122results about How to "Guaranteed computing speed" patented technology

The invention discloses an electricity reliability index rapid calculation method based on caching data multithread processing, solving the problem that the electricity reliability index analytical calculation efficiency is too low. The method comprises the steps of creating a data cache region in a memory, and creating data blocks by taking administrative units, equipment register dates or power failure occurrence dates as main keys by dividing according to business types; establishing a change cache region in each data block, recording data change, and merging the data of the change cache region and basic data in regular time; evenly distributing data relating to the index calculation to a plurality of threads to carry out basic index calculation, merging the basic index calculation result of each thread, and calculating the reliability comprehensive index of each level of management unit. According to the method, multiple data blocks are used for caching business data, technical measures such as the data change cache regions and the multithread processing are adopted, the basic data retrieval and calculation efficiencies of the electricity reliability business are improved, and the rapid calculation of the electricity reliability index is realized.

The invention belongs to the technical field of autonomous navigation of intelligent traffic vehicles, and particularly relates to a vehicle mileage calculation method based on double-eye vision. The vehicle mileage calculation method comprises the following steps of obtaining a video stream of a double-eye camera which is fixedly arranged at the top part of a vehicle, and transmitting to a vehicular processor; respectively extracting the features of a left image and a right image from the image of each frame of video stream of the double-eye camera, combining with the features of a left image and a right image of the previous frame, and searching a matched feature point set by a feature matching method; according to the matched feature points of the previous frame, using a three-dimensional vision method to calculate the space coordinates of a corresponding three-dimensional point; re-projecting the space coordinates of the three-dimensional point in the previous step to the coordinates of a two-dimensional image of the existing frame, and using a GN iteration algorithm to solve the minimum error of re-projection, so as to solve the motion transformation value of the vehicle of the adjacent frame; according to the motion transformation value of the vehicle, accumulating and updating the mileage information of the vehicle motion. The vehicle mileage calculation method has the advantages that by matching and combining the quick features and the high-precision features, the precision is improved, and the calculating speed is guaranteed.

The invention discloses a moving target detecting and tracking algorithm evaluation method based on data set compensation. The moving target detecting and tracking algorithm evaluation method automatically adds quantification factors of eight performance interference factors for data sets, and allows the user to select variation factors when evaluating an algorithm conveniently; the moving target detecting and tracking algorithm evaluation method can customize weight sizes of the variation factors according to degree of sensitivity of a specific evaluated algorithm to specific variation factors, so that comprehensive quantification factors corresponding to the data sets truly reflect the algorithm application environments, and finally the objectiveness of evaluation results is ensured; at last, since the comprehensive quantification factors are comprehensive quantification embodiments of the variation factors affecting algorithm performances in the corresponding data sets, the comprehensive quantification factors of the data sets are adopted for performing weighted compensation for threshold values used for carrying out success determination of the evaluation index precision OverlapP, can show different influences of the data sets with different variation factors on the algorithm performances in the evaluation result, can simulate results of the algorithm under some interference conditions, and provides certain guidance for the further improvement direction of the algorithm.

The invention discloses a neural network model training method. The method comprises a step of obtaining a first image feature and a second image feature, wherein the first image feature is an image feature outputted by a picture A through a trained model, and the second image feature is an image feature outputted by the image A through a model to be trained, a step of obtaining a classification probability which is a classification probability outputted by the second image feature through a classification layer of the model to be trained, and a step of updating the parameters of the model tobe trained until the second image feature and the first image feature are fitted according to the first image feature, the second image feature and the classification probability. The invention also discloses an electronic device and a storage medium. The invention provides the neural network model training method, the electronic device and the storage medium, the parameters of the model to be trained are updated according to the first image feature, the second image feature and the classification probability, a small model with the same precision of a trained large model is obtained through training, and the calculation speed and accuracy are ensured.

The invention discloses a frequency-domain aeroelectromagnetic method 2.5 dimension band landform inversion method. The method comprises the following steps of 1) defining a target function, setting iteration times i, fitting precision and the maximum iteration time and inputting an initial model and inversion data, wherein the i equals to 0; 2) carrying out forward calculation, calculating a forward equation KF=b so as to acquire secondary magnetic fields Hx and Hz; 3) calculating a fitting error, if the fitting error reaches setting precision or the maximum iteration time, quitting calculation, otherwise, continuously calculating; 4) using pseudo-forward modeling to calculate a Jacobian matrix and acquiring a model updating step; and 5) updating a model parameter and satisfying the following equation: mk+1=mk+deltam. In the invention, band landform inversion is considered; a landform influence is eliminated; and frequency-domain aeroelectromagnetic method 2.5 dimension inversion under a fluctuating land surface condition is realized. An effective calculating method is provided for mountain-area aeroelectromagnetic data processing and interpretation.

