48results about How to "Improve traversal" patented technology

The invention provides a cooperative air combat firepower distribution method based on an improved multi-target leapfrog algorithm and belongs to the technical field of computer simulation and method optimization. The method comprises the steps that firstly, required data information is obtained through a cooperative air combat formation command and control system; secondly, a multi-target optimization module of cooperative air combat firepower distribution is established; then a multi-target quantum leapfrog algorithm based on a self-adaptive mesh method is carried out, and a Pareto non-inferior solution of firepower distribution problem is solved; finally, rules can be selected independently according to the optimal distribution scheme, and the optimal firepower distribution scheme is selected from the non-inferior solution. The cooperative air combat firepower distribution method based on the improved multi-target leapfrog algorithm has the major functions that weapons on a fighter aircraft on an attack mission are distributed to multiple targets according to the optimal firepower distribution scheme, the formed cooperative air combat is enabled to realize the optimal cooperative attack effect, and the maximum operational effectiveness is obtained.

The invention discloses an auto-disturbance rejection position servo system optimization design method based on an improved CPSO. By aiming at problems of permanent magnet synchronous motor servo systems on high position control precision, fast response, and stable performance, a double-loop control structure is adopted, and a PMSM auto-disturbance rejection position servo control system is established. By aiming at a parameter setting problem of an auto-disturbance rejection position controller, the improved Chaos Particle Swarm Optimization (CPSO) is provided. By adopting the CPSO, a position of a particle is initialized according to cubic chaotic mapping, and an index self-adaptive way having adjustable parameters is used to adjust inertia weight in a non-linear way, and at the same time, the position of the particle is updated by adopting a chaos and stability alternate way, and therefore the convergence rate and the global optimization ability of the CPSO are effectively improved, and the CPSO is used for the optimization of the auto-disturbance rejection position controller parameters. By combining with a fitness function including position control requirements, the optimization design of the PMSM position servo control system is realized, the position control precision and the response speed of the servo system are improved, and a strong disturbance rejection ability is provided.

The invention relates to a network technique, especially an IP address searching method. In order to solve the problem of traversal performance and memory consumption of multi-branch Trie tree, the invention discloses a traversal method of multi-branch Trie tree for increasing traversal performance and reducing memory consumption. The technical scheme is that: when a node is treating, firstly, a treated record last time is compared with the node, if the node has been treated already, then, next node is traversal, if not, the node is treated by processing function; after the node has been treated, the node position information, IP prefix information corresponding to the position, block pointer and IP prefix information of its each level ancestor are recorded. The method provided by the invention has advantages that memory capacitance is reduced, distributing and releasing memory are not needed, other IP prefix information of the level needs not cached, comparison with other IP prefix of the level is not needed and traversal performance is increased. Because of employing preorder traversal method, treating order can be ensured.

The invention discloses a distribution network overcurrent protection method with a distributed power supply, and a fixed value optimization method and system, and belongs to the technical field of distribution network relay protection. When a fault occurs, a data acquisition system acquires a fault current flowing through each inverse time limit overcurrent relay in the distribution network witha distributed power supply; according to the inherent characteristics of the fault current and the inverse time limit overcurrent relay and the selectivity, the sensitivity and reliability requirements of the relay protection, a mathematical model is established, wherein the mathematical model comprises an objective function and a constraint condition; a particle swarm optimization based on crowdsearch is employed to perform particle optimizing of the time setting coefficient and starting current of the inverse time limit overcurrent relays; and according to the optimal particles, the time setting coefficient and starting current of each inverse time limit overcurrent relay are subjected to re-assigning, and faults are cut off in the shortest time. The problem is solved that the relay fixed value is improperly set after the distributed power supply is accessed into the distribution network.

The invention relates to an alternating-current / direct-current-system reactive power optimization method based on a good-point set quantum particle swarm algorithm. The method comprises the following steps of step1, establishing an alternating-current / direct-current-system reactive power optimization model; step2, improving a quantum genetic algorithm; and step3, using the improved quantum genetic algorithm to solve the alternating-current / direct-current-system reactive power optimization model. In the invention, the QPSO is improved and a problem of premature convergence generated because the QPSO is easy to fall into local optimum is solved.

