. The method does not need to use an article entity, thereby improving the packing efficiency. Moreover, the packing algorithm can satisfy the preset packing condition in the process of solving, the packing conditions may include any one or more of a minimum number of package containers, a minimum package container size, and a closest picking path for articles within the same package container for any one or more of a maximum utilization of package container space, a minimum package container cost, and a shortest picking path for articles.

A connection controller (402) includes a network topology cache (418) coupled to receive network topology data (405) of a network (400). Connection controller also can include a packing algorithm (414) coupled to receive a requested traffic pattern (403) of a packet (408), where the packing algorithm computes an actual traffic pattern (412) using the network topology data and the requested traffic pattern such that the network operates as a strictly non-interfering network (419). Connection controller further includes a logical network state entity (420) coupled to communicate the actual traffic pattern to a source (406) corresponding to the packet.

A texture atlas scheduling method comprises the following steps of determining basic information for texture streaming scheduling and a data structure; creating the actual physical texture and save the texture loaded into memory; creating an indirect index buffer to store the location information of Mipmap on the physical texture; according to the level of detail information of texture, carrying out the inflow and outflow of texture, and according to the rectangular texture packing algorithm, finding the position information of the current inflow texture on the physical texture; rendering thetexture and relocating the UV coordinates for sampling calculation. The texture atlas scheduling method of the invention is based on a rectangular texture packing algorithm, and effectively reduces the number of DrawCalls by merging texture maps, so that the rendering efficiency is improved. Through the scheduling of textures, the memory usage can be reduced, the necessary texture resources can beloaded gradually, and the memory pressure can be reduced. The invention can effectively reduce the pressure of the graphics processing unit in the rendering process.

The invention discloses a scheduling optimization method based on time-triggered communication service. The method combines the distributed integrated modular avionics system, based on the service characteristics thereof, with a time trigger mechanism to enable the system to support both time-triggered service and event-triggered service, thereby improving the timeliness and stability of the system. In order to further improve the utilization rate of system resources, the scheme provides a static scheduling table generation algorithm for time-triggered service, and the optimization goal is toarrange the time-triggered service in a dispersed way as much as possible, so as to obtain the largest number of idle time slots and provide uniform time resources for subsequent event-triggered service to improve the stability of the system. The invention provides a new two-dimensional packing algorithm and introduces constraints to achieve the goal of optimizing uniform and dispersed arrangementof the time-triggered service, thus improving the time delay performance of the system.

The invention discloses an intelligent decomposition control planning method for paths of a carrying robot in an intelligent environment. The method includes the following steps: step one, constructing a global map three-dimensional coordinate system for carrying areas of a carrying robot, and obtaining a walkable area coordinate in the global map three-dimensional coordinate system; step two, obtaining a training sample set; step three, constructing a global static path planning model of the carrying robot; and step four, obtaining an optical path in real time, and then finishing a transportation task. A path planning model is established through construction of a kernel extreme learning machine optimized by a wolf pack algorithm, a global optimal solution can be quickly found in an intelligent environment, and the problem of local optimum of conventional path planning is avoided.

The invention belongs to the field of electronic design automation technique, concretely a functional model of FPGA logic cells and a universal logic cell packing algorithm. The model firstly extracts functional components from the FPGA logic cells, then using the connection of the functional components and multi-channel switch selectors to describe the whole structure of logic cells, successively making different configurations on the logic cells to generate many available functional circuits formed only by connection of logic cells and the model can widely describe the structure of logic cells of the existing FPGAs and obtain all logic functions of logic cells by their corresponding available functional circuits. Based on the functional model of FPGA logic cells, the invention advances a universal logic cell packing algorithmí¬FDUPack whose kernel idea is to repeatedly make circuit diagram mode matching on each available functional circuit in the user circuit and which is a universal algorithm of processing various logic cell packing problems.

The invention discloses a method for pre-generating a face sheet with one ticket and multiple pieces, which comprises the following steps: determining whether the order is an order of one ticket and multiple pieces type according to the received order information; when the order is a multi-item order, obtaining a stock quantity unit parameter of each item in the order; determining the package material parameters corresponding to the order according to the stock quantity unit parameter of each commodity and the preset package material packing algorithm; a delivery order is generated before theorder enters the warehouse for sorting completion according to the determined package parameters. The invention also provides a computer-readable storage medium. After receiving the order information,the method of the invention generates a shipping noodle list according to the commodity information of one ticket and multiple types in the order before the order enters the warehouse for sorting, which improves the speed of packing and sorting of multiple commodities in the order, thereby improving the timeliness of processing the order and high work efficiency.

