430 results about "Swarm intelligence" patented technology

The invention discloses a block chain-based online taxi-hailing service system. The system includes a traffic cloud database, a traffic cloud artificial intelligence system (TAI) and a mobile client. With the block chain-based online taxi-hailing service system adopted, a decentralized, trustless, collectively maintained, asymmetric cryptography reliable database basic infrastructure and underlying internet protocol can be provided for traffic cloud; and a mobile SaaS (Software-as-a-Service) application pattern, a distributed computing normal form and a group intelligence model which can establish high-degree connections for travelers based on time stamp, achieve travel resource allocation consensuses and provide services according to demands can be realized.

A plurality of swarm intelligence-based mobile robots, each having multiple legs and multiple joints, the mobile robot includes: an environment recognition sensor for collecting sensed data about the surrounding environment of the mobile robot; a communication unit for performing communication with a remote controller, a parent robot managing at least one mobile robot, or the other mobile robots located within a predefined area; and a control unit for controlling the motions of the multiple legs and multiple joints to control movement of the mobile robot to a given destination based on control data transmitted from the remote controller through the communication unit or based on communication with the other mobile robots within the predefined area or based on the sensed data collected by the environment recognition sensor.

A system and method of use for self-tuning a PID controller utilized with an exciter and generator is disclosed. The system includes a power source, an exciter electrically connected to the power source, a generator that is electrically energized by the exciter, and a processor that provides a PID controller that calculates an estimated exciter time constant and an estimated generator time constant using particle swarm optimization (“PSO”) to control exciter field voltage. The particle swarm optimization (“PSO”) technique includes increasing the voltage reference by a predetermined percentage over a predetermined time period, initializing each particle position of exciter time constant and generator time constant, calculating generator voltage, performing a fitness evaluation and then obtaining and updating best values. This is followed by repeating the steps of determining the generator voltage, the fitness evaluation, and the best values over a predetermined number of iterations.

A swarm intelligence routing robot device includes wherein multiple swarm intelligence robot devices configure a cluster, and the swarm intelligence routing robot device configures and manages a wireless communication network to relay communication between the swarm intelligence robot devices which move in an atypical environment in the cluster, and selects a location thereof in order to maintain a communication state with the swarm intelligence robot devices with which the swarm intelligence routing robot device wants to establish a connection based on a network resource information management of the swarm intelligence robot devices.

The method comprises: creating a air-way planning model of a unmanned combat air vehicle (UCAV); using a shortest air-way and minimum detectable air-way weighted method to compute the cost function used as the performance index of describing the air-way; after using ant swarm intelligence to find the current candidate path node for the UCAV, using the satisficing-decision making principle to evaluate the satisficing degree of each candidate path node so as to select the satisfied candidate node; meanwhile, using a 'wheel of fortune' modified policy to improve the global search capacity of the algorithm; after each ant completes the selection of its own candidate air-way, making a overall correction for the biological information elements at each side.

The invention provides a method for determining an optimal route of the airway of an unmanned aerial vehicle. According to the method, the threat of an operation area is more sufficiently considered, more efficient global searching ability is achieved and a more accurate flying route is provided for the unmanned aerial vehicle. The method comprises the following steps: by adopting a quantum encoding mode, changing the state of a basic quantum bit by using a quantum rotating gate and a quantum not-gate, and further updating the position of a bat individual. Because of the diversity of the quantum state, a quantum bat algorithm (QBA) is relatively high in global searching ability and an available or even optimal route avoiding the threat and limiting conditions can be found for the unmanned aerial vehicle. The experiment result shows that the quantum bat algorithm is an effective and stable method for solving the airway route planning problem of the unmanned aerial vehicle, and the search performance of the quantum bat algorithm is superior to that of other swarm intelligence algorithms.

A method for generating a three dimensional grid of terrain includes receiving data representing a three-dimensional point cloud and generating a plurality of slices of the data. The method evaluates the slices using a swarm intelligence algorithm. The method is able to identify both terrain and objects. The terrain can also be identified as traversable terrain.

In some embodiments, the invention involves information routing in networks, and, more specifically, to defining a framework using swarm intelligence and utilization of the defined framework for routing information in the network, especially for cloud computing applications. In an embodiment, information about available information/services is pushed to network nodes using information packets (ants). Nodes requiring services send query packets (ants) and a node may send a response to a query ant when information is available. Ants may be forwarded throughout the network based on popularity of nodes, freshness of information/requests, routing table information, and requests or interest by consumer nodes captured in information routing table. Other embodiments are described and claimed.

