41results about How to "Improve search speed" patented technology

The invention provides a method for calculating a flexural capacity characteristic value of a steel and UHPC (Ultra High Performance Concrete) composite structural beam. Different constitutive models are adopted according to performance characteristics of UHPC, a T-shaped steel beam and a steel bar in the composite structural beam, and the contribution of material strain hardening performance to the flexural capacity is fully considered; compared with an ideal elastic-plastic model, the real performance of the structure can be better obtained; meanwhile, the cross section curvature, the curvature iterative calculation increment and the neutral axis position iterative calculation increment are given for initial values of conventional bridge calculation, and the neutral axis position and the cross section curvature are searched by adopting an influence coefficient method, so that the search speed is increased, and the problem that convergence cannot be realized due to manual setting of the iterative increment is solved; and finally, the condition that the UHPC top strain reaches the UHPC compression limit strain or the T-shaped steel beam bottom strain reaches the steel limit strain is taken as a judgment condition, and two damage modes of UHPC crushing and the condition that the steel reaches the tensile ultimate strength can be considered at the same time.

The invention relates to the technical field of path planning, in particular to a global path planning method for a robot in a complex static environment, and the method comprises the steps: constructing a static environment model; setting parameters; initializing a population position; calculating the fitness; adjusting the position of the sparrow to avoid static obstacles; after each iteration, carrying out local optimization by using cubic spline interpolation; and repeating the steps until the maximum number of iterations or path convergence is achieved, and outputting the optimal path. Compared with the prior art, the method has the advantages that the nonlinear attenuation factor mu is introduced into the discoverer algorithm, so that the discoverer can perform wide exploration in the early stage and can perform accurate exploration on the developed area in the later stage, the early-stage search speed of the discoverer is increased, and the convergence speed is increased; and different search paths are determined according to the alarm value, and large-range search is performed when the alarm value is low, so that the optimal path is quickly searched, and the convergence speed is further improved.

The invention discloses a memory management method for reducing fragmentation and an operating system, and belongs to the technical field of computer storage. Memory management is performed through a memory pool, a logic space is created for mapping with the memory pool, a page table is utilized to record a mapping relation between memory pages of the memory pool and memory pages of the logic space, and a memory recovery table records memory information supporting automatic recovery and the total memory capacity in the memory recovery table according to an application sequence. Memory allocation, memory access, memory release and memory recovery are all carried out through a logic space. According to the invention, the rapid allocation of the memory is realized, and the fragmentation of the memory is reduced.

The invention discloses a demand side flexible resource participation multi-objective optimization modeling method and a related device. The method comprises the following steps: determining initial data including wind power generation, thermal power generating units, electric vehicle disordered charging, distributed electric heating and user side energy storage; an objective function of the multi-objective optimization model is determined, boundary conditions of the multi-objective optimization model are designed according to the objective function and the basic data, and the multi-objective optimization model is constructed; designing a conventional thermal power adjustment scene and a typical demand side flexible resource adjustment scene based on the auxiliary service market; based on a conventional thermal power adjustment scene and a typical demand side flexible resource adjustment scene, the obtained initial data are utilized, an improved particle swarm optimization algorithm is adopted to solve a multi-target optimization model, and an optimization solving result is output. According to the method, the regulation capability of the power grid is optimized, the wind curtailment rate and the wind curtailment cost are effectively reduced, the absorption capability of new energy is improved, the problem of power curtailment caused by blockage of a delivery section is reduced, and the method has important significance in promoting sustainable development of the new energy.

The application discloses a multimedia resource search method, related device and vehicle, and relates to the field of human-computer interaction. When a user performs resource search, it is first judged whether there is a running multimedia application. When it is judged that there is a running multimedia application, it is further judged whether to perform search in the multimedia application according to the obtained search voice content. When it is judged that search is performed in the multimedia application, the multimedia application is directly called to perform in-application search according to the search voice content, so that the searched multimedia resource is more consistent with the user's expectation, the user's use experience is improved, the search speed is improved due to the direct search in the application, and the user's use experience is further improved. When it is judged that there is no running multimedia application, search can be performed in a media resource library outside the multimedia application, so that the user's resource search demand is met, and the user experience is ensured.