The invention discloses a fast FAT32 disk partition traversal and file searching method and relates to an FAT32 file system and a magnetic disk data search and recovery technology. The method of the invention comprises the steps as follows: raw-disk-reading disk sector data of a root directory and subdirectories to internal storage, and performing directory entry search to all levels of directories: gradually adding 32 bytes in the same directory to perform horizontal search, and performing downward search to the searched subdirectory entry at a start position of the subdirectory, and returning to a search position stored in a parent directory to search by performing upward search after finishing the search of the current subdirectory, and performing traversing to finish the search of each directory entry sub-tree of the root directory in turn, and performing file attribute search, classification and ordered storage to a file directory entry. The method of the invention improves traversal and search speed, and improves overall performance for subsequent file search, read or download. The method of the invention is especially suitable for FAT32 file system partition traversal and file search in an embedded system.

The invention relates to a central air conditioner energy consumption control method based on improved particle swarm optimization. The method includes the following steps that COP curves of differentunits of a central air conditioner are acquired, and energy consumption functions of all the units are obtained through fitting; based on the energy consumption functions of all the units, in combination with an exterior penalty manner, a central air conditioner energy consumption optimization model is established; the central air conditioner energy consumption optimization model is solved through the improved particle swarm optimization, and optimal load distribution rates of all the units are obtained; and load switching of all the units is controlled according to the optimal load distribution rates of all the units, and central air conditioner energy consumption optimization control is completed. Compared with the prior art, by means of the improved particle swarm optimization, the early ergodicity of the particle swarm optimization is improved through a sinusoidal chaotic sequence, particle groups have the mechanism of getting away from local optimal points by adding sinusoidal chaotic disturbance, the inertia weights of particles are adaptively adjusted in the optimization process, the optimization speed can be effectively increased, the optimization precision can be effectively improved, thus, the method is suitable for complex operation working conditions of the central air conditioner, and it is guaranteed that the operation energy consumption of the air conditioner isminimized.

The invention relates to an active power distribution network multi-period dynamic reconstruction method based on improved recursive ordered clustering. The method comprises the following steps of: S1, employing the Euclidean distance to describe the similarity degree of data in a segment, and employing the improved recursive ordered clustering to take the maximum similarity degree in various types of power prediction curve segments as a target for period division; S2, calculating an uncertain power flow by using an affine-linear optimization interval power flow algorithm so as to calculate aninterval value of the network loss, and taking a midpoint of the interval value as a fitness function; S3, according to the determined fitness function, solving the model by adopting an adaptive quantum particle swarm algorithm of a Blot spherical surface to obtain an optimal solution which is a disconnecting switch set corresponding to the optimal network topology; and S4, adjusting the topological structure of the power distribution network by adopting the obtained disconnection switch set. Compared with the prior art, the method has the advantages of quick optimization, safe and economic operation of the power grid and the like.

The invention provides an intelligent setting method for unmanned aerial vehicle attitude control parameters based on a quantum firefly algorithm, and belongs to the field of automatic control. The method comprises the steps of building an unmanned aerial vehicle attitude motion model, designing a fractional order PID controller, determining a to-be-set parameter, and selecting an error index function as an objective function; setting parameters of the quantum firefly algorithm; executing the quantum firefly algorithm to perform controller parameter setting optimization, and obtaining the optimal controller parameter and the objective function value of the current setting; judging whether the objective function value meets requirements or not; if the objective function value meets the requirement, determining that the firefly position is the optimal attitude controller parameter, and ending the setting; otherwise, returning to the step 2, resetting the parameters of the quantum fireflyalgorithm, and executing the steps 2-4. According to the method, on the basis of a standard firefly algorithm, improvement is carried out by utilizing a quantum theory, elite retention and mutation behaviors, and the defects that the later convergence speed of the standard firefly algorithm is seriously reduced, the convergence precision is not high, and local optimum is likely to happen in the prior art are overcome.

The invention discloses a UVM-based RFID tag chip verification device. The device comprises static components including a virtual interface module, a runtest calling method, a clock generation module,a DUT of an RFID tag and an EEPROM (Electrically Erasable Programmable Read Only Memory) module. Dynamic components comprise a base test module, a mycase module, an ENV module, a CONFIGURATION module, an agent module, a scoreboard module, a refresh module, a driver module and a monitor module. According to the method, the methodology of the UVM is adopted, so that the verification efficiency is higher. The hierarchy is clear, and the verification efficiency and credibility are improved by generating different test cases and a large number of randomized tests.