A method for planning an operation point sequence and a path of an industrial robot based on a swarm intelligence algorithm mainly solves the problem that a method for planning an operation point sequence and a path of an industrial robot in the prior art is low in automation degree and poor in scientific rationality. According to the method of the present invention, a discrete sequence similarity index (SD) is used for representing the difference (distance) between individuals in planning of the sequence; a collision evaluation index (O) is constructed for avoiding collision between the industrial robot and a barrier as well as between joint rods of the robot; for a constraint condition, a penalty function is adopted for performing processing, and a constraint penalty term C is introduced, so that dynamic characteristics of the industrial robot meet margin requirements. The method of the present invention obtains the optimal operation point sequence of the industrial robot and the corresponding optimal path based on the above evaluation indexes and an improved swarm intelligence algorithm, can effectively improve execution efficiency and operation performance of the industrial robot, can reduce cost, and can meet requirements of actual production.

The invention relates to a short-term load predicting method of a power grid. The method comprises the steps: step 1, acquiring historical data and pre-treating the data; step2, decomposing the historical load sample data into a plurality of different-frequency sub-sequences by using wavelet decomposition; step 3, performing single-branch reconstruction to each sub-sequence; step 4, dynamically choosing training samples and establishing a neural network predicting model optimized by a vertical and horizontal intersection algorithm; step 5, predicting each sub-sequence 24 hours in advance by using the optimal neural network predicting model; and step 6, superposing the predicted value of each sub-sequence to obtain a whole prediction result. The inherent defects of the neutral network can be overcome by optimizing BP neutral network parameters by a brand-new swarm intelligence algorithm, that is, the vertical and horizontal intersection algorithm instead of the traditional algorithm; the burr problem caused by the impact load processing is solved by the wavelet decomposition, the precision declining resulting from the removal of the effective load in the burr pre-treatment is solved and the predicted value of the hybrid algorithm is more approximate to the actual measured load value.

The invention provides an intelligent target scoring system and method based on a multi-bullet-hole mode recognition algorithm. The system comprises a sound control sensor, a swarm intelligence camera, a supervisory control computer and a mobile client. The intelligence camera is arranged in front of targets. Shooting signals trigger the intelligence camera to collect images through the sound control sensor. A target surface positioning correction algorithm, a multi-bullet-hole recognition sorting algorithm and a target scoring algorithm are used for acquiring the bullet hole type and shooting scoring information in real time, and then the information is uploaded to the supervisory control computer and the mobile client through an internal WIFI module. The mobile client conducts voice target scoring, the supervisory control computer displays the shooting scoring information of each shooter in real time, and a user interface is provided for performing statistical query and intelligent management on the shooting information. According to the intelligent target scoring system and method, shooting target scoring of each target is independent and does not depend on the supervisory control computer, the structure is simple, and safety and reliability are achieved; and meanwhile, the provided mode recognition method is high in antijamming capability, the bullet hole recognition rate is high and real-time performance and accuracy can be achieved.

The invention discloses a stock prediction method based on depth learning, which includes: obtaining the latest transaction data for the target stock and associated stock, generating a multi-dimensional feature matrix corresponding to the latest transaction data, inputting a multi-dimensional characteristic matrix corresponding to the latest transaction data into a composite neural network for processing, obtaining a prediction result of the target stock. The invention also discloses a stock prediction device based on depth learning, stock prediction apparatus and storage medium based on in-depth learning; the invention firstly utilizes the convolution neural network of the composite neural network to learn the characteristics of the transaction data of the target stock and the associatedstock, Features are input into the composite neural network of short-term and long-term memory network for processing, and the prediction of the stock price rise and fall is obtained. A stock forecastmethod based on depth learning and swarm intelligence is provided, which can accurately forecast the stock price rise and fall.