The present invention discloses a search and scanning control method for an optoelectronic turret to control a target area identified by a radar, and belongs to automatic control technology. The scanning search mode and the search range are automatically calculated according to the field of view size of the turret sensor and the possible target range given by the radar, so as to realize fully automatic search and scanning. During the regional search and scanning process, the optoelectronic turret will switch the search and scanning mode according to the target area range given by the radar and the current sensor field of view angle information range, select a suitable scanning mode from four scanning modes according to the size of the target area range and the field of view range, and calculate the scanning parameters, so as to ensure that the entire target area range can be scanned even when the target area is constantly changing. Actual experiments have proved that this search and scanning control method realizes fully automatic search and scanning without human intervention, and can find the target even when the range given by the radar is not accurate enough, which greatly improves efficiency and reduces the probability of missing the target.

This invention discloses a data storage method and system based on a scalable multidimensional learning index structure, belonging to the field of computer data storage. This system fully utilizes storage characteristics to organize data into memory and disk, significantly reducing disk I / O with minimal memory usage, accelerating data search speed, and supporting data expansion. This invention can be applied to multidimensional data indexing, typically represented by geospatial data, meeting the need for rapid multidimensional key searching in the high-dimensional key space of such data.

The application discloses a model management method and device, the method comprises the following steps: obtaining a first model, editing the first model based on a first input, and saving the edited first model as a first template, the first input is used for adding running dependency files corresponding to N first modules respectively, the running dependency files store parameter information of at least one simulink model, at least one second model is created according to the first template, and nodes corresponding to modules at all levels of the second model are displayed on a model configuration interface of the second model, in response to a second input to a node corresponding to a target module in the N first modules, the target module is updated based on parameter information of a target simulink model, and the target simulink model is a simulink model matched with the target module. According to the embodiment of the application, the difficulty of finding the to-be-replaced module in the model can be reduced, and the finding and replacing efficiency can be improved.

The application provides a battery group arrangement equalization strategy optimization method based on a QTA-TLBO double-layer multi-objective algorithm, which comprises the following steps: establishing an upper-layer battery group optimization model comprising an upper-layer optimization objective function and an upper-layer optimization constraint condition, and obtaining an optimal single cell arrangement structure of the battery group; establishing a lower-layer battery group optimization energy scheduling model comprising a lower-layer optimization objective function and a lower-layer optimization constraint condition, and obtaining an optimal equalization strategy of the battery group; establishing a coupling optimization model between the upper-layer battery group single cell arrangement structure and the lower-layer battery group equalization strategy; and using the proposed quantum tunneling annealing-teaching and learning double-layer multi-objective optimization algorithm to optimize and solve the model, so as to obtain an optimal grouping scheme of the battery group and an optimal equalization strategy of each cycle. Through the application, the reasonable arrangement of the single cells of the battery group can be obtained, the long-term reliable operation of the battery group can be realized, and the energy scheduling strategy of the battery group between the single cells can be planned, so that the maximization of battery energy utilization is realized.

The invention discloses a space station gyroscope orthogonality judgment method and device, and belongs to the technical field of data processing. The method comprises the following steps: pre-constructing a plurality of configuration sets; determining LTU components contained in each configuration set to form a plurality of component sets in one-to-one correspondence with the plurality of configuration sets; when gyro orthogonality judgment is carried out on the current configuration, determining a target configuration set to which the current configuration belongs according to the plurality of configuration sets; determining all LTU components contained in a corresponding target component set according to the target configuration set; whether a target LTU assembly exists in all LTU assemblies contained in the target assembly set or not is determined, three gyroscopes in the target LTU assembly participate in fault diagnosis and have no faults, and if yes, it is determined that the three gyroscopes in the target LTU assembly have orthogonality. According to the invention, the operand can be reduced and the searching speed can be improved.