The invention relates to an optical cable fault prediction method based on temperature and resistance, and relates to the technical field of optical cable communication state prediction, solving the problems that the current method only can process the occurred faults in the optical cable line and cannot analyze the grounding insulation resistance value of an optical cable protection sleeve. The optical cable fault prediction method based on temperature and resistance can predict the grounding insulation resistance of the optical cable protection sleeve through an improved Gauss Wolf Pack Algorithm-Extreme Learning Machine (GWPA-ELM) prediction algorithm, can predict the temperature of the optical cable protection sleeve through a difference autoregression moving average model (ARIMA) algorithm, and can evaluate the state of the optical cable protection sleeve through the predicted resistance value and temperature. The optical cable fault prediction method based on temperature and resistance can predict the possibly occurred fault of the optical cable in the future, and can make maintenance strategy in advance.

The invention discloses a wolf pack algorithm-based multi-target disassembly line setting method under spatial constraint. The method comprises the following steps of: (1) establishing a mathematicalmodel taking minimization of the number of workstations, an idle time balance index, disassembly cost and a positive difference value of an actual use surface of the workstations as targets; (2) generating an initial population, comparing objective function values of the initial population through Pareto to obtain a Pareto optimal solution, and storing the Pareto optimal solution in an external file; (3) calculating to obtain a new population by adopting a multi-target discrete wolf pack optimization algorithm; (4) comparing the target function value of the mixed population composed of the newpopulation calculated in the step (3) and the external archives by adopting Pareto, and further updating the external archives; (5) comparing the target function value of the new population calculated in the step (3) by adopting Pareto, and further updating the population; (6) repeating the steps (3)-(5) according to the set number of times; and (7) outputting a Pareto optimal solution in the external file as a disassembly task allocation scheme. The method provided by the invention has stronger search capability and robustness.

The invention discloses a conflict-free test scheduling method based on link distribution in NoC. The method includes the steps of dividing a network into multiple areas through an improved packing algorithm, setting a route tree for each boundary node to find a communicating route in the link distribution process, and enabling a parallel test of all the areas to be free of conflicts through a distribution link after alternative route collection information of all the sub-areas is synthesized. By means of the method, the link conflict problem of the parallel test in the network on chip is solved, test time of a chip can be effectively shortened, and test reliability is ensured.

The invention discloses a mobile robot path planning method based on an improved ant colony algorithm. The method comprises the steps: resetting an initial pheromone concentration, improving a heuristic function, and updating the pheromone concentration, wherein the resetting initial pheromone concentration is the setting of different pheromone concentrations for each grid; adding an A* algorithm valuation function and a corner constraint factor into the heuristic function, using the A* valuation function for searching for a global optimal solution, and meanwhile using the corner constraint factor for angle constraint; and in the pheromone updating part of the ant colony algorithm, adding a distribution principle of a wolf pack algorithm, using the principle to distribute pheromones, and meanwhile, using a maximum-minimum principle in an MMAS algorithm to limit the pheromone concentration. According to the method, the global search capability of the algorithm can be enhanced, a path length is shortened, a smoother moving track is planned for a mobile robot, the method is not limited to the fields of computers and artificial intelligence, and the method is also suitable for similar problems in the fields of traffic, logistics, management and the like.

The invention discloses a train axle space straightness measurement method, which comprises the following steps of: obtaining point cloud data of multiple groups of measured train axle information through the axial movement of a line structured light 3D sensor along a train axle; preprocessing the point cloud data by using a neural network; performing spatial circle fitting on the preprocessed point cloud data to generate circle center coordinates of each section of the train axle; projecting the circle center coordinate of each section; and fitting the spatial line projected to the plane according to a wolf pack algorithm, restoring the spatial line into a spatial straight line, and calculating the straightness of the spatial straight line. The method comprises the steps that train axle section information is collected through a line structured light sensor, and denoising processing is conducted on point cloud data through a neural network; acquiring the section circle center of the train axle by using a space circle fitting algorithm, and completing train axle space straightness measurement in combination with a wolf pack algorithm; accurate train axle section information and straightness can be obtained, and the measurement accuracy is high.

