146results about How to "Guaranteed convergence speed" patented technology

The invention relates to a three-dimensional entity model surface finite element mesh automatic generation method which has the characteristics of good dimension control performance of mesh quality, density and dimension, good convergence in a dividing process, high calculation efficiency, stability and reliability. The method comprises the following steps: (1.1) establishing a three-dimensional geometrical model, reading an STL neutral file of a model surface, and reconstructing a topology relation of triangle patches; (1.2) according to a relation between a triangle patch and an adjacent patch, grouping surface triangle patches, wherein each group of triangle patches is a characteristic face; (1.3) determining boundaries of each characteristic face, and determining characteristic edges on each characteristic face boundary; (1.4) according to mesh density distribution, generating mesh nodes on each characteristic edge, and expressing boundaries of the characteristic face with mesh node rings on the boundaries; (1.5) selecting a cut surface and an optimal dissection surface, and generating mesh nodes on cut lines and dissection lines; (1.6) generating a surface mesh on each characteristic face, and carrying out smoothing process.

The invention discloses an online lithium ion battery SOC estimation method based on extended Kalman filter, belongs to the technical field of SOC prediction of a lithium ion battery, and aims to solve the problem that the reliability of the online estimation of a conventional lithium ion battery SOC is low due to influence of initial value selection. The method comprises the steps as follows: a voltage and current relation of a first-order RC (resistance / capacitance) equivalent circuit of a detected lithium ion battery and a voltage and current relation of a second-order RC equivalent circuit are established firstly; a charge-discharge experiment is performed on the detected lithium ion battery to establish a polynomial fitting function of a Kalman filter initial value SOC 0 of the detected lithium ion battery; a covariance P (0) of the Kalman filter initial value SOC 0 and a Kalman filter initial error of the detected lithium ion battery is obtained; and then, battery SOC estimation based on extended Kalman filter is performed, so that the online estimation of the lithium ion battery SOC is realized. The method is used for online estimation of the lithium ion battery SOC.

A method of acquiring a part-processing optimal scheduling scheme based on an improved chicken flock algorithm comprises the following steps: step 1, determining an evaluation index of an optimization object for a multi-objective flexible workshop scheduling problem; step 2, establishing an optimization object function; step 3, determining a constraint condition of a scheduling optimization process; step 4, designing Pareto improved chicken flock algorithm; step 5, carrying out iterative operation, outputting a Pareto non-dominated solution, selecting an optimal solution according with an enterprise need, carrying out decoding on the optimal solution and taking the solution as a final scheduling scheme. In the invention, under the condition of satisfying a resource constraint, an operation constraint and the like, time of completion, a maximum load of a single machine and a total load of all the machines are taken as an integration optimization object, the improved chicken flock algorithm is used so that an optimal scheduling scheme of part processing can be rapidly acquired. In a chicken position updating formula, a cock learning portion in the group where the chicken belongs is added. A algorithm convergence speed is guaranteed and simultaneously solution quality is greatly increased.

The invention relates to a modeling method of plate rolling in an online control model, which carries out the modeling by a rigid-plastic finite element method. The modeling method comprises the following steps: taking a central line of a plate as an x shaft and the thickness direction of the plate as a y shaft to build a two-dimension plane strain rolling model; inputting rolling conditions and parameters; dividing finite element grids in a rolling deformation region at the lower side of the rolling contact region by adopting a quadratic element and carrying out finite element pretreatment; setting the initial speed field of the finite element; building a rigid-plastic finite element energy functional by taking the initial speed field as an initial value, iterating and solving a minimum value point of the energy functional by adopting the damping Newton method, and obtaining the actual speed field; calculating the strain field and the strain field according to the actual speed field, further calculating online control parameters of the rolling force, the rolling torque and the forward slip value, and obtaining the plate rolling model. The invention improves the calculation speed of the finite element, realizes the online rapid calculation and control of the rigid-plastic finite element of the plate rolling and has strong antijamming capacity and good stability.

