61results about How to "High degree of adaptability" patented technology

The invention discloses a fault positioning method for a power distribution network by combining simulation calculation and real-time monitoring, and belongs to the technical field of fault detection of the power distribution network. The method comprises the following steps of: extracting information of a target power distribution network from the conventional power distribution network geographic information system and a power distribution network energy management system through an organizational data analysis interface to automatically generate a real-time simulation calculation model of the target network; putting forward a virtual node-based fault positioning algorithm according to the simulation calculation model so as to solve functional relation between voltage amplitude of any node and a fault position parameter when a symmetric and asymmetric fault occurs at any point in the target network; and finally calculating an accurate position of the actual fault of the power distribution network by using a small amount of real-time data of voltage drop amplitude in a fault process. By using the fault positioning method for the power distribution network by combining the simulation calculation and the real-time monitoring, the state and the parameter of the network are accurately and effectively reflected when the fault occurs, the accuracy of the fault positioning is improved, and the method is suitable for various network structures and fault types.

The invention provides a sorting method of vehicle-mounted laser scanning point cloud data of urban ground objects. The method mainly comprises two steps as follows: original vehicle-mounted laser scanning point cloud data are subjected to road surface filtering in combination of vehicle tracking data, and point cloud data of a road surface are separated from point cloud data of other ground objects; and the obtained point cloud data of other ground objects are further identified to be different ground object types. In the road surface filtering algorithm, the vehicle tracking data are combined, and a histogram statistic and analytical method is introduced, so that the self-adaption degree, the automation degree and the accuracy of the filtering algorithm of the road surface in an urban environment are improved greatly; and in the sorting algorithm of other ground objects, a point cloud feature image sorting method based on the maximum elevation value is improved, mistakenly sorted point cloud data are corrected with a cellular automaton technology, and a new thought and method are provided for the sorting algorithms of vehicle-mounted laser scanning point cloud data.

A multifunctional tool for cutting (crushing or shearing), crimping, perforating and other operations that require high force to be applied to a small area, plus a method of designing such tools for a variety of applications. A sectorial eccentrically pivoted cam is implemented as a part of a lever of first stage. A working surface of this cam fits precisely onto a corresponding surface of a lever of a second stage so that when the cam is partially rotated about its pivot, the lever of second stage receives impetus to move about its own pivot at hyperbolic trajectory and a distance lesser than that of the lever of first stage.

The invention discloses an embedded information processing method, a client side and a server. The method comprises loading first multimedia information to be played, detecting whether the first multimedia information carries identification information of preprocessing, and obtaining a judgment result; when the judgment result shows that the identification information of the preprocessing is carried, determining a target object and second multimedia information capable of being triggered and displayed by the target object according an indication of the identification information of the preprocessing; and, with the play of the first multimedia information, displaying the second multimedia information in response to the first multimedia information. The second multimedia information and the target object are associated.

The invention discloses a photon point cloud denoising algorithm based on a Gaussian mixture model, and the method comprises the steps: firstly building a local elevation frequency histogram based onthe original photon counting laser radar data, roughly determining the position of a signal photon, and removing most photon noise; extracting characteristic parameters based on the photon point cloudafter coarse denoising, and establishing a Gaussian mixture model; using k-mean algorithm to realize the initial estimation of mixed Gaussian model parameters ; and finally, carrying out parameter optimization by using an EM algorithm to realize accurate separation of noise photons and signal photons. According to the method, the problems of nonuniform distribution of photon point cloud, incomplete boundary data and the like can be solved, the method has the characteristics of no need of parameter setting, high adaptive degree and the like, and the automation degree and precision of photon point cloud denoising are improved.

The invention relates to a method for converting pixel data of a tooth CT image into 3D printing data, and belongs to the technical field of image processing. The method comprises the steps of acquiring a CT image of a tooth, extracting pixel data of the image so as to generate pixel contour point cloud, acquiring outer surface point cloud of the tooth, performing coarse registration and fine registration on the pixel contour point cloud and the outer surface point cloud, respectively forming fine registration point cloud databases, setting a judgment value, verifying a fine registration result, combining the fine registration point cloud databases, acquiring a complete point cloud database of the tooth, rebuilding a three-dimensional grid database based on the complete point cloud database of the tooth, and forming an STL format supported by 3D printing equipment. According to the invention, a slice contour high-precision extraction method is adopted to realize adaptive extraction in order to adapt to a non-free curved surface of the tooth surface, CT scanning and non-contact scanning are combined innovatively, and existing mature algorithms are integrated, thereby realizing conversion of the pixel data of the tooth CT image into the 3D printing data, effectively improving the image conversion efficiency and precision, and being high in pertinence.