The invention relates to a human body behavior recognition method based on heterogeneous layered PSO and SVM, and belongs to the technical field of human body behavior recognition and pattern recognition. Firstly, a particle fitness function is established according to input data, and then particle initialization is carried out on parameters needing to be optimized in a classifier based on a mixed random chaotic mapping method; the particles is layered by adopting a dynamic threshold rule, the acting force of the heterogeneous particles is fused into the particle position and speed updating process of each layer, and a layering speed updating principle is set; and finally, carrying out iterative optimization on each dimension parameter in the classifier to obtain a classification model based on heterogeneous layered optimization. And human motion behavior data input by the sensor is classified based on the model. Compared with the prior art, the optimization algorithm solves the problem that parameters are prone to falling into local optimum when a support vector machine classification model is established, the established heterogeneous hierarchical classification model parameter optimization convergence block is high in fluctuation interference resistance, and the recognition precision of human body behaviors is improved.

The invention discloses a power system small-signal stability analysis method based on a fractional order differential theory. The method comprises steps: 1) a power system small-signal fractional order model is built; and 2) robust stability analysis on the power system small-signal fractional order model is carried out. The power system small-signal stability analysis method based on the fractional order differential theory has the beneficial effects that construction of a simplified nonlinear equation for idealized processing of a complex system is not needed, linearization on the constructed simplified nonlinear equation is also not needed, an equivalent simplified model for a power system complex small-signal model is constructed according to an intermediate equivalence process theory, a large number of differential operators reflecting the characteristics of the system are not lost, the characteristics of the system can be reflected more accurately, the power system small-signal stability analysis is more accurate, the control is more reliable, and the operation is safer.

The present invention provides an intermodulation test method and device. The method comprises the steps of controlling a plurality of modulation signal sources to send the broadband modulation signals of different frequencies respectively; adjusting the output signal amplitudes of the modulation signal sources to enable the amplitudes to be the preset values when the signals arrive at the entrances of the tested devices; setting an analysis frequency range of a signal analyzer, and obtaining the intermodulation products generated by the tested devices after the plurality of modulation signal sources output signals to the tested devices; measuring the intermodulation products, and comparing the measurement results with a first threshold value; according to the comparison results, determining whether the intermodulation performances of the tested devices are qualified.

The invention discloses a lithium ion battery model parameter recognition method based on a chaotic cat group. The invention discloses a lithium ion battery parameter recognition method, relates to the technical field of electric power, and aims at carrying out parameter recognition on a lithium ion battery model by introducing a chaotic cat swarm algorithm which can carry out local search and global search at the same time, and introducing the chaotic algorithm on the basis of a cat swarm, so that the algorithm ergodicity is improved, and the convergence degree of a globally optimal solutionis greatly improved.

The invention provides a digital platform based on data service and data management. The platform comprises a data service module which is used for collecting service information, and generating corresponding service data according to the service information; a data management module used for acquiring a service demand, transmitting the service data to control ends of corresponding managers according to the service demand, matching the service data with a preset service service mode, and determining a matching result, wherein the managers comprise a service demander, a service manager and a service partner; and a digital platform used for linking the data management module and the data service module when the matching result is successful, pushing and guiding the matching result to the intelligent terminal based on the preset intelligent terminal to construct the digital platform.

An embodiment of the invention discloses a file traversal method, apparatus and system. The file traversal method is applied to a traversal server; the traversal server generates an initial traversal task according to a received traversal request for a target storage cluster, and receives a sub-task set obtained after splitting the initial traversal task by a first storage node; each storage node in the target storage cluster generates a sub-task request; and storage nodes corresponding to sub-tasks in the sub-task set finish file traversal operations respectively and receive traversal data returned by the storage nodes to generate a traversal result. By applying the technical scheme provided by the embodiment of the invention, the traversal server performs generation of the initial traversal task and distribution of the sub-tasks in a file traversal process; each storage node executes the corresponding sub-task in the local; and the storage nodes are not interacted, so that the file traversal performance is improved and the load pressure of the storage cluster is reduced.

The invention discloses a secret key sequence generating method based on a self-feedback evolution sequence. According to the method, individuals in a population are sequenced according to frequency number adaptive values and sequence adaptive values of the population, a breeding pool is established based on ranking sampling, through evolution operation in the breeding pool, new individuals prevail over the best individual in the breeding pool are obtained, the new individuals are adopted for replacing the best individual in the breeding pool and the best individual in the population, the best individual in the population is searched for in a self-feedback mode and migrated in a self-feedback mode, when the population reaches the maximum number of iterations, the best individual in the population serves as a final secret key sequence, and the best individual in the population is selected as the final secret key sequence through an evolutionary algorithm and self-feedback. The secret key sequence generating method has the advantages of being high in safety, execution efficiency and randomness.