The invention discloses a temperature influence-considering optimal power flow algorithm of a power system. As the traditional optimal power flow is processed by taking power system network parameters as constants and the influence of the electric heating coupling relation of the actual power grid line is neglected, the calculation result of the traditional optimal power flow has great deviation to the scheduling operation result of the actual power grid. On the basis, the temperature influence-considering optimal power flow algorithm of the power system is characterized in that a temperature influence-considering optimal power flow model of the power system is established according to the electric heating coupling relation between line temperatures and line resistance. Besides, the problem of the temperature influence-considering optimal power flow algorithm of the power system is solved by use of a simplifying primal-dual interior point method, and generalized inequality constraints containing only upper limit constraints are formed by simplifying the inequality constraints in the optimization model, and the writing of a program can be greatly simplified. Finally, the simulation and analysis results of a plurality of examples indicate that the model and the algorithm are higher than the traditional optimal power flow in calculation accuracy and calculation efficiency and have significance to the scheduling operation of the power system.

The invention discloses a remote sensing image classification method of a double-branch fusion multi-scale attention neural network. The method comprises the following steps: reading a multispectral image from a data set; after an image matrix is obtained, preprocessing image data by using superpixels; performing normalization operation on the data, and taking a block from each pixel in the normalized image matrix to form an image block-based feature matrix; selecting a training set and a test set; constructing a classification model of the convolutional neural network based on two-channel sparse feature fusion; training the classification model by using the training data set; and utilizing the trained classification model to classify the test data set. According to the method, consideringfrom the characteristics of the image, the characteristics can be extracted by self-adapting to the size of the target region object in the image, and a new central pixel offset strategy is adopted for the boundary pixels, so that the classification accuracy of the boundary pixels is improved, and the operation speed of the whole training process is also improved.

The present invention proposes a multiplier device performing multiplication of different powers of two serially in time (not in parallel) in order to further reduce the area needed for a hardware realization. By virtue thereof, it is enabled to use only one adder in connection with the multiplication which contributes to a reduced hardware amount and reduced required area for the hardware. A shifter means based on binary weighted shifting is used for shifting in connection with the multiplication, thereby reducing the required hardware amount (number of multiplexers and hardwired shifting elements) and thus reducing the area for hardware implementation still further. The present invention can be used in applications using digital multiplication, such as in digital signal processing DSP, digital filters and / or finite impulse response filters FIR filters as well as programmable and / or adaptive digital filters. As the multiplier is represented in CSD coding, the number of necessary shifting operations can be reduced and the number of necessary additions can be reduced, thus contributing to a reduced area needed for a hardware realization of a shifting means and a multiplier device on a silicon chip.

The invention discloses a multi-constraint scheduling calculation method and a device for an aviation part machining automatic production line. The method comprises the steps of acquiring process information and order task information of to-be-machined parts of an automatic production line; determining the priority of each process task in the order task information; sorting the process tasks according to the priorities of the process tasks on the premise of meeting various constraint conditions; on the premise that various constraint conditions are met, simulating the actual operation processof the automatic production line, obtaining a production scheduling calculation result, and the production scheduling calculation result comprises the predicted starting and ending time of each procedure task and a task list of equipment. Obviously, on the basis of solving all constraint conflicts, the priority of task scheduling is formulated, a process-level scheduling scheme meeting all conditions is generated and serves as a reference basis of the actual production process, and high practicability is achieved.

The invention discloses a weak and small airspace target detection method based on super-resolution feature enhancement. The method comprises the following steps: 1, performing illumination compensation on an airspace video stream to obtain an airspace video image; 2, acquiring a foreground target gray feature map by adopting a foreground detection algorithm based on background modeling and inter-frame difference, extracting a foreground target contour by morphological filtering, and calculating to obtain a prediction frame set of a foreground target; 3, adopting a multi-target tracker based on a KM algorithm and Kalman filtering to track the prediction frames, and screening out suspected target frames; 4, training a super-resolution neural network model on the spatial domain target super-resolution data set, and performing super-resolution enhancement on a foreground image region mapped by the suspected target frame by using the super-resolution model in a detection stage; and 5, training a neural network model based on a YOLOv4 framework on the airspace target detection data set, and detecting a target in the foreground target area after super-resolution enhancement by using a detector model in a detection stage to obtain a target detection result.

