381results about How to "Expand your search" patented technology

The invention discloses an electric vehicle travel planning method based on multi-target optimization, generally comprising the following steps: (1) a travel planning problem model is established; (2) drivers provide travel information; and (3) an optimal scheme is solved based on a timed multi-target ant colony optimization algorithm. The problem model comprises a road network model, a vehicle model, and travel target and travel constraint definition. Travel information includes: not providing any information, providing constraint information, and providing optimization goal and constraint information. The ant colony optimization algorithm includes the steps of pheromone initialization, route transfer probability calculation, travel scheme search, air conditioner use determining, travel scheme ranking, pheromone updating, and loop optimization. A dynamic stochastic road network model is used to describe the traffic environment and plan the travel of electric vehicles, and target characteristics corresponding to different travel schemes can be reflected. The ant colony optimization algorithm ensures that a multi-target and multi-constraint optimized electric vehicle travel scheme is generated as the number of iterations increases.

The present invention provides an order sequencing optimization method for logistics. The process of the method includes the following steps that: since the same type of commodities may be stored in a plurality of different containers, containers from which corresponding commodities in orders should be taken out are determined according to the expiration dates of the commodities in the orders, the number of remaining commodities in the containers where the commodities are located or the distances of the containers before order sequencing optimization is performed; and the orders of the commodities of which the containers are determined are optimally sequenced. According to the order sequencing optimization method for logistics of the invention, an evolutionary computation optimization method is adopted to optimally sequence the orders, so that the orders of the same commodities can be arranged at adjacent positions as much as possible, and therefore, commodities which have taken out can be repeatedly utilized, and the problem of low efficiency caused by frequent transport of the commodities can be avoided; and a constructed learning group concept is utilized to expand the search range of the algorithm, so that optimization calculation can be prevented from being trapped in local optimum, and convergence precision is greatly increased. With the order sequencing optimization method for logistics of the invention adopted, the optimal commodity picking efficiency of the commodities in the orders in a logistics process can be realized.

The invention relates to the field of computers, in particular to a keyword extracting method based on a seq2seq (sequence to sequence) deep neural network model. The keyword extracting method comprises the following steps: first, extracting target information through a pre-processing module; then, converting and labeling the target information through a word vector conversion module and a part-of-speech labeling module; next, obtaining a candidate word sequence through a candidate word weight calculating module; finally, obtaining a keyword through a candidate word screening module. Accordingto the keyword extracting method based on the seq2seq deep neural network model, the importance of each word to a document can be analyzed better and the keyword which can represent the theme of thedocument better is selected by regarding a document vector as the average of a word vector, and combining the word vector and the document vector to serve as the vector representation of a word; meanwhile, the investigation scope of keyword extracting is expanded; the defect that keywords beyond a vocabulary and the keywords which are not in the contents of a source document cannot be predicted byan existing extracting technology is overcome.

The invention discloses a related word mining method. The method comprises: obtaining parallel sentence pairs expressing the same meaning with different expression forms based on large-scale user search behavior data; performing word segmentation processing on each set of parallel sentence pairs; performing word alignment processing on the parallel sentence pairs subjected to the word segmentation processing to obtain first aligned word pairs; calculating co-occurrence frequency of the first aligned word pairs; and determining the first aligned word pairs with the co-occurrence frequency higher than a predetermined threshold as related words. With the related word mining method, related words with higher relevancy can be mined, the retrieval word search range also can be expanded, and the probability of finding a better search result is increased. At the same time, the invention furthermore discloses a search method and a search system.

The invention discloses an equipment preventive maintenance and flexible job shop scheduling integrated optimization method. The method is characterized by specifically comprising: first of all, according to an operation sequence, an equipment maintenance period, and a constraint condition of uninterrupted production process of each operation and non-conflict production process and equipment maintenance process of a flexible job shop scheduling problem, establishing an integrated optimization model of a flexible job shop scheduling and equipment maintenance plan, which takes cost and efficiency into consideration; secondary, optimizing multiple targets by use of a hybrid multi-objective chemical-reaction optimization method, the multiple targets comprising maximum completion time, total production cost and total equipment preventive maintenance cost; and finally, obtaining an optimization solving result, such that a flexible job shop scheduling plan can be obtained. The method can realize the goals of reducing the maximum completion time, the production cost and the equipment maintenance cost and can obtain an optimal flexible job shop scheduling scheme.

