64 results about "Window model" patented technology

The invention relates to an AGV path planning method based on time window optimizing. The AGV path planning method based on time window optimizing comprises the following steps: setting information of key joints, entering joints and exiting joints in a map of a transportation system, and establishing a grid path map in which a transportation line is the same as that of entities of workshops; establishing a storage path of AGV between an entering joint and a target position; detecting feasibility of the path on spatial planning according to the exiting joint and the entering joint, and planning and establishing time window models M of all paths; taking transformation time of the paths as an unique assessment parameter for path optimizing in the time window models M, and selecting the path with the shortest transportation time as an optimal path; and combining a delivery path, the optimal path and the storage path to generate a complete transportation path, establishing a complete path and time window model, successively reading complete path information, and finishing dispatching management of AGV. The time window models are adjusted in time, the working efficiency of the system is greatly improved, a multi-path time window planning method is adopted, and the models are established to solve the collision problem.

Embodiments of the invention provide a robot path planning method and a robot planning a route. The method comprises the following steps of according to a pose of a robot and control information of a speedometer, acquiring an initial pose of the robot; according to the initial pose and data observed by a sensor, creating a prior map; according to a rapid expansion tree algorithm of adaptive adjustment, calculating a global path used to control walking of the robot in the prior map; acquiring real-time detection data of the sensor of the robot, and determining whether the sensor detects a barrier in the global path when the robot is in a walking process; and when the sensor detects the barrier in the global path, establishing a dynamic window model to plan a local barrier avoiding route of the robot so as to make the robot avoid the barrier according to the local barrier avoiding route.

Method for simultaneous full-wavefield inversion of gathers of source (or receiver) encoded geophysical data to determine a physical properties model for a subsurface region, especially suitable for surveys where fixed receiver geometry conditions were not satisfied in the data acquisition. First, a shallow time window of the data (202) where the fixed receiver condition is satisfied is inverted by simultaneous encoded (203) source inversion (205). Then, the deeper time window of the data (208) is inverted by sparse sequential source inversion (209), using the physical properties model from the shallow time window (206) as a starting model (207). Alternatively, the shallow time window model is used to simulate missing far offset data (211) producing a data set satisfying the stationary receiver assumption, after which this data set is source encoded (212) and inverted by simultaneous source inversion (214).

The invention discloses a wireless sensor network abnormal event detecting method based on multi-attribute correlation and belongs to an anomaly detection technology in data mining.The wireless sensor network abnormal event detecting method comprises the specific steps that non-space-time attribute dependency model is established based on a Bayesian network, the attribute correlation confidence is calculated according to a conditional probability table, the similarity of points to be detected and abnormal points in a non-space-time attribute correlation mode is reflected; time correlation detection is performed based on a sliding window model, readings simultaneously meeting the time correlation mode and abnormal event attribute correlation mode are marked as temporal abnormal points to detect whether abnormal events occur or not by cooperating with neighbor node information.In addition, the wireless sensor network abnormal event detecting method calculates recent abnormal nodes closest to an abnormal event area center by adopting a Ritter's smallest enclosing circle algorithm, uploads the abnormal information of the abnormal nodes and can effectively reduce the data transmission amount and reduce the energy consumption of sensor nodes.The wireless sensor network abnormal event detecting method can be applied to wireless sensor network event detection of multiple sending components.

The invention discloses a data transmission method and a system based on a UDP protocol, belonging to the technical field of communication. The system of the invention comprises a connection module and a data transmission module; and the method comprises the following steps: connecting a transmitting end with a receiving end; creating a sliding window model, and setting a flag for each data packet, wherein the flag is a continuous serial number; and performing message receiving and transmitting synchronization and traffic control by the transmitting end and the receiving end through the sliding window. Embodiments of the invention can be used in the field of cloud computing, the method and the system extends based on the prior UDP protocol, and on a condition that high-efficiency data transmission is ensured, reliability of the UDP is improved. Simultaneously, the embodiments of the invention can not only provide transmission service for various cloud computing, but also can ensure high performance under different cloud computing environments.