The invention discloses a multi-beam passive monopulse angle measurement method based on a digital array, and aims to improve the robustness of a traditional multi-beam amplitude comparison directionfinding method. The method comprises the specific steps that step 1, electronic reconnaissance equipment carries out digital simultaneous multi-beam processing on multi-array-element data, and signalinterception of a specified airspace is completed; step 2, threshold detection is carried out on the data of the multiple beams, and a beam sequence number and a moment sequence number corresponding to the target are determined; and step 3, the data with the target is processed by adopting a passive self-adaptive monopulse method to obtain the angle value of the target. The precision and the robustness of the traditional multi-beam amplitude comparison direction finding method are improved, and the direction finding capability of the electronic reconnaissance equipment is improved.

The invention discloses a camera internal reference calibration method based on an adaptive mutation longicorn swarm optimization algorithm, and the method comprises: enabling a prefabricated calibration template to be placed in a field of view of a to-be-calibrated camera, and collecting a calibration template image; extracting feature points in the calibration template graph, and solving a homography matrix, a camera internal parameter matrix, a camera external parameter matrix and lens distortion parameters; and calibrating camera internal parameters by using an adaptive mutation longicornswarm optimization algorithm, wherein the camera internal parameters comprise a camera internal parameter matrix and lens distortion parameters. The adaptive mutation longicorn swarm optimization algorithm designed by the invention is simple in algorithm flow, small in operand, higher in convergence speed, relatively high in global optimization capability, capable of reducing premature convergenceand jumping out of local extremum, high in calculation result precision and particularly suitable for high-dimensional complex optimization. The camera internal reference calibration method is high in precision and convergence, and the optimal solution of the camera internal reference can be rapidly and accurately obtained in real time.

The invention discloses a flexible material detection method and a storage medium thereof. The method comprises the following steps: S1, acquiring image data of a flexible material; S2, detecting a plurality of pieces of surface defect information in the image data, judging the obvious degree of the surface defect information, and classifying each piece of surface defect information according to the obvious degree; S3, selecting a feature matching identification step or a neural network identification step according to the classification condition; s4, performing a feature matching identification step, and analyzing the surface defect information based on a feature matching identification model to obtain first defect type information; s5, performing a neural network identification step, and analyzing the surface defect information based on a neural network identification model to obtain second defect type information; s6, integrating the first defect type information and / or the seconddefect type information and outputting a detection result. According to the design, reasonable classification is carried out, the accuracy of an overall result is ensured, and the overall detection rate is improved through reasonable distribution.

The invention discloses a neural network quasi-PR photovoltaic grid-connected inverter control method, which adopts a single-phase full-bridge inverter, a quasi-PR controller and a neural network parameter regulator. Compared with quasi-PR control, the neural network quasi-PR photovoltaic grid-connected inverter control method is characterized in that a total harmonic distortion ratio of current tracking based on BP neural network quasi-PR control is reduced, the dynamic response performance is faster and the system self-adaptability is higher. PR parameters set by means of a neural network are considered to be globally optimal solutions of the control system, enable the whole system to have better self-adjustment capability and anti-interference capability, make quality of output waveforms higher and response time shorter, and have high application value.

The invention relates to a high-confidence reconstruction quality and false reduction transient quantitative evaluation method based on a cascaded neural network, which is used for carrying out data analysis and extraction on an information database of prisoners and evaluating the reconstruction quality of the prisoners in prisoners. According to the method, a criminal risk assessment scale is used as guidance; the cascade neural network and the migration reconstruction neural network which have a mutual supporting effect are constructed by comprehensively utilizing the full-cycle multi-dimensional related data of the prisoners, and training is performed by using the preprocessed structural data, so that the reconstruction quality evaluation method with high precision and high practicability is obtained. Compared with a traditional model and algorithm without using the neural network, the method provided by the invention is more suitable for multi-dimensional nonlinear original data, the prediction accuracy and efficiency are higher, and it is indicated that the method provided by the invention is effective and practical.