The invention discloses a wireless sensor node alliance generating method based on an improved particle swarm optimization algorithm, comprising the following steps of: 1, collecting the capacity information and the task information of each node and quantizing the capacity information and the task information of each node by using vectors; 2, dividing a particle swarm into m sub-swarms at t moment according to the number of tasks to be executed, and initializing the current position and setting the maximum iterations for each particle in each sub-swarm; 3, evaluating the benefit value of the current position of each particle by using the following utility functions for m sub-swarms; 4, comparing the benefit values a1 of the current positions of the particles with the preset benefit values a2 of the local optimum positions of the particles and the preset benefit values a3 of the optimum positions of swarm bodies and updating the local optimum positions of the particles and the optimum positions of swarm bodies, wherein the benefit values a1 is obtained in step 3; 5, calculating the particle speed vector and the particle position at the t+1 moment by using the particle swarm optimization algorithm; and 6, repeating step 3-step 5 to obtain the final optimum positions of the swarm bodies. The invention has the advantages of high executing efficiency and stability.

The invention discloses a two-stage particle swarm optimization algorithm including independent global search. The two-stage particle swarm optimization algorithm comprises the following steps: species initializing; adopting the chaotization method for initializing the positions X and the speeds V of particles; adopting the fitness function (fitness) to calculate the adaptive values of all current particles, and initializing the record optimal position (pbesti) of each particle and the global optimal position (gbest) of all the particles; carrying out the first stage iterative-global search; carrying out the second stage iterative-local search. The two-stage particle swarm optimization algorithm has the benefits that during each iteration of the first stage iterative-global search, one non-self particle is randomly selected from all the particles for learning, and the random selection guarantees that the species is prevented from tracking the specific particle and that the aggregate phenomenon is avoided; the second stage iterative-local search can quickly converge and can obtain solutions high in accuracy, the accuracy of the optimal solution is increased, and the prematurity defect is remarkably improved.

The invention discloses an unmanned aerial vehicle target tracking method, which comprises the following steps: (1) obtaining a left-eye image and a right-eye image by using a binocular camera image acquisition device; (2) calculating the position coordinates of the output UAV in the world coordinate system by the visual SLAM algorithm; (3) detecting the target in the panoramic image composed of the left-eye and the right-eye images, and calculating the three-dimensional coordinates of the output tracking target in the camera coordinate system; (4) taking the real-time position of the UAV in the world coordinate system and the real-time position of the tracking target in the camera coordinate system as the input of the speed control algorithm, and calculating the desired speed as the inputof the flight controller of the UAV to control the flight parameters of the UAV and realizing the target tracking. The method performs autonomous tracking through visual positioning while detecting targets, and is particularly suitable for tracking tasks in weak GPS or GPS-free environments.

The invention provides an integrated stabilization chaotic system based PID (Proportion Integration Differentiation) controller optimization control method. The analysis is performed on an integrated stabilization system dynamic model equation to obtain a chaotic system so as to solve the problem of ship stabilization. The chaotic behavior of the system under the certain conditions is verified by a phase diagram and Lyapunov exponent spectrum analysis method, controlled parameters are selected, and the chaotic behavior of the system can be effectively controlled by a nonlinear feedback control method. According to the integrated stabilization chaotic system based PID controller optimization control method, the chaotic dynamics behavior of the system is improved and the original dynamic characteristics of the system are reserved; a chaotic search algorithm is combined with an ant colony algorithm to implement the optimization of the PID control parameters and accordingly the global optimization capability of the ant colony algorithm is high, meanwhile the system convergence speed is improved, and accordingly the control system performance is significantly improved; the value of application to a controller device is high, wherein the ship rolling motion is effectively designed through the controller device.

The invention discloses a method and a mobile terminal for searching a cell. The method comprises the following steps: recording frequency of a historic cell in an alternate search list of the mobile terminal, wherein the historic cell frequency is frequency of a cell resided by the mobile terminal and/or frequency of a cell adjacent to the cell resided by the mobile terminal; and if no residing cell is searched according to the frequency of an alternate cell recorded in the alternate search list and a primary scrambling code, searching a residing cell according to the frequency of the historic cell in the alternate search list, wherein the alternate cell comprises the cell resided by the mobile terminal last time and/or the cell adjacent to the cell resided by the mobile terminal last time. The embodiment adds the frequency of the historic cell into the alternate search list, enlarges the searching range of the cell, can improve the searching efficiency of the cell and shorten the searching time of the cell.

The invention discloses a mobile phone book and call records quick-speed standby searching system and method. The system includes a keyword module, for receiving the keyword information in phone book and call records arbitrarily input by users in the standby interface; a matching search module, searching entries meeting the requirements in the phone book and call records according to the proposed matching search logic, and displaying on the standby interface the entries meeting the requirements, to complete the rapid operation of fuzzy matching. The present invention can rapidly match all phone book and call records entries meeting the requirements by only inputting several simple keyword symbols on the mobile phone standby interface, so that users do not need to access to the telephone directory to search the relevant contact telephone, and can expand search range by a quick-speed fuzzy matching, reduce the search time, but also can provide users with a more convenient and efficient operation.