The invention discloses a method of generating a 3D house type model by a 2D house type model based on camera shooting. The method comprises steps: 1, a 2D house type picture is acquired through camera shooting; 2, an essential matrix reduction method is used for correcting the 2D house type picture acquired in the first step; 3, image enhancement processing is carried out on the2D house type picture after processing of the second step; 4, a contour line of the 2D image after processing of the third step is extracted; 5, a door and window model base is built, matching is carried out according to the contour line extracted in the fourth step, and door and window positions are recognized; 6, a wall structure is stretched to a fixed height position, and generation of a 3D house type model is completed. The method has the following advantages that automatic generation of the 3D house type model from the 2D house type model is realized, the generation method is simple, applicability is universal, the generation method can be embedded in a portable mobile device to form APP application software to be combined with a camera of the portable mobile device, and practicability is provided.

The invention provides a video size switching system and a video size switching method facing a display terminal. The method includes the steps: firstly, computing the size of interested windows according to interactive choices of users and by designing perception window models; then detecting long shots, fusing various factors of human eyes to view videos into each frame of image in each long shot by adopting a knowledge-based fuzzy reasoning decision-making system, automatically identifying interested areas and cutting the interested areas to serve as display content of the display terminal; and finally, performing time-domain smoothing for central points of all the interested areas identified in the long shots, so that consistency and continuity of time domains of the interested areas are guaranteed. Additionally, the video size switching system facing the display terminal is based on the method. From the perspective of video source format switching technology, the method with the technical scheme can be self-adaptive to switch a common video format into a target video format which can be played by a small-sized terminal.

The invention provides a stream type repetitive data detection method. According to the method, a TBFA (Timing Bloom Filter Array) is constructed for flexibly and efficiently detecting repetitive data in a sliding window model, wherein the TBFA consists of a plurality of TBFs (Timing Bloom Filters) with the same structure, each TBF comprises a bloom filter and a separated timer array used for storing timestamps, the whole TBFA works in a looped first-in first-out mode and gets rid of old elements removed from a data stream monitoring window while recording new elements. The stream type repetitive data detection method is implemented under the sliding widow model, element monitoring is correct to one element, therefore the statistic result based on the stream type repetitive data detectionmethod has good stability; in addition, a part of the timer arrays in the TBFA can be unloaded into a disc, therefore the overhead of an internal memory can be reduced. Theoretical analysis and experimental data show that more than 95% of query efficiency can be maintained when DCBA (Detached Counting Bloom filters Array loads less than 10% of data contents to the internal memory, therefore the method provided by the invention is superior to the traditional technical scheme in space efficiency and expandability.

The invention discloses a GPS (global positioning system) dual-frequency non-difference cycle slip detecting and restoring method and device. The method includes: reading a GPS observing value, and generating first detection quantity and second detection quantity according to the GPS observing value; utilizing a self-adaptive sliding window model to smooth the first detection quantity, adjusting the first detection quantity after being smoothed according to a first cycle slip judgment threshold value for cycle slip detection to acquire a first detection result; utilizing a epoch difference-solving method for difference solving of the second detection quantity to generate third detection quantity, and performing cycle slip detection on the third detection quantity to acquire a second detection result; analyzing the first detection result and the second detection result, and calculating the first detection quantity and the second detection quantity at a cycle slip epoch to acquire a first cycle slip value and a second cycle slip value; restoring the GPS observing value according to the cycle slip values. By the GPS dual-frequency non-difference cycle slip detecting and restoring method and device, cycle slip detection success rate and cycle slip restoring accuracy can be improved, so that needs on high-accuracy positioning of GPS navigation can be met, and small cycle slip, big cycle slip, special cycle slip and continuous cycle slip can be detected and restored.

The invention provides a method for splitting, making and recombining house type data. The method comprises the following steps of: drawing data information of all rooms according to a house type figure, calling an uploading functional module, educing the data of each room and storing to a remote server; acquiring data of one single room from the remote server, transmitting a request and then downloading and analyzing the data, and generating virtual figures of the walls and the door and window models of the rooms according to the existing data; arranging a plurality of functional region modules and auxiliary points in the figure; calling a three-dimensional rendering module to render the single room; calling the uploading functional module to transmit the data of the single room to the server, combining the data information of all the rooms by the remote server after the data information of all the rooms of the user are uploaded, and then rendering a whole house overlook effect figure and a line drawing figure of the user. The invention provides a method which is convenient for merchants or designers of different brand categories to serve the same proprietor and provides richer choices for the proprietor.