The invention provides a method for automatically sorting and rating metallographic structures of different materials, comprising the following steps: firstly creating a database of crystal grain series of three different materials (20CrMnTi, CF steel and 55 steel) at different multiples; reading in one metallographic structure image F with the size of M*N, wherein M and N are positive integers; recognizing metallographic materials in the read-in metallographic structure image F, and recognizing kinds of the materials by adopting a convolutional neural network; and then carrying out rough sorting and fine sorting on the database, and rating grain size scales of the recognized materials by adopting a data enhancement and transfer learning method. The method provided by the invention firstlyapplies a convolutional neural network algorithm and a transfer learning algorithm to recognition and rating of metallographic images and can realize automatic recognition and rating of the metallographic structures of different materials, and accuracy and efficiency of the method provided by the invention are also greatly improved.

The invention provides a method for calculating the dynamic characteristics of an electromagnetic mechanism based on a temperature compensation matrix, relates to the calculation method of dynamic characteristics of the electromagnetic mechanism, and aims to solve the problems that an existing technology cannot simultaneously achieve the effects of high efficiency and high accuracy and cannot be applied to analytical test on the dynamic characteristics of the electromagnetic mechanism at different temperatures. The method comprises the following steps: 1, acquiring a key parameter of the electromagnetic mechanism; 2, searching or measuring a magnetizing curve and a resistance value R; 3, setting the initial state of the dynamic characteristics of the electromagnetic mechanism; 4, calculating the dynamic characteristics of the electromagnetic mechanism at t+delta t moment; 5, acquiring coil current at the t+delta t moment; 6, acquiring the dynamic characteristics of the electromagnetic mechanism at a temperature T of the current electromagnetic mechanism; and 7, acquiring the dynamic characteristics of the electromagnetic mechanism at different electromagnetic mechanism temperatures, and the like. The method is applicable to the field of electromagnetic mechanism dynamic characteristic calculation.

The invention discloses a massive blackout recovery technique which is realized through massive blackout recognition, effective isolation of a transfer region, analysis on a transfer load, optimized reconstruction containing distributed power supplies, dynamic adjustment operation and optimization and recovery on a running mode, power can be recovered and supplied within a short time after generation of massive blackout in a distribution grid, by means of convenience of distribution automation, a larger number of switching operation can be completed within the short time, during the process, the transfer region is effectively isolated to guarantee power supply safety, during optimized reconstruction of the distributed power supplies, a reasonable starting principle and a calculation mode are adopted to ensure the feasibility and rationality of a reconstitution scheme, during dynamic operation adjustment, small-range network reconstruction is adopted to the greatest extent during secondary network reconstruction so as to ensure time requirement of calculation speed and power recovery, and after fault removal and bus decompression relief, the original running mode can be timely recovered so as to guarantee optimized running of the running mode.

The invention discloses a deep Q network driven power system operation mode automatic optimization adjustment method. The method comprises steps of determining a load fluctuation range by taking a typical operation mode as an adjustment reference mode, and generating a large amount of target mode sample data for training and testing in combination with a Latin hypercube sampling method; determining all feasible single control actions in the power grid model, numbering the control actions, and setting the control actions as action spaces; initializing a power grid model, judging whether an untrained sample exists or not, if yes, assigning load data in the sample to the power grid model, performing convergence optimization processing on the output data of the generator in the current operation mode, and if not, terminating training and the like. According to the method, the problem that the optimal power flow method is difficult to converge when solving the multi-target optimal power flow is solved while the calculation speed is ensured, various indexes of the mode obtained after adjustment have no overlarge deviation, and method reference is provided for application of deep reinforcement learning to power grid optimization and control problems.

The invention belongs to the technical field of electric vehicle charging, particularly relates to an electric vehicle charging system and device based on mobile charging pile scheduling, and aims tosolve the problems that an existing mobile charging pile scheduling method is small in scheduling range, a charging vehicle cannot be found, and the electric vehicle is inconvenient to charge due to inconvenient recovery and maintenance. The system disclosed in the present invention comprises: an information interaction unit for acquiring charging request / reservation / charging completion information; a data acquisition unit used for acquiring a current charging demand, current mobile and fixed charging pile distribution diagrams and residual electric quantity information of each mobile chargingpile; a charging demand prediction unit used for predicting the charging demand of the next time period; a charging pile scheduling unit used for generating a charging demand prediction distributiondiagram and performing charging pile scheduling in combination with the data of the data acquisition unit; a charging unit used for selecting the optimal charging pile to charge the electric vehicle.According to the system, the mobile charging pile is scheduled in advance on the basis of predicting the charging demand, the charging efficiency of the electric vehicle is high, the accuracy is high,and the real-time performance is good.