An intelligent power generation control method based on multi-agent reinforcement learning having time tunnel thought includes the following steps: determining a state discrete set S; determining a combined action discrete set A; collecting real-time operating data of each power grid, calculating an instantaneous value of each area control error ACE(k) and an instantaneous value of a control performance standard CPS(k), and selecting search action a<k>; in the current state s, obtaining a short-term award function signal R(k) by a certain area power grid i; obtaining value function errors rho<k> and delta<k> through calculation and estimation; updating a Q function table and a time tunnel matrix e(s<k>, a<k>) corresponding to all states-actions (s, a); updating Q values and updating a mixed strategy pi(s<k>, a<k>) under the current state s; then updating a time tunnel element e (s<k>, a<k>); selecting a variable learning rate phi; and updating a decision change rate delta (s<k>, a<k>) and a decision space estimation slope delta<2>(s<k>, a<k>) according to a function. The intelligent power generation control method based on multi-agent reinforcement learning having time tunnel thought aims to solve the problem of equalization of multi-area intelligent power generation control, has a higher adaptive learning rate capability and a faster learning speed ratio, and has a faster convergence rate and higher robustness.

The invention relates to a clustering method, in particular relates to an improved fuzzy C-mean clustering method based on quantum particle swarm optimization, and belongs to the technical field of data mining and artificial intelligence. The improved fuzzy C-mean clustering method comprises the steps of: firstly, based on the conventional fuzzy C-mean clustering algorithm, improving the fuzzy accuracy of the conventional clustering algorithm by using a novel distance standard in place of a Euclidean standard; meanwhile classifying singly and quickly through using an AFCM (Adaptive Fuzzy C-means) algorithm to replace a randomly distributed initial clustering center to reduce the sensitivity of the clustering algorithm on the initial clustering center; and finally, introducing a QPSO (AQPSO (Adaptive-Quantum Particle Swarm Optimization)) parallel optimization concept based on distance improvement in a clustering process, so that the clustering algorithm has relatively strong overall search capability, relatively high convergence precision, and can guarantee the convergence speed and obviously improve the clustering effect.

The invention discloses a variable-step LMS adaptive filtering method. The variable-step LMS adaptive filtering method comprises the following steps that (1) input signals are generated by means of a two-order AR model; (2) the products of input signals generated at all moments and the corresponding two-order prediction coefficients of a two-order linear prediction filter are added, so that output signals at the moments are obtained; (3) the differences between the input signals and the output signals are obtained, so that an error value is obtained; (4) the product of the error value, a step value and the input signals is taken as the two-order prediction coefficient of the two-order linear prediction filter with the updated transient variation; (5) the steps from the step (2) to the step (4) are iterated, so that w [1] (n) and w [2] (n) are made to converge, and an output signal is what is needed. According to the variable-step LMS adaptive filtering method, both the errors with the index being first power and errors with the index being second power are considered, the rate is convergence is maintained, and better steady state errors are obtained.

The invention belongs to the technical fields of guidance, navigation and control of a deep sky object (DSO) detector, and particularly discloses an autonomous dimensionality reduction navigation method for a DSO landing detector. The method comprises the following steps of: determining the attitude, position and initial speed value of the detector relative to an inertial coordinate system at the current time; determining the distance of the detector relative to the center of the DSO; determining the three-dimensional (3D) speed of the detector relative to the inertial coordinate system; constructing the state quantity, state equation, observed quantity, observation equation and measurement noise variance matrix of a navigation system; carrying out non-dimensionalization on the measurement noise variance matrix, and determining observability; processing the measurement noise variance matrix, the observation equation, the observed quantity and an observation matrix by a decomposition transformation method; and determining the distance and speed of the detector relative to the center of the DSO by utilizing extended Kalman filtering (EKF) based on UD covariance factorization. By means of the method disclosed by the invention, the stability of autonomous navigation filtering can be ensured, and the convergence speed and estimation accuracy of key navigation parameters can be improved.