The invention discloses a three-dimensional spherical path planning method based on a dynamic spherical window. The method comprises the steps of performing filtering integration based on environmentinformation sensing through a sensor, and furthermore converting to a spatial grid map; establishing a dynamic spherical window model, performing dynamic sampling on the inner part and the surface ofa sphere for obtaining local candidate target points; establishing a uniform evaluation function, performing optimization screening on the candidate points according to the evaluation function; and performing successive approximation to a destination through the local target points until arriving at the destination. The three-dimensional spatial path planning method can be applied to a quick pathplanning problem of an unmanned aerial vehicle / unmanned underwater vehicle in a three-dimensional space. High freedom in the three-dimensional space is sufficiently considered, and the no-collision three-dimensional path with considered safety and efficiency can be quickly obtained.

The invention relates to a dynamic route planning method of multi-AGV (automatic guided vehicle) based on an intelligent parking lot, and belongs to the technical field of the route planning. The method specifically comprises the following steps: S1, establishing a K shortest path algorithm by combining an A* algorithm and a Yen algorithm, and solving the shortest route to a target parking space from a starting station; S2, establishing a dynamic time window model; S3, establishing a multi-AVG conflict type analysis mode, if the conflict is existent among multiple AGVs, analyzing different route planning methods for different conflict types; and S4, establishing the route planning algorithm for multiple AVGs based on the dynamic time window model and the multi-AGV conflict type analysis model. The location utilization efficiency of the traditional parking lot and the parking efficiency are improved, the running efficiency of each AGV vehicle is improved, the energy consumption of the AVG vehicle is reduced, and a beneficial path is provided for the realization of the intelligent parking lot.

The invention discloses a simulation experiment device of high-rise building three-dimensional fire behaviors under the effect of environment wind. The device comprises a high-rise building model, a rotary platform, a direction adjustable wind hole, a three-dimensional movable thermoelectric coupling frame and a fire source simulating device. The high-rise building model is a cuboid shell and is divided into four chambers, wherein the ignition experiment chamber is provided with the fire source simulating device and a size adjustable building window model. The bottom of the high-rise building model is connected with the rotary platform and matched with the external direction adjustable wind hole so that environment wind in any direction at any wind speed can be simulated. The three-dimensional movable thermoelectric coupling frame is of a combined rectangular structure, and the parameter measurement function of any position of an external wall surface can be achieved. The simulation experiment device for studying high-rise building three-dimensional fire spreading under the effect of environment wind is established for the first time, a wind direction adjusting system is safe, convenient to use and precise, the experiment device is provided with a complete parameter measuring system, and the complete process of a high-rise building from fire starting, flame spreading to final spreading towards the upper layer and adjacent side of the building can be studied.

The invention discloses an infrared image sea-sky-line self adaption detection method which includes the following steps that (1) an original infrared image Forg is obtained; (2) edge detection is conducted on the original infrared image through a Canny operator, and an edge image Fedge is obtained; (3) a sliding window model MWin, the sea-sky-line length and a model LEN sum <j> are built; (4) full-figure traversal is conducted on the edge image Fedge through the sliding window model MWin, and the sum of lengths of sea-sky-lines of different lines is solved; (5) the length of the sea-sky-lines and the line with the maximum LEN sum are defined to be the positions of the current image sea-sky-lines; (6) smoothing is conducted on the current image sea-sky-lines, and the filter value Lineseasky <p+1> of the p+1th frame of the sea-sky-lines is output.