The invention provides a vehicle-mounted retrieval system, a vehicle-mounted end and a cloud service center. The vehicle-mounted retrieval system comprises the vehicle-mounted end and the cloud service center. The cloud service center stores historical data of the vehicle-mounted end. The vehicle-mounted end sends a retrieval request carrying the current position information of the vehicle-mounted end and identity data of the vehicle-mounted end to the cloud service center. The cloud service center retrieves related historical data related to the identity data in the historical data of the vehicle-mounted end based on interest points in a retrieval preset area of the current position information and based on the identity data after the retrieval request is obtained and the identity data is confirmed to be valid. The cloud service end regards interest point data of the interest points matched with the related historical data as a retrieval result of the retrieval request and feeds the retrieval result back to the vehicle-mounted end. The vehicle-mounted end receives the retrieval result. The vehicle-mounted retrieval system, the vehicle-mounted end and the cloud service center improve retrieval efficiency of vehicle-mounted devices.

The invention discloses a software upgrading method and system based on the WiFiDirect. A menu item undergoing software upgrading through the WiFiDirect function and a submenu item sending a software upgrading request are set in advance; a main mobile device receives an operation order on the menu item by a user, the WiFiDirect function is started, an auxiliary mobile device with the WiFiDirect function is searched for, matching is performed, if matching succeeds, an operation order on the submenu item by a user is received, and the software upgrading request is sent to the auxiliary mobile device; the auxiliary mobile device sends upgrading files conforming to the software upgrading request to the main mobile device; the main mobile device carries out software upgrading according to the upgrading files. Thus, the function of obtaining upgrading resources between the mobile devices to carry out software upgrading in a situation that a network and a WiFi hot spot do not exist is achieved, the searching range is large, the transmission speed of the upgrading files is high, reliability is high, and the upgrading time is greatly shortened.

The invention discloses a cooperative relay selection method based on an improved genetic-particle swarm optimization mixed algorithm. The method comprises following steps: an emitting terminal detects N accessible relay nodes and obtains channel state information of the nodes; the particle population of a relay selection scheme is expressed in an initialization manner; the speeds and the positions of particles are updated, and the fitness value of the particles is calculated; the individual extremum and the global extremum of the particles are updated; particle position arrays serve as genes, the genes are selected to be recombined and added to the population; the genes are selected for mutation, and the original genes are replaced; new particles which never appear in an iteration process are generated in a random manner and added to the population; M particles with the highest fitness value are selected in the population to form a new generation of the particle population; and whether the particle population is in accordance with the termination condition is determined: if so, the particles with the highest fitness value are selected as the optimal relay selection scheme; and if not, the speeds and the positions of the particles are continuously updated. According to the method, the transmission rate of the emitting terminal is increased as fast as possible, and the power consumption is reduced as low as possible.

The invention discloses an ensemble classification method based on randomized greedy feature selection, and belongs to the field of bioinformatics and data mining. The method is used for classifying gene expression data related to plant stress response. The method includes the following steps that 1, randomness is introduced into a traditional greedy algorithm to conduct feature selection; 2, a weighting local modular function serving as a community discovery evaluation index in a complex network is used as heuristic information of the randomized greedy algorithm; 3, base classifiers are trained in each feature subset with a support vector machine algorithm; 4, clustering partition is conducted on the base classifiers with an affinity propagation clustering algorithm; 5, base classifiers serving as class representative points in the cluster are used for conducting integration, and an ensemble classification model is formed with a simple majority voting method. By means of the method, whether plant samples are stressed or not can be recognized according to gene expression data, and the microarray data classification precision is greatly improved; besides, the algorithm is high in generalization capability and has very high stability.

The invention discloses a service selection method oriented to a mobile edge computing environment. The method comprises the following steps: (1) collecting all equipment information, service information and connection information between equipment in a system; (2) receiving a service request; (3) selecting a service for the service request by using a GAMEC algorithm; and (4) computing a destination edge server according to a service selection scheme and a user path. Compared with the prior art, the service selection method has the advantages that the location movement of a user in a service selection process is considered, and movement information of the user is fused into the service selection, so that the overall response time of the service is reduced through the service selection. Moreover, a selection scheme of the destination edge server is proposed, thereby further reducing the service response time. Moreover, a temperature control mechanism of a simulated annealing method is introduced into a genetic algorithm, so that the searching range of the algorithm can be enlarged at the initial stage of the algorithm; local optimum is avoided effectively; the convergence speed is increased at the termination stage of the algorithm; and the efficiency of the algorithm is increased.