The present application discloses a robot positioning method, a chip and a laser robot based on a multi-layer grid map. The robot positioning method comprises: the robot uses a laser sensor to collect laser point data; the robot obtains the multi-layer grid map layer by layer in order of resolution from low to high; the robot traverses each candidate solution in the current layer grid map; when the robot determines that the currently traversed candidate solution is a feasible solution, the robot controls the laser point data to stop adding the multiple occupancy probability values ​​corresponding to the currently traversed candidate solution, and then sets the determined feasible solution as the candidate solution of the next layer grid map based on the ratio between the resolution of the next layer grid map and the resolution of the current layer grid map, and then recursively determines the feasible solution in the candidate solutions of each layer grid map in order of resolution from low to high, until the optimal feasible solution is determined, and the optimal feasible solution is set as the repositioning result of the robot.

This invention belongs to the field of thermal power unit automation control technology, and discloses a configuration batch replacement method and related device based on hash caching technology. The configuration batch replacement method includes: reading a modified measurement point mapping table to obtain all source KKS and target KKS; updating the original hash table cached in memory based on the obtained source KKS and target KKS to obtain an updated hash table; traversing the updated hash table to perform configuration batch replacement; wherein, during configuration replacement, location information is obtained through the measurement point type, and then the source KKS in the indexed variable is replaced with the target KKS through the location information index variable. This invention can achieve fast and accurate replacement of KKS within the configuration, helping engineers to perform efficient configuration design; this invention solves the technical problems of slow configuration replacement speed and high system resource consumption in existing technologies, and has high engineering application value.

The application discloses a kind of based on three-dimensional voxelization map small unmanned aerial vehicle fine inspection path planning method, system, setting and medium, it is related to unmanned aerial vehicle control technical field, the method includes: S1: three-dimensional voxelization modeling is carried out to inspection object and environment, and safety inflation processing is carried out to obstacle;S2: the original inspection view point set is clustered and clustered, and the view point in clustering cluster is sorted according to S type path order;S3: the three-dimensional A* algorithm of optimization is used to generate collision-free polyline path;S4: the line path generated in step S3 is smoothed using line-of-sight algorithm, removes redundant node, generates smooth flight trajectory;S5: the path point after smoothing is converted from local model coordinates to WGS84 latitude and longitude coordinates using decoupling fitting strategy based on control point, generates the GPS waypoint sequence executable by unmanned aerial vehicle.The method provided by the application can solve the problems of unsafe path planning during inspection, low inspection point traversal efficiency, non-smooth flight path and the like in the prior art.

The application provides a UAV group sensing method for city monitoring data time delay guarantee, comprising the following steps: a main process of a scheduling center creates a data time delay and a trajectory pool; the scheduling center starts a plurality of sub-processes for simulating city monitoring data generation and time delay updating; a UAV group collects simulated trajectories according to a current sensing strategy; the simulated trajectories are evaluated; the scheduling center optimizes the sensing strategy by using an asynchronous backtracking algorithm according to data of the data time delay and the trajectory pool, and synchronizes the optimized sensing strategy to each sub-process; until the UAV group sensing strategy no longer changes, the optimal sensing strategy is obtained; the UAV group is dynamically scheduled to guarantee city monitoring data time delay; the method is suitable for participating in a task sensitive to a time threshold, and therefore, compared with a traditional guarantee task, the UAV can be used to participate in city monitoring data time delay guarantee tasks to improve the completion satisfaction of the task.

This invention discloses a method for obtaining K shortest paths in a city that considers real-time traffic conditions, comprising: 1. Constructing a city road network based on traffic information at the initial time; 2. Defining and initializing parameters; 3. Searching for the k-th shortest path from the starting intersection node to the ending intersection node at the current time; 4. Searching for the (k+1)-th shortest path from the starting intersection node to the ending intersection node at the current time; 5. Obtaining the K shortest paths from the starting intersection node to the ending intersection node at the next time after updating traffic conditions, based on real-time traffic conditions. This invention can obtain K optimal travel paths that better reflect actual traffic conditions while improving computational efficiency, thereby ensuring the stable and efficient operation of social traffic.