The invention discloses a three-dimensional image registration method based on a reselection point strategy and an artificial bee colony optimization, which comprises the following steps: (1) samplinga dynamic point cloud to obtain a sampling point set; (2) generating a certain number of bee colonies and initializing the position of the individual bee colonies; (3) determining the Euclidean transformation moment of each honeybee, and transforming the position of the sampling point set according to the Euclidean transformation matrix; 3) transform that sample point set by the Euclidean transform moments and calculate the objective function value; (5) comparing the variation of the optimal objective function value, and if the variation is smaller than the threshold value for successive times, reselecting the point operation to obtain a new sample point set, otherwise, entering the step (6); 6) if that maximum evolutionary algebra is reach, entering the step 7), otherwise return to the step 3); (7) obtaining the optimal Euclidean transformation moment from the optimal solution of the population; And moving the dynamic point cloud to complete image registration. In this method, the re-selection strategy is introduced into the sampling process and the bee-colony algorithm is combined to effectively reduce the time of image registration and improve the performance.

The invention provides a walking robot control method and system. The method comprises the following steps of: providing a closed line patrol path; driving the walking robot to walk for a circle alongthe line patrol path, and recording the perimeter of the line patrol path; automatically acquiring a position value corresponding to each starting point in a current line patrol path according to therecorded perimeter of the line patrol path, wherein the position value is a length from the current starting point to an initial point on the line patrol path; and driving the walking robot to walk according to the line patrol path again, and driving the walking robot to enter a working state when the walking robot reaches each starting point according to the corresponding position value. According to the walking robot control method and system, parameters of all starting points do not need to be input manually in an assisted mode, the labor cost is saved, the traversal performance of the walking robot in the working interval is improved, and the working efficiency is improved.

The invention provides a method and system for efficiently traversing a large data volume set, a terminal and a storage medium. The method comprises the steps of obtaining the number of CPU cores of acurrent server; averagely splitting the big data set into subsets with the same quantity according to the quantity of the CPU cores; creating a unique corresponding sub-thread for each subset, and controlling the sub-thread to execute a processing task of the corresponding subset; and collecting calculation results of all the sub-threads, combining all the calculation results, and outputting thecombined calculation results to the main thread. The big data set can be rapidly processed, the processing steps are simple, computing resources can be fully utilized, and the method is suitable for conventional application programs.

The invention discloses a user page testing method and device, electronic equipment and a storage medium, and relates to a page testing technology. The specific implementation scheme comprises following steps of capturing a screenshot of a current page; obtaining a control tree corresponding to the current page according to the screenshot of the current page; according to the control tree corresponding to the current page, dividing the current page into a page type corresponding to the current page; and testing the current page according to the page type corresponding to the current page. According to the embodiment of the invention, a unified test method can be adopted for user pages of different containers, the generalization ability is high, and the test efficiency is higher.

The invention discloses a safe unloading method for load balancing in a mobile edge computing network, which comprises the following steps of: A, constructing a multi-cell mobile edge computing systemarchitecture, and initializing a to-be-processed task of a mobile equipment end and related parameters of an edge server; B, proposing a data security transmission strategy for the unloaded data; S,proposing a multi-objective optimization problem, taking the total time for completing the task as a first objective function, taking the total energy consumption of the user side as a second objective function, and taking the system load mean square error as a third objective function; and D, optimizing the three objective function values by using an improved multi-objective optimization algorithm, obtaining a Pareto optimal unloading strategy set, and obtaining an optimal unloading strategy according to user preferences. The method is suitable for a multi-cell and multi-server scene, safe task unloading is achieved, time delay and energy consumption are reduced, and system loads are balanced.

The invention discloses a sea skimming aircraft tail end flight parameter design method. The method comprises the steps of obtaining sea skimming aircraft tail end flight scheme parameters and a target function; a quantum seagull algorithm is adopted, the tail end flight parameters are set and optimized by solving the maximum value of the target function, and the optimal flight parameters are determined; the determination of the quantum seagull algorithm comprises the following steps: on the basis of a standard seagull algorithm, initializing a seagull population by adopting Tent chaotic mapping, and determining the state of the quantum seagull by adopting a quantum bit probability amplitude coding mode. According to the method, the Tent chaotic mapping and the quantum theory are fused to improve the sea gull algorithm, each quantum sea gull state represents two positions in a parameter optimization space and correspondingly optimizes two solutions of an objective function, the global search efficiency of the algorithm is improved, and the convergence speed of the algorithm is increased.