The invention provides a method for detecting the parameters of a double-fed wind generating set. The method includes the steps that first, the double-fed wind generating set is connected into a power grid for generating electricity, and a voltage signal and a current signal of the double-fed wind generating set are collected through wave recording equipment; second, a master controller of the wind generating set is used for collecting the wind direction, the wind speed, the pitch angle, the generator speed, the generator torque and a master control low voltage ride-through signal; a converter controller is used for collecting a converter low voltage ride-through signal, a converter Chopper actuating signal and a converter Crowbar actuating signal; third, the equivalent parameter and the low voltage ride-through response delay time of a double-fed wind generator are acquired according to the current signal and the voltage signal. Compared with the prior art, by means of the method for detecting the parameters of the double-fed wind generating set, errors caused by an experimental platform which is composed of parts of devices are avoided, the reciprocal influence of the double-fed wind generating set and the power grid can be reflected, and the detection method is high in operability, simple and practical.

The invention discloses a dynamic bridge parameter extracting system and a dynamic bridge parameter extracting method based on laser Doppler and relates to the technical field of bridge detection. The dynamic bridge parameter extracting method includes the steps of S1, collecting photoelectric signals of a photoelectric detector by a high-frequency signal collector, and converting the photoelectric signals into voltage values to obtain a photoelectric signal discrete sequence ; S2, by the aid of a computer, subjecting the photoelectric signal discrete sequence to fast Fourier transform so as to obtain a frequency movement sequence, seeking the specific position of the photoelectric signal corresponding to a peak value of the frequency movement sequence, subjecting a frequency-subdivided interval to further subdivision to compute a frequency spectrum within the frequency-subdivided interval, seeking the specific position of the photoelectric signal corresponding to a peak value of a frequency spectrum sequence, and computing a Doppler frequency shift; S3, computing moving speed of an object; S4, judging whether vibrating frequency of the object needs to be computed or not; S5, computing the vibrating frequency of the object according to a speed sequence. The dynamic bridge parameter extracting system and the dynamic bridge parameter extracting method based on the laser Doppler are high in extracting precision and used for measuring the low moving speed and the vibrating frequency of an engineering structure like a bridge.

The invention provides a commodity association degree determination method and device, and relates to the technical field of electronic commerce. The method comprises the following steps: according tothe category purchase data and the user non-purchase behavior data, obtaining an association relationship corresponding to a category purchase behavior of the purchase target category and a user non-purchase behavior and a category purchase behavior of other commodity purchase categories; and based on the association relationship and the weights corresponding to the category purchasing behaviorsand the user non-purchasing behaviors, determining the association degree between the commodity purchasing categories, and selecting the associated categories according to the association degree. According to the method and the device, user behavior data of browsing, shopping cart adding, attention, purchasing and the like are comprehensively analyzed. The product association degree is calculatedthrough the quantifiable association coefficient, the storage space can be saved, the calculation speed can be ensured, the method can be applied to different commercial marketing scenes, accurate product positioning analysis can be met, marketing activities with high recall rate can be met, and the use experience of users can be improved.

The invention provides a large-scale nerve network 3D visualization method and relates to the field of analogue simulation of a large-scale nerve network. The large-scale nerve network is decomposed into multiple layers of nerve networks; link relation between the layers is expressed through 3D curves with arrows, and all link curves are not overlapped; link between different layers of neurons is in one-to-many stereoscopic display, and 3D dynamical representation can be carried out; and pulse activities of all neurons can realize complete dynamic display in a 3D space. A brain is a hypercomplex 3D network body formed by a lot of nerve cells through links; the link scale of the neurons is very huge, and conduction of information stream is very complex; and the method solves the technical problem of visualization in analogue simulation of the large-scale nerve network.

A shielding door obstacle detection method is characterized by firstly judging whether that scene is changed significantly after the image is taken; if the scene is changed significantly, the detection area in the taken image is compared with the detection area in the train sample image to determine whether an obstacle exists or not; if the scene is not changed significantly, the obstacle recognition model is used to identify whether an obstacle exists or not. The invention also relates to a shielding door obstacle detection system, comprising a camera arranged above a gap between the shielding door and a vehicle door, a data processing unit electrically connected with the camera, a data storage unit electrically connected with the data processing unit, and a background computer communicatively connected with the data storage unit through a network communication interface. The screening door obstacle detection method and the system adopt different recognition schemes according to the scene change situation, thereby effectively avoiding the influence of the scene change on the foreign object recognition and improving the accuracy of the obstacle recognition.