The application provides an improved tabu search topology optimization method of a marine wind farm power collection system, which is based on the positions of wind turbines to intelligently preliminarily group and sort, to form an initial connection scheme, to connect all the wind turbines in each group with the minimum cost, to calculate the group cable cost, to check whether the lines cross, to impose a penalty in the total cost if the lines cross, to ensure that the scheme is feasible, to compare the current scheme with the historical best, to update the record and intelligently exchange the wind turbine groups to explore a new scheme, to repeat the optimization process until the number of iterations is satisfied, and to finally output a globally optimal topology scheme. The application has the advantages of better optimization effect, significantly improved economy, higher calculation efficiency, stronger engineering practicability, better algorithm robustness, better scheme reliability, high design automation degree and strong auxiliary decision-making capacity.

The invention discloses a gravitational lens gravitational wave pairing method and system based on deep learning, and the method comprises the steps: obtaining time sequence strain data of a gravitational wave detector, carrying out the frequency domain whitening processing of the data, converting the data back to a time domain, and carrying out the interception to obtain a whitening strain fragment with a fixed length; inputting into a twin encoder; selecting a plurality of other events with the highest similarity for each gravitational wave event based on the cosine similarity between the feature vectors to form a candidate pairing set; a generalized cross-correlation-phase transformation algorithm is adopted to calculate waveform cross-correlation, and refined similarity scores and relative time delay estimation between event pairs are obtained; and constructing a weighted graph, and obtaining a final pairing result by solving the maximum weight matching of the graph. According to the method, dimension reduction is carried out on the high-dimensional time domain data by using the twin encoder, and full-amount pairwise comparison on directories is avoided through a coarse screening strategy, so that the search speed on a large-scale data set is greatly improved.

The application belongs to the field of communication measurement and control, and particularly relates to a measurement and control transponder system and equipment for quickly and reliably capturing a mixed spread spectrum signal, and aims to solve the problem of insufficient system precision and weak reliability. The application comprises a digital down-conversion module for receiving a signal and performing digital down-conversion processing on the signal; a direct spread code capture module for capturing the zero intermediate frequency signal and performing modulation to obtain a spread spectrum signal; a frequency hopping interval division module for dividing the bandwidth of the spread spectrum signal into N parallel frequency hopping intervals according to a frequency hopping pattern; a matched filter module for filtering the signal and performing N-point Fourier operation; a capture decision module for comparing the code phase with a threshold value, and if the threshold value is exceeded, determining that capture is successful; and a synchronization refresh module for performing dynamic refresh through a synchronization refresh algorithm. The application improves the capture speed and reliability of the measurement and control transponder.

The invention belongs to the field of artificial intelligence and proteomics, and particularly relates to a mass spectrum dimensionality reduction method oriented to a large mass spectrum library and application of the mass spectrum dimensionality reduction method. The method comprises the steps that precursor features are processed through a full-connection network, and ion peak features and hash features are processed through a convolutional layer and a maximum pooling layer which are cascaded; splicing the processed features together, and inputting the spliced features into a full connection layer; the output features of the full connection layer, the precursor features, the ion peak features and the Hash features are used as the input of a Transform layer, and the Transform layer carries out processing to obtain an initial feature vector; splicing the initial feature vector with the mass-to-charge ratio feature of the mass spectrum to obtain a final feature vector after dimension reduction; according to the method, similar spectrogram retrieval can be quickly carried out in a large-scale mass spectrogram archive library, the calculation speed of PTM / SAAV related knowledge discovery is increased, the spectrogram search speed is increased, and the spectrogram search time is shortened.