The invention relates to a time window online correction-based multi-AGV (Automated Guided Vehicle) path planning method. The method includes the following steps that: the operation point sites of anAGV are extracted according to workshop layout information and path information, independent road sections are obtained through division according to the operation point sites; a scheduling system updates an AGV status list and a real-time task list in real time, assigns a task in the real-time task list to an idle AGV, and informs the AGV of a start point and an end point; a feasible path list isestablished, whether a feasible path exists is judged, a task corresponding to the feasible path is added in a time window model, the AGV executes the tasks and feeds back current AGV position information in real time; and whether the current AGV position information is consistent with the position information of the AGV in the time window model is judged, and the time window model is corrected online and updated synchronously. With the time window online correction-based multi-AGV (Automated Guided Vehicle) path planning method of the invention adopted, error accumulation caused by actual errors can be decreased; and a multi-AGV path conflict problem can be solved. The time window online correction-based multi-AGV (Automated Guided Vehicle) path planning is a new dynamic path planning solution suitable for any environment and any number of AGVs, and is suitable for being popularized.

The invention discloses a superpixel local information measurement-based polarized SAR ship target detection method, and mainly aims at solving the problem that the target detection rate lower complicated scenes is low. The method comprises the following steps of: 1, carrying out superpixel segmentation on an original image so as to obtain superpixel segmentation results under different scales; 2,for the segmented results, calculating three superpixel level-based difference measurements by utilizing a sliding window model; 3, converting the superpixel level-based difference measurements intopixel level-based difference measurements; 4, mapping pixel-level difference measurement vectors into different measurement values by utilizing kernel fisher discrimination, so as to a difference measurement value of each pixel point; and 5, classifying the difference measurement values of the pixel points by utilizing a linear SVM classifier, determining a category of each pixel and carrying outautomatic target detection. The method is capable of enhancing the target detection performance under complicated scenes an realizing the automatic detection process, and can be used for subsequent ship target discrimination, recognition and classification.

The invention discloses a transmission method based on a learning energy-efficiency model. The method comprises the following steps: firstly, periodically recording historical change trends of a network, carrying out weighted smoothing on round-trip delays, and judging change trends of a congestion control window; then establishing models for a network energy efficiency and the congestion control window, when a new ACK (Acknowledgement) is received, updating the energy-efficiency and window models; finally combining with the change trends of the congestion window and the energy efficiency, predicting the size of the congestion control window in a next time period; for a network packet loss or timeout event, using the traditional TCP (Transmission Control Protocol) data packet retransmission mechanism, and when the packet loss is over, reusing the energy-efficiency model for processing. The transmission method disclosed by the invention lowers influences of network random events and estimation errors of the traditional algorithm, and can weigh the delays and particular emphasis of throughputs; in a high-speed network, the higher occupancy rate of a link bandwidth and lower end-to-end delays can be approximated, and the end-to-end delays performed on packet transmission are lowered.

The invention discloses a pedestrian detection method based on aggregation channel characteristics, and belongs to the technical field of digital image processing. The method is used for training a plurality of models (a big window model and a small window model), so as to solve a problem of false detection and detection leakage caused by the application of a single model. Moreover, the method greatly improves the confidence level of a window detected by the big window model in a process of window inhibition through a non-extreme value inhibition algorithm, and a non-extreme value inhibition threshold value is updated according to the sliding window scale ratio of the big and small models, thereby inhibiting a body part which is wrongly detected as a small target. Compared with a pedestrian detection method which employs a single model and is based on the aggregation channel characteristics, the method can effectively reduce the false detection rate and the detection leakage rate, andcan meet the real-time requirements.

The present invention discloses a method, a system and an apparatus for detecting a large-scale complex network community structure. The method comprises: abstracting a to-be-detected large-scale complex network as atlas data; using a multi-thread parallel sliding window model to carry out optimized storage on the abstracted atlas data; using a multi-thread parallel adaptive tag propagation algorithm to carry out tagged processing on the stored atlas data; and carrying out post-processing according to a tagged processing result and outputting a community structure detection result. The systemcomprises an atlas abstraction module, an optimized storage module, a tagged processing module and a post-processing module. The apparatus comprises a memory and a processor. According to the technical scheme of the present invention, time complexity is reduced and the execution efficiency is improved; the technical scheme of the present invention can also compute the large-scale atlas through anordinary personal computer, so that the cost is reduced; the technical scheme of the present invention can adaptively identify overlapping and non-overlapping communities, so that the community detection accuracy is improved; and the technical scheme of the present invention can be widely applied in the field of complex network service computing.