The invention relates to a self-adaptive dual-harmony optimization method which comprises the following steps of: initially optimizing a harmony memory base and putting a generated initial solution into the harmony memory base; then equally dividing the initial solution into two groups, i.e. a master harmony library and an auxiliary harmony library respectively and respectively determining the tone trimming probabilities and the tone trimming bandwidths of the master harmony library and the auxiliary harmony library; iteratively searching from the opposite direction under the situation that the algorithm convergence rules are not satisfied to obtain two groups of new solutions; and replacing a solution in the existing memory bank by using an optimal solution in the two groups of new solutions obtained by multiple iteration, thereby obtaining an optimal solution to finally achieve the wonderful harmony. The self-adaptive dual-harmony optimization method has the beneficial effects that tone trimming probability and tone trimming bandwidth factors are continuously adjusted to improve the dynamic adaptability of an algorithm and the coordination ability of local search and full search; two groups of master and auxiliary harmonies which are different in the search direction and are mutually coordinated are constructed, so that the search range is expanded, the iteration number is reduced, and the global optimization is more quickly realized; the problem of complicated function optimization is solved; and the full search ability and the convergence rate are good.

A distributed photovoltaic grid-connected acceptable capacity calculating method based on improved Big Bang-Big Crunch. The distributed photovoltaic grid-connected acceptable capacity calculating method based on the improved Big Bang-Big Crunch comprises the steps of basic data acquisition, model building, algorithm parameter initialization, initial fragment solution generation, fitness evaluation, gravitational solution generation, explosion iteration cycle, cycle termination judgment and the like. According to the present invention, modeling is carried out on the problem of the maximum accessibility capacity of distributed photovoltaics in a power distribution system fro the angle of planning of a power distribution network so as to establish a grid-connected photovoltaic maximum accessibility capacity model which uses the distributed photovoltaic access capacity maximization as a target function and uses a system power flow constraint, an operating voltage horizontal constraint, a branch circuit current limitation, a voltage harmonic distortion rate limitation, a short circuit current limitation and the like as constraint conditions; solution is carried out by an improved Big Bang-BIg Crunch optimal searching method; and the distributed photovoltaic grid-connected acceptable capacity calculating method can provide guidance for planning, building and operation of a photovoltaic access system.

The invention discloses a method, device and system for recommending an application program to open a file, wherein the method is applied to terminal equipment and comprises the following steps of: receiving a request for opening one file; extracting the suffix name of the file; matching the suffix name with a first pre-set corresponding relationship; when the suffix name is successfully matched with the first pre-set corresponding relationship, obtaining all application program information capable of opening the file corresponding to the suffix name; generating a first selection message including all the application program information; judging whether a first application program corresponding to first application program information is installed on the terminal equipment or not while receiving a first return message including the first application program information; and opening the file by using the first application program when the first application program is installed. According to the method, the device and the system disclosed by the invention, the application program capable of opening the file can be recommended to the file; and thus, the file opening efficiency is increased.

The invention discloses a method for improving a standard shuffled frog leaping algorithm.The method comprises the steps of initializing parameters; calculating the adaptive value of each frog individual, and finding the adaptive value and position of the global optimum frog individual of a frog population; conducting optimum drawdown ranking on the frog population; conducting dividing for obtaining frog sub-populations; finding the positions of the optimum and the worst frog individual of each frog sub-population; conducting updating operation on the position of the worst frog individual of each frog sub-population; calculating the adaptive value of the frog individual with the position updated in each frog sub-population, and finding the global optimum adaptive value and the position of the frog population at this moment; implementing prediction of the global optimum adaptive value of the frog population obtained after iteration is completed next time, and furthermore adjusting the movement step-length variable coefficient dj and skip among steps; judging whether the ending conditions are met or not.By means of the method, the defects that at the later stage, the convergence rate of the standard shuffled frog leaping algorithm is severely lowered, convergence precision is insufficient, and the algorithm is prone to getting into local optimum are overcome.

A motion detection block includes a wavelet transform section which receives a current image I 1 and a reference image I 2 as inputs. The motion detection section detects a motion vector using size-reduced images RI 1 and RI 2 generated by transform in the wavelet transform section. Thus, the search range of a motion vector can be apparently extended at the same processing amount.

The invention provides a spark-based support vector machine parameter optimization parallel particle swarm optimization method which is applied to machine learning model training. The initialization information of all nodes is converted into information of an RDD form and is stored. The cross validation of node particles of different address coordinate parameters is carried out, and the node particles are mapped as an accurate rate of cross validation and the individual extremums of particles. The individual extremums of all particles are used to find a global extremum. A node particle is updated in each RDD according to the global extremum. Whether the global extremum reaches a target accurate rate or a number of iterations reaches a limit or not is judged, an optimization process is ended if so, otherwise the parallel cross validation of the node particles is repeated. The running speed of the optimization process is fast, the search range is large, and the searching of global optimum by the particle swarm algorithm is accurate and rapid.