The invention provides a hypersonic flight vehicle attitude control method considering input saturation, which comprises the following steps of: 1, combining uncertainty of parameters, unmodeled dynamics and external disturbance in an unpowered reentry process mathematical model of a hypersonic flight vehicle to serve as total disturbance, and establishing models of an attitude loop and an angularrate loop, 2, designing a performance function to constrain steady-state and transient-state performances of state variable tracking errors of the attitude loop and the angular rate loop of the flight vehicle, 3, converting the inequality constraints obtained in the step 2 into equality constraints so as to facilitate controller design, 4, designing a linear extended state observer, and obtainingan output estimation value and a total disturbance estimation value of each loop, and step 5, designing a controller, so that the tracking error of the system can be converged to a preset area underthe condition of facing input saturation constraints. According to the method, the design and parameter optimization of the linear active disturbance rejection controller of the hypersonic flight vehicle are realized, and the dynamic performance, the robust performance and the anti-interference performance of the hypersonic flight vehicle are improved.

The invention relates to a path planning method based on an improved ant colony algorithm. According to the algorithm, an improved artificial potential field (APF) algorithm and an ant colony algorithm are combined, and the improved APF algorithm is adopted to carry out initial map planning, so that the blindness of initial planning of the ant colony algorithm is reduced. According to the algorithm, a heuristic function is improved by utilizing an evaluation function of an A * algorithm and a path turning angle, and a heuristic information incremental function is introduced, so that the convergence rate is ensured while local optimization is avoided. The state transition rule is improved, and the threshold of the state transition function is adaptively adjusted, so that the operation efficiency of the algorithm can be improved, and the diversity of solutions can be improved. The pheromone updating mechanism and the path evaluation function of the algorithm are improved, the global optimality of the algorithm is improved, and the obtained path better meets the actual requirement.

The invention belongs to the logistics distribution site selection technical field and relates to an adaptive Levy distribution hybrid mutation improved artificial fish swarm algorithm-based distribution center site selection optimization method. The method includes the following steps that: (1) relevant parameters are initialized, and a distribution center site selection optimization model is established; (2) the distribution center site selection optimization model is solved through using the optimization method according to which adaptive Levy distribution hybrid mutation is utilized to improve an artificial fish swarm algorithm; and (3) a distribution center site selection result is compared with the result of using the adaptive Levy distribution hybrid mutation to improve the artificial fish swarm algorithm in solving a distribution center site selection problem. According to the method of the invention, Levy mutation and chaotic mutation are introduced into the basic fish swarm algorithm, so that the diversity of artificial fish states in the basic artificial fish swarm algorithm can be increased, the capability of the basic artificial fish swarm algorithm to jump out of local optimum can be improved, and the optimization of distribution center site selection can be enhanced.

A method for optimal deployment of reader antennas of an ultrahigh-frequency radio-frequency identification positioning system based on an improved chicken swarm optimization algorithm specifically comprises the following steps: determining the evaluation indexes of an optimization target for a deployment problem of multiple target reader positioning antennas; establishing an optimization objective function; determining the constraint conditions in the process of deployment optimization; designing an improved chicken swarm optimization algorithm; and carrying out iterative operation, outputting a global optimal fitness value and an optimal solution, getting a solution satisfying the need of ultrahigh-frequency radio-frequency identification positioning, and carrying out final scheduling on reader antennas. On the premise of meeting the reader antenna number and space constraint conditions, the geometric accuracy of positioning, the coverage of positioning and the communication interference are taken as optimization objectives, and the optimization problem is solved using the improved chicken swarm optimization algorithm. Therefore, the quality of the optimal solution is improved greatly while the convergence speed of the algorithm is ensured.

The invention relates to a distributed flight control system fault diagnosis design method based on the multi-agent technology. The method is characterized by including the steps that firstly, a multi-agent system connected graph with a leader is established and represented through an undirected graph and a Laplacian matrix L is acquired; secondly, a state equation and an output equation of each node of a flight control system are established; thirdly, according to each node, a distributed error equation and a global error equation based on the undirected graph are established; fourthly, according to the established undirected graph, the collected state equations of the flight control system, the collected output equations of the flight control system, and the established global error equations, a distributed fault diagnosis observer gain matrix of the flight control system can be acquired. According to the method, a multi-target distributed fault diagnosis observer is designed, online diagnosis can be conducted when any node in the system breaks down or multiple nodes break down simultaneously, and online fault diagnosis and real-time fault estimation can be conducted on the flight control system.