The present invention discloses a passive underwater sound positioning method based on moving a time window periodically. The method is composed of a strapdown inertial navigation system (SINS), a single hydrophone (receiver) at the bottom of an autonomous underwater vehicle (AUV) and a seafloor single hydrophone (band sound source), adopts a time window model which moves based on the cycle of an ultrasonic wave sent out by the seafloor hydrophone, and comprises the steps of carrying out the generalized cross correlation on the sound source signals received when the AUV is located at different positions in a time window to obtain the delay inequality, and then calculating a time window internal AUV multi-point model to obtain the latest position coordinate of the AUV. According to the present invention, by calculating the time window internal AUV multi-point model, the AUV does not need to navigate too far, so that a positioning error of an inertial navigation system which accumulates along with the time continuously is reduced effectively. According to the present invention, the AUV does not need to emerge from the water to update the position, does not need the digital communication, and receives an ultrasonic signal passively, so that the position of the AUV is difficult to expose, and the invisibility and the safety of the AUV are improved.

The invention discloses a multi-satellite coordinated real-time tracking method for a spatial dynamic target. The method comprises the steps of firstly establishing a low earth orbit satellite and staring detector model, a tracking task model and a visible time window model; then introducing a rolling time domain concept when real-time tracking is performed on the spatial dynamic target, taking the forward-looking fixed time length T as the rolling time domain to obtain a tracking task of a ballistic missile in the rolling time domain, and establishing a single-layer to-be-tracked task set with the single rolling time domain as a reference, and splitting each to-be-tracked task into a plurality of atomic tasks in the rolling time domain by use of a reverse tree structure to obtain a set ofdetector resources corresponding to each atomic task; and finally, constructing a fitness function based on constraints and objective functions of resource scheduling, and then solving the optimal particle individual based on the fitness value of the particle individual through an evolutionary particle swarm optimization algorithm with crossover and mutation operations, and performing resource scheduling of the detector resources on the single-layer atomic task set according to the optimal particle individual.

The invention discloses an abnormal detection method of dew point data. The abnormal detection of dew point data is realized mainly through using a machine learning method, the time sequence data areprocessed through a sliding window model, and an abnormal identification model and an abnormal source judgment model are constructed. The abnormal data are found according to an abnormal recognition model, the abnormal source judgment model is used for judging the reasons of the abnormal data, and abnormal messages are pushed in time. According to the abnormal detection method of the dew point data, the safety of pipeline transportation is greatly improved, and the consumption of manpower and material resources is reduced.

The invention belongs to the technical field of video coding, and in particular relates to an H.264 / AVC (Advanced Video Coding) macroblock-level code rate control algorithm based on weight window models. By using spatial correlation of a window which is formed by macroblocks around a current macroblock, and time correlation of a window which is formed by a macroblock of on a previous frame, which is in same position as the current macroblock and macroblocks around the macroblock, a low-complexity prediction model of a mean absolute deviation (MAD) and a quantization parameter (QP) calculating model are provided. Based on the two models, the macroblock-level code rate control algorithm used for an H.264 / AVC coder is provided. Shown by the experiments, the macroblock-level H.264 / AVC code rate control algorithm based on the weight window models has higher peak signal to noise ratio (PSNR) and less bit deviation compared with a code rate control algorithm of H.264 / AVC reference software.

The invention discloses a high dynamic range image adaptive reproduction display method. The method comprises the steps that a high dynamic range image is read so as to obtain the grayscale or brightness channel of the image; local mapping transformation is performed on the grayscale or brightness channel of the image by using a nonlinear tone mapping window model; local contrast measure calculation is performed based on a PLIP model; adaptive optimization double guidance image mapping constraints are designed to guide an objective optimization function to obtain the optimal result; and the result image is outputted. The high dynamic range image can be effectively displayed, the image edge and image detail information can also be maintained and the halo effect can be suppressed so as to have great adaptive capability and robustness, and the processing effect proves that the method has high practicality.