The invention discloses a personalized recommendation method based on GWO-FCM, and the method comprises the following steps: S1, obtaining data information of a user in a certain time period from a movie watching platform, and obtaining the hobbies and interests of the user; S2, according to the behavior information of the user, extracting movie information by applying an optimized collaborative filtering algorithm to form an algorithm recommendation list; S3, processing an algorithm recommendation list, carrying out screening according to watching records and browsing records of the user, predicting and sorting filtered movie scores and sorted, obtaining an actual recommendation list, and forming personalized recommendation; and S4, arranging the recommended contents in a descending orderaccording to the prediction score, and pushing the specific information of the movie to a corresponding position. According to the method, interests and hobbies among users are fully understood for recommendation, and the personalized recommendation model of the fuzzy Cmean clustering algorithm based on wolf pack algorithm optimization is used, so that the data sparsity can be relieved to a certain extent, and recommendation can be performed more accurately.

The invention provides an intelligent economic scheduling method and device based on a wolf pack algorithm. The method comprises the following steps: establishing a target function according to the coal consumption and cost of a thermal power generating unit, establishing a constraint condition equation set by taking the power balance of a new energy resource system, unit exertion constraint, up-down climbing rate limits of a unit and spinning reserve requirements of a wind power system as constraint condition, so as to form a corresponding optimal mathematic model; forming an initialized wolfpack position meeting the constraint condition equation set according to a wolf pack search algorithm, and executing the wolf pack search algorithm by taking a target function as an adaptability index calculation formula of the wolf pack search algorithm, so as to obtain a corresponding optimal solution; and scheduling the operations of the thermal power generating unit and the new energy resource system according to the corresponding optimal solution. According to the intelligent economic scheduling method, the optimal mathematic model which adopts an optimal operational economic benefit asa target and meets the requirements of the thermal power generating unit and new energy source operational constraints is established, the wolf pack algorithm is applied to the model, and the optimalsolution of the model is obtained, so that the intelligent optimized dispatching of new energy sources is considered.

The invention discloses an energy efficiency preferential cloud resource allocation and scheduling method. According to the method, on the basis of establishing a cloud resource allocation system model, an energy efficiency preferential virtual machine allocation method and a dynamic virtual machine migration method are designed, and the energy efficiency management of cloud resources is reinforced. According to the virtual machine allocation method, through utilization of an expansion bin packing algorithm, the number of working servers is reduced. According to the dynamic virtual machine migration method, through utilization of an integer linear programming method, virtual machines are dynamically integrated into the least number of cloud servers, the number of the working servers is further reduced, and the energy efficiency of a computing center is improved. The migration method and the allocation method work cooperatively, so the total energy consumption of the cloud computing center can be effectively reduced. A simulation study result shows that according to the energy efficiency preferential cloud resource allocation and scheduling method designed by the invention, the energy efficiency of the cloud computing system can be effectively improved.

The invention discloses a wind power plant-containing electric power system optimal scheduling method utilizing an improved wolf pack algorithm. The method comprises the steps: 1, establishing a windpower generation cost model and a thermal power generation cost model; 2, establishing a wind power plant-containing power system optimal scheduling model comprising an objective function and constraint conditions; and 3, solving the wind power plant-containing power system optimal scheduling model by utilizing an improved wolf pack algorithm. When the method is used for optimal scheduling of a power system containing a wind power plant, the total power generation cost can be reduced, and the economy of the power system can be improved.

The invention discloses an unmanned aerial vehicle attack and defense decision-making method based on a self-adaptive step discrete wolf pack algorithm. The unmanned aerial vehicle attack and defensedecision-making method comprises the steps of S1, obtaining an air combat situation, combat performance and a target intention, and building a comprehensive threat function; S2, determining an artificial wolf code length L according to the missile number m owned by an unmanned aerial vehicle and the enemy unmanned aerial vehicle number n, and establishing an unmanned aerial vehicle attack and defense distribution model according to constraint conditions; S3, designing the self-adaptive step discrete wolf pack algorithm, executing the self-adaptive step discrete wolf pack algorithm by taking the comprehensive threat function as a fitness index calculation formula of the self-adaptive step discrete wolf pack algorithm, and solving a corresponding optimal solution; and S4, performing unmannedaerial vehicle attack and defense decision making according to the corresponding optimal solution. According to the unmanned aerial vehicle attack and defense decision-making method based on the self-adaptive step discrete wolf pack algorithm, the intelligent behavior of the discrete wolf pack algorithm is described by defining the crossover operator and the motion operator, and the convergence speed of the discrete wolf pack algorithm is increased by adopting a self-adaptive running step length mode, so that the problem that the attack and defense decision-making speed of the unmanned aerialvehicle is difficult to meet the real-time air combat requirement when the problem scale is too large is solved.