The invention discloses a distributed system data redistribution method and device. A distributed system comprises multiple object storage devices, wherein each object storage device comprises multiple objects. The method comprises the steps that each object storage device periodically collects load information thereof and transmits the load information to a main object storage device; the main object storage device dynamically adjusts a concurrent process number of data redistribution according to the received load information of all the object storage devices and a first preset strategy; the main object storage device sends a data redistribution request to the corresponding objects according to the concurrent process number obtained after adjustment and performs data redistribution operation; and the data redistribution request comprises a reading request and a writing request. Through the distributed system data redistribution method and device, the concurrent process number of data redistribution can be adjusted according to front-end business conditions of the object storage devices, and the effect of adaptive adjustment is good.

The invention relates to a data preprocessing method as well as an implementation system and application thereof. The method comprises the following steps: (1) data structuralization: treating original data which comprises quantizable fields and text fields, specifically, for the quantizable fields, conducting label coding on discrete category fields in the quantizable fields, conducting normalization on continuous numeric fields, performing rule extraction on the text fields, extracting keywords by utilizing an information extraction and knowledge representation technology, representing corresponding rules, and establishing a structured knowledge base; and (2) feature vector extraction and construction: for the quantizable fields processed in the step (1), judging the similarity, deletinginvalid features with small sample similarity distinction, and selecting the most effective feature as a feature vector. Aiming at a textual data file, the invention provides a keyword and rule extraction and quantification method based on a knowledge extraction and representation technology, and a structured knowledge base is established for quantitative evaluation.

The invention provides a computer device and method for realizing pulmonary nodule image classification detection, and the method comprises the steps: scanning a lung CT image of a patient through a CT scanner, and carrying out the adaptive form segmentation of the lung CT image to obtain a pulmonary nodule image; marking the pulmonary nodule images into different categories, and storing the marked pulmonary nodule images into an image database; establishing a pulmonary nodule image unit library based on the pulmonary nodule images in the image database; calculating the distance between everytwo image units in the pulmonary nodule image unit library to obtain a distance matrix; clustering the distance matrix; calculating pulmonary nodule CT value density distribution characteristics of each pulmonary nodule image unit; based on a supervised machine learning model, using CT value density distribution characteristics to realize training and classification of pulmonary nodule risk degrees; and eliminating false positive pulmonary nodule images according to the CT value density distribution of each pulmonary nodule image. According to the invention, the accuracy of pulmonary nodule image classification detection is improved, and the method has a wide application range.

The invention discloses a vibration damping type motorcycle foot rest. The vibration damping type motorcycle foot rest comprises a foot rest body and a foot rest rack used for installing the foot rest body and capable of being fixed to a motorcycle frame. The inner end of the foot rest body is hinged to the foot rest rack. At least two rubber vibration damping bases are arranged on the foot rest rack. Positioning poles capable of being embedded into the rubber vibration damping bases to achieve positioning are arranged at the bottom of the foot rest body. Vibration, transferred to a driver and a passenger, of a motorcycle body can be effectively buffered, and the comfort degree of the driver and the passenger is increased; the driver and the passenger can keep balance easily. The influences on gear shifting and braking of the driver are avoided or reduced, so that potential safety hazards of driving are effectively eliminated and the driving safety of a motorcycle is ensured to the maximum extent.

The invention provides a physiological parameter monitoring device of a wearable optical fiber sensor based on a neural network. The physiological parameter monitoring device comprises a monitoring unit and a signal detection unit, wherein the monitoring unit monitors a physiological signal and inputs the monitored signal into the signal detection unit to realize monitoring of physiological parameters; the monitoring unit comprises a phase-sensitive optical time domain reflectometer system and a monitoring body; the phase-sensitive optical time domain reflectometer system comprises a phase-sensitive optical time domain reflectometer, a fan-in unit and a fan-out unit, a detection signal of the phase-sensitive optical time domain reflectometer enters the monitoring body from a signal outputport through the fan-in unit, and a scattering signal generated by the monitoring body is received by the phase-sensitive optical time domain reflectometer through the fan-out unit; and the signal detection unit comprises a model establishing subunit, a model applying subunit and a result evaluating subunit. The physiological parameter monitoring device of the wearable optical fiber sensor based on the neural network can be used for intelligent feedback guidance of rehabilitation training of a human body, skill training of athletes and the like.