The invention discloses a reactive power grid capacity configuration method for a random inertia factor particle swarm optimization algorithm. The reactive power grid capacity configuration method includes steps that I, acquiring system parameters of a WAMS system in real time and setting particle boundary conditions; II, initializing a swarm and determining an adaptive value of the particle; III, dividing iterative stages; IV, updating the speed and position of the particle; V, judging whether the iteration times arrives at the maximum iteration times of a global search stage; VI, judging whether the iteration times arrives at the maximum iteration times of a primary solution stabilization stage; VII, judging whether the iteration times arrives at the upper iteration limit; VIII, iterating till arriving at the maximum times, and outputting an online reactive capacity configuration method. Compared with a standard algorithm and an adaptive mutation algorithm, the reactive power grid capacity configuration method for the random inertia factor particle swarm optimization algorithm enables the optimization precision to be improved and realizes to improve the early global search capability and the late local search precision based on guaranteeing a convergence rate through combining with actual situations of reactive optimization, and the global optimal solution is ultimately obtained.

The invention discloses an unmanned surface vehicle distributed formation control method under collision avoidance and connection retention constraint. The method comprises the following steps: building a kinematic and dynamic model of an unmanned surface vehicle; describing information interaction among individuals in an unmanned surface vehicle formation system through an unoriented topologicalgraph; building a distance error equation, a relative course angle error equation and an azimuth angle error equation of neighboring unmanned surface vehicles on the k side, and describing the collision avoidance and connection retention constraint into a constraint problem of formation error; using a logarithmic barrier lyapunov function to guarantee that the formation error satisfies a constraint condition of preset transient state performance, and by a backstepping design method, performing design of a virtual controller for a relative course angle error system, a distance error system andan azimuth angle error system; by a dynamic surface control technology, avoiding a problem of repeated derivation to the virtual controller and solving a problem of failure in measurement of acceleration in design of a formation controller; and designing the distributed formation controller based on a disturbance observer.

The invention discloses an electric power Internet of Things container migration method for edge network load balancing. The method comprises the steps of establishing a container information vector and a container deployment matrix of an edge network; carrying out load balancing detection on the edge network to obtain an overload edge node list; considering the resource utilization balance degree, the residual resource balance degree, the network transmission delay and the container migration downtime, and establishing a container migration model of the load balance joint migration cost; andbased on an improved ant colony system algorithm, performing container migration according to the container deployment matrix, the overload edge node list and the container migration model. The invention can effectively solve the problems of limited edge node resources, obvious business busy degree differentiation among edge nodes caused by obviously unbalanced business request space-time distribution of an edge network, and the like.

The invention discloses a multi-fighter cooperative fault diagnosis method under switching topology. The method includes the steps that firstly, a multi-fighter system connecting diagram of each topology structure is constructed and represented with a digraph to obtain the Laplacian matrix and self-cycle matrix of each topology structure; secondly, a state equation and an output equation of each node fighter are obtained, and a system model with an executor fault is set up for each node; then, a global augmented system state equation based on the digraphs is constructed for each node; finally, a multi-fighter cooperative fault diagnosis observer is set up, collected input data and output data of the all the nodes are sent into the observer to obtain the fault estimated value of each node, and therefore the executor faults of the flight control system are estimated. By means of the method, the fault of any node and the faults simultaneously appearing in multiple nodes can be diagnosed on line in the cooperative flight control system, and online fault diagnosis and real-time fault estimation of the flight control system can be carried out.