The invention discloses an ecological simulation-based electric car battery charging and replacing service network simulation system and a method. According to the method, an electric car user system model, a battery charging and replacing service network system model and a power grid system model are processed into a simulated ecosystem, the electric car user model is regarded as a biological population of the ecosystem, the motion and the battery charging and replacing actions of the electric car population in one region are simulated by virtue of an ecological simulation method, a large quantity of individuals complying with simple rules present a swarm intelligent optimization algorithm without central control, an optimal matching scheme is generated, and various index parameters are output, so that the service ability of battery charging and replacing facilities is judged. Therefore, the battery charging and replacing facilities can be guided to be planned and constructed, and the electric car development is promoted.

The invention belongs to the field of artificial intelligence of a computer application technology, relates to an application of a swarm intelligence optimization method of an intelligence optimization algorithm, and particularly relates to a traffic flow forecasting method for an intelligent traffic system. A mixed artificial fish swarm optimization support vector regression method is applied to traffic flow forecasting. The construction process of the mixed optimization method is characterized in that a particle swarm algorithm is applied to improve the behavior selection of the artificial fish swarm algorithm aiming at the problem that the effect of a step-length factor in the artificial fish swarm algorithm on the algorithm is insufficient to reduce the step-length effect, then the support vector regression is optimized to conduct parameter selection to further build a mixed artificial fish swarm optimization traffic flow forecasting model. The method has the advantages of being capable of overcoming the shortcomings of the artificial fish swarm algorithm, acquires better combination regression parameters compared with the single swarm intelligence optimization algorithm application, and improves the traffic flow forecasting accuracy accordingly. The mixed optimization method is applicable to actual traffic flow predication and other engineering optimization.

A distributed swarm intelligence system comprises an underlying protocol layer used for adapting to a network protocol; a core software platform layer, used for calling computing resources of an external system platform and supporting a distributed system, a consistency algorithm and a computing model; an open software layer, used for providing an open source system framework. The distributed system at least comprises one or more of an HLA system, a DDS system and a Multi-Agent system. According to the system, the problems of large-scale calculation, calculation model splitting, cooperation of multiple intelligent expert systems, swarm intelligence decision making, intelligent system decision making flexible organization and the like are solved.

The invention discloses a method for determining automatically mosaic line of orthophoto and performing automatic seamless mosaic of orthophoto, including selecting the optimum route to evade obstacle area in a difference image using the positive feedback of ant algorithm and elicitation type search characteristic of swarm intelligence; obtaining the mosaic lines of two orthophotoes to be spliced, performing mosaic fusion to the orthophoto pairs using the mosaic lines, thereby implementing the automatic seamless mosaic of orthophoto. The method which determines automatically the position of mosaic lines in the orthophoto mosaic process is capable of improving greatly the production efficiency of orthophoto map in mapping industry by replacing the prior method for determining or modifying mosaic lines manually, in which the mosaic lines evades automatically ground feature higher than the ground, such as houses, crown of trees, and the like, and evades the areas with large contrast of imaging colors in adjacent orthophoto maps.

The invention relates to a pre-stack non-linear fluid identification method for a fuzzy neural network of a chaotic quantum-behaved particle swarm. Fluid identification is always a key point and difficult point problem in the oil-gas exploration field. By aiming at deficiency in the common fluid identification method at present, a multi-attribute angle gather combination fluid identification factor is built by researching an AVO (amplitude versus offset) response characteristic comprising different fluids; a chaos search mechanism, a quantum-behaved particle swarm and a fuzzy system theory are organically combined to fully perform respective advantages and complementarities of the chaos search mechanism, the quantum-behaved particle swarm and the fuzzy system theory; a novel group intelligent optimization algorithm of a ''chaotic quantum-behaved particle swarm fuzzy system'' is developed and researched, and a mechanism and an optimizing performance of the pre-stack non-linear fluid identification method are researched from two aspects of the theory and practicality; problems of poor global search capability, premature convergence and the like in a traditional optimization algorithm are fundamentally improved; the optimization algorithm is introduced into fluid identification to form the pre-stack non-linear fluid identification method for the fuzzy neural network of the chaotic quantum-behaved particle swarm; the problem existing when a traditional fluid detection means is used for carrying out fluid identification is effectively solved; fluid identification precision is improved; and a new scientific and effect technical method is provided for the fluid identification.