Provided is a method for detecting the saturation of a current transformer (CT) on the basis of a third-order central moment. The method comprises: sampling current at the secondary side of the CT; acquiring a current waveform from a position near the peak value of a waveform by using a data window in order to obtain a waveform symmetric about the peak value and with less sampling points; computing a dispersion coefficient by using the third-order central moment after the acquired waveform is subjected to waveform mirror image transformation; and outputting a detected result. In the method, a dispersion coefficient after a standard sinusoidal current waveform is subjected to mirror image transformation. By means of analysis and computation, the dispersion coefficient can be considered as a fixed value so as to be used as a comparison basis. The method mainly solves problems of low detection rate and low detection precision when the CT saturation occurs, may fast and accurately detect the generation of the CT saturation just by using sampled data in half cycle so as to improve relay protection reliability to a certain extent and have certain practical significance in the aspect of guaranteeing the accurate and reliable operation of the CT.

The invention discloses a method for improving power supply reliability. The method comprises the steps of determining a system of power supply reliability influence factors; analyzing a reliability planning and improving measure; and determining a reliability planning scheme; wherein, the power supply reliability influence factors comprise a factor influencing the power failure times of various devices in different power failure types, a factor influencing the power failure duration of the various devices in different power failure types, and a factor influencing the power failure various devices in different power failure types. The method for improving power supply reliability analyzes relevance between a power supply reliability status index and each of the influence factors, and specifically an influence degree of each of the factors on the reliability index, constructs the complete and systematic system of the influence factors, and mines a weak link for the status reliability according to the system.

The invention provides a lazy interaction mode-based video tracking method. The invention aims to eliminate the defect of low computational speed or insufficient robustness of an existing video tracking algorithm. According to the method, when a target is partially obscured and illumination changes obviously, the abnormal conditions of blocks are judged; the judgment results of the blocks can be divided into three situations, wherein the first one is the departure of the blocks from a target, the second one is proportional imbalance of positive samples and negative samples, and the third one is the excessively low confidence value of the trackablility of the blocks; with the above conditions adopted, abnormal blocks can be screened out, and re-modeling and tracking are performed; and since a plurality of blocks are required to be tracked, the KCF algorithm is introduced to be adopted as a basic tracking operator to ensure the efficiency of the tracking. With the method of the invention adopted, the robustness of resampled samples can be ensured to the greatest extent, and therefore, the continuity and correctness of a tracking effect can be achieved, and at the same time, a higher calculation speed can be guaranteed.

The invention discloses a power system frequency response analysis method based on data-model fusion driving, and the method comprises the steps of carrying out the preliminary prediction on the system frequency response through employing a model driving method, and carrying out the error correction on a preliminary measurement result through employing a data driving method; and further providing a power system frequency response analysis method based on data-model fusion driving. The method specifically comprises a data processing link, an offline training link and an online application link. According to the method, the calculation precision is greatly improved on the premise of ensuring the calculation speed, meanwhile, the dependence degree of the model on the sample data is reduced, and the interpretability of a result is improved; the method can be applied to situation awareness and real-time evaluation of frequency security and stability, and provides a rapid and effective decision basis for the subsequent scheduling and control so as to prevent frequency collapse events and improve the stability of a power system.

The invention belongs to the field of electric logging value simulation, and discloses a method for simulating fracture-vug type reservoir body electric logging response. The method includes establishing an equivalent fracture-vug type reservoir body stratum model according to regional geological data, determining basic parameters of the equivalent fracture-vug type reservoir body stratum model; establishing finite element functions according to the DC logging response of the equivalent fracture-vug type reservoir body stratum model; carrying out grid discretion and resistivity assignment on the equivalent fracture-vug type reservoir body stratum model based on finite element theory; and assembling and solving finite element conductance array and obtaining depth lateral logging value. The method solves the problem of trans-scale grid dissection of a fracture-vug type reservoir body, effectively carves the boundary of the fracture-vug type reservoir body, scientifically simulates the values of fracture-vug type reservoir body electric logging, and provides theoretical basis for identifying and evaluating electric logging of carbonatite and volcanic fracture-vug type reservoir body according to electric logging data.