The invention provides an integrated least-mean-square (LMS) adaptive filter, which comprises a digital filter, wherein the digital filter integrates a step length 2 mu which is a decimal fraction into an integer of more than or equal to 1, and outputs an output signal y(n) which is integrated and amplified at the same time; an output end of the digital filter is connected with an error calculation module which is used for performing order-of-magnitude matching calculation on a desired signal d(n) and an error signal e(n); and by integrated processing, the decimal multiplication operation of the LMS adaptive filter is avoided, and the complexity of an implementation circuit is reduced. The invention also provides a method for acquiring an adaptive weight coefficient by using a variable step length. The invention has the advantages that: by arranging an error judgment module, the step length is variable; and a large signal can be stably and quickly converged during the guaranteeing of the convergence speed of a small signal.

The invention discloses a discrete manufacture workshop scheduling method based on an improved cat group algorithm. The discrete manufacture workshop scheduling method comprises steps of 1) establishing a discrete manufacture workshop multi-target scheduling model, 2) determining a scheduling constraint condition, 3) initializing a cat group speed and position, 3) determining a ratio MR through a linear mixed ratio, dividing the cat group into a searching mode and a tracking mode, 5) determining whether the cat group is in a searching mode, if yes, executing a behavior under the searching mode, if not, executing a behavior under the tracking mode, 6) using a fitness function to calculate fitness of each cat to choose a cat with an optimal fitness, 7) determining whether an iteration termination condition is satisfied according to a chosen optimal solution , if yes, terminating the program, if not, continuing to execute the step 4. The discrete manufacture workshop scheduling method comprises a global searching and local searching, guarantees rate of convergence of the algorithm, and overcomes problems that the traditional algorithm is low in searching speed and easy to trap in local optimum.

The invention discloses a method for estimating an SOC of a power battery based on anti-outlier robust unscented Kalman filter, and belongs to the technical field of power batteries. The method comprises the following steps that a state and observation equation of the power battery is designed through the combination of a composite model method and an ampere-hour method, a model equation of the vehicle-mounted battery is determined, and a battery equivalence model is established; model parameters are identified, relevant parameters of the battery model observation equation are identified by means of a recursive least square method, the iteration frequency is identified with the system input amount as continuous excitation, and therefore a final result is converged and tends to be stable; an improved anti-outlier robust unscented Kalman filter algorithm is adopted for estimating the SOC of the battery. By means of the method, a measurement error model is corrected into a normalized contaminated normal distribution model, a posterior probability of the occurrence of outliers is calculated in combination with the Bayesian theorem to serve as a weighting coefficient for the self-adaptive adjustment to measure and predict related variances and gain matrices, and the problem of outlier interference can be effectively solved.

The invention discloses an unmanned aerial vehicle automatic landing locus control method based on double models. The method comprises the following steps: 1, establishing an unmanned aerial vehicle and aircraft carrier dynamical model, and according to relative positions between an unmanned aerial vehicle and an aircraft carrier, establishing a relative movement equation; 2, according to a feedback linearization theoretical method, designing an unmanned aerial vehicle to aircraft carrier locus controller; 3, designing an expected space locus of the aircraft carrier; designing an expected relative tracking value; and designing an expected relative speed; 4, calculating to eliminate errors between expected and actual relative longitudinal (xe<~>), (ue<~>), and transversal (ye<~>)and vertical (ze<~>) relative positions; and calculating to eliminate an error theta e<~> between an expected relative pitch angle and an actual relative pitch angle, a pitch angular speed Pe<~> and a deflection ratio We<~>; and 5, each execution part controlling signal calculation: calculating an execution part control variable [delta T, delta a, delta e, delta r] needed by an execution part control variable u needed for realizing a control amount. A control process is shown in attached drawings.

The invention provides an SOC online estimation method for a storage battery based on an EKF algorithm, and the method comprises the following steps: determining a battery equivalent circuit model, and building a discrete state space model of a battery nonlinear system; building a fitting function relation between the SOC initial value SOC(0) of the battery and an open-circuit voltage initial value UOC(0) through a constant current charging and discharging experiment, solving and obtaining the SOC initial value SOC(0) of the battery; taking SOC(0) as an initial state quantity of input, carrying out the estimation of the SOC of the battery through the EKF algorithm, and generating an SOC estimated value; carrying out the temperature, battery service life and self-discharging effect compensation for the generated SOC estimated value, and outputting a corrected SOC estimated value. Through the optimization of the SOC initial value, the method enables the initial state of the EKF to approach to the real-time state of the storage battery as much as possible, guarantees the convergence speed of the SOC online estimation of the storage battery, and improves the estimation precision through the temperature, battery service life and self-discharging effect compensation.