A middle-high voltage series-connection electronic switch comprises a main electronic power switch module, a basic dynamic voltage-sharing resistance-capacitance branch formed by a non-polarized capacitor (C1) and a resistor (R1) in series connection, an H bridge diode rectifying module, an attached dynamic voltage-sharing resistance-capacitance branch, a discharging resistor and a surge absorption module, wherein the input end of the H bridge diode rectifying module is connected with the basic dynamic voltage-sharing resistance-capacitance branch, the output end of the H bridge diode rectifying module is connected with the attached dynamic voltage-sharing resistance-capacitance branch, two ends of the attached dynamic voltage-sharing resistance-capacitance branch are further connected with a surge absorption module metal oxide varistor (MOV) in parallel, the attached dynamic voltage-sharing resistance-capacitance branch is formed by a capacitor (C2) and a current-limiting resistor (R2) in series connection mode, and two ends of the capacitor (C2) are further connected with the discharging resistor (Rp) in parallel. The middle-high voltage series-connection electronic switch enables a system to meet general dynamic voltage-sharing requirements and meet requirement for short-time voltage shock absorption under the condition of not adding resistance-capacitance branch power consumption, and is long in service life and high in self-adaption degree.

The invention discloses an obstacle avoidance shortest path planning method based on a Voronoi diagram, and the method comprises the steps: collecting the information of an obstacle, including the position of a starting point, the position of a destination point, the size of the obstacle, and the position of a central point of the obstacle; according to the positions of the center points of the obstacles, regarding the center points of the multiple obstacles as a point set S on a plane, constructing optimal triangulation for the point set S through adoption of a Delaunay triangulation method, and obtaining a triangulation straight line dual graph, namely a Voronoi graph corresponding to the point set S and the center points of the obstacles, through construction; expanding each edge of the Voronoi diagram in the direction in which the two obstacles determining the edge are located to obtain a simple polygon; and solving an Euclidean shortest path of two points in the simple polygon to obtain a final obstacle avoidance shortest path. The method is low in complexity, and has high self-adaptability and certain flexibility for obstacles with different threat degrees.

The invention provides a vehicle obstacle avoidance method and a vehicle obstacle avoidance system based on machine vision. The obstacle avoidance method comprises the following steps: judging whether an obstacle exists in front of a micro vehicle by a processor by using a obstacle detection algorithm based on a single image; calibrating two cameras, determining the height of the obstacle through a stereoscopic vision method and transmitting height information to a controller by the processor; detecting conditions of vehicles in two adjacent lanes by distance measuring equipment to provide a running area for the vehicles to avoid the obstacle and change a lane and transmitting running area information to the controller; and according to the obtained information, transmitting a command to a operation and control module by the controller through using an adaptive lane change strategy to complete autonomous lane change of the vehicles. The obstacle avoidance method provided by the invention has the advantages of being stable, high in adaptive degree, fluent in vehicle obstacle avoidance posture and up to 98% in obstacle avoidance success rate.

The invention discloses a motorcycle pedal assembly which comprises a foot containing body and a foot rest which is used for installing the foot containing body and can be fixed to a frame of a motorcycle. The inner end of the foot containing body is hinged to the foot rest and an air bag is additionally arranged between the foot containing body and the foot rest. By the adoption of the motorcycle pedal assembly, vibration generated by the motorcycle and transmitted to a driver and a rider can be effectively buffered, the comfort degree of the driver and the rider is increased, balance can be kept for the driver and the rider, gear shifting operation and braking operation performed by the driver are not affected or slightly affected, potential traveling safety hazards are effectively eliminated, and the traveling safety of the motorcycle is guaranteed to the maximum degree.

A city sensing base station disclosed by the present invention comprises a sensing terminal, a control terminal and a communication terminal, the sensing terminal comprises a panorama sensing module, a sound source sensing module, a space and state sensing module, an environment sensing module and a biological detection module, and the above modules respectively carry out information interaction and adaptive sensing with the control terminal through a CAN bus. The control terminal is in communication connection with the communication terminal, and achieves information sharing and transmission with an external terminal through the communication terminal; the communication terminal comprises a short-range communication module and an optical fiber communication module; the control terminal comprises an end-side operation module, and the end-side operation module is an SOC chip integrated with an NPU acceleration module by adopting an ARM architecture; multi-element information required by urban comprehensive treatment is comprehensively captured, information entries are unified, meanwhile, data management cost is reduced, data quality is improved, data realization ability is improved, and urban comprehensive treatment is efficiently supported at low cost.