The invention provides a method, a device and an electronic device for detecting a CC attack, which relate to the technical field of network anomaly detection. The method for detecting the CC attack comprises the following steps of: setting a window size of a number of times and a single sliding distance of the window of times; calculating the information entropy value of the times window at eachsliding position in turn according to the times window setting; if the information entropy value of any sliding position is less than the preset information entropy threshold value, the number of times window is determined to be CC attack at the sliding position. This method can detect the network anomaly more quickly and has stronger real-time detection ability through the number of times windowmodel and information entropy algorithm. The method uses the dimension of request access times to define the sliding window, which can effectively avoid the entropy difference caused by the differentrequest times per unit time, and the detection result is more accurate.

The invention belongs to the technical field of data mining and particularly provides a Bayes classifier based on pattern discovery in data flow. A method mainly includes a pattern discovery stage and a classifier establishment stage. In order to establish a Bayes classification model based on patterns in the data flow, a single sweep algorithm FFI is provided for excavating frequent item sets on the continuous data flow through a sliding window model. The method has high performance on the aspects of running time and classification precision, and better adapts to the data flow dynamic environment.

The invention discloses a multiwindow-heuristic three-dimensional space path planning method based on a magic cube model. The method includes: sensing environment information through a sensor, and filtering and integrating on this basis to convert into a three-dimensional space grid map; building 26 degree-of-freedom search space based on the magic cube model; using a multiwindow model to quicklydetermine accessibility of 26 degrees of freedom in batches according to a priority sequence; establishing a heuristic evaluation function, calculating a function value according to weight parameters,and utilizing the evaluation function for screening to acquire a local target; iterating step by step to finally acquire a globally unique optimal path. The method can be applied in quick path planning of unmanned aerial vehicles / unmanned underwater vehicles containing prior information in three-dimensional space, high degree of freedom in the three-dimensional space is fully taken into consideration, and a globally-optimal collisionless three-dimensional path can be acquired quickly.

The invention discloses a multiple data stream processing method based on an MIC co-processor. The method comprises the steps that a central processing unit (CPU) acquires a data source and transmits multiple data streams which arrive in parallel to the MIC co-processor through a PCIE bus; the MIC co-processor performs parallel calculation on the data streams by adopting a four-layer sliding window model and carries out a multiple data stream query algorithm to obtain a data stream query result; the MIC co-processor returns the data stream query result to the CPU; the CPU obtains an IO output task of a user according to the data stream query result and carries out the IO output task. According to the multiple data stream processing method based on the MIC co-processor, the parallel performance and real-time performance of multiple data stream processing are improved.

The invention provides a high spectrum exception detection method purifying backgrounds based on the local density. The method comprises the steps of: firstly acquiring an initial background corresponding to a currently detected pixel by using a concentric double-layer window model; then calculating the local density of each pixel of the initial background; setting the maximum exception proportionand selecting the pixel corresponding to the minimum local density according to the proportion; dividing the background by using the maximum between-cluster variance method; detecting a high spectrumimage based on an LRXD exception detection method; setting a threshold value, marking pixels with the detection values greater than the threshold value as exceptional points. The method removes exceptional data in backgrounds by purifying the backgrounds, thereby facilitating analysis of differences in targets and backgrounds and effectively reducing the false alarm rate.

The invention belongs to the field of image processing, and relates to a hyperspectral image abnormity detection method based on constrained sparse representation. The method comprises the following steps: (S1), carrying out the linear standardization of a hyperspectral image; (S2), extracting a local background dictionary for each test pixel according to a double-window model; (S3), solving a constrained sparse representation model according to the local background dictionary, and obtaining a first model optimal solution; (S4), deleting all abnormal atoms from the local background dictionaryaccording to the first model optimal solution, and obtaining a new background dictionary; (S5), solving the constrained sparse representation model according to the new background dictionary, and obtaining a second model optimal solution; (S6), calculating a detection value of the pixel according to the second model optimal solution; (S7), traversing the whole hyperspectral image, calculating thedetection values of all pixels of the hyperspectral image, and outputting an image formed by the detection values, i.e., an abnormal detection image. The method does not need the background statistical information and the setting of sparsity, and improves the reconstruction precision.