The invention discloses a target tracking method oriented to a video with a low frame rate. The method comprises the following steps: (1), representing a target region by a method integrating a dominant color and a space distribution characteristic thereof; (2), employing a cross color ratio-based matching criterion to carry out similarity matching on a candidate region and the target region; (3), employing a parameter integrogram-based fitness function to characterizing a matching degree of a sample particle and a target template; and (4), utilizing an annealing particle swarm optimization framework with simulation of biological swarm intelligence to search abrupt motions caused by a video with a low frame rate. According to the invention, the effective target tracking method is realized; and moreover, experimental results show that the provided method, compared with other classical low frame rate tracking methods, has good effectiveness and robustness.

The invention discloses a task scheduling method and a system in cloud computing. The task scheduling method in the cloud computing comprises performing parameterization on characteristic information of tasks and classifying the tasks, computing and obtaining an optimal working node through a bacterial foraging algorithm according a classifying result and matching the working node and the task. The task scheduling method in the cloud computing has the advantages of enabling scheduling of user task groups through the cloud computing to have advantages of allowing swarm intelligence parallel search, being easy to jumping out of a local minimum and the like and being contributed to maintenance of diversity of the task group in the cloud computing due to the fact that the task scheduling and resource allocation problem in cloud computing is achieved through the bacterial foraging algorithm and satisfying user requirements and improving satisfaction of user experience.

The present invention provides a swarm intelligence based behavior clustering system. The data representation of the system comprises a data structure and a data type, and a k-means mixed clustering algorithm is employed; the k-means mixed clustering algorithm which combines an ant colony clustering algorithm and a k-means clustering algorithm is employed and is mainly divided into two parts, a first part is used for carrying out ant colony clustering, a second part is used for collecting an ant colony clustering result by using a k-means algorithm; in the k-means mixed clustering algorithm, a similarity formula is similar to a fundamental model for ant colony clustering and an LF algorithm, but simpler probability transformation functions are adopted and are of two straight lines with the slope of k; and proved by subsequent experiments, due to improvements, both the precision and efficiency of the algorithm are more excellent compared with the existing algorithms.

The invention discloses a swarm intelligence optimization fault diagnosis system based on hybrid optimized parameters. The system is used for performing fault diagnosis on a Tennessee Eastman processand comprises a data preprocessing module, a principal component analysis module, a relevance vector machine module and a swarm intelligence algorithm module. Fault diagnosis prediction of important parameter indexes of the Tennessee Eastman chemical process is performed, the defect that the fault diagnosis effect is affected due to the fact that the instrument test sensitivity of existing chemical fault diagnosis technologies is poor and optimal system parameters are difficult to find is overcome, the swarm intelligence algorithm module is introduced to optimize relevance vector machine parameters, so that the swarm intelligence optimization fault diagnosis system based on hybrid optimized parameters is obtained, and the effects that the test sensitivity of the fault diagnosis system is good and globally optimal solutions are easy to find in the Tennessee Eastman process are realized.

The generator includes enterprise Intranet and/or Internet. In Intranet, there is Web server and database server. In Web server and database server, there is generator composed of decision support modules oriented to object. These modules include class libraries, objects, attributes and methods needed by swarm intelligence decision support system under Internet environment. The said decision support modules include modules of problem-oriented solution, data mining oriented and one from knowledge management oriented as well as main control of decision-making and coordination control possibly. These modules are connected to each other, and main control decision-making module coordinates and controls whole decision process. Assembling modules or adding them to frame of decision support system generates dedicated decision support system or application system with decision support functions so as to raise develop efficiency. The method is easy to be integrated to other application system.

The invention discloses a PID controller parameter optimal setting method relating to the field of automatic control and based on swarm intelligence optimization searching technology. Integral performance indexes which can comprehensively measure stability, rapidness and accuracy of an automatic control system are adopted as fitness functions. Through utilization of a global optimization function of a differential evolution algorithm, a proportionality coefficient K[p], an integral coefficient K[I] and a differential coefficient K[D] which can realize global minimization of the performance index function values of a PID control system are searched to be regarded as optimal setting parameters of the PID controller. The PID controller parameter optimal setting method based on the differential evolution method is utilized to carry out simulation experiment direct current motor rotating speed closed loop control system. The experiment result shows that a PID control system obtained after undergoing setting by the method has outstanding advantages of a fast adjusting speed and small overshoot compared with control systems set obtained through a common setting method. The PID controller parameter optimal setting method is a PID controller parameter setting method having a popularization value.