The invention discloses a parameter optimization method based on an improved genetic algorithm, computer equipment and a storage medium, and belongs to the field of parameter optimization. The optimization method comprises the following steps: 1) defining an initial chromosome population; 2) constructing a dynamic evaluation index function of the electric vehicle control system, and optimizing toobtain chromosome fitness; 3) selecting by using a brocade selection algorithm to serve as a parent population; performing operation by using an adaptive crossover and mutation algorithm to generate afilial generation population; adjusting the optimization region of the nth chromosome in the jth step by adopting a self-adaptive search strategy, checking whether j reaches the maximum allowable optimization step number or not after the search is completed, and if not, returning to the step 2); and 4) finding out the chromosome individual with the minimum fitness in the current population, wherein the value corresponding to each dimension of the chromosome is the parameter value of the electric vehicle control system, and the invention solves the problems of complex modeling and large calculation amount in the parameter optimization process of the electric vehicle control system.

The invention discloses a model-free control method for an aerodynamic heat superhelix nonlinear fractional order sliding mode, and the method comprises the steps: building a mathematical model between the input electric energy and the output temperature of an aerodynamic heat ground simulation system of a hypersonic aircraft based on the law of conservation of energy, and converting the mathematical model into a model-free control superlocal model; constructing a nonlinear fractional order sliding mode surface according to the defined output tracking error, the nonlinear function and the fractional order calculus; and in combination with the nonlinear fractional order sliding mode surface, the super-spiral approaching rate, the super-local model and a time delay observer, a model-free controller of the super-spiral nonlinear fractional order sliding mode is built, and buffeting in the control process is suppressed. According to the design of the nonlinear fractional order sliding mode surface, the stability of control is ensured, the convergence speed is high, the steady-state error and the saturation error are reduced, and the shaking problem of the sliding mode surface is improved through the combination of the super-spiral approaching rate.

The invention discloses an intelligent navigation algorithm based on real-time traffic status. According to the invention, an ant colony algorithm is applied to the field of automobile navigation; a traffic flow factor is considered; the traffic flow is served as a parameter influencing ant colony pheromone and the road section with short distance and smooth traffic is selected for the automobileas far as possible, so that the purposes of avoiding congested road section and balancing the traffic flow of a road network can be achieved; the performance of the algorithm is greatly promoted in amanner of dynamically adjusting the evaporation rate and introducing the thought of elitist ants, in order to overcome the defects of low convergence rate and possibility of falling into local part ofthe ant colony algorithm when the road network node is increased.

The invention discloses a dynamic demand response solving method considering time-of-use pricing. The method comprises steps of adopting a fuzzy clustering algorithm to divide a load prediction curvein a scheduling period into a peak time period, a flat time period and a valley time period; determining corresponding points (p, q) on the basis of historical data fitting demand and price functionsaccording to the load mean value of each time period, and linearizing the functions near the points (p, q); according to the coefficients of the linear function in each time period, respectively calculating the elastic coefficients of the peak time period, the flat time period and the valley time period so as to establish a dynamic elastic coefficient matrix; establishing a dynamic demand responsemodel according to the dynamic elastic coefficient matrix; solving the dynamic demand response model so as to obtain the prices of the peak, flat and valley time periods in the scheduling period anda new load prediction curve formed after implementing the dynamic demand response. According to the invention, the technical problem that the optimal effect cannot be achieved when the demand side response is stimulated through the price change due to the fact that the fixed price elastic coefficient cannot dynamically reflect the relationship between the load characteristics and the time-of-use price in different scheduling periods is solved.