This invention discloses a UAV state estimation method based on a two-stage search, relating to the field of low-altitude sensing and control technology. The method includes: constructing a three-dimensional reference coordinate system for a substation area, a set of no-entry flight zones, and a constraint grid table; generating an environmental constraint data packet; configuring and transmitting OTFS sensing frames based on the environmental constraint data packet, acquiring echo sequences, performing time synchronization, frequency offset correction, static clutter suppression, and frequency domain pre-whitening processing to generate a weighted observation sequence and a gridded environmental cost table; performing a coarse search using two-dimensional orthogonal matching pursuit under environmental constraints to obtain a unique candidate state group; performing a continuous domain fine search based on the unique candidate state group to complete position and velocity correction and generate current cycle state data; and generating initial state data for the next cycle based on the current cycle state data. This invention balances search efficiency and estimation accuracy through a two-stage search; and improves the accuracy of UAV state estimation through precise time delay and precise Doppler fitting.

The utility model discloses a bedside table convenient for placing articles, which relates to the technical field of bedside tables and comprises two groups of fixing plates. A bottom box is jointly connected between the two groups of fixing plates, and a placement cabinet is arranged above the fixing plates. The bottom box is connected between the two sets of fixing plates, the bottom box can be used for containing objects which are not commonly used, the sliding rods slide in the sleeve rods, meanwhile, the telescopic plates slide in the fixing plates, and therefore the height of the containing cabinet is adjusted, and after the containing cabinet is adjusted to the proper height, the limiting screws are inserted between the sliding rods and the sleeve rods, and the objects can be placed conveniently. The relative positions of the sliding rod and the sleeve rod are fixed; then the limiting frame is pulled, the limiting frame can serve as a handle, articles on the top plate can be protected and prevented from falling off, the top plate slides relative to a sliding block of a sliding groove, when the sliding block slides to the end close to the limiting plate, limiting is conducted through the limiting plate, and therefore the top plate is prevented from falling off from the containing cabinet.

The application provides a distributed blocking flow shop scheduling optimizer driven by a learning mechanism, and proposes a distributed estimation algorithm applying reverse learning and differential evolution to optimize energy-saving distributed blocking flow shop scheduling. The application fully considers the energy consumption in actual production, and designs an initialization method considering total delay time and total energy consumption; in order to improve the quality of the population, a multi-population collaborative operation guided by reinforcement learning and reverse learning is designed, information interaction is realized through the multi-population collaborative mode, and the search speed is accelerated. Based on the specific characteristics of different populations, adjustable parameter variables meeting the exploration and development capacity are designed. In order to optimize the distributed blocking flow shop scheduling problem with the objective function of reducing total delay time and total energy consumption, acceleration and deceleration operations on different paths are proposed. Through comparison on the 2017 test set and examples composed of different numbers of machines, workpieces and machine arrays, the overall performance of the optimizer is better than that of other optimizers.

The invention provides a method for searching a target high-quality semiconductor material based on lightweight and heavy neural networks and a related device, and belongs to the technical field of high-quality semiconductor material positioning. The method comprises the following steps: acquiring an original microscopic image of an uncompressed target high-quality semiconductor material; inputting an original microscopic image of an uncompressed target high-quality semiconductor material into the trained lightweight neural network to obtain a potential target area; inputting the potential target area into the trained heavy-weight neural network to obtain the size or thickness of the target high-quality semiconductor material; and determining coordinates of the target high-quality semiconductor material according to the size or thickness of the target high-quality semiconductor material. According to the method, the problems of low speed and low precision of searching the target high-quality semiconductor material are solved.

The invention discloses a vehicle soft switch memory test method and system and a vehicle, and belongs to the technical field of vehicle testing, and the method comprises the steps: setting the initial states of all soft switches of a vehicle; collecting and recording initial state values of all soft switches of the vehicle machine; operation is carried out according to a preset test working condition, and current state values of all the tested soft switches are obtained; and comparing the obtained initial state value with the tested current state value, and outputting a test result. According to the invention, the problems of low efficiency and easy misjudgment of the conventional test means for the soft switching memory of the vehicle-mounted infotainment system are solved.