The invention relates to a fault identification method for fall-off of a cab apron of a railway wagon. The invention relates to the fault identification method for fall-off of the cab apron of the railway wagon. The objective of the invention is to solve the problems of low fault detection accuracy and low efficiency of an existing method. The method comprises the following steps: 1, acquiring original image data; 2, obtaining a sample image containing a cab apron part; 3, marking a cab apron, a vehicle body and a triangular base; 4, obtaining a segmented image data set; 5, drawing the edge of the target on the mask image; when the target edge is located at the peripheral boundary of the mask image, setting the mask image at the target edge as 0; otherwise, setting the mask image at the target edge as 255, and expanding the mask image to obtain an edge image; 6, acquiring an edge image data set; 7, establishing an edge detection network; 8, building an instance segmentation network; 9, obtaining a segmentation network; 10, carrying out railway wagon cab apron falling fault judgment by utilizing the segmentation network. The invention belongs to the field of fault image recognition.

The invention provides an aluminum alloy smelting automatic feeding tower and an automatic feeding method thereof, and relates to the field of feeding machine.The aluminum alloy smelting automatic feeding tower comprises a feeding assembly and evaporation assemblies, wherein the evaporation assemblies are fixedly connected to the two sides of the feeding assembly, sleeve bases are embedded into the outward wall bodies of the evaporation assemblies in a penetrating mode, and water pipes are connected into the sleeve bases in a sleeved mode; two supporting pipes are symmetrically fixed to the bottom end of each evaporation assembly, smoke guiding pipes are fixedly connected to the bottom ends of the supporting pipes, and the smoke guiding pipes are inserted and fixed to the top wall of the smelting furnace; the feeding assembly comprises a vibration part, a valve part, a spray head, an outer barrel, an inner barrel and an air guide base, wherein the inner barrel is sleeved by the outer barrel, the opening and the opening degree of the valve part are adjusted, and manual intervention is not needed in the adjusting process; and compared with traditional program control, the aluminum alloy smelting automatic feeding tower has the advantages of being high in self-adaption degree, high in use firmness and the like; and the problem of poor combustion efficiency caused by dry burning in the smelting furnace or excessive materials in the furnace can be greatly improved.

Supersonic mixing layer hybrid enhanced closed-loop control method and system, comprising a first supersonic flow channel, a second supersonic flow channel, a controller, a console, a velocity field measurement system and a computer, between the first supersonic flow channel and the second supersonic flow channel A mixing intensifying plate is provided, the mixing intensifying plate separates the first supersonic flow channel and the second supersonic flow channel, and the first supersonic air flow and the second supersonic air flow realize enhanced mixing outside the end of the mixing intensifying plate; The console acts the excitation function signal on the hybrid intensifier; the velocity field measurement system measures the velocity of the flow field outside the end of the hybrid intensifier in real time and transmits the measured data to the computer. The computer is preloaded with a machine learning algorithm. The machine learning algorithm generates the excitation function signal to the controller. The invention has the characteristics of being adaptable to a wide range of working conditions, strong robustness, and remarkable in mixing enhancement effect.

The invention discloses an ultrasonic imaging noise reduction method and ultrasonic imaging equipment. The noise reduction method comprises the steps that when it is judged that the ultrasonic imaging device meets a preset precondition, outputting an experiment beam trigger signal, and controlling at least two ultrasonic units located at different transverse positions to emit experiment beams to execute empty scanning; extracting at least two groups of calibration beams which represent the same transverse position and correspond to the experimental beams, analyzing detection data of the calibration beams at preset depths, and correspondingly obtaining at least two groups of sampling data; calculating to obtain standard noise data according to a preset discrete relational expression and the sampling data; and performing noise reduction on the ultrasonic imaging process by using the standard noise data. The ultrasonic imaging noise reduction method has the technical effects of high noise reduction speed and high adaptive degree, meanwhile, the algorithm process is simpler, the characteristics of the formed wave beams are fully utilized, and the noise reduction effect is more obvious when the method is applied to ultrasonic imaging of parallel receiving wave beam forming.