The invention discloses a method and system for predicting wind speed. The method includes: obtaining the original sequence of wind speed data; using a particle swarm algorithm to determine the optimal preset scale parameter and optimal bandwidth parameter of the variational mode decomposition method, and converting the original sequence Decompose into several modal function subsequences; use the differential evolution algorithm to determine the kernel parameters of the least squares support vector machine model of each modal function subsequence, the variation factor of the mutation operation decreases with the increase of the evolutionary algebra, and the generated mutant individuals It is related to the optimal individual of the previous generation, and the crossover probability factor of the crossover operation increases with the increase of the evolutionary algebra; according to the autocorrelation of each modal function subsequence and each kernel parameter, determine the least squares of each modal function subsequence Support vector machine wind speed prediction sub-model, and predict the decomposition wind speed of each sub-sequence through each wind speed prediction sub-model; determine the final wind speed prediction value according to each decomposition wind speed. The method and system provided by the invention can accurately predict wind speed.

The invention discloses a control plane load balancing method and system in a software-defined network that are aimed at dynamically regulating and controlling a connection relation between a control plane and a data plane via monitoring of a request flow sent to the control plane from the data plane in the software-defined network, and therefore all controller loads of the control plane in the software-defined network can be balanced. The method comprises the following steps: in a first step, monitoring flow tables that are needed are distributed to all software-defined exchange devices via application program interfaces provided by controllers, all the software-defined exchange devices are monitored in real time according to monitoring parameters given by a cloud platform service provider, and the exchange devices are used for uploading collected data onto the control plane via an OpenFlow protocol; in a second step, the collected monitoring data is subjected to processing and analyzing operation, the connection relation between the controllers of the control plane and the exchange devices of the data plane is determined, the flow tables are distributed to all the exchange devices via the OpenFlow protocol, all controller loads can be controlled and balanced, and therefore control plane delay can be reduced.

The invention discloses an energy-saving transmission self-adaption recursive least squares (RLS) distributed-type detection method of a wireless sensor network. Through the updating process of an increment of a distributed-type RLS weight number, the situation that measurement data of all-network nodes are utilized to train the distributed-type RLS weight number of a medium bridge of a bridge node set is achieved, convergence performance of an RLS algorithm is ensured, and the convergence performance is equivalent with that of an existing all-network distributed-type RLS algorithm. Distributed-type RLS weight number estimation is conducted on bridge nodes, adjacent transmission of the weight number is conducted on a sub-network of the bridge nodes, transmission updating of the weight number is achieved, and distributed-type detection is achieved. The energy-saving transmission self-adaption RLS distributed-type detection method of the wireless sensor network has the advantages that all-network parameter calculation and transmission in the prior art are avoided, communication traffic and node operation quantity are reduced, energy consumption is saved, and stability is high. In addition, the RLS weight number is calculated on the bridge nodes, all-network node data information is utilized, and performance of the algorithm is ensured. Under conditions of less energy consumption and lower network communication traffic, all-network detection performance can be achieved, and performance of the wireless sensor network is improved.

The invention relates to a negative feedback self-adaptive mechanism kinematic chain isomorphism identification method for an ant colony algorithm. The method comprises the following steps of forming a topological graph corresponding to the structure of the mechanism kinematic chain; ranking the mechanism framework of the kinematic chain according to structural feature, wherein the step of ranking mainly comprises two steps of layering of the topological graph and initial ranking in the layer; obtaining structural feature set of the mechanism, and converting into a depressed TSP (traveling salesman problem); introducing negative feedback mechanism and self-adaptive parameter adjustment into the ant colony algorithm, and working out condition maximum structural codes corresponding to the structural feature set of the two mechanisms through the improved anti colony algorithm; judging whether the condition maximum structural codes are equal, wherein if the condition maximum structural codes are equal, the two mechanisms are isomorphism, and if the condition maximum structural codes are not equal, the two mechanisms are not isomorphism. According to the method, the defect of the ant colony algorithm that local optimum is likely to be converged is overcome, and the global searching ability and rate of convergence of the ant colony algorithm in operation can be guaranteed.