The invention discloses an intelligent setting method for attitude control parameters of a carrier rocket. The method comprises the following steps: acquiring nonlinear controller parameters and an objective function of the attitude of the carrier rocket; adopting a quantum dogvessel algorithm, setting and optimizing nonlinear controller parameters by solving the maximum value of an objective function, and determining optimal nonlinear controller parameters, wherein the determination of the quantum dogvessel alveolar algorithm comprises the following steps: on the basis of the standard dogvessel alveolar algorithm, determining the state of the quantum dogvessel alveolar by adopting a quantum bit probability amplitude coding mode; and according to the sine and cosine position variables corresponding to the two positions in the parameter solution space, determining the target function value of the two position coordinates, and taking the maximum value as the target function value of the quantum dogvessel state. According to the method, the quantum theory is combined with the dogvessel alveolar algorithm, each quantum dogvessel alveolar state is equivalent to two positions occupied in an optimization parameter space, each dogvessel alveolar state corresponds to two solutions of an optimization problem, the ergodicity of the algorithm is improved, and the global convergence speed of the algorithm is increased.

The invention provides a walking robot control method and system, and the method comprises the steps: S1, counting a corner temporary counting value and a corner counting value in each first period; wherein the rotation angle count value is used for representing the sum of the number of rotation angles passed by the robot in the line patrol state in each first period, and the same rotation angle is counted once; wherein the corner temporary counting value is used for representing the sum of the number of corners which the robot starts from an initial point, enters a line patrol state and passes when walking along a line patrol path in each second period; and S2, determining a working starting point according to the size relationship between the corner temporary count value and the corner count value, and driving the robot to leave the line patrol path at the working starting point to enter a working area, so that the robot is changed from the line patrol state to the working state. Thetraversal of the walking robot in the working interval is improved, and the working efficiency is improved.

The invention discloses and proposes a reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms. The reservoir dispatching method comprises the following steps of S11, acquiring basic information data of a reservoir; S12, building a multi-target optimal dispatching mathematic model considering constraint conditions of water balance, machine set output, discharged volume and generation power, wherein the target function is built based on maximum generation power, minimum water deficit, minimum surplus water and minimum output; S13, figuring out Pareto optimal solution of the multi-target optimal dispatching mathematic model by using the multi-target shuffled frog leaping and differential algorithms; and S14, automatically determining a reservoir optimal dispatching scheme by a multi-target decision method on the basis of the Pareto optimal solution. By the reservoir dispatching method, global optimization is achieved, the calculation efficiency is improved, and the requirement of automatically selecting the multi-target optimal dispatching scheme of the reservoir is met.

The invention discloses a robot path planning method based on local optimal convolution evaluation. The method comprises the steps of building a two-dimensional grid map of a working environment through a mobile robot, and determining the positions of a starting point and a target point; enabling the mobile robot to sense environment information around the current position, and screening out a feasible moving direction from the candidate moving directions; calculating the projection of a unit vector in each feasible moving direction in the negative gradient direction of the target point distance function, and obtaining a local optimal moving direction; checking the rationality of the local optimal moving direction by using a convolution evaluation index; and when the feasible path cannot be found in the primary path planning, performing secondary path planning, and canceling a link of checking the rationality of the local optimal moving direction. According to the method, the path planning of the mobile robot in an unknown environment can be realized, the decision-making thought conforms to the actual operation characteristics of the mobile robot, and the method has the advantages of small operand, high ergodicity and high operation efficiency.

The present invention provides an intermodulation test method and device. The method comprises the steps of controlling a plurality of modulation signal sources to send the broadband modulation signals of different frequencies respectively; adjusting the output signal amplitudes of the modulation signal sources to enable the amplitudes to be the preset values when the signals arrive at the entrances of the tested devices; setting an analysis frequency range of a signal analyzer, and obtaining the intermodulation products generated by the tested devices after the plurality of modulation signal sources output signals to the tested devices; measuring the intermodulation products, and comparing the measurement results with a first threshold value; according to the comparison results, determining whether the intermodulation performances of the tested devices are qualified.

The invention provides an end-to-end delay improvement method and device for a hysteresis network based on a behavior similarity model, and the method comprises the steps: enabling a hysteresis network application scene to be mapped on a map, and dividing the map into regions; setting initialization time, calculating and recording the access frequency of each node of the tolerance network to eachregion within the initialization time, and reserving the weight value vector table of all the regions by each node; obtaining a node pair of two nodes entering the communication range of the oppositeside in the hysteresis tolerance network, wherein the node pair comprises two nodes entering the communication range of the opposite side; entering a node pair emission stage; and judging whether thenumber of message copies of the node in the node pair is 1 or not, and if so, entering a waiting stage. According to the scheme provided by the invention, the effects of high message submission probability in the DTN and reduction of submission delay can be achieved.