The invention relates to the technical field of space-time prisms, in particular to a network space-time prism construction method and system. The method comprises the following steps: acquiring a starting point, an ending point, departure time and arrival time; according to an A * algorithm and a branch and bound algorithm, determining the earliest arrival time of a plurality of reachable edges starting from the starting point according to the starting time and reaching the end point at the arrival time; according to an A * algorithm and a branch and bound algorithm, determining the latest departure time when the edge arrives at the end point at the arrival time; and generating space-time polygons of the path according to the earliest arrival time and the latest departure time, and generating a network space-time prism based on the plurality of space-time polygons. According to the method, the efficiency of constructing the space-time prism can be improved.

The invention provides a method for calculating a cross section curvature-resistance bending moment curve of a steel and UHPC (Ultra High Performance Concrete) combined structural beam, which comprises the following steps of: iteratively calculating resistance bending moments Mr corresponding to different cross section curvatures # imgabs0 #, and respectively taking an obtained # imgabs1 # sequence and an obtained Mr sequence as a horizontal coordinate and a vertical coordinate, so as to obtain the cross section curvature-resistance bending moment curve of the combined structural beam. According to the calculation method, the problem that a method for obtaining a cross section curvature-resistance bending moment curve through design calculation is lacked in the prior art is solved, engineers can conveniently evaluate the performance of the material in the elastic and plastic stages, and optimization of the structural design of the composite structural beam is facilitated.

The utility model relates to the technical field of clothing, and discloses a cold protective clothing with a heating function, which comprises a coat, a lining and a heating component, the coat is detachably connected with the lining, the heating component is arranged between the coat and the lining, and the heating component is detachably mounted on the lining. By arranging the heating assembly, the heating assembly is divided into a left area, a right area and a neckline area, when a certain position of the heating assembly is damaged, a user can quickly determine the area where a damaged point is located according to the temperature of the certain area, the user does not need to search the whole body, and therefore the searching speed is increased, and the overhauling efficiency is improved; through an independent working mode, the three areas do not influence one another, and even if a certain area is damaged, the other areas can still be normally heated; the upper body of the user can be uniformly heated and warmed in all directions, the limitation of movement of the user is avoided, and the heating effect is good; and through the arrangement of the coat pocket, the mobile power supply is not easy to shake off when the user exercises.

The present invention discloses an operation optimization method for offshore wind power participating in the electricity spot market considering carbon-green certificate trading. By constructing an offshore wind power carbon trading model and a green certificate trading model, an operation optimization model for offshore wind power participating in the electricity spot market considering carbon-green certificate trading is constructed, and an improved particle swarm algorithm is used to solve the economically optimal offshore wind power target power. The present invention aims to improve the economic efficiency of offshore wind power operation, studies the mechanism of offshore wind power participating in the electricity spot market, and considers the impact of carbon trading and green certificate trading, and constructs an operation optimization model for offshore wind power participating in the electricity spot market considering carbon trading and green certificate trading. The optimization is performed with the goal of optimal economic efficiency, which helps to improve the overall benefits of offshore wind power participating in the electricity spot market.

The present invention relates to a method for trajectory planning of a UAV when taking low-altitude wind and UAV energy consumption into consideration when flying in mountainous areas, and belongs to the field of UAV trajectory planning methods. The energy consumption cost of the UAV is added to the objective function of the trajectory planning of the present invention, and a calculation model of the UAV energy consumption is given. The energy consumption constraint of the UAV is added to the model constraint, and the impact of low-altitude wind on the trajectory planning is considered to correct the trajectory, that is, the passing time of the UAV is corrected according to the vector relationship. In terms of algorithm, the model is solved by improving the A* algorithm. A comparative analysis is performed on the algorithm with that before the improvement, and the trajectory planning model considering low-altitude wind and energy consumption is solved with the improved algorithm. The present invention greatly improves the practicality and accuracy of the research on UAV trajectory planning when considering the actual operating environment.