The invention discloses an image distortion method based on matrix inverse operation in a virtual reality (VR) mobile end. The method comprises the following steps: 1, reading a current frame and an equipment state; 2, converting coordinates under a screen coordinate system at current-frame time into coordinates under a standard equipment system at the current-frame time; 3, according to the coordinates under the standard equipment coordinate system at the current-frame time, obtaining coordinates in a world coordinate system; 4, according to the coordinates in the world coordinate system, obtaining coordinates under the standard equipment coordinate system at corresponding next-frame time; 5,performing linear transformation on the coordinates under the standard equipment coordinate system at the next-frame time so as to finally obtain coordinates under the screen coordinate system through the transformation; and 6, endowing the coordinates under the screen coordinate system at the corresponding next-frame time with pixel RGB values of the coordinates under the screen coordinate system at each current-frame time so as to obtain a final distortion image. The method is a method for generating intermediate frames in VR, can effectively reduce jittering in a VR game, and improves user experience.

The invention discloses an unmanned aerial vehicle obstacle avoidance flight method based on digital spatial slices. The method comprises the following steps of: step 1, acquiring a picture shot by anunmanned aerial vehicle; 2, adjusting the resolution of the picture and performing processing to obtain a grey-scale image; 3, for the single grayscale image, extracting a plurality of slices from different regions of the picture to form a set I by using a digital spatial slicing algorithm; 4, simultaneously inputting the slice picture sets in the set I into a neural network, and outputting corresponding obstacle calibration information to form a set R; 5, estimating the position and direction of an obstacle according to the set R; 6, planning a flight mode of the unmanned aerial vehicle, andsetting a yaw angle and a speed; and step 7, enabling the flight control system of the unmanned aerial vehicle to perform motion control according to the acquired speed and yaw angle information. Themethod has a very good effect on tree detection in an under-forest environment, and hardware adopted by the method is light in weight and low in energy consumption, and therefore, the method is suitable for being used on a small unmanned aerial vehicle.

The invention discloses a forest fire identification method and system, a program and a storage medium, and belongs to the technical field of forest fire prevention. The method comprises the steps offirstly obtaining a visible light image and a near-infrared image of a scene; identifying a suspected fire area in the visible light image and a suspected fire area in the near-infrared image; matching the suspected fire area in the visible light image with the suspected fire area in the near-infrared image, and performing primary verification on the forest fire situation in the scene; synchronously adjusting the focal lengths of the visible light camera and the near-infrared camera, and carrying out matching verification on the suspected fire area in the visible light image and the suspectedfire area in the near-infrared image again; and finally, carrying out cloud identification verification. According to the invention, the accuracy of forest fire identification is effectively improved,and misinformation is reduced.

The invention relates to a visual inertial odometer method and system for online calibration of equipment parameters, which adopts a Kalman filter to online calibrate external parameters of a camera and bias of an IMU (Inertial Measurement Unit), and adopts nonlinear optimization and a sliding window to online calibrate internal parameters of the camera. Image data and IMU data are acquired by using the camera and the inertial measurement unit carried by the equipment, parameters are calibrated without depending on the equipment and a calibration plate, the calculation speed is ensured by using a Kalman filter to calibrate external parameters of the camera and bias of the IMU, and the precision requirement is met by using nonlinear optimization and a sliding window to calibrate internal parameters of the camera. And the Kalman filter and the nonlinear optimization sliding window are in a tight coupling relationship, so that the method is more robust. The invention provides a set of perfect algorithm process and system of the real-time Kalman filter and the nonlinear optimization sliding window visual inertial odometer, the performance of the visual inertial odometer in different environments and different devices is remarkably improved, and the algorithm has the advantages of universal process, strong pertinence, simple and easy online operation process and the like.

A tetrahedral mesh virtual cutting method based on route separation comprises the steps that 1, data of a tetrahedral mesh is read, and a surface triangular patch set of the tetrahedral mesh is obtained; 2, a collision detecting thread is started, and a thread from the surface triangular patch set to a screen is displayed; 3, after collision detection occurs, track discretization of a tool is started, feature point processing is carried out, and meanwhile selection of an optimal route is started; 4, a tetrahedron shared by two first points on the optimal route is separated; 5, the two points in the step 4 are deleted. In the cutting process, each colliding patch is evaluated through an evaluation function, the optimal point is selected to serve as a route of a cutting track, and then tetrahedron separation is carried out; due to the fact that only topological separation exists in the cutting process, and no pathological tetrahedrons are generated, the stability of finite element computing is improved, the number of the tetrahedrons is not increased, and the